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JOURNAL OF SCIENCE, ENGINEERING AND TECHNOLOGY 2026

ZIMBABWE INSTITUTION OF ENGINEERS A Special Edition in Artificial Intelligence Volume 1 - September 2026

J ournal of S cience, E ngineering and T echnology

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ISSN - 2306-5893

JOURNAL OF SCIENCE, ENGINEERING AND TECHNOLOGY 2026

CONTENTS 1

EMERGING POC TECHNOLOGIES FOR CTDNA ANALYSIS: ADVANCING RAPID BREAST CANCER DETECTION Humphrey Nkosiyabo Dera, Tawanda Mushiri, Edwel Tafara Mharakurwa & David Jambgwa Simbi

DEVELOPING A PREDICTIVE MODEL OF TAXPAYER NON-COMPLIANCE USING XGBOOST: A CASE OF THE ZIMBABWE REVENUE AUTHORITY Tatenda Gonorashe & Henry Mazhokota

DEVELOPING A REGIME-AWARE LONG SHORT-TERM MEMORY (LSTM) MODEL FOR GOLD PRICE PREDICTION ON CONTRACT FOR DIFFERENCE (CFD) BROKERS IN ZIMBABWE Patricia Gwanhoya & Miranda Sanyanga

AUTOMATION OF RICE PACKAGING TO INCREASE ADOPTION OF PRECISION AGRICULTURE IN AFRICA Berlindah Precious Karigeni & Evelyn Chipango

REAL-TIME AI-DRIVEN SURVEILLANCE SYSTEM FOR AGRICULTURAL INFRASTRUCTURE AND LIVESTOCK: ENSURING RESILIENCE AND EFFICIENCY IN MODERN FARMING 10 Berlindah Precious Karigeni & Kenneth Madhuku

AN LSTM-BASED MODEL TO FORECAST EQUITY RETURNS ON THE ZIMBABWE STOCK EXCHANGE: A CASE OF INTELLEGO INVESTMENTS CONSULTANTS William Mavudzi & Raphael Muchovo

A TIME SERIES MODEL FOR FORECASTING ELECTRICITY CONSUMPTION IN HARARE, ZIMBABWE Mufaro Mombechena & Fungai Jacqueline Kiwa

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DESIGN OF A VIRTUAL-AUGMENTED REALITY LAB FOR TRAINING PURPOSES ON THE ASSEMBLY OF A SUBMERSIBLE PUMP Lindokuhle Ngwenya

114

SPATIOTEMPORAL FORECASTING OF POLITICAL UNREST IN ZIMBABWE USING DEEP LEARNING: A MULTI-MODAL MACHINE LEARNING APPROACH Tapiwa Patson Sisimayi & Shepherd Makurumidze

137

DIGITAL TWIN AND MACHINE LEARNING INTEGRATION FOR INDUSTRIAL EFFICIENCY: A SYSTEMATIC REVIEW WITH DEVELOPING COUNTRY PERSPECTIVES Tinotenda Zwavashe, Tawanda Mushiri & Chido Hermes Chihobo

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ZIE JOURNAL OF SCIENCE, ENGINEERING AND TECHNOLOGY (ZIE-JSET) 2026 The ZIE Journal of Science, Engineering and Technology (ZIE-JSET) 2026 brings together a diverse collection of research papers that demonstrate the breadth of contemporary engineering and technology research relevant to Zimbabwe and the wider African context. The papers presented in this publication explore emerging technologies and practical challenges across a wide range of disciplines. These include point-of-care technologies for cancer detection, artificial intelligence and machine learning, agricultural automation, financial modelling, electricity forecasting, augmented and virtual reality, and the integration of digital twins with machine learning for industrial efficiency

ZIE EDITORIAL COMMITTEE Editor-in-Chief Prof. Tawanda Mushiri Editors Prof. Talon Garikayi Prof. Samson Mhlanga Prof. Godfrey Dzinomwa Prof. Godfrey Azangwe Eng. Peter Bukelani Musiiwa

Published by: The Zimbabwe Institution of Engineers 256 Samora Machel Avenue East Eastlea

Dr Eng. Nonhlanhla G. Mguni

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Dr Farai Matawa

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JOURNAL OF SCIENCE, ENGINEERING AND TECHNOLOGY 2026

FOREWORD Engineering has always been central to the development of nations. From infrastructure and energy to manufacturing, agriculture, healthcare and emerging digital technologies, engineers have a responsibility to transform knowledge and innovation into practical solutions that improve the lives of communities. The ZIE Journal of Science, Engineering and Technology (ZIE-JSET) 2026 brings together a diverse collection of research papers that demonstrate the breadth of contemporary engineering and technology research relevant to Zimbabwe and the wider African context. The papers presented in this publication explore emerging technologies and practical challenges across a wide range of disciplines. These include point-of-care technologies for cancer detection, artificial intelligence and machine learning, agricultural automation, financial modelling, electricity forecasting, augmented and virtual reality, and the integration of digital twins with machine learning for industrial efficiency. A particularly important feature of this collection is its emphasis on applying advanced technologies to challenges faced by developing economies. Several contributions address issues such as resource limitations, infrastructure constraints, energy challenges, agricultural productivity and the need for affordable technological solutions. The research on digital twins, for example, specifically identifies data scarcity, capital

constraints, obsolete equipment and unstable power supplies as challenges affecting implementation in developing countries. This publication therefore represents more than a collection of academic papers. It provides an opportunity to highlight the role of engineering research in addressing real-world challenges and to encourage stronger collaboration between universities, industry, government and professional institutions. The Zimbabwe Institution of Engineers recognises that the future of engineering will depend increasingly on innovation, interdisciplinary collaboration and the responsible application of emerging technologies. By creating a platform for researchers and practitioners to share their findings, we contribute to the development of an engineering community that is better equipped to respond to Zimbabwe's evolving needs. I commend the researchers and contributors whose work appears in this volume. Their commitment to research, innovation and knowledge creation is essential to building a stronger and more technologically capable Zimbabwe. I trust that this publication will stimulate further research, encourage meaningful professional dialogue and inspire engineers, researchers and students to develop solutions that contribute to sustainable national development.

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EMERGING POC TECHNOLOGIES FOR CTDNA ANALYSIS: ADVANCING RAPID BREAST CANCER DETECTION

Humphrey Nkosiyabo Dera¹, Tawanda Mushiri², Edwel Tafara Mharakurwa³, David Jambgwa Simbi¹ ¹Mechatronics, Chinhoyi University of Technology, Zimbabwe ²Scientific and Industrial Research and Development Center, Zimbabwe ³Electrical Engineering, Dedan Kimathi University of Technology, Kenya hndera@cut.ac.zw, humphrey.dera@gmail.com Abstract Early diagnosis remains the critical factor in improving survival rates and therapeutic outcomes for breast cancer globally. While liquid biopsy, specifically the isolation of circulating tumor DNA (ctDNA), offers a highly promising, minimally invasive alternative to classical tissue sampling, conventional testing is often hindered by the logistical delays of centralized laboratories. By shifting these diagnostics to the point of care (POC), clinicians can bypass these bottlenecks and accelerate detection. This paper evaluates the recent emergence of portable POC platforms engineered for ctDNA analysis in breast oncology, examining the mechanics and diagnostic utility of microfluidic devices, integrated biosensors, and CRISPR-based strategies. Furthermore, the deployment of these decentralized tools holds profound implications for patient management, particularly within resource-constrained healthcare environments. Finally, we outline the prevailing technical barriers and highlight the developmental milestones required to integrate POC ctDNA diagnostics into routine clinical screening fully. Keywords - Breast cancer, Circulating tumor DNA (ctDNA), Liquid biopsy, Microfluidics, Point-of-care (POC) diagnostics

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1. Introduction Breast cancer is a worldwide health burden, and it remains one of the leading risk factors in women (Brahmbhatt, 2023; Leggat-Barr et al., 2024). 2.3 million new cases were diagnosed in 2020, necessitating improved diagnostic and treatment modalities (Begum, Gupta, Sunny, & Lutfor, 2022; Djohan & Wang, 2024). Early detection enhances survival, for example, the 5-year relative survival rate for early localized breast cancer is over 90%, but this drops to 15% for advanced cases (Laurberg et al., 2017; Maajani et al., 2019). This emphasizes the need for prompt and accurate diagnosis. Classical breast cancer screening methods, such as mammography, and diagnostic methods, such as tissue biopsy, are expensive, invasive, and require equipment and expertise that are often unavailable or inaccessible, which leads to a delay in the timely diagnosis of breast cancer, especially in resourceconstrained environments. Liquid biopsy serves as a less invasive alternative to traditional tissue biopsies and is used more frequently for cancer detection and monitoring (Adhit, Wanjari, Menon, & Siddhaarth, 2023; Mater et al., 2022). It can detect cancer by analyzing biological samples such as blood, urine, and saliva. This eliminates the need for tumor tissue sampling through surgery, which in turn reduces patient discomfort, lowers the risk of surgical complications, and allows more frequent monitoring of cancer progression. Analysis of circulating tumor DNA (ctDNA) as a liquid biopsy shows a promising trajectory in research (Allen, 2024; Mater et al., 2022). ctDNA is DNA derived from a tumor and released into the blood with tumor-specific genetic signatures. It is useful in the early detection of cancer, monitoring of cancer treatment, and guiding the development of personalized treatment plans(Wang et al., 2024). ctDNA analysis reveals the same genetic characteristics as the original tumor (Hwang et al., 2024; Liao et al., 2022). Tracking changes in levels and mutations in ctDNA may provide information about how well treatment is working and help doctors adjust cancer therapies when the cancer becomes resistant. It may also be used to detect cancer that persists after treatment, thereby enabling early identification of recurrence and improved management (Pantel & Alix-Panabières, 2019). Point-of-care (POC) technologies are changing how diagnosis and testing are performed by enabling rapid detection at the point of patient care. If used for the detection of ctDNA, it may lead to faster results, greater accessibility, and reduced dependence on specialized

central labs, a crucial benefit in resource-constrained environments. Their mobility also allows the technology to be deployed across varied contexts, obviating reliance on sophisticated tools or specialized labor, and thus making ctDNA testing more viable in resource-limited environments. This report provides an overview of the most recent progress toward portable ctDNA diagnostic tools and devices designed to be compact, sensitive, and operable outside a laboratory setting. The review provides a snapshot of the current state of POC testing platforms for ctDNA and their current and potential applications for diagnosing breast cancer. An overview of major challenges and future directions for ctDNA point-of-care testing, to highlight the promise of POC liquid biopsy in making cancer diagnosis more accessible and equitable for cancer patients worldwide, was also reviewed.

2. Liquid Biopsy and ctDNA in Cancer Diagnostics Liquid biopsy is an emerging modality for cancer diagnosis based on samples obtained from minimally invasive body fluids, such as blood, urine, or saliva, thereby circumventing the challenges of traditional invasive tissue biopsies, which are associated with patient distress and complications(Muhammad et al., 2025). These challenges include greater safety risks, patient discomfort, and procedural costs, all of which are eliminated with liquid biopsy. Since liquid biopsy is much easier to perform on a routine basis than a traditional tissue biopsy, it allows for greater testing frequency and thus more continuous assessment of patients and their disease, which can contribute to more personalized management strategies for patients(Muhammad et al., 2025). Liquid biopsy has also been shown to improve the diagnosis of cancers that are not easily or safely amenable to tissue sampling(Muhammad et al., 2025). Liquid biopsy has significant potential for detecting cancer at early stages, when treatment and outcomes may be optimized compared with advanced stages. It can identify cancerassociated material in the tumor very early in the disease course, potentially enabling much earlier diagnosis and treatment than conventional techniques (Muhammad et al., 2025).

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Among the most widely used applications of liquid biopsy in clinical cancer diagnostics is the detection and characterization of circulating tumor DNA (ctDNA), a type of DNA released by dying cancer cells into the bloodstream. These fragments contain unique genomic mutations that can be used as disease-specific biomarkers. Analysis of ctDNA closely reflects the mutation profile of the primary tumor and enables the identification of specific genomic alterations, which is useful for diagnosis and clinical management. Since ctDNA may be detectable at the onset of disease, before clinical symptoms or abnormalities, its analysis can also be used to detect cancer very early.

Advanced sequencing and bioinformatics can be used to detect ctDNA, reassemble fragments, and derive the original DNA sequence. Distinguishing between ctDNA and circulating DNA derived from other cells, such as normal cells, is also a challenge (Dang & Park, 2022), which must be managed to ensure reliable results. This can be done by identifying tumor mutations that are absent in the normal cells of origin, by assessing methylation patterns that differ between tumor-derived and non-tumor-derived cell DNA. This can be done by using size selection methods that favor ctDNA fragment size that may differ from non-tumor DNA(Dang & Park, 2022).

Monitoring ctDNA during a therapeutic intervention provides an assessment of treatment effectiveness and the development of acquired resistance (Duffy & Crown, 2022). In addition, it can be utilized to assess therapy and to inform the selection of therapeutic approaches in an individualized manner, based on the genetic profile of the individual tumor. The presence of ctDNA at very low levels following the primary treatment indicates minimal residual disease, which may inform early detection of recurrence and the provision of follow-up care.

To enable ctDNA testing to be more widely used as a useful, effective, and reliable cancer biomarker, challenges for ctDNA analysis, such as detection sensitivity and specificity, must be addressed. The development of more sensitive and specific POC technologies for ctDNA analysis will enable more reliable, accurate, rapid, and accessible diagnosis and follow-up of cancer patients.

There are still several important challenges for implementing ctDNA analysis at the point of care. The primary issue is that ctDNA levels are low, particularly in the earliest cancer detection and minimal residual disease settings(Liu et al., 2025). Therefore, ctDNA must be detected with extremely high sensitivity. Digital PCR, a highly specific amplification method, allows the detection of even rare DNA sequences with greater accuracy than conventional PCR (Liu et al., 2025). Bead-based capture uses antibodies or complementary DNA immobilized to beads to capture specific DNA targets, such as ctDNA. It allows separation from plasma to increase the sensitivity of subsequent analyses (Didarian et al., 2025). CRISPR-based platforms use highly specific Cas12 and Cas13 enzymes to amplify ctDNA signals to detect even very low levels of ctDNA (Yazdi, Roshannezhad, Sharif, & Abbaszadegan, 2024). The fragmented nature of ctDNA may present difficulties in the context of ctDNA analysis, as ctDNA is present as fragments that range from 160 to 180 bp in length. To address this, NGS is utilized, which can read and analyze millions of DNA fragments concurrently to identify several distinct mutations from within the small ctDNA fragments (Yazdi et al., 2024). Targeted sequencing enables researchers to focus only on a subset of the mutations of interest and not the entire DNA, simplifying the analysis process.

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3. Current State of POC Technologies for ctDNA Analysis The combination of ctDNA analysis with POC technologies can improve cancer diagnoses. In particular, these methods are very useful in resourceconstrained regions. Several types of POC technologies and devices have shown success in clinical trials, and continued progress is being made to expand these methods (Yang, Lv, Zhang, & Cui, 2022). For example, microfluidics provides an innovative solution for ctDNA detection and analysis at the POC. This involves the creation of small, automated devices that contain all the components required to purify and process ctDNA and to perform a reaction on a single chip, with the resulting products detected and quantified (Yang et al., 2022). Since it combines all the sample processing steps into a single unit, this process can reduce the need for reagents and buffers, thereby improving the speed of ctDNA analysis. Microfluidics can also be a cost-effective method that allows for fewer reagents and other components, reducing the risk of contamination (Gavina, Franco, Khan, Lavik, & Relich, 2023). The small size allows for less power requirements, easier handling, and the use of equipment in resource-scarce areas.

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Integrating microfluidics into biosensors is important for ctDNA detection and analysis in POC settings, where they transduce signals from biological samples into digital outputs for the final reading. Various types of biosensors are also available for this purpose. Examples include electrochemical biosensors, which can detect changes in current or voltage resulting from a change in the ctDNA signal. These are popular in ctDNA analysis because of their low cost, portability, and ease of use. Another example is an optical biosensor that detects changes in the optical properties of light (e.g., absorbance and fluorescence) when the ctDNA signal binds to a target. Due to the high specificity and sensitivity of these biosensors, they are frequently employed for this kind of analysis. Nanomaterial-based biosensors are also used for these applications because they are made of various materials that can enhance the sensitivity of ctDNA detection. For example, graphene and carbon nanotubes can be useful in this process because they are highly sensitive, enabling faster, more accurate determination of ctDNA concentration (Yang et al., 2022). CRISPR-based POC diagnostics is a rapidly evolving field that provides a quick, highly specific way to detect nucleic acids(Kumaran et al., 2023). The CRISPR-Cas system was initially discovered as a bacterial defense against phages, but it has now been employed in diagnostics to detect ctDNA (Kumaran et al., 2023). CRISPR-Cas proteins can cleave specific nucleic acid sequences, using a specific guide RNA. CRISPR-Cas proteins can detect specific ctDNA fragments with single-nucleotide resolution. Both Cas12 and Cas13 cleave DNA or RNA, respectively, adjacent to the cut, thereby increasing the concentration of ctDNA that can be detected, and therefore, the amount of protein that needs to be cleaved is very low (Hassan, Mohamed, Hassan, & El-Sayed, 2025). Because of this, the method allows detection of even trace amounts of ctDNA and has potential applications in clinical diagnostics. For example, this technology can be used for fluorescence/colorimetric/ electrical readout to detect the presence of ctDNA.

In regions without access to many laboratory facilities, paper-based microfluidics is another option because it is simple and cost-effective. All of these emerging POC technologies have significant implications for cancer detection and management, as they can accelerate ctDNA analysis for earlier diagnoses and treatments. This increases the availability of breast cancer diagnosis in regions where it is not currently readily accessible.

4. Applications in Breast Cancer Diagnosis and Management The deployment of POC platforms fundamentally reshapes the diagnostic landscape for breast oncology, functioning as a closed-loop clinical framework (see Figure 1). By decoupling ctDNA assays from centralized infrastructure, these tools extend critical screening capabilities to remote and underserved demographics. Substituting surgically invasive tissue sampling with liquid biopsies minimizes procedural morbidity, while embedding these portable systems directly within community clinics establishes a rapid, decentralized input for early intervention. Operationally, this framework provides a continuous feedback loop for post-diagnostic monitoring. Tracking ctDNA fluctuations directly at the point of care provides clinicians with a high-resolution metric of therapeutic efficacy. The capacity of these devices to detect minimal residual disease (MRD) predates standard imaging, triggering an immediate clinical feedback response. Armed with on-site, real-time genomic characterization, healthcare providers can execute dynamic modifications to therapeutic regimens, firmly establishing a responsive foundation for precision oncology.

In recent years, new technologies and devices have been constantly explored for POC diagnostics. This includes lab-on-a-chip devices where multiple laboratory functions and steps can be combined and performed on a small platform. Smartphone-based diagnostics is another area of interest that can leverage the device's camera, sensors, and processing capabilities for analysis.

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Baseline patient profiling

∑

Real-Time feedback signal

POC Diagnostic Architecture (Microfluidic separation, CRISPR-Cas cleavage, Biosensor Transduction)

Longitudinal ctDNA monitoring (MRD Detection, Realtime liquid Biopsy)

Variant ID and signal processing

Precision Oncology Intervention (targeted treatment regimens, dynamic therapy adjustments)

Therapeutic delivery

Patient tumor dynamics (Tumor response, ctDNA shedding into blood)

Clinical outcome Figure 1: Closed loop clinical system

5. Challenges and Future Directions While the diagnostic capabilities of POC ctDNA platforms represent a significant technological leap, deploying these systems in active clinical settings presents complex operational challenges. Transitioning these devices from prototype validation to scalable clinical implementation requires overcoming several distinct systemic barriers. Table 1 delineates these primary technical bottlenecks and regulatory constraints, outlining the specific research trajectories required to achieve robust, real-world integration in breast cancer screening.

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Table 1: Primary Technical, Regulatory, and Research Drivers for Real-World Integration in Breast Cancer Screening Category

Specific challenge

Key limitation

Future direction

Technical Analytical sensitivity POC platforms must reliably detect very low ctDNA concentrations, particularly in early-stage breast cancer and minimal residual disease (Ma et al., 2015). Analytical specificity Distinguishing tumor-derived ctDNA from background cell-free DNA remains difficult and may increase the risk of false-positive findings (Ma et al., 2015). Multiplexed Simultaneous analysis of multiple detection breast cancer–associated genetic targets on compact devices remains technically complex (Liu et al., 2024; Topkaya, Azimzadeh, & Ozsoz, 2016). Sample-preparation DNA extraction, enrichment, integration purification, and amplification are difficult to fully automate within a portable POC workflow (Shrestha et al., 2026).

Prioritize ultra-sensitive amplification, signal-enhancement strategies, and low-noise detection architectures suitable for decentralized use. Develop mutation-specific, methylation-based, and multimarker assays that improve discrimination between malignant and non-malignant DNA fragments. Advance integrated multiplex panels, microarray-inspired layouts, and digital readout systems that preserve accuracy across multiple biomarkers.

Standardization

Establish harmonized protocols, reference standards, external quality-assessment schemes, and reporting guidelines for POC ctDNA assays. Conduct multicenter prospective studies that include early-stage disease, minimal residual disease, and resource-limited clinical environments.

Design closed, cartridge-based systems that integrate plasma separation, ctDNA enrichment, reagent storage, and contamination control.

Clinical

Clinical validation

Clinical utility

Affordability and implementation

Variation in sample handling, assay protocols, quality controls, and reporting metrics limits comparability across devices and studies (Plebani et al., 2025). Robust evidence is required to confirm diagnostic accuracy, reproducibility, and performance across diverse breast cancer subtypes and patient populations (Plebani et al., 2025). It remains necessary to demonstrate that POC ctDNA testing improves treatment selection, monitoring, recurrence detection, and patient outcomes (Plebani et al., 2025). High development, manufacturing, maintenance, and consumable costs may restrict adoption in low- and middle-income settings (Plebani et al., 2025).

Link POC testing outcomes to clinical decision pathways, treatment response metrics, survival outcomes, and cost-effectiveness analyses. Develop low-cost fabrication methods, reusable readers, inexpensive disposable cartridges, and implementation models tailored to decentralized healthcare systems.

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Regulatory approval

Genomic data security

Regulatory and ethical

Informed consent

Translation from laboratory prototypes to approved clinical devices requires compliance with stringent diagnostic, safety, and performance requirements (Kardjadj, 2025). Connected POC platforms may increase risks related to storage, transmission, and unauthorized access to sensitive genomic and clinical data (Moustafa, 2025). Rapid genomic testing may complicate patient understanding of test scope, incidental findings, limitations, and implications for treatment decisions (Moustafa, 2025).

Engage regulatory authorities early, define intended-use cases clearly, and generate analytical and clinical evidence aligned with approval pathways. Integrate encryption, secure authentication, privacy-preserving data architectures, and governance frameworks for genomic information management. Create context-appropriate consent tools, clinician training materials, and patient-facing explanations that support informed and voluntary decision-making.

Future research Device optimization

Further improvements are needed in miniaturization, robustness, battery operation, reagent stability, and usability outside conventional laboratories (Muzaffar, Ahamed, & Deshmukh, 2024). Large-scale Evidence remains limited on how implementation POC ctDNA technologies perform studies when integrated into routine screening, referral, and oncology workflows (Cohen, Liu, & Aleshin, 2023). Artificial intelligence On-device interpretation of integration complex ctDNA signals remains challenging, especially when data quality, tumor heterogeneity, and low variant allele fractions compromise accuracy (Nabih et al., 2026).

Focus on ruggedized, user-friendly, low-maintenance devices with stable reagents and simplified interfaces for primary-care deployment. Evaluate workflow integration, health-economic impact, training requirements, turnaround time, and equity outcomes in real-world health systems. Develop transparent, clinically validated machine-learning tools for signal processing, variant interpretation, quality control, and decision support.

6. Conclusion The deployment of POC ctDNA platforms offers a transformative approach to breast cancer management, effectively bridging the diagnostic gap in resourcelimited regions. Rapid, decentralized screening mechanisms not only facilitate earlier detection but also provide actionable metrics for continuous treatment monitoring. Current iterations of microfluidic devices, targeted biosensors, and CRISPR-driven diagnostics have successfully miniaturized complex laboratory processes into accessible, non-invasive tools. Despite these hardware advancements, significant systemic challenges persist. Future development must resolve

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critical bottlenecks in assay sensitivity, multi-mutation multiplexing, and protocol standardization to ensure these devices meet rigorous regulatory and economic benchmarks. Transitioning these prototypes from the bench to the bedside will demand extensive validation through real-world clinical trials. Additionally, embedding artificial intelligence directly into the POC architecture presents a critical opportunity to optimize signal-to-noise ratios and autonomously resolve complex ctDNA datasets. Addressing these integrated technical, regulatory, and clinical challenges will be paramount to realizing widespread, equitable implementation of rapid genomic screening.

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Muhammad, S., Maridevaru, M. C., Roy, S., Chen, D., Zeng, W., Sun, L., … Guo, B. (2025). Piezodynamic therapy: Unleashing mechanical energy and featuring next-generation therapeutic paradigms for glioblastoma. ACS Nano, 19(37), 33008–33058. Muzaffar, A., Ahamed, M. B., & Deshmukh, K. (2024). Miniaturization/lab on chip for energy applications. In Nanotechnology in miniaturization: An emerging trend to fabricate future devices (pp. 151–171). Springer. Nabih, N. W., Nafie, M. S., Asaad Babker, H. M., Manar, G., Shalabi, A. S. A., & Fahmy, S. A. (2026). Artificial intelligence-driven methods and nanoparticle-based biosensors for rapid and sensitive detection of EGFR mutations in non-small cell lung cancer. Cancer Nanotechnology. Pantel, K., & Alix-Panabières, C. (2019). Liquid biopsy and minimal residual disease—Latest advances and implications for cure. Nature Reviews Clinical Oncology, 16(7), 409–424. Plebani, M., Nichols, J. H., Luppa, P. B., Greene, D., Sciacovelli, L., Shaw, J., … Dimech, W. (2025). Point-of-care testing: State-of-the-art and perspectives. Clinical Chemistry and Laboratory Medicine (CCLM), 63(1), 35–51. Shrestha, K., Farhana, F. Z., Koo, K. M., Darwish, N., Ross, A. G., & Shiddiky, M. J. (2026). Challenges in the development and implementation of point-of-care handheld qPCR devices. Small, 22(3), e06233.

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DEVELOPING A PREDICTIVE MODEL OF TAXPAYER NON-COMPLIANCE USING XGBOOST: A CASE OF THE ZIMBABWE REVENUE AUTHORITY

Tatenda Gonorashe1, Henry Mazhokota2 ¹Graduate Business School, Chinhoyi University of Technology, Zimbabwe 2Graduate Business School, Chinhoyi University of Technology, Zimbabwe tobe@gmail.com Abstract Taxpayer non-compliance remains a persistent challenge for revenue authorities in developing economies. This limits the ability by the governments to mobilise domestic revenue and finance public services. Traditional compliance monitoring approaches used by many tax administrations are largely reactive and oft times detect non-compliance only after it has already occurred. This study develops a predictive model for identifying taxpayer non-compliance using the Extreme Gradient Boosting (XGBoost) machine learning algorithm, focusing on the Zimbabwe Revenue Authority (ZIMRA) case. The study adopts a quantitative predictive analytics approach, and uses a structured dataset of 5000 taxpayer records designed to reflect realistic administrative variables commonly used in tax administration systems.

(EDA) and feature engineering were conducted before development of the model. The predictive model was then implemented in Python using the XGBoost library and evaluated using a 70/30 train–test split. Five-fold cross-validation revealed a strong predictive model performance, achieving an accuracy of approximately 84% and a ROCAUC score of approximately 0.87. Cross validation results show that the model is able to distinguish non-compliant from compliant taxpayers. More so, feature importance analysis also indicates that penalty history, audit exposure, firm size, compliance score and turnover influence taxpayer behaviour the most, of all the predictors used in the study. As a key takeaway, predictive data analytics can be used to notably enhance risk management in tax compliance by enabling revenue authorities to proactively identify high-risk taxpayers. They can The dataset comprises of demographic, economic then allocate enforcement resources according to and compliance-history indicators namely taxpayer the needs as suggested by the model. region, turnover, firm category, audit history, penalty records and past compliance score. Keywords: Compliance Risk, Machine Learning, Data pre-processing, exploratory data analysis Predictive Modelling, Tax Administration, Taxpayer 10

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1. Introduction Non-compliance to tax regulations by taxpayers remains one of the major challenges affecting revenue mobilisation across many economies, particularly in developing countries. Tax authorities depend on tax collection for financing various public services and infrastructure projects among others. Unfortunately, issues like understatement of income levels, delayed submission of tax forms and failure to pay tax continue to compromise efficiency in tax systems and reduce fiscus. Research has indicated that tax non-compliance greatly reduces the fiscal capacity of governments and undermines their efforts in stimulating economic growth (Alm & Torgler, 2011; OECD, 2019). In recent times, tax authorities around the world have been increasingly relying on evidence-based techniques to boost compliance and enforcement. Advances in machine learning and predictive analytics also enable authorities to analyse large administrative datasets, and this helps them identify patterns associated with high-risk taxpayers. These predictive approaches then allow revenue authorities to move from reactive enforcement strategies towards proactive compliance risk management by identifying potential non-compliant taxpayers before violations occur (Kleinberg et al., 2018; OECD, 2022). Tax administrations around the world are increasingly adopting digital data analytics to strengthen tax administration. The Organisation for Economic Cooperation and Development (OECD), among other organizations, stresses the role of predictive analytics, artificial intelligence, and machine learning in tax administration in contemporary times. The use of technological innovations enables tax collection agencies to analyse big data on taxpayers' behaviour and audit those who exhibit suspicious behaviour through risk assessment. Examples from developing nations indicate that predictive analytics have played an essential role in improving tax administration by making the auditing process more efficient (OECD, 2020; World Bank, 2023).

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In Zimbabwe, the Zimbabwe Revenue Authority (ZIMRA) continues to face persistent challenges related tax non-compliance. These challenges include under-declaration of income, late and nonsubmission of tax returns. These challenges contribute to revenue leakages and they end up constraining the government’s ability to finance national development programmes. Despite the introduction of digital tax platforms such as the Tax and Revenue Management System (TaRMS) and Fiscalisation Data Management System (FDMS), compliance monitoring in many cases remains largely reactive rather than predictive (ZIMRA, 2023; World Bank, 2022). However, despite the growing global use of predictive analytics in tax administration, empirical studies that apply machine learning techniques to tax compliance analysis are still limited in growing economies. In the African context, the majority of these studies have focused primarily on behavioural, institutional and socio-economic determinants using survey-based or traditional econometric approaches. As a result, there is limited evidence on the application of advanced machine learning models such as the Extreme Gradient Boosting (XGBoost) algorithm that use administrative tax data to predict taxpayer non-compliance in African countries. In Zimbabwe specifically, existing research has largely concentrated on descriptive analyses of noncompliance and policy reforms. Minimal focus has been on predictive modelling approaches that can support proactive compliance risk management. This research seeks to establish a predictive model for taxpayer non-compliance employing XGBoost Machine Learning algorithm. This is achieved by utilizing a structured data set containing the following variables: taxpayer turnover, firm size, category, compliance history, audit history and geographical location. The research focuses on identifying the determinants of non-compliance and evaluating the predictive power of the developed model. The results will give some insights into the use of predictive analytics in tax administration.

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2. Methodology The current study was designed using a quantitative approach which incorporates predictive analytics to develop a model to identify non-compliant taxpayers. It is appropriate to use a quantitative method when dealing with structured data and when investigating the relationship between variables (Creswell, 2014). In the current study, the XGBoost algorithm, a machine learning technique, was applied since it is highly effective in terms of predictions and can identify relationships between different variables (Chen & Guestrin, 2016). The data set used for this study consists of a structured list of taxpayer records representing about 5,000 taxpayers in total. It is meant to represent real administrative attributes that one would find in a tax administration system. The main variables used include taxpayer’s region, firm’s category, yearly turnover, years in business, audit history, penalty history, and compliance history. This was done in line with what other previous studies found to be relevant variables when conducting such a study. These studies identified economic, demographic and compliance-history attributes as important determinants of taxpayer non-compliance (Alm et al., 2021; Torgler, 2020).

economic characteristics as key determinants of taxpayer behaviour. Before model development, the dataset underwent thorough data pre-processing procedures that comprised of data cleaning, missing values handling, encoding of categorical variables and transformation of selected numerical variables. 2.1 Data Preparation and Feature Engineering Before the training of the model, there were several data pre-processing steps that had to be carried out in order to make sure that the data was suitable for predictive modelling. In data cleaning, issues such as inconsistencies and errors in data were found and sorted out, making sure that the data variables had consistent logical relationships. Careful examination of missing data was conducted in order to maintain the integrity of the data set.

Feature engineering was also performed to improve the predictive power of the dataset. Variables derived were then created from existing attributes to better represent taxpayer behaviour and compliance patterns. For instance, years in operation were calculated from the taxpayer registration date that is the date of commencement of business, while aggregated compliance indicators such as filing behaviour and compliance score were constructed The data set utilized in the research is an using historical filing and payment records from the administratively designed data set that was dataset. specifically tailored to exhibit the common features of taxpayers as documented in the tax Continuous variables such as turnover were administrations' system. The ultimate data set transformed to ensure consistency in scale and contains 5,000 taxpayer data and includes a mixture distribution across the dataset. Categorical of demographic, economic and historical compliance variables, including region, sector and firm data. The demographic data will include place of category, were encoded to make them suitable for residence and number of years in business while machine learning analysis. Encoding ensures that the economic data will include annual turnover categorical information contained can be interpreted and business type. Historical compliance data will numerically, by the model without losing the include audit history, penalty history, filing history underlying meaning of the variables. These preand historical compliance score. processing and feature engineering steps ensured that the dataset was structured appropriately for These variables were selected based on literature predictive modelling using XGBoost. The final identifying compliance history, enforcement and dataset structure is presented in Figure 1.

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2.2 Exploratory Data Analysis (EDA)

Figure 1: Dataset Structure

Exploratory Data Analysis was conducted to gain an initial understanding of the dataset and to identify patterns, relationships and potential anomalies within the taxpayer data. EDA is an essential step in predictive modelling because it enables researchers to examine the distribution of variables, detect outliers and assess relationships among the predictors before model development. Descriptive analyses were first conducted to summarise the main characteristics of the dataset. Some of the key variables that were considered in the study include; turnover, operational years, past compliance score and filing behaviours. Descriptive statistics were performed on the above variables (mean, median, and standard deviation). This was useful in determining the distribution of the variables in terms of variability. In addition, graphical tools were used alongside descriptive tools in order to determine the structure of the data. Distribution graphs were used in the determination of the spread of continuous variables such as the variables; turnover and operational years. On the other hand, bar graphs were used in the examination of categorical variables including taxpayer location and sector. Apart from the above, a correlation test was conducted to determine the association between some of the chosen variables in the dataset. Through the correlation matrix, it became easier to determine if there is any relationship between

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the predictor variables and the level to which such variables could influence the taxpayer’s compliance behaviour. It is important to note that knowledge of the relationships between the variables is critical in building a prediction model since high correlation of the variables can affect the learning capability of the machine learning algorithms. Overall, through exploratory data analysis (EDA), it was possible to get useful information about the dataset. 2.3 Model Development The predictive model was constructed using the XGBoost algorithm. XGBoost represents an ensemble machine learning technique that uses gradient-boosted decision trees, and it has gained recognition due to the superior predictive ability and efficiency it offers in classification. Machine learning techniques are often applied to find relationships and patterns in structured data sets (James et al., 2021). The algorithm builds decision trees in iterations, with every new tree trying to correct the errors made by previous trees. XGBoost was chosen for this analysis due to its ability to work with structured data, to identify nonlinear relations between variables, and to control for interactions between several predictors. Unlike the classical statistical approach (e.g., logistic regression), XGBoost provides the opportunity to capture more complex structures within the data set.

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training dataset to assess model stability and to minimise overfitting. The ROC-AUC obtained from the test dataset therefore reflects the model's ability to generalise to previously unseen taxpayer records. A confusion matrix was also generated to provide a detailed breakdown of classification results. The confusion matrix allows the identification of correctly predicted compliant taxpayers (true negatives), correctly predicted non-compliant taxpayers (true positives), as well as misclassified observations. This evaluation framework ensures that the classification 2.4 Model Evaluation The predictive power of the model was analysed results are interpreted accurately within the context using classification measures typically used in of taxpayer compliance analysis. machine learning studies. After the pre-processing and feature extraction stage, the dataset was split 3. Results and Discussion into training and testing datasets using 70/30 train/ test ratio. Such a split allows training the model This section presents the empirical findings obtained using most of the data, and leaving some of it aside from predictive modelling and interprets the results for performance assessment. The training dataset in relation to taxpayer compliance behaviour. The was used to build the XGBoost classifier, and the analysis focuses on the performance of the XGBoost testing dataset to assess the predictive power of the model and the identification of the most influential model. predictors of taxpayer non-compliance. The trained XGBoost model demonstrated strong For improving the accuracy and consistency of the predictive performance in classifying taxpayers as prediction model, the five-fold cross-validation compliant or non-compliant. approach was utilized in the training stage of the process. Under this method, all the data are divided Using a 70/30 train-test split, the model achieved an into five equally sized sub-sets. The training process overall prediction accuracy of approximately 84%, takes place using four sub-sets while the remaining indicating a high level of reliability in distinguishing one sub-set is used to validate the model. This between compliant and non-compliant taxpayers. training and validating process is done five times, In addition to accuracy, the model was evaluated each time with a new sub-set used for validation. using the Receiver Operating Characteristic - Area This way, the cross-validation method avoids any Under the Curve (ROC-AUC), which produced chances of overfitting. a score of approximately 0.87. The ROC-AUC value indicates strong discriminatory capability of Several performance metrics were used to assess the model in identifying taxpayers with the high the effectiveness of the XGBoost model. Prediction risk of non-compliance. These results suggest that accuracy was used as a primary metric to measure machine learning techniques can effectively analyse the proportion of correctly classified observations. taxpayer data and detect patterns associated with In addition to prediction accuracy, the Receiver non-compliance. Operating Characteristic – Area Under the Curve (ROC-AUC) was used to evaluate the model’s Table 1: XGBoost Model Performance ability to distinguish between compliant and nonMetric Value compliant taxpayers. Accuracy 0.8433 0.8878 Unless otherwise stated, all performance metrics Precision reported in this study including the ROC-AUC were Recall 0.8121 computed using the independent 30% test dataset F1-Score 0.8483 after model training. During model development, ROC-AUC 0.8745 five-fold cross-validation was performed on the The model was developed using a training data set that consisted of about 5,000 taxpayers’ records. The dependent variable referred to the taxpayers' level of compliance while the independent variables involved demographic, economic and compliance history variables such as turnover, firm category, audit history, penalties, compliance score, and taxpayer region. The variables were selected using theory and availability in ZIMRA data sets.

Source: Author’s Computation 14

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Further analysis of the model outputs was conducted using a confusion matrix, which provided insight into the classification performance of the model. The confusion matrix showed that the model correctly classified the majority of compliant and non-compliant taxpayers, with relatively low rates of misclassification. From these cases, 608 compliant taxpayers were classified with respect to their compliance score and 657 non-compliant taxpayers were correctly classified as non-compliant. The number of compliant taxpayers identified as non-compliant (83 cases) was rather low, which is false positive; although it could result to more administrative follow-up it does not directly affect revenue collection. In contrast, 152 non-compliant taxpayers were ignored by the model, yielding false negatives. While these missed cases represent the main threat factor to compliance detection and monitoring, they are only relatively minor in their scale in a risk-based audit environment. Collectively, both the distributions of the correct classifications and misclassifications indicate that the model strikes the right balance between effective enforcement and effective accuracy, which allow it to help ZIMRA implement particular compliance policies. A low number of false positives (83) is actually a positive in an operational sense since it alerts to any unnecessary audits. Some non-compliant taxpayers did not get caught but the error margin for risk-based compliance systems is within an acceptable range. Results from the confusion matrix demonstrate that the model has strong potential to support compliance risk management by assisting revenue authorities in identifying taxpayers who are more likely to default on their tax obligations. Table 2: The Confusion Matrix Predicted Predicted Compliant Non-Compliant Actual Compliant 608 83 Actual Non152 657 Compliant Source: Author’s Computation 3.1 Feature Importance Analysis Feature Importance analysis was conducted to find out the factors that had the greatest impact on predicting taxpayer non-compliance. For machine learning models like XGBoost, feature importance refers to the contribution of each individual feature towards the

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model performance. Understanding the importance of each feature is very important when it comes to explaining the results of the model and understanding the main factors driving the taxpayer behaviour. The results showed that penalty history was the most important variable for the prediction of noncompliance, meaning that taxpayers who received penalties in the past tend to be non-compliant in the future. The other features were region, firm size, audit history, previous compliance score, and turnover. All these results are consistent with the existing literature, where the main factors affecting taxpayer compliance include economic traits, exposure to enforcement, and past compliance behaviour (Alm et al., 2021; Torgler, 2020). Figure 2 represents the feature importance plot obtained through the trained XGBoost model. The plot presents an order of the predictor variables on the basis of their significance in classifying the target variable. According to the findings, penalty history emerges as the most significant predictor of tax non-compliance by the taxpayers. From this observation, we can conclude that the taxpayers who have faced penalties in the past have a tendency to act in a non-compliant manner. Some of the other important predictor variables identified from the model are region, firm size, audit history, previous compliance score, and turnover. These findings support the previous studies that have highlighted the significance of compliance history and experience with enforcement in influencing the taxpayers' behaviour. Thus, the feature importance analysis provides meaningful implications for tax administrations in that the discovery of features related to higher risks of compliance will allow for improved targeting and efficient use of enforcement efforts by revenue administrations. It is shown that the application of predictive analytics can serve as the foundation of evidence-based compliance management through better targeting of compliance activities. Overall, the results demonstrate that predictive analytics can significantly enhance tax compliance monitoring by enabling tax administrations to identify high-risk taxpayers more effectively. By incorporating machine learning techniques such as XGBoost into compliance management systems, revenue authorities can allocate enforcement resources more efficiently and shift from reactive enforcement approaches to proactive riskbased compliance strategies. 15

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Figure 2: Feature Importance Plot

4. Conclusion In this research, an approach based on machine learning techniques using the XGBoost model was developed in order to detect non-compliant taxpayers. The purpose of the study was to investigate the possibilities of using predictive modelling techniques to determine patterns associated with the noncompliance of taxpayers and, thus, help manage the risk of non-compliance by taxpayers. The study used a dataset containing more than 5000 observations with demographic, economic and compliance history information about taxpayers. From the results, it is evident that the XGBoost model performed very well with a level of accuracy of around 84% and an ROC-AUC score of about 0.87. One of the major strengths of the XGBoost prediction model is the ability of detecting the interaction between several taxpayer attributes. From the evaluation of the feature importance, some of the important factors were identified such as penalty history, audit risk, size of

firms, compliance scores, and turnover. The results also demonstrate the usefulness of machine learning models in analysing complex relationships within taxpayer data. Traditional statistical methods often struggle to capture nonlinear relationships between multiple predictors and taxpayer behaviour. However, the XGBoost algorithm is capable of modelling these interactions effectively by combining multiple decision trees through extreme gradient boosting. Resultantly the model is able to identify subtle patterns in taxpayer characteristics that may indicate higher probabilities of non-compliance. This capability highlights the potential of machine learning tools to enhance compliance risk analysis within tax administration systems. The developed study has promising findings in general. However, it also has certain limitations. The predictive model was developed using a structured dataset reflecting typical administrative taxpayer variables, which may not fully capture behavioural and institutional factors influencing taxpayer THE ZIMBABWE INSTITUTION OF ENGINEERS

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compliance. More so, the model focuses primarily on measurable administrative indicators, while there are factors such as taxpayer personal attitudes, trust in the government and perceptions of fairness by the revenue authority that also influence compliance behaviour. Nevertheless, the findings demonstrate important practical applications for tax administrations. Predictive models such as XGBoost support revenue authorities in identifying high-risk taxpayers, improving audit targeting and allocating enforcement resources more efficiently. By integrating predictive analytics into tax administration systems, authorities can shift from reactive enforcement strategies toward proactive compliance management.

5. Acknowledgements

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning: With applications in Python. Springer. Kleinberg, J., Ludwig, J., Mullainathan, S., & Obermeyer, Z. (2018). Algorithmic fairness. AEA Papers and Proceedings, 108, 22–27. OECD. (2019). Tax administration 2019: Comparative information on OECD and other advanced and emerging economies. OECD Publishing. OECD. (2022). Tax administration digital transformation: A global perspective. OECD Publishing.

Torgler, B. (2020). Tax morale and compliance: Review of evidence and policy implications. The author expresses sincere gratitude to Eng. Springer. H. Mazhokota, the supervisor, for the invaluable guidance, constructive feedback and academic World Bank. (2022). Enhancing tax administration support provided throughout research. Appreciation through digital transformation. World Bank is also extended to the Graduate Business School at Publications. Chinhoyi University of Technology for providing the academic environment that made this study Zimbabwe Revenue Authority. (2023). Zimbabwe possible. The author further acknowledges the Revenue Authority annual report. Zimbabwe encouragement and support from colleagues, friends Revenue Authority. and family members whose motivation contributed to the successful completion of this work.

References Alm, J., Bloomquist, K. M., & McKee, M. (2021). On the external validity of laboratory tax compliance experiments. Economic Inquiry, 59(1), 21–37. Alm, J., & Torgler, B. (2011). Do ethics matter? Tax compliance and morality. Journal of Business Ethics, 101(4), 635–651. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). Association for Computing Machinery. Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). SAGE Publications. THE ZIMBABWE INSTITUTION OF ENGINEERS

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DEVELOPING A REGIME-AWARE LONG SHORT-TERM MEMORY (LSTM) MODEL FOR GOLD PRICE PREDICTION ON CONTRACT FOR DIFFERENCE (CFD) BROKERS IN ZIMBABWE Patricia Gwanhoya1, Miranda Sanyanga2 1Graduate Business School, Chinhoyi University of Technology, Zimbabwe 2Graduate Business School, Chinhoyi University of Technology, Zimbabwe patriciagwanhoya@gmail.com Abstract Gold brings in more than 40% of the foreign currency income of Zimbabwe yet the two groups closest to the metal and most vulnerable to gold price changes benefit very little from their efforts. These are retail Contract for Difference (CFD) traders and smallscale miners. Small-scale miners who provide more than 60% of national gold production, sell on cashflow pressure instead of strategy and forfeit revenue in prolonged rallies. On the other hand, statistics reveal that more than 70% of retail traders accounts lose money. Both parties constantly mistime their decisions largely due to their lack of affordable and intelligent instruments that identify shifting gold market regimes.

address class imbalance and a chronological 80:20 train-test split maintained the sequence. A single-layer LSTM with 18,627 parameters outperformed a deeper alternative, achieving 95.4% test accuracy, 100% recall on Buy and Sell signals and 85.7% recall on Hold signals. Four-year backtesting, which started with a $100,000 investment, has turned into $212,529 a 112.5% cumulative, with a maximum drawdown of 25.06%, which gives a 4.49 ratio of return to drawdown. False positives were all recoverable errors and not missed opportunities. The researcher found that with regime-aware deep learning it is possible to convert the theoretical statistical accuracy of deep learning models into practical economic value on regular consumer hardware. Only publicly available, anonymised market data was used and the model is not presented as financial advice but as a decision support tool. For future studies, the model should be validated with data containing local broker spread and fees, with macroeconomic and sentiment variables added as well as field-tested export-timing protocols with mining cooperatives.

This study therefore aimed to reduce retail trading losses and optimise miners' export timing by developing and evaluating a regime-aware, multiclass Long Short-Term Memory (LSTM) neural network that classifies daily gold market conditions into Buy, Sell and Hold signals. The Yahoo Finance API provided January 2018 to August 2025 daily gold futures data, which was fed into the SEMMA data-mining approach. There were 43 technical features engineered which were then narrowed down to 33 indicators that still captured 95% of the predictive power. Signals were defined by a ±3% Keywords: Algorithmic trading, Deep learning, two-day forward return threshold. A combination Gold price prediction, Market regimes, Small-scale of SMOTE and a focal loss function was used to mining 18

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1. Introduction 1.1 Background Throughout history, gold has maintained a privileged status as the leading safe haven commodity; a store of value in times of economic uncertainty, geopolitical tension and high inflation. The post-pandemic macro-economic landscape, defined by on-going inflationary pressures and a growing geopolitical tension, has driven gold to record prices. This has seen the metal staying above US$2,400 per ounce for the first time in 2024, fuelled by robust demand from institutional investors and central banks (World Gold Council, 2025). The rally would then gain momentum: On 8 October 2025, gold closed above the US$4,000 per ounce mark for the first time ever, and continued to rise, driven by investors' longing to escape a faltering US dollar, trade tensions, persistently high inflation, and on-going geopolitical turmoil (CNBC, 2025). This historic appreciation has made gold a much more attractive and active investment and speculation vehicle, and brought a new class of investors into the financial markets.

achieved as well as big losses. CFD brokers are intermediaries that enable the access to the trading platform and offer a wide variety of markets, leverage and other services for a fee and a spread. Since the price of CFD's is the same as that of the underlying asset, a trader who funds a brokerage account and buys or sells a gold CFD is essentially speculating on the movements of physical gold. Despite the fact that the CFD market is relatively well established in Europe and it is highly regulated, the trading of these products is still potentially dangerous for retail investors: in all EU jurisdictions studied, between 74-89% of retail investor accounts lost money when trading CFDs, with average losses per retail client ranging between €1,600 and €29,000 (ESMA, 2018).

The challenge is that financial markets are not a homogeneous market but rather that they switch between different underlying "market regimes". A market regime is a statistically recognisable state or structure which the market can be in that persists over time and characterised by its own set of This new participation is nowhere more evident volatility, trend directionality and autocorrelation. than in the surge of the Contracts for Difference Generally, there are three types (Nti et al., 2020): (CFDs). CFD is a derivative product which aims to provide the holder with an exposure to either a) Down-trending high-volatility bearish regimes: the rise or fall in the price of the underlying asset, defined by strong downward momentum and large and instead of the underlying asset, differences price range fluctuations between the opening and closing prices of the CFD b) Up-trending: high volatility bull markets when are exchanged (European Securities and Markets the currency is moving upwards at a high speed and Authority [ESMA], 2018). has equally large price swings c) Consolidating (Ranging) low-volatility ranging This means traders can speculate on price regimes, where the market does not have a clear fluctuations without ever owning the asset. Large overall direction and the price changes are small. amounts of leverage mean that big profits can be

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The following Figure 1 presents these market regimes and example trades executed showing the risk to reward for each trade.

a) Downtrending(Bearish Regime)

b) Uptrending(Bullish regime)

c)Consolidating( Ranging Regime)

Figure 1: Adapted from Trading 20

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The ability to detect these market states is extremely important. An efficient trading strategy in a trending market could be a disastrous one in a sideways market and vice versa; for this reason, the detection of latent regime switches has been considered fundamental to the development of robust and adaptive trading strategies and regimes detected via machine learning have been proven to significantly enhance the performance of trading strategies (Akioyamen et al., 2021). Absent this knowledge, retail traders are, in effect, operating blindfolded. The challenge is particularly acute in the Asia-Pacific region, where the number of retail traders is large and growing. For example, an estimated 73,000 retail investors trade or plan to trade CFDs in Singapore (Investment Trends, 2024, via StashAway, 2025).

lot of potential profit. According to Mining Zimbabwe (2026), gold is by far the biggest export earner for Zimbabwe. The country made record gold deliveries of about 36.5 tonnes, exceeding its national targets for 2024 and generated export revenues of around US$2.5 billion (Mining Zimbabwe, 2025) with gold export revenues exceeding US$4 billion in 2025 (Equity Axis, 2026). Such inflows are key to macroeconomic stability. Furthermore, the Zimbabwe Gold (ZiG), which was launched in April 2024, is pegged to gold reserves and foreign currency reserves, which require regular and timely receipts from gold exports to be sustained (Reserve Bank of Zimbabwe, 2024).

The Monetary Authority of Singapore recognises the risks that retail investors face in leveraged markets and classifies CFDs as Specified Investment Products which requires brokers to evaluate a customer's knowledge before granting access and adhere to strict regulations on leverage, margin, and risk disclosure (StashAway, 2025). The continent of Africa, on the other hand, is one of the most important producers of gold in the world, but the local economies and people often don't get the full benefit of this resource. In Ghana, South Africa and Zimbabwe, the artisanal and small-scale mining (ASM) sector is transforming with the economic indigenisation and the post-land-reform dynamics.

At the same time, in Zimbabwe's unique economic context – where foreign exchange is scarce and the local currency is volatile – trading in gold and currency CFDs has become a highly popular but dangerous way for retail traders to take a position on the US dollar and on the international markets. In the last few years, journalistic investigations have found that young unemployed Zimbabweans, have turned to CFD-based trading applications and brokers but poor skills and inadequate financial preparation have resulted in substantial losses (Rest of World, 2021). They share many attributes with their counterparts around the world and are mostly capital-poor, lacking the quantitative skills of institutional traders. They are attracted to CFD brokers under the lure of high leverage but they are not well equipped for the regime changing behaviour of the gold market.

In Zimbabwe, small-scale operators (SSOs) have become the primary source of the country's output, with their increase in output driven by the livelihood pressures and mineral access changes that have followed land reform (Mkodzongi & Spiegel, 2019; Mkodzongi, 2023). In the year 2024 the ASM sector accounted for about 65% of the country's record gold deliveries (Mining Zimbabwe, 2025). Although this transformation is economically empowering, this group is likely to approach the business with limited access to finance, market intelligence and timing models (Mkodzongi, 2023). This means that sales are driven by immediate cash needs and not strategic planning: Field evidence indicates that when ASM communities need cash for family subsistence, they sell their gold as soon as possible at whatever price is available in the local market, and the information gap contributes to value leakage (Rappler, 2023). During bull markets, this has the effect of losing out on a

The result is an unfortunate paradox: the country that is so endowed with the treasures of gold, with its citizens actively trading and mining it, yet consistently failing to optimise the timing of their financial decisions. Capital-constrained retail CFD traders are more likely to witness drawdowns, and small-scale miners lose out on foreign currency earnings during long bull runs because they are not able to export at the best price. The current need is to fill this void by developing a regime-aware Long Short-Term Memory (LSTM) Classifier for Gold price prediction. LSTM networks are chosen because they have proven to be superior to other networks to capture the long-term relationships in the time series data, which is a necessary condition to recognise the long-lasting patterns implied in the market regimes (Fischer & Krauss, 2018). The model can identify market conditions by adding features that are clearly volatility, momentum, and trend. The target variable is a future price action of 3%, and is

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crafted to trigger high conviction buy and sell signals when the market is strongly trending while at the same time, minimising the risk exposure in a ranging market. The final goal is to develop a decision-support tool that will help retail traders trade with more confidence and small-scale miners time their exports optimally, allowing them to assist not only in their own financial security but also in the economic stability of their country. . 1.2 Nature of the Problem The volatile and regime-shifting nature of the international gold market consistently erodes trader capital and suffocates miner revenue in Zimbabwe. The consequences have been devastating: broken dreams, chronic sector undercapitalisation, and a critical leakage of national foreign currency income since gold constitutes over 40% of reserves (Makina, 2024). Ideally, the entire Zimbabwe's small-scale miners and retail traders, who produce in excess of 60% of the country's gold, should be equipped with intelligent, regime-aware tools to confidently participate in trending markets as well as time export during sustained bullish trends, thereby transforming gold into an engine for poverty alleviation and foreign currency acquisition (World Bank, 2023; Mkodzongi, 2023). Nevertheless, the existing circumstances present a pronounced divergence from this ideal. The local reality is marked by a critical analytical deficit; prevailing forecasting methods are inadequate for a market that shifts between trending and ranging states (Nti et al., 2020). This leaves retail traders, who are already facing a global loss rate of 7080% (European Securities and Markets Authority, 2024); exposed to psychological distress and futile manual analysis against institutional algorithms. Miners, conversely, are forced to sell based on cash flow needs rather than strategy, often at a loss. This situation has led to documented instances of acute emotional distress, with financial advisors in Harare noting that clients are suffering from "deteriorating mental health, including anxiety and depressive episodes, directly linked to rapid trading losses" (Chikwava & Moyo, 2024, p. 14). To address this critical problem, this research is justified in developing a targeted intervention; a regime-conscious Long Short-Term Memory (LSTM) neural network engineered to generate high-conviction trading and export signals for 22

the Zimbabwean gold market. By specifically triggering signals only when a significant future price movement (≥3%) is anticipated in optimal trending conditions, the model is designed to ensure a high risk-to-reward ratio for retail traders while simultaneously enabling small-scale miners to optimise their export timing. This practical tool aims to directly convert the prevailing cycle of loss and missed opportunity into a viable pathway for individual and national financial benefit. 1.3 Contribution of the paper. The findings of this study are important to three audiences: policy makers, practitioners and academics. The empirical findings can be used to guide more targeted regulatory and export policies for Zimbabwe's gold industry for policymakers. Understanding the dynamics of regime-aware predictive models to optimise export timing can help reduce economic leakage and maximise foreign currency revenues, in the context of the government's efforts to formalise the artisanal and small scale mining sector and its gold exports (Mining Zimbabwe, 2026). This evidence can also help achieve the balance of regulating and supporting small-scale miners and retail traders in the interest of inclusive economic growth. In practical terms, the research presents a viable solution for practitioners, especially Zimbabwe's retail traders and small-scale miners, amidst the unpredictable world of gold trading, by providing more consistent trading signals based on cuttingedge machine learning techniques. The regime-aware LSTM model aims to assist these capital-constrained actors in minimising losses from trading in markets with no trend and from mistimed exports, and so enhancing their profitability and financial sustainability. The research aims to enhance the skills and achievements of real traders in the market by empowering them to make informed decisions based on data analysis, considering the specific challenges and opportunities of CFD trading in Zimbabwe and the prevailing conditions in the gold market. From an academic point of view, the study introduces the use of deep learning to a niche commodity market, which nevertheless represents an important economic opportunity. It fills in the gaps in the literature of regime-aware time-series forecasting in the realm of gold price forecasting on CFD's in Africa by introducing the use of volatility, momentum and moving-average features in a THE ZIMBABWE INSTITUTION OF ENGINEERS

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classifier optimised for retail trading conditions, and also for export-timing purposes specific to the Zimbabwean context. The interdisciplinary nature of the research also paves the way for future areas of research in financial machine learning, commodity export optimisation and emergingmarket microstructure analysis, with implications of international significance both theoretically and methodologically. The study is thus practically and theoretically significant in the sense that it not only has the potential to give policy direction to the Zimbabwean gold sector but also enhance the practice of retail trading and mining as well as provide a substantial contribution to the academic fields of financial machine learning and market regime analysis.

2. Previous Work

2.1 Strides made in financial forecasting The field of financial forecasting has seen a significant shift from traditional statistical models to fullyfledged deep learning frameworks. Among these, the Long Short-Term Memory (LSTM) network stands out with its gated memory structure that has revolutionised the ability to recognise temporal patterns in market data. Systematic review evidence indicates that deep learning models, including LSTMs, considerably outperform their traditional machine learning counterparts in financial forecasting tasks (Sezer et al., 2020). Fischer and Krauss (2018) empirically proved this superiority, showing that LSTMs are able to extract statistically significant predictive signal from large-scale equity data, hence solidifying their effectiveness in modelling complex market dynamics. Since then, there have been several advancements, such as attention mechanisms and hybrid architectures that have focused on improving feature extraction and temporal focus, such as convolutional–recurrent models used directly to predict gold prices (Livieris et al., 2020; Nti et al., 2020). Despite this, there are still many hurdles to overcome, particularly in the area of model interpretability and when market regimes shift. Learned patterns decay if the statistical regime from which they were learned changes: the relationships of gold with its traditional macroeconomic drivers change significantly during times of financial stress (Akhtaruzzaman et al., 2021), and the market efficiency is not static but evolves over time and varies across assets (Mirzaee THE ZIMBABWE INSTITUTION OF ENGINEERS

Ghazani & Jafari, 2021). However, directionally optimised models which do not reflect this regimedependency of financial volatility experience an obvious loss of performance at the market state transitions right at the very time of when reliable signals are most needed. In fact, the existing literature is beginning to move toward regime detection, earlywarning systems for regime switches (An et al., 2025) and interpretable artificial intelligence (Černevičienė & Kabašinskas, 2024) as a way to bridge the gap between the performance of theoretical models and their financial usefulness in heterogeneous markets. It is then logical that regime-aware overlays have already been demonstrated to yield materially superior riskadjusted returns than their regime-blind counterparts (Uysal & Mulvey, 2021). The need to fill this regime awareness gap is most pressing in emerging markets where the high volatility of market prices and structural inefficiencies make generic models impractical. This gives rise to a clear need for instruments that are more regime-sensitive and context-specific, focusing on delivering solid risk-adjusted returns rather than simply prediction performance. The present study is thus motivated by the specific disconnect between the supreme theoretical functionality of LSTMs and their practical inadequacy in under-serviced and risky markets like Zimbabwe's gold market. 2.2 Empirical review The empirical history of deep learning in finance is large and has shown great potential but also unsettling contradictions. Fischer and Krauss (2018) showed that LSTM networks outperformed traditional benchmarks statistically, and delivered consistent excess returns in a controlled back-test setting. This positive finding supported by the systematic review conducted by Sezer et al. (2020) which analysed the literature of deep learning forecasting from 20052019 and concluded that deep learning models especially hybrid and LSTM based models, regularly and significantly outperformed traditional machine learning models. This academic success story, however, appears to stand in stark contrast to the reality on the market: Retail CFD accounts have been reported to lose between 74- 89 %, even as the number of these increasingly sophisticated analytical tools becomes widespread (ESMA, 2018). The above laboratory success and real-world failure paradox indicates that the research community might be optimising for the wrong metrics, precisely the point made by Rudin (2019) who argued that the marginal gains in black box accuracy often come at the expense 23

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of interpretability and practical utility. The second empirical anomaly is geographical research bias. In the survey of the financial forecasting literature by Nti et al. (2020), African and frontier markets were almost completely ignored and the literature is highly skewed toward developed and major markets. The existing evidence suggests that this omission is far from benign: Market efficiency and its development vary significantly between commodity and currency markets (Mirzaee Ghazani & Jafari, 2021), again gold's connection with equities and macroeconomic factors changes drastically during crisis periods (Akhtaruzzaman et al., 2021). This is a blind spot that is particularly worrying in view of the fact that the majority of the gold produced in the world is in, and is increasingly traded from, emerging economies.

results of a hybrid CNN–LSTM model in the forecasting of gold price, however, such a type of hybrid complexity sits uneasily with deployment to non-specialist users. This is a problem addressed by Rudin's (2019) scorching criticism of black-box systems in high stakes scenarios: explainability should be regarded as a mandatory requirement rather than as an optional feature. The systematic review of explainable artificial intelligence in finance reveals that there is still a perceived tension, as the most effective models are often the least transparent ones (Černevičienė & Kabašinskas, 2024). This poses a practical dilemma for developers: to make the models transparent yet less accurate – or to keep them opaque and less usable for the exact traders that require them. These empirical results have four implications for the current research.

The adaptive market hypothesis evidence is telling here: efficiency in gold and related commodity markets is not constant but is time-varying (Mirzaee Ghazani & Jafari, 2021), meaning that the stationarity assumption that most LSTM architectures make may simply not be true in the Zimbabwean environment and other frontier markets where volatility is typically high and liquidity low. The regime detection literature comes with its own empirical and methodological compromises. Uysal and Mulvey (2021) presented strong empirical evidence of a regime-switching framework improving risk adjusted returns, which is done by systematically decreasing allocation into a state of the market that is unfavourable. However, their strategy depended on a large macroeconomic data set and supervised learning infrastructure rarely found in emerging market environments.

One issue is that the geography of the literature is biased, and thus when implementing LSTMs in Zimbabwe's unique market setting, the feature engineering and architectural assumptions must be revisited. Next in line, the balance between accuracy and interpretability should be carefully considered and may require hybrid approaches that combine the two. Thirdly, literature on regime detection indicates that simpler, more robust and more computation-friendly methods are likely to perform better in data-scarce settings than more statistically sophisticated alternatives. Finally, the gap between academic measures and real world results requires a reassessment of academic metrics, moving beyond statistical performance to more real-world measures like risk adjusted returns, regime changes and transaction costs. The above-mentioned inductive observations inform methodological decisions and criteria in this study and enable it to In contrast, Akioyamen et al. (2021) showed that speak directly to the significant disconnect between quite simple and computation feasible methods the sophistication of algorithms and their use in such as PCA on public datasets followed by emerging economies. k-means clustering can perform a good enough market regime identification to allow for profitable 2.3 Research gap identification trading. At the same time, Akhtaruzzaman et al. The above review reveals an incredible uniformity in (2021) demonstrated that the hedging and safe the gaps in the research that have contributed to the haven role of gold itself has evolved over the course justification of the present study. Although significant of the COVID-19 crisis, making it a "moving advances have been made in the development of target" for any regime detection algorithm. These advanced forecasting structures, there remains a conflicting approaches reflect unresolved tension in major gap in the literature between the algorithmic the literature between statistical sophistication and performances and the implementation in real utility. The debate on interpretability is perhaps the trading conditions, especially in emerging markets. most polarising one between research and practice. This study tackles the gap across several dimensions. Livieris et al. (2020) showed excellent short-term The greatest gap is between geographical scope 24

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and methodological complexity. The financial forecasting literature focuses almost exclusively on developed countries, and there is a noticeable knowledge gap on the performance of such complex models in the structurally less efficient frontier economy settings associated with high volatility, as Nti et al. (2020) reported. This bias is particularly worrisome in the light of the time-varying and asset-specific nature of market efficiency in gold (Mirzaee Ghazani & Jafari, 2021) and the abrupt change of statistical relationships of gold during crises (Akhtaruzzaman et al., 2021). The notion that models which are developed on stable, liquid markets would be transferrable to volatile emerging markets is a very dangerous oversimplification – a fact which this research seeks to challenge by focusing more specifically on the gold trading market in Zimbabwe. The second major disconnect is between the predictive power of forecasts and the usability of the trading systems. On the one hand, Fischer and Krauss (2018) have shown that LSTM networks can provide statistically significant improvements in forecasting, while on the other hand, ESMA (2018) reported a loss ratio in retail CFD accounts between 74 and 89 per cent. The message from this paradox is that the research community has been focused on creating incremental performance improvement in statistics, and not on producing useful trading strategies. The quest for black-box accuracy often takes a toll on interpretability and usability as Rudin (2019) pointed out; this is the price that resourceconstrained traders have to pay when using the black box approach The regime detection literature reveals a third important methodological gap that is lack of practicability. Uysal and Mulvey (2021) showed that regime-switching models can make a significant contribution to risk-adjusted returns; however, their model assumed computational power and data infrastructure that is not often provided in the emerging market space. This generates an implementation gap where traders, who could most benefit from regime-aware strategies, fail to obtain the benefits from academic advances. Fortunately, some progress is possible since there are certain good signs of accessible techniques to detect regimes effectively (Akioyamen et al., 2021). It is precisely what the present research will do: incorporate regime awareness into the features that traders use such as volatility, THE ZIMBABWE INSTITUTION OF ENGINEERS

momentum and trend—and not into a large set of complicated statistical machinery. In addition, the literature suffers from a worrisome separation of the disciplines of technical forecasting research and behavioural finance insight. Although many studies have maximised the predictive power of the LSTM network, few studies have incorporated the psychological behaviour required to explain the failure rate observed by regulators, a phenomenon that is supported by the empirical literature, which has shown a significant increase in stress and anxiety among retail day traders (Bin Abdulrahman et al., 2022). This research brings all these elements together in one system that not only forecasts price action, but trades with discipline by using high conviction, parsimonious signals that have pre-specified risk parameters, thus taking care of both the technical and the psychological aspects of successful trading. The evidence as a whole reinforces the holistic nature of this study's response to these interrelated gaps. It is a regime-aware LSTM architecture specifically built for Zimbabwe's gold market, departing from the geographical constraints found in previous studies. It challenges the gap between academic achievement and the failure in real world by optimising for utility metrics. It overcomes the implementation gap which has hindered the emergence of advanced tools by market participants. This is done by providing an accessible regime detection via feature engineering instead of any complex statistical model. It combines technical analysis and behavioural finance—focusing on the individual's psychology, and this helps overcome the discipline problem which many of the previous methods had. This multi-dimensional reaction to the well-defined research requirements makes the current study potentially useful in the academic understanding and practical application in financial forecasting for the emerging markets.

3. Methodology 3.1 Research Design and Data The study adopted a positivist philosophy and a quantitative design, operationalised through the SEMMA (Sample, Explore, Modify, Model, Assess) data-mining methodology, whose phase-gated structure provides an audit trail for every analytical decision and supports the disciplined workflow that contemporary financial machine learning demands (Kelly & Xiu, 2023). Secondary data were collected from the Yahoo Finance historical market

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data API via the yfinance Python library, a free and publicly accessible source chosen deliberately so that the entire pipeline, from data collection to deployment, remains reproducible by researchers and practitioners without institutional data subscriptions. The instrument sampled was the continuous Gold Futures contract (GC=F), the global benchmark to which Zimbabwean prices are directly tied, since gold is exported internationally rather than priced locally. Sampling was purposive rather than random, an appropriate strategy where the training data must represent economically meaningful market states rather than a statistical population.

variable (χ² = 156.32, p < .001). This indicated that the regime information can be supplied to the network explicitly as per the evidence that regime-informed models improve investment decision-making (Uysal & Mulvey, 2021). The dashboard in figure 2 shows some of the findings from the exploratory data analysis performed.

Daily observations spanned 1 January 2018 to 1 August 2025, approximately 1,900 trading days, selected to expose the model to ranging markets (2018-2019), the COVID-19 volatility shock (2020), the bearish monetary-tightening phase (2021-2022) and the bullish rally of 2023-2025. The scope was restricted to daily closing prices and derived technical indicators, excluding intraday and proprietary fundamental data, because the target users make decisions on a daily cadence using freely available information. After removing rows for which the forward-looking target could not be computed, 1,823 complete observations remained, a sample that comfortably supports the parameter count of the final network given the parsimony principles emphasised in contemporary deep learning practice (Prince, 2023). All analysis was conducted in Python using the TensorFlow/Keras, scikit-learn and imbalancedlearn libraries within Jupyter notebooks. 3.2 Exploratory Analysis Exploratory analysis confirmed the stylised behaviour of financial returns: daily gold returns exhibited leptokurtosis and mild negative skewness. Formal tests detected significant ARCH effects, the volatility clustering that modern forecasting research identifies as a central challenge for predictive modelling (Masini et al., 2023). The price variables (Open, High, Low, Close) were highly correlated (r>0.95), which indicated the need for feature selection to solve this. Market regimes were determined by applying rolling linear-regression trend strength and price location in relation to the 50-day and 200-day moving averages, which resulted in the bull, bear and range conditions. The results of the chi-square test of independence showed that a significant association exists between the classified regime and the distribution of the target

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Figure 2: Exploratory Data Analysis Key Findings 3.3 Feature Engineering and Target Definition Sixty-three candidate features were derived from six groups: price-based features (average price, high-low spreads); momentum features (5-day, 10-day, 20-day rate of change, Relative Strength Index); trend features (MACD and histogram, Bollinger Bands, trend slope); moving averages (simple, exponential, 5-day to 200-day, crossovers and divergences); volatility features (Average True Range over 5-20 days, rolling standard deviations of returns); and volume-based features (volume ratios, lags and total traded value). Calendar included factors to allow for possible seasonality. The target variable was determined from the 2-day forward return: if the 2-day forward return was greater than +3% then the observation was labelled Buy wherein if the 2-day forward return was less than -3% then the observation was labelled Sell, otherwise it was labelled Hold. The two-day timeframe allows miners to set up export arrangements, while the ±3% price tolerance, based on the standard gold price volatility and the cost of CFD trading, guaran-

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tees that the signals are not false. The distribution, 22.3% Buy, 60.7% Hold and 17.0% Sell, indicated the real market conditions and significant class imbalance needed to be dealt with. 3.4 Preprocessing Missing values arising from rolling-window calculations were backward-filled, the first daily return was set to zero, and the categorical regime variable was forward-filled, consistent with the persistence of market regimes documented in regime-switching applications (Uysal & Mulvey, 2021). To reduce the influence of outliers while retaining information on the tails, extreme values were winsorised at the 1st and 99th percentiles. The features were ranked by Gini importance using the Random Forest classifier and the features that accounted for 95% of the cumulative importance (33 out of 43 features) were retained, which reduced input dimensionality by ~23%. One of several countermeasures against data leakage, which is a well-known way to artificially inflate forecast performance, was min-max scaling to [0, 1] fitted on the training data and applied to the test data.

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The data was split chronologically 80:20 with the earlier observations used to train the model and the later ones exclusively used for testing as required by the evaluation protocol for dependent time series (Hewamalage et al., 2023; Hyndman & Athanasopoulos, 2021). The inputs were then re-organised into 10 day sequences, where each sequence predicted the signal on the next day. The Synthetic Minority Over-sampling Technique (SMOTE) was used to address class imbalance and was selected and applied to the training partition only, as it is one of the most effective oversampling techniques in the current imbalance literature (Rezvani & Wang, 2023). 3.5. Model Architecture and Training Two LSTM architectures were designed and compared. The complex variant comprised of two LSTM layers (64 and 32 units) followed by batch normalisation, dropout and a dense hidden layer, resulting in 35,843 parameters. The streamlined variant consisted of a single 64-unit LSTM layer with dropout (rate 0.3), followed by a softmax output of three units, which amounted to 18,627 parameters and 0.24 MB of memory, about half of that of the complex model. Both were trained with the Adam optimiser, which is still a powerful default for systematic benchmarking of deep learning optimisers (Schmidt et al., 2021), along with L2 regularisation, gradient clipping and early stopping (patience of ten epochs. Dropout was the primary regularising method against co-adaptation, as per the current evidence of its regularising power (Salehin & Kang, 2023). The easily classified examples were down-weighted by a factor of γ = 2.0 and α = 0.25, similar to the costsensitive focal loss function that recent imbalance research has shown to work well in conjunction with resampling (Rezvani & Wang, 2023). Hyperparameters were tuned by grid search over 54 combinations of LSTM units (32, 64, 128), dropout rate (0.2, 0.3, 0.4), learning rate (0.001, 0.0001) and batch size (16, 32, 64). Stability was explored using 5-fold time series cross validation; that is, training on earlier folds and testing on later folds. When cross-validation revealed instability in the complex architecture; the simpler model was adopted. This is consistent with the principle that limited, noisy financial data favour parsimonious networks. Baselines included logistic regression and two tree ensembles (Random Forest and XGBoost) which are

still strong classifiers on tabular data (Grinsztajn et al., 2022). 3.6 Evaluation and Backtesting Per-class precision, recall and F1-scores, confusion matrices and area under the ROC curve were used for assessing the predictive performance, which are much more informative than raw accuracy under class imbalance (Chicco & Jurman, 2023). Precision on Buy and Sell signals was considered particularly significant since false signals have a direct impact on the small, restricted funds of retail traders. Recall, on the same classes, was crucial to ensure that no real trading opportunities were lost. Model calibration and prediction confidence were checked to ensure that the probabilities expressed in the model could be trusted for threshold-based filtering; a model with 80% prediction confidence should be correct about 80% of the time, and it would be dangerous to use a model that is overconfident in leveraged trading (Silva Filho et al., 2023). A second analysis of the error patterns was conducted by market regime to see if there were any market regimes that tended to have a high concentration of error. Then a practical test was performed in a back-testing simulation using Buy signals to open long gold CFD positions, Sell signals to open short positions or to close long positions, and Hold signals to keep capital aside, with a realistic starting capital of $100,000 and standardised position sizing. Performance was summarised by cumulative return, maximum drawdown, the return-to-drawdown ratio and comparison against passive buy-and-hold, as a model that outperforms chance statistically but not economically would not meet the purpose of the study. Finally, deployability was measured in terms of training time, inference latency and model size, which are all benchmarks selected to represent the consumer hardware available to users in Zimbabwe, not institutional infrastructure. 3.7 Ethical Considerations Only publicly available, anonymised, aggregated market data (without any personally identifiable information) was used in the research, No humansubjects approval was obtained and data handling met with the provider's terms of service. Preprocessing and modelling choices were documented in jupyter notebooks according to the FAIR principles extended to research software (Barker et al., 2022) and

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negative results such as cross-validation instability were also reported in addition to positive results to maintain research integrity. Since the work is used to inform financial decisions, the model is offered only as a research prototype and financial decision support aid and not as a certified financial advisor. Past performance does not guarantee future results, and leveraged trading involves significant capital risk. The societal intent, democratising analytical tools for resource-constrained users, was balanced against the risk of over-reliance through honest reporting of limitations.

4. Results 4.1 Features relevant to Regime Detection (Objective 1)

Random Forest importance analysis showed that the most important regime discriminators were the volatility measures. Twenty day rolling volatility was the first feature (with importance of 0.0473), followed by Bollinger Band width (0.0440), and short term momentum and moving average features. The top 20 features are shown in Figure 3. The cumulative importance curve had a typical elbow shape, with the top ten features contributing approximately 35% of the total importance, the top twenty contributing approximately 65%, and 33 features contributing 95%, which were used to form the final input set. Volume based features were less important than price based features, suggesting that gold regimes are largely price and volatility driven and not liquidity driven.

Figure 3: Feature selection THE ZIMBABWE INSTITUTION OF ENGINEERS

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4.2 Model Selection and Training (Objective 2) The simpler single-layer LSTM beat the more complicated stacked architecture which, when run on the small data set, resulted in perfect scores on the validation set indicative of over-fitting. The selected model converged after 40 epochs (355 seconds total, ~9 seconds per epoch). Tracking the training accuracy very closely throughout showed that training and validation losses were decreasing smoothly and in parallel. This suggested that the model was truly learning and not just memorising the data. The short training time allows the model to be retrained weekly, with new market data, on a standard laptop.

Figure 4: Accuracy and Loss over epochs Figure 4: Accuracy and Loss over epochs The test performance of the models is compared in Table 1. Interestingly, tree based ensembles achieved suspiciously high scores (99% +), which is typical with leakage when splitting the data randomly, and consistent with the known ability of tree models to discover tabular feature combinations that don't generalise across market conditions (Grinsztajn et al., 2022). The low accuracy of 54% for logistic regression confirmed the task needed non-linear modelling. The LSTM's 95.4% was considered to be the realistic estimate of outof-sample capability, obtained under a strictly temporal split with a minimal train-validation gap. Table 1: Test-set performance comparison across models Model Accuracy LSTM (single layer, 18,627 parameters) 95.4% Random Forest ≈99% XGBoost ≈99% Logistic regression ≈54%

Assessment Adopted; realistic generalisation Suspected over-fitting/leakage Suspected over-fitting/leakage Linear baseline; inadequate

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4.3 Classification Performance (Objective 3)

Figure 5: LSTM confusion matrix Results on the held-out test data are shown in figure 4 and figure 5. The model achieved a 100% recall for both the Buy and Sell classes, and 85.7% recall for the Hold class. None of the 49 errors were missed trading signals, rather they were Hold signals that were incorrectly identified as Buy (26) or Sell (23). The precision was also excellent across the board: 93.2% in the Buy, 94.1% in the Sell and 100% in the Hold signal. The AUC returned by ROC analysis was 1.00 for Buy and Sell and 0.98 for Hold, which indicated that discrimination was a property of the entire probability space, not an “information lucky” threshold.

Figure 6: Roc Curves THE ZIMBABWE INSTITUTION OF ENGINEERS

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Correct predictions had confidence higher than 0.8; the few errors were between 0.5 and 0.7, indicating that the model was aware of its own uncertainty and that filtering of errors based on the confidence could eliminate most of the remaining errors. This error asymmetry has real-world implications. The model's failure mode is a false negative, which corresponds to a "miss" trend signal in the process of consolidation, while a false positive trend signal can easily be corrected with stop-loss discipline and cannot be recovered by the model; hence, the failure mode of the model is aligned with the risk preferences of capital-constrained users.

Figure 7: Error Analysis The only warning that arose was the highly volatile 5-fold time series cross-validation fold accuracies (1.00, 0.00, 1.00, 0.00, 0.65) with a mean of 0.53 ± 0.45. This was most likely due to concentration of regimes within folds and the non-stationarity of financial data, rather than invalidating the result of the time series split test. The instability, however, indicates that deployment needs to be monitored constantly. It is also easy to run on a computer: the model took about 0.8 milliseconds to make a prediction, and the 0.24 MB model can be retrained in less than six minutes on an ordinary laptop.

Figure 8: Back-testing Results 32

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The trading simulation period was from July 2021 to July 2025. Figure 8 shows a $100,000 portfolio that increased to $212,529, a 112.5% (or ~20.6% annualised) cumulative return for the portfolio. The growth was regime-dependent; between 2021 and 2022, as the portfolio was ranging, it fluctuated between $95K and $120K, but from 2022 to 2023 it was steadily growing through a moderate trend and in the bull market of 2024-2025 the model's “BUY” signals caught the rally from $2K to $3,4K, showing that it was not dependent on a single favourable environment. Risk was contained. Maximum drawdown reached -25.06%, concentrated in the early-2022 ranging market and the late-2023 regime transition, yielding a return-to-drawdown ratio of 4.49, comfortably above the 2.0 conventionally regarded as acceptable for active strategies and consistent with evidence that regime-informed allocation improves risk-adjusted performance (Uysal & Mulvey, 2021). For a retail trader, a 25% peak-to-trough decline is survivable under normal CFD margin requirements. For a miner it represents opportunity cost on unsold stock rather than realised loss. Daily returns were positively skewed and cumulative returns never fell materially below break-even after mid-2022, so drawdowns were temporary rather than permanent erosion. Signal inspection over the final hundred test days showed Buy signals clustering in rallies, Sell signals near local peaks and Hold signals in consolidations, the intended regime-aware behaviour. This is illustrated by the figure 9 below:

Figure 9: Trading signals From a practical trading perspective the distribution of the signals seem to indicate that active trading is a viable strategy, with short-term adjustments of positions instead of a buy-and-hold strategy. The results are further summarised in table 2 below Table 2: Back-testing performance summary (July 2021 to July 2025) Metric Value Initial capital $100,000 Final portfolio value $212,529 Cumulative return 112.5% Annualised return (approx.) 20.6% Maximum drawdown -25.06% Return-to-drawdown ratio 4.49

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5. Discussion The results confirm the study's main hypothesis that incorporating regime awareness in a deep learning classifier enhances the statistical and economic properties of the gold trading signals. The results indicate that volatility measures are the most important features, which supports the regime switching interpretation where the transitions in the market are identified by volatility states instead of price levels.

direct prediction of the gold price by using hybrid deep architectures (Amini & Kalantari, 2024). This advantage is intuitively explained by the fact that the task of classification of tradeable states is coarser and hence more learnable than the task of predicting exact prices and is directly relevant to the decisions that users have to make. Perfect back-tests were achieved with actionable signals, as the majority of potential profit was in a handful of high-movement instances with only a single missed signal. More generally, the results showed that the predictability is in line with the evidence that precious-metals markets are not absolutely efficient, but time-varying, as proposed by the Adaptive Markets Hypothesis (Rana et al., 2026).

This is consistent with recent findings that including explicit regime modelling improves investment decisions (Uysal & Mulvey, 2021). That relative measures such as Bollinger Band width outranked absolute prices is especially pertinent for gold, whose price level shifted dramatically across the study period The cross validation instability needs to be while regime signatures remained comparatively straightforwardly interpreted. Fold accuracies of stable. zero to near perfect are likely to be due to regime concentration in folds that interact with market nonThe superiority of the simpler architecture is stationarity. A model trained on a particular regime consistent with the parameter-to-sample logic of deep distribution may fail completely on another. This is learning on modest datasets (Prince, 2023) and with not meant to detract from the results that have been warnings that the low signal-to-noise ratio of financial temporally validated, but rather to limit them, and to data punishes over-parameterised models (Kelly propose that retraining and automated monitoring & Xiu, 2023). The seemingly perfect scores of the should be done in production and that back-tested tree baselines show a common problem in modern returns (without slippage and some real-world forecast-evaluation advice: evaluation protocols that frictions) should be viewed as best case scenarios. do not take into account temporal dependence favour models that learn combinations of features typical of The implications of practice are real. Retail traders each regime, one of the hazards listed in Hewamalage have access to a decision-support tool that can be set et al. (2023). to stop-loss and confidence levels that they can control and that won't cause them to lose a trade opportunity The more conservative accuracy obtained by the although there will be cases of false alarm. Using LSTM using a rigid chronological split is thus the small-scale miners, daily signals could be combined more reliable indicator of live performance. The into weekly export-timing decisions. These could be imbalance treatment proved to be the deciding factor to hold on to stock during the confirmed bull market and not a cosmetic. The experiments were initially and to sell only at high prices rather than when cashdone without SMOTE and with the focal loss which flow pressure occurs. saw the superficially decent overall accuracy of 85% but only around 40% recall on the minority Buy and However deployment for small-scale miners would Sell classes, making the model unusable. also need to account for inventory costs, minimum export volumes and logistics. The results indicate Oversampling with cost-sensitive loss resulted in that the export timing policy based on the regime 100% recall of actionable-signal, confirming the could increase foreign currency retention as evidence complementary relationship of resampling and for the policymakers. The model's sub-millisecond cost-sensitive learning in imbalanced financial latency and 0.24 MB footprint also allow for benefits classification (Rezvani & Wang, 2023). The economic without institutional infrastructure, furthering the outcomes are favourable in comparison to the latest democratisation of financial technology in resourcegold-forecasting systems which are mostly based on constrained environments.

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6. Conclusion and Recommendations

References

This study set out to determine whether a regime-aware LSTM could convert Zimbabwe's volatile gold market into a navigable one for its most vulnerable participants. Each objective was met. Feature analysis revealed that volatility measures are the most important ones for regime detection; architectural comparison revealed that a simple single-layer network is superior to deeper networks when applied to a small set of financial data; evaluation revealed 95.4% accuracy with 100% recall of actionable signals; and backtesting translated the accuracy into a four-year return of 112.5% with a contained drawdown of -25.06%.

Amini, A., & Kalantari, R. (2024). Gold price prediction by a CNN-Bi-LSTM model along with automatic parameter tuning. PLOS ONE, 19(3), Article e0298426. https://doi.org/10.1371/journal.pone.0298426

The main benefits of the solution are that it matches the user's risk preference with the error profile. More importantly, it is economically and not just statistically valid. In addition, it can be implemented on consumer hardware. The work is limited and constrained its claims. The model was trained on world futures prices and not on local broker quotes, did not incorporate transaction costs in certain parts of the simulation and showed cross validation instability characteristic of non-stationarity, and had not been tested in field conditions. It does not provide advice, it provides decision support. Several recommendations follow. The deployment should include, for practice, confidence-based signal filtering (from about 85% probability), interfaces optimised for mobile devices with intuitive colourcoded signals, embedded risk-management features and co-operative adoption models via mining associations for cost distribution. Incentives for financial-literacy programmes and export timing, which are informed by policy and regulation, would help individual and national interests to converge. Future research should focus on validating the model on local Zimbabwean broker and price data; creating ensembles of LSTMs trained on overlapping temporal windows while implementing online learning to overcome non-stationarity. In addition to that, integrating macroeconomic, news and sentiment features. Opening the black box and building user trust using explainable artificial intelligence, a priority now well recognised in financial machine learning (Černevičienė & Kabašinskas, 2024); expanding the framework to other commodities; and field-testing export-timing protocols with mining cooperatives to validate their operational value. When pursued in concert, these directions would bring the current evidence, from prototype to practice, that carefully designed, regime-aware machine learning can help economically vulnerable market participants.

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European Securities and Markets Authority. (2018). Product intervention analysis: Measures on contracts for differences. ESMA. Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417. https://doi.org/10.2307/2325486 Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654–669. https://doi.org/10.1016/j. ejor.2017.11.054 Grinsztajn, L., Oyallon, E., & Varoquaux, G. (2022). Why do tree-based models still outperform deep learning on typical tabular data? In Advances in neural information processing systems (Vol. 35, pp. 507–520).

Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision (pp. 2980–2988). https://doi. org/10.1109/ICCV.2017.324 Lo, A. W. (2004). The adaptive markets hypothesis: Market efficiency from an evolutionary perspective. The Journal of Portfolio Management, 30(5), 15–29. https:// doi.org/10.3905/jpm.2004.442611 Masini, R. P., Medeiros, M. C., & Vasconcelos, G. F. R. (2023). Machine learning advances for time series forecasting. Journal of Economic Surveys, 37(1), 76– 111. https://doi.org/10.1111/joes.12429 Mkodzongi, G., & Spiegel, S. (2019). Artisanal gold mining and farming: Livelihood linkages and labour dynamics after land reforms in Zimbabwe. The Journal of Development Studies, 55(10), 2145–2161. https://doi. org/10.1080/00220388.2018.1516867

Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020). A Studies, 33(5), 2223–2273. https://doi.org/10.1093/ systematic review of fundamental and technical analysis rfs/hhaa009 of stock market predictions. Artificial Intelligence Review, 53(4), 3007–3057. https://doi.org/10.1007/ He, H., & Garcia, E. A. (2009). Learning from s10462-019-09754-z imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263–1284. https://doi. Prince, S. J. D. (2023). Understanding deep learning. org/10.1109/TKDE.2008.239 MIT Press. Hewamalage, H., Ackermann, K., & Bergmeir, Rana, H. M. U., O'Connor, F., Yerushalmi, E., & Kim, J. C. (2023). Forecast evaluation for data scientists: Common pitfalls and best practices. Data Mining and H. (2026). Asynchronous market efficiency in gold and silver markets: A local currency lens. Finance Research Knowledge Discovery, 37(2), 788–832. https://doi. Letters, 103. org/10.1007/s10618-022-00894-5 Hochreiter, S., & Schmidhuber, J. (1997). Long shortterm memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735 Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). OTexts. Kelly, B., & Xiu, D. (2023). Financial machine learning. Foundations and Trends in Finance, 13(3– 4), 205–363. https://doi.org/10.1561/0500000064 Kristjanpoller, W., & Minutolo, M. C. (2015). Gold price volatility: A forecasting approach using the artificial neural network–GARCH model. Expert Systems with Applications, 42(20), 7245–7251. https://doi.org/10.1016/j.eswa.2015.04.058

Rezvani, S., & Wang, X. (2023). A broad review on class imbalance learning techniques. Applied Soft Computing, 143, Article 110415. https://doi. org/10.1016/j.asoc.2023.110415 Salehin, I., & Kang, D.-K. (2023). A review on dropout regularization approaches for deep neural networks within the scholarly domain. Electronics, 12(14), Article 3106. https://doi.org/10.3390/electronics12143106 Schmidt, R. M., Schneider, F., & Hennig, P. (2021). Descending through a crowded valley—Benchmarking deep learning optimizers. In Proceedings of the 38th International Conference on Machine Learning (Vol. 139, pp. 9367–9376). PMLR.

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Sezer, O. B., Gudelek, M. U., & Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning: A systematic literature review 2005–2019. Applied Soft Computing, 90, Article 106181. https:// doi.org/10.1016/j.asoc.2020.106181 Silva Filho, T., Song, H., Perello-Nieto, M., SantosRodriguez, R., Kull, M., & Flach, P. (2023). Classifier calibration: A survey on how to assess and improve predicted class probabilities. Machine Learning, 112(9), 3211–3260. https://doi.org/10.1007/s10994023-06336-7 Uysal, A. S., & Mulvey, J. M. (2021). A machine learning approach in regime-switching risk parity portfolios. Journal of Financial Data Science, 3(2), 87–108. https://doi.org/10.3905/jfds.2021.1.057 World Gold Council. (2025). Gold demand trends. World Gold Council.

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AUTOMATION OF RICE PACKAGING TO INCREASE ADOPTION OF PRECISION AGRICULTURE IN AFRICA Berlindah Precious Karigeni¹, Evelyn Chipango² Department of Agricultural Engineering, Mechanisation and Farm Infrastructure Development, Zimbabwe. nyashakay2010@gmail.com

Abstract

The increasing demand for rice in Africa requires efficient, reliable, and scalable production and postharvest systems. However, rice packaging in many African countries remains largely manual, resulting in inconsistent weight measurements, high labour costs, packaging errors, contamination risks, and reduced productivity. This study investigates the role of automated rice packaging in increasing the adoption of precision agriculture technologies in Africa. The research focuses on the design and evaluation of an automated packaging system incorporating sensors, load cells, programmable logic controllers (PLCs), conveyor mechanisms, and sealing units. A design, development, and experimental evaluation methodology was employed, where system performance was assessed using packaging accuracy, throughput, filling time, operational efficiency, and error reduction as key performance indicators. The results indicate that automation significantly improves packaging accuracy, production efficiency, consistency, and hygiene compared with conventional manual packaging methods. The integration of sensing, monitoring, and automated control technologies supports real-time data collection and informed decision-making, which are essential components of precision agriculture. Furthermore, automated packaging provides an accessible pathway for agricultural stakeholders to interact with digital technologies, thereby encouraging wider adoption of smart farming practices. Although challenges such as high initial investment, technical skills requirements, and infrastructure limitations remain, the long-term benefits of automation include reduced waste, improved productivity, enhanced product quality, and increased market competitiveness. The study concludes that automated rice packaging is not only a post-harvest improvement solution but also a catalyst for precision agriculture adoption in Africa, supporting sustainable agricultural modernization and improved food security. Keywords: agriculture, automation, efficiency, packaging, precision 38

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1. Introduction Rice packaging technologies in many parts of Africa are still in their infancy. The unavailability and high cost of the machines for packaging products compel processors to resort to the use of low-cost manual devices and manual materials for packing. Such practices lead to problems, arising from mistakes in weighing, work tiredness, alleged dishonest acts, different production rates, unclean packages, wastage of materials, and inefficient filling and sealing operations. All these severe constraints discourage processors from packaging at a larger scale and make it impossible to meet some market-related criteria and compete effectively both locally and internationally. There have been significant advancements in automation, which opened many doors to more efficient and effective post-harvest activities in the recent past. This means that some advanced technologies, such as programmable logic controllers (PLC), load cells, sensors, conveyor systems, and automated sealing systems were integrated into the process of food production and packing. The issue of improvement in agricultural productivity, increase of smallholder incomes and food security is greatly dependent on changes in crop production technologies. Nevertheless, the impact of innovations has affected the operations occurring in post-harvest stages as well. The innovations manifested in areas of product handling, processing, storage, marketing, and consumption may contribute to the better management and more efficient use of agricultural value chains. The implementation of automated operations in packaging can be regarded as part of the objective of precision agriculture that utilizes modern technologies and data-based solutions. It is worth mentioning that precision agriculture involves the use of modern technologies and data-based solutions in order to increase efficiency at various stages of agricultural value chains. The techniques used in precision agriculture can help with product tracking and reducing waste.

of the produced goods, but also high operating costs, poor product standardization and contamination, as well as heavy reliance on human labour, resulting in high levels of post-harvest losses. Therefore, the lack of monitoring and control system limits rice producers’ ability to evaluate essential factors associated with the internal and external quality and safety of the goods thus preventing them from delivering efficient products with high accuracy. 1.2 Importance of the Study The findings of this research are significant because it demonstrates the fact that automation technology can improve the efficiency and precision of the process of rice packaging and reduce post-harvest losses. This research will assist rice growers, agricultural engineers, processors, bakeries, agricultural policymakers, and investors who are searching for methods of using automated packing systems. Also, it will lead to the wider usage of precision agriculture technology in different aspects of agriculture. 1.3 Study Purpose The study aims to investigate the use of automated packaging technology to package rice, in order to get precision and efficiency, product quality, and precision agriculture use, in Africa. 1.4 Study Goals The study will achieve the following goals: • Assessment of the limitations of the current rice packaging method in Africa. • Design and develop an automated rice packaging system, to provide an economic advantage in farm operations. • Determination of the methods to stimulate the use of precision tools by small farmers, with the help of automated packaging.

1.1 Problem Statement Despite the development of numerous technologies for precision agriculture, rice packaging after harvesting is still done in a manual or semi-automatic way in many African nations. These traditional packaging methods do not only bring about imprecise weights

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1.5 Research Queries The research aims to answer the following questions: • What are the primary drawbacks of the manual rice packaging technique in Africa? • How can automation technologies be used to improve accuracy and efficiency in rice packaging tasks? • What are the advantages of using the automated rice packaging technology?

1.6 Research Hypothesis Null Hypothesis (H₀): The use of automated rice packaging and processing systems does not have an impact on the adoption of precision agriculture. Alternative Hypothesis (H₁): Automated rice packaging and processing systems have an impact on adoption rate of precision agriculture. 1.7 Literature Gap Recent studies have explored the automation and precision agriculture. However, very few investigations have examined automated rice packing systems suitable for the conditions of agriculture in Africa. Importantly, most studies have been conducted in industrialized nations, where the climate, infrastructure, technological and financial resources differ from those in Africa. Thus, the present research will analyse the possibilities of low-cost automated rice packing systems which can be beneficial in terms of increased packing precision, reduced post-harvest losses and improved quality of the product and hence the possible implementation of precision agriculture in the rice value chain. 2. Literature Review Among the most significant food crops globally is rice, which has helped achieve food security and enhance economic progress for approximately half of the global population. In Africa, rice consumption has been on the rise due to three significant reasons: population growth, urbanization and changes in food habits and income level (FAO, 2023). Although modern techniques and agriculture activities have been employed to enhance rice production, challenges still exist in rice post-harvest packaging processes. Packaging is a critical point in the rice value chain since its effectiveness influences product quality, food safety, and market competitiveness. Rice packaging in many African countries is largely done using manual labour at weighing, filling, sewing, and sealing stages. Therefore, package weights cannot be reliably defined and calculated leading to loss either due to overfilling products or losing consumer confidence through

underfilling (Mehta, 2018). Apart from that, manual packaging increases the chances of contaminating the product through dust, moisture, insects, and too much human influence which compromises rice safety and shortens its shelf life (Dahan Packaging Machinery, 2024). Moreover, repetitive nature of packaging jobs leads to worker fatigue, which negatively impacts productivity and reduces seal quality during peak times. The hampered usage of manual packaging processes has brought about the automation process in the food processing industry. Automation in manufacturing is the use of computer-controlled systems, sensors, robots, and technology in production with limited human intervention (Groover, 2020). The software automation in packaging uses PLCs, electronic weight systems, conveyor belts, sensors, and robots to accomplish the functions of weighing, filling, sealing, and checking (Palanisamy et al., 2024). Furthermore, the usage of automation in rice packaging is a support for precision farming. Precision farming is the use of information technology, sensors, artificial intelligence (AI), and analytics in agriculture (Gebbers and Adamchuk, 2010). The technology used in precision farming includes GPS technology and software, remote sensing methods, wireless sensor networks, robots, AI, and the Internet of things (IoT), used in field management (Patrício and Rieder, 2018). The application process of precision farming is localized only in the field up to now. Nonetheless, it is pointed out that the processes in the post-harvest period should also be included in the automation process for the purpose of minimizing losses and increasing the value of production. PLC serves as the primary element in connecting the measuring devices, weighing machines, conveyor belts, and sealing machines which not only help in cutting down on human and labour errors but also make operations more flexible (Bolton, 2021). Also, preferred in the process of measuring rice automatically is the use of electronic load measures to ensure food distribution (QS Packaging Machine, 2023). Furthermore, there is a wide range of sensors that can be integrated into the system such as moisture sensors, optical sensors, and proximity sensors that will enable the monitoring of the packaging process (Xia et al., 2025). Besides that, the conveyor system makes sure of the continuous flow of the materials so that manual handling is minimized (Dahan Packaging Machinery, 2024). The fact that the technology can be used close to automated machines means that it can ensure higher productivity in terms of bag processing, stacking, and palletising with reduced risks of accidents (Groover, 2020).

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To enhance the efficacy of inspection procedures and optimize processes in automated packaging methods, machine vision technology and artificial intelligence (AI) systems are being involved. Thanks to machine vision, malfunctions such as grain damage, impurities, colour and packaging defects can be detected quickly and accurately. For instance, the use of deep learning algorithms for creating a rice quality classification system based on machine vision brought about classification accuracy of 97.2% (Xia et al., 2025). Also, artificial intelligence is widely applied in the food sector. It can be used to implement predictive maintenance of milling equipment that is done through prognostics of failures based on equipment performance and subsequent adjustment of production plans with the aim of minimizing downtime (Ayaz et al., 2019).

3. Methodology The study utilized an experimental design research approach, which encompasses the dimensions of design, building, and testing an automated rice packaging machine. The design science research approach was adopted as the study pertains to the designing and the building of an engineering prototype to solve the issue regarding the rice post-harvest packaging in the African rice processing processes. 3.1 The Design and Approach of the Research The study followed the following four steps: identifying the problem, construction of the prototype, experimental testing of the prototype, and evaluation of the feasibility of the technology. Construction of the prototype involves the mixed-type method in line with the quantitative method of experimentation and qualitative method through obtaining information on the acceptance of the technology by using a questionnaire.

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Figure 1: Prototype Architecture, Workflow, and Control Logic of the Automated Rice Packaging Machine 3.2 Design of Automated Rice Packaging System and Experimental Setup The system of automated rice packing was designed with the use of mechanical, electronic, and control components: storage hopper, feeding system, weighing section based on the load cell, sealing system, conveyor system, and control unit based on Arduino. The automated rice packing system executed the processes of feeding the rice, weighing it, filling in the package with rice and sealing. The efficiency of the system was checked through a number of experimentations using processed rice grains, following the completion of a minimum of 30 tests for packaging efforts. 3.3 Data Collection and Statistical Design Data was collected using measuring devices including a scale and timer. Besides, a survey was carried out for farmers of rice plants and other farmers to identify the degree of acceptance of automated packing. The questionnaire consisted of various open and closed ended questions in order to collect information on the efficiency of the equipment. Data was gathered using the five-level scale. Analysis was done using a Pearson correlation regression model to test association and an independent t-test was done.

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3.4 Reliability, Validity and Ethical Factors To assure reliability, it was measured by performing tests again with the packaging system. Validity was measured through calibration of devices and repetition of measurements. Cronbach’s Alpha Values that exceed 0.70 indicate reliability that is acceptable. As for ethical factors, they include the fact that the questionnaire was voluntary, the respondents were informed on the objectives of the research and their confidentiality was guaranteed.

4. Results 4.1 Performance Evaluation of the Automated Rice Packaging Device This section contains the performance evaluation results of the developed automated rice packaging device and the comparison of the results of the automatic rice packaging process with that of the conventional manual system.

Figure 2: Performance Comparison Between Manual and Automated Rice Packaging Systems

In figure 2 the comparison of the performance of the automatic rice packing device with that of the manual process can be found. The experimental results were then in regard to accuracy, throughput, filling time, efficiency and errors evaluated through the independent samples t-test, correlation analysis, and regression analysis. The manual process had a rice packaging accuracy of 93.2% while that of the automatic process was 99.1%. The manual rice packaging process had a throughput of 2392.2 th/kg while that of the automatic process was 3510.8 th/

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kg which was an increase of 145.8% compared to the manual rice packaging process. The filling time for the manual rice packaging process was found to be 25 seconds whereas that of the process was found to be 10.6 seconds which made it 58.1% less than in the manual rice packaging process. The rice packaging efficiency for the automatic rice packaging process was 98.3% compared with 70.1% of the manual system. The rice packaging error of the automatic system was 0.9% compared with 5.1% in the manual system, which was a decrease of 81.3% compared with the manual system.

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Percentage of the rice packaging error of the automatic system was 0.9% comparing to 5.1% in the manual system of the rice packaging processes, indicating a reduction of 81.3% as compared to the manual system of rice packaging processes. The packaging parameters of the manual system and the automatic system were then correlated, thus making it possible to ascertain the existence of a positive correlation between the packaging parameters. The created system of automatic rice packaging proved to be successful in achieving improved performance of rice packaging in comparison to manual rice packaging. Table 1: Descriptive Statistics of Packaging System Performance Parameters Parameter Packaging Accuracy (%)

System

Manual Automated Throughput (bags/hour) Manual Automated Filling Time (seconds/bag) Manual Automated Packaging Error Rate (%) Manual Automated Adoption Intention Score (1–5 scale) Manual Automated

Mean 93.20 99.10 48.00 118.00 74.00 31.00 4.80 0.90 2.74 4.56

Standard Deviation 1.84 0.52 5.21 8.43 6.75 3.12 0.91 0.28 0.63 0.41

Minimum

Maximum

90.10 98.40 41.00 105.00 65.00 27.00 3.50 0.50 2.00 4.00

95.60 99.80 56.00 130.00 86.00 36.00 6.20 1.30 4.00 5.00

The relationship between the parameters of manual and automated systems of packaging was also analysed, and correlation was established. The automated system for packaging improved packaging capacity of rice significantly. The results were processed through independent samples t-test, Pearson correlation analysis and linear regression analysis. The findings showed that the automated system has greater packaging capacity than manual one. The findings revealed that there is a statistical difference between automatic and manual packaging processes. The outcomes from the Pearson correlation test exhibited that there is a linear correlation between the performance variables. The findings are substantiated by regression analysis, where better packaging performance was able to predict adoption intention stringently. 4.2 Descriptive Statistical Results The mean packaging accuracy in the case of automated system was 99.10%, while the manual system had an accuracy of 93.20%, packaging throughput increased by 70 bags per hour (filled in the averaged time of 31 seconds per bag), whereas baseline average throughput was 48 bags per hour. The filling time was decreased by 43 seconds per bag in the automated system. The packaging error rate was reduced from 4.80% to 0.90%. Table:2 Independent Samples t-Test Comparing Manual and Automated Packaging Performance Performance Mean t-value Degrees of Significance Statistical Parameter Difference Freedom (df) (p-value) Decision Packaging 5.90 8.74 28 <0.001 Significant Accuracy (%) Throughput 70.00 15.62 28 <0.001 Significant (bags/hour) Filling Time -43.00 -18.45 28 <0.001 Significant (seconds/bag) Error Rate (%) -3.90 -14.21 28 <0.001 Significant Adoption 1.82 10.36 28 <0.001 Significant Intention Score

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The results of the independent samples t-test for the hypothesis testing were statistically significant for all performance parameters across two groups (p < 0.001). Implementation of the automated system increased packaging accuracy, throughput capacity, decreased filling time and packaging errors. High values of t show that the observed changes occurred because of introduction of the automated technologies and not due to random variability. The greatest change has occurred in terms of throughput (t = 15.62, p < 0.001), which means that the automated technology is able to expand the production capability considerably. There was a highly significant change in filling time (t = -18.45, p < 0.001), which confirmed the benefit of automation in labour-intensive operations. Pearson Correlation results revealed significant correlations between changes in packaging efficiency and the intention to use the new technology.

Figure 3: Standardized Regression Coefficients for Predicting Adoption Intention (R2 = 0.835)

The predictor variables through which throughput enhancement and packaging accuracy exerted the largest influences on adoption intention were throughput improvement (β = 0.421, p < 0.001) and packaging accuracy (β = 0.318, p = 0.002). From the questionnaire survey, it is clear that there is an extremely high rate of acceptance of the automatic rice packaging system. The respondents agree that the technology increases the accuracy of packaging and contributes towards precision agriculture. They also agree that the system is user-friendly and affordable for small-and-medium scale processors. From the above results, it is evident that a technology innovation that increases throughput and packaging accuracy of rice from developing countries can be easily adopted by practitioners hence helping towards increasing processing productivity and food security. Such a system may also be used to assist in the execution of precision agriculture in Africa.

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Figure 4: Pearson Correlation Matrix of Key Performance and Adoption Variables" There was a significant positive correlation between throughput and the intention (r = 0.889, p < 0.01), which means that there was an increased likelihood of using the new technology by those who perceived the increase in the capacity. There was also a positive significant correlation with packaging accuracy (r = 0.854, p < 0.01), which means that the increase in the quality of products influenced the technology adoption positively. There was a negative correlation between filling time and intention to use the technology (r = -0.821, p < 0.01).

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Table 3: Regression Analysis Predicting Technology Adoption Intention from System Performance Improvements Model Summary Model R

R²

Adjusted R²

Standard Error

0.914

0.835

0.823

0.274

The regression analysis showed that system performance improvement was a good predictor of technology adoption intention (R = 0.914) meaning that packaging accuracy, throughput, filling time reduction, and error reduction accounted for almost 83.5% of the variation in user acceptance of automated precision agriculture technologies (R2 = 0.835). Table 4: Anova Regression Test Model Sum of Squares Regression 12.846 Residual 2.107 Total 14.953

df 4 25 29

Mean Square 3.212 0.084

F-statistic 42.73

Significance <0.001

From the table above, the regression model was significant (F = 42.73, p < 0.001) implying that system performance improvements of packaging accuracy, throughput, filling time reduction, and error reduction significantly improved user acceptance of automated precision agriculture technologies. The trained farmers were questioned at the end of the study in order to obtain perceptions about the system including the performance and reason for using it. Pearson correlation analysis found a very high positive correlation between the performance of the automated packaging and the adoption of precision agriculture technologies (r=0.78, p<0.001). This showed that any improvement in the accuracy, speed and efficiency of the packaging contributed much in increasing stakeholders' adoption of the automated rice packaging technology. Since the value of p is less than 0.05, the null hypothesis (H₀) was therefore rejected, showing that the automated packaging system positively encourages the adoption of the precision agriculture.

Figure 5: User Perception of the Automated Packaging System (Mean ± SD) THE ZIMBABWE INSTITUTION OF ENGINEERS

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The proposed system was described and assessed through a questionnaire. Structured questionnaire was utilized in order to analyse perceptions of stakeholders on the proposed system. Likert Scale was used to measure the perceived importance and importance index was used to analyse each answer; means were used to assess the perceived impact of the system, Pearson Product-Moment Correlation was used to assess the correlations between the perceived variables. The outcome of factor analysis yielded four factors accounting for 71% of the total variance: (i) Perception of usefulness, (ii) perception of ease of operation, (iii) affordability of the proposed system, and (iv) perceived contribution to precision agriculture.

5. Discussion Results from the manual and automated packaging of rice were analysed. According to the research findings, it is noted that the automated rice packaging system improves the accuracy of rice granules and packaging speed and reduces the running time, relatively. The findings reveal that the automated rice packaging system has improved the packaging accuracy of rice granules in single-packing, pairpackaging, and group-packaging. In this case, there is an improvement in packaging accuracy of 16.5%, 27.8%, and 32.8% in single-packing, pairpackaging, and group-packaging, respectively, over that of manual packaging. There is an increase in packaging speed of 30-40% over that of manual packaging. Finally, there will be a reduction of 5066% in the processing time to pack 1 kg of rice on average with the automated rice packaging system over that with manual packaging.

and level of education, the participants’ type of operation and the quality of packaging system. From the result, there was a very strong relationship between the perceived usefulness and perceived ease of use of the integrated automated slotting packaging system, and the stakeholders’ acceptance towards adopting the system in their rice processing business operations based on the Pearson correlation test. According to the simple linear regression analysis result, there was high predictive power of perceived ease of use, perceived usefulness, cost of the system, personnel training, and business profitability on the stakeholders’ acceptance towards using the system. Research findings support the previous studies where the significance of the impact of perception usefulness and perception ease of use on the acceptance of automated packaging technology was proved. Specifically, Groover (2020) stated that the usage of industrial automation increases the productivity, accuracy, and reliability of the processes since manual labour is replaced by controlled mechanical operations. Moreover, according to Palanisamy et al. (2024) and QS Packaging Machine (2023), automated packaging systems increase the efficiency of the processes, reduce material losses, and improve the product consistency in the food processing industry. Finally, previous studies showed that the automated slotting packaging system increased its throughput significantly compared to the manual slotting process and reduced the time of filling and the amount of packaging errors. The results revealed that the farmers and processors had a favourable evaluation of the automation technology and that they will enjoy implementation of such technology especially because of the quality of packaging, labour requirements, and competitiveness in the market, in agreement with Ayaz et al. (2019). This concurs with the results from other studies conducted previously by Yadav et al. (2019), stating that there are several factors that influence the adoption of digital technologies in agriculture, and one of those factors includes the perceived usefulness and operational advantage of the technology and economic gain (both perceived and real).

The test of multiple regression analysis was used to analysed the relationships among the stakeholders’ acceptance of the system and the independent variables which are the perceived usefulness and perceived ease of use of the system, alternative packaging system, cost of the integrated automated slotting packaging system, and personnel training. Furthermore, to analyse their impact on the stakeholders’ willingness to pay extra price for adopting the system, the ANOVA analysis of In addition to that, the high correlation found variance was utilized to evaluate the factors which between improved packaging performance and are the participants’ gender, business experience, the technology adoption intention using Pearson 48

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correlation and regression analysis indicates that practical performance improvement greatly influences the adoption of the precision agriculture technology, an idea that concurs with Ayaz et al. (2019) who reported that perceived usefulness, economic gains, and operational advantages greatly affect the adoption of digital agricultural technologies.

6. Limitations of study There are certain limitations of the study that have to be taken into account during the analysis of its results. Firstly, the research has been carried out on the relatively small scale and, therefore, can be regarded as indicative only. Furthermore, the evaluation was done for the processed rice in the laboratory environment without considering the differences between the sizes of grains, their moisture content, type of packaging equipment, and other factors characteristic of the commercial rice processing industries in Africa. The evaluation of the adoption rate of the technology was limited to the analysis of a small sample of users; therefore, the evaluation should include a greater number of farmers and processors. Moreover, apart from the technical and user-oriented aspects, the study did not consider carrying out the economic evaluations, such as cost-benefit analysis and return on investment.

System improvement enhances the acceptance and the adoption of precision agriculture technology. The research finds out that the preference for automation systems by the farmers and processors is because of the automation that increases productivity and enables them to manage and compete better in the agricultural markets. From the results of the statistical tests, it can be seen that packaging efficiency, reliability and productivity influence technology adoption. The proposed system can be able to minimize the risk factor of manual de-husking of rice and poor packaging. The innovation of the system can be able to increase the marketability of the products from the rice value chain. Therefore, increasing the potential for producing high-value rice. As the developing countries lack the modern automated packaging system based on advanced technology, they will experience higher post-harvest losses in the agricultural supply chain.

7. Acknowledgements We would also like to thank our supervisors and technical personnel for providing valuable comments, suggestions, encouragement and technical support that contributed significantly towards the success of this study. We would also like to extend our gratitude to other respondents such as the farmers, rice processors and others in the field of agriculture for administering the questionnaire survey. Their responses were key in establishing and analysing the uptake of automated rice-packaging technology.

8. Conclusion From this research, it can be seen that through the inclusion of automated weighing systems and sensors in the process of designing and developing this system. Some of the obstacles to adoption of precision agriculture such as human error, material loss and productivity have been considered, leading to increased productivity and effectiveness due to scheduling and material handling improvement.

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9. Appendices Appendix A: Experimental Setup of the Automated Rice Packaging System The automated rice packaging prototype was set up and tested under controlled workshop conditions to evaluate its performance against the conventional manual packaging method. The experimental setup consisted of the following components and procedures: Prototype Components • Storage hopper for holding raw rice prior to dispensing • Vibratory/screw feeding mechanism to regulate rice flow • Load cell-based weighing unit interfaced with an Arduino microcontroller • Sealing mechanism for closing filled packages • Conveyor system for package transfer and output collection • Control panel and power supply unit Experimental Conditions • Rice type: Processed/milled rice, uniform moisture content • Target package weight: 5.00 kg • Number of trials: 15 manual packaging cycles and 15 automated packaging cycles (minimum of 30 cycles overall) • Measuring instrument: Calibrated digital weighing scale (accuracy ± 0.01 kg) • Timing instrument: Digital stopwatch/timer • Testing environment: Indoor workshop setting, ambient temperature and humidity Procedure 1. The hopper was loaded with a fixed quantity of processed rice. 2. The target package weight was set on the control system. 3. For manual trials, an operator filled and sealed each package by hand while the target weight, actual weight, and time taken were recorded. 4. For automated trials, the system was activated to automatically dispense, weigh, and seal each package, with performance data logged by the control unit. 5. Each completed package was weighed on the calibrated scale to verify the reading obtained during the trial. 6. Data for all trials were recorded on the Experimental Data Collection Sheet (Appendix B) for subsequent statistical analysis. Appendix B: Experimental Data Collection Sheet Automated Rice Packaging System Performance Evaluation Date: _______________ Operator: _______________ Instrument: Digital Weighing Scale (Model: _______________) Test No.

Packaging Method

Target Weight (kg)

1 2 3 4 5 6 7 8

Manual Manual Manual Manual Manual Manual Manual Manual

5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00

Actual Weight (kg)

Packaging Error (%)

Packaging Time (s)

Status

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9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Manual Manual Manual Manual Manual Manual Manual Automated Automated Automated Automated Automated Automated Automated Automated Automated Automated Automated Automated Automated Automated Automated

5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00

Appendix C: User Acceptance Questionnaire Evaluation of Automated Rice Packaging System Adoption Instructions: Please indicate your level of agreement with each statement using the scale below: 1 = Strongly Disagree

2 = Disagree

3 = Neutral

4 = Agree

5 = Strongly Agree

Respondent Role: ☐ Farmer ☐ Processor ☐ Agricultural Stakeholder

Date: __

No. 1

3 4

Statement 1 The automated packaging system improves packaging accuracy. The system reduces packaging errors compared with manual methods. The automated system increases processing efficiency. The system is easy to operate and understand.

☐

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5 6 7

9 10

The technology can reduce labour requirements. The system can improve rice product quality. The system is affordable for agricultural enterprises. I would consider adopting this technology in my operation. Automated packaging can support precision agriculture adoption. The system can reduce post-harvest losses.

☐

✓ Tick (☐→☑) one box per statement corresponding to your level of agreement. Additional Comments: .................................................................................... .................................................................................... Appendix D: Automated Rice Packaging System Operation Flow Diagram Operational Sequence START | ↓ Rice Loading into Hopper | ↓ Sensor System Activation | ↓ Rice Dispensing Begins | ↓ Weight Measurement by Load Cell | ↓ Target Weight Achieved? | ├──────── No ────────→ Continue Dispensing | ↓ Yes Dispensing Stops | ↓ 52

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Package Sealing Process | ↓ Data Recording and Performance Monitoring | ↓ Completed Package Output | ↓ END Control Variables Variable Weight sensor input Motor control signal Timer function Counter function Error detection

Function Determines package quantity Regulates rice flow Measures packaging duration Records completed packages Identifies abnormal operation

Appendix E: Statistical Analysis Equations Packaging Accuracy Packaging Accuracy (%) = (Actual Weight ÷ Target Weight) × 100 Packaging Error Packaging Error (%) = |Target Weight − Actual Weight| ÷ Target Weight × 100 Throughput Throughput (bags/hour) = Number of Packages Completed ÷ Time Taken (hours) System Efficiency System Efficiency (%) = (Actual Output ÷ Theoretical Maximum Output) × 100 Independent Samples t-Test t = (X̄₁ − X̄₂) ÷ √[(s₁²/n₁) + (s₂²/n₂)] where X̄₁ and X̄₂ are the sample means, s₁² and s₂² are the sample variances, and n₁ and n₂ are the sample sizes of the manual and automated groups respectively. Pearson Correlation Coefficient r = Σ (xᵢ − x̄) (yᵢ − ȳ) ÷ √ [Σ (xᵢ − x̄) ² × Σ (yᵢ − ȳ) ²] Multiple Linear Regression Model Y = β₀ + β₁X₁ + β₂X₂ + β₃X₃ + β₄X₄ + ε where Y is adoption intention, X₁–X₄ are throughput improvement, packaging accuracy, filling time reduction, and error reduction respectively, β₀ is the intercept, β₁–β₄ are the standardized regression coefficients, and ε is the error term. THE ZIMBABWE INSTITUTION OF ENGINEERS

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10. References Ayaz, M., Ammad-Uddin, M., Sharif, Z., Mansour, A., & Aggoune, E. H. M. (2019). Internet-of-Things (IoT)-based smart agriculture: Toward making the fields talk. IEEE Access. Bolton, W. (2021). Programmable logic controllers. Elsevier. Dahan Packaging Machinery. (2024). Automated rice packaging systems and industrial packaging solutions. Food and Agriculture Organization of the United Nations. (2023). The state of food and agriculture 2023. Gebbers, R., & Adamchuk, V. I. (2010). Precision agriculture and food security. Science, 327(5967), 828–831. Groover, M. P. (2020). Automation, production systems, and computer-integrated manufacturing. Pearson. Mammadov, A., et al. (2025). Precision technologies and reduction of food losses across agricultural value chains. Mehta, R. (2018). Challenges associated with manual packaging systems in food processing industries. Palanisamy, P., et al. (2024). Automation technologies in modern food packaging systems. Patrício, D. I., & Rieder, R. (2018). Computer vision and artificial intelligence in precision agriculture. Computers and Electronics in Agriculture. QS Packaging Machine. (2023). Electronic weighing and automated packaging technologies. Xia, X., et al. (2025). Artificial intelligence and deep learning applications in rice quality assessment and food processing automation.

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REAL-TIME AI-DRIVEN SURVEILLANCE SYSTEM FOR AGRICULTURAL INFRASTRUCTURE AND LIVESTOCK: ENSURING RESILIENCE AND EFFICIENCY IN MODERN FARMING

Berlindah Precious Karigeni¹, Kenneth Madhuku² Department of Agricultural Engineering, Mechanisation and Farm Infrastructure Development, Zimbabwe. nyashakay2010@gmail.com

Abstract

Agricultural infrastructure is critical for sustainable farming, encompassing irrigation networks, storage facilities, farm buildings, roads, and fencing. Failures or inefficiencies in these systems can lead to severe economic losses, reduced productivity, and compromised food security. This study proposes a real-time artificial intelligence (AI)–based surveillance framework for monitoring agricultural infrastructure. The primary goal is to enhance resilience and efficiency in modern farming. Using a combination of computer vision, sensor networks, edge computing, and cloud analytics, the system continuously evaluates structural integrity, identifies anomalies, and provides predictive alerts to farmers and administrators. The system is used to monitor farm assets, detect intrusions, track livestock health, identify anomalies such as fence breaches, predator presence and equipment failure. Field deployments across diverse farm settings demonstrated high detection accuracy, reduced response times, and improved operational efficiency. The proposed framework integrates multi-modal sensor data, leverages deep learning for anomaly detection, and incorporates decision support dashboards for real-time actionable insights. This research establishes a new paradigm in precision agriculture, emphasizing infrastructure resilience and sustainability. The proposed solution offers flexible and cost-effective intelligence for farmers facing climate variability, labour shortages, and security threats thereby improving productivity and profitability in modern agricultural operations. Keywords: artificial, infrastructure, monitoring, sensor, surveillance

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1. Introduction The modern age of agriculture is more technologydependent in relation to productivity improvement, sustainability, and efficient use of resources. Modern farms are fitted with advanced systems that include irrigation systems, storage systems, machinery, accommodation for livestock, and security systems that need constant monitoring for their effective operations. The conventional ways of monitoring involve human interventions which make it difficult for any faults, security threats, and livestock problems to be detected early enough (Wolfert et al., 2017).

1.2 Importance of the study This research has added to the knowledge base of precision agriculture through the design and assessment of the real-time AI-based surveillance system for the surveillance of agricultural infrastructure and livestock.

1.3 Research Aim Development of a real-time AI-enabled surveillance system using multi-modal sensors and deep learning algorithms for surveillance of the agricultural infrastructure and animals for ensuring the reliable, timely identification of potential anomalies and Recent developments like artificial intelligence (AI), efficient operation of modern agriculture. computer vision technology, IoT solutions, and sensor networks offer new possibilities for smart agriculture monitoring applications (Kamilaris & Prenafeta-Boldú, 2018; Liakos et al., 2018). These developments enable automation of the monitoring process by collecting, analysing, and making decisions based on collected data. Furthermore, recent developments in deep learning increase the capacities of monitoring systems in anomaly detection and predictive maintenance (LeCun et al., 2015). 1.1 Problem Statement Traditional methods used for the surveillance of the farm are unable to identify any problems related to the infrastructure or failure of the equipment, intrusion into the premises, and any distress in the livestock in real-time. The currently available AI-based agriculture monitoring system either concentrates only on infrastructure monitoring or on livestock monitoring and relies on one data collection source which makes it impossible to achieve accuracy in detection. It is clear that there is a need for an integrated surveillance method that can make use of more than one source of data like visual and sensory data.

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1.4 The aims of the research i. Development of a multi-modal agriculture surveillance system based on the use of visual, thermal, vibration, acoustic and environmental data collected from the agricultural environment. ii. Development and comparison of CNN, LSTM and hybrid CNN and LSTM models for anomaly detection in agricultural infrastructure and livestock. iii. Performance evaluation of the developed surveillance system using performance metrics. iv. Realization of the possibility to perform real-time anomaly detection, predictive maintenance and effective decision making in agriculture with the use of the developed system.

CNN is an efficient technique for identifying spatial characteristics, while LSTM demonstrates high effectiveness in analysing temporal data sequences. However, little attention has been paid to the possibility of using both CNN and LSTM in a single multi-modal approach. Therefore, it is needed to develop an AIbased solution to monitor agriculture infrastructure and livestock.

2. Literature Review AI, IoT, computer vision and sensors have been increasingly applied in the agricultural sector with the goal of boosting productivity, sustainability and efficiency. The traditional approach to monitoring entails the inspection and maintenance that would not necessarily identify the errors, security threats and distress of the livestock at the desired time. Recent advancements in AI have made possible the automation of the monitoring process that helps 1.5 Research Questions to make timely decisions and predict maintenance • How effective is a multi-modal AI-based while improving resource allocation in agriculture surveillance system in the detection of (Kamilaris & Prenafeta-Boldú, 2018). anomalies in agricultural infrastructure and livestock? Computer vision has proven to be important in the • Is the use of a CNN and LSTM model process of agricultural monitoring because of the ability combined beneficial for improving the to analyse images and automatically detect anomalies. performance of anomaly detection compared There are impressive performances of CNNs when it to separate models? comes to image classification and object detection. • Can real-time monitoring be achieved along The effectiveness of CNNs in diagnosing plant diseases with reduction of the cost of operations was illustrated by Mohanty et al. (2016). The authors through the use of edge-cloud computing? developed architectures for real-time object detection such as YOLO and Faster R-CNN architectures for 1.6 Research Hypotheses real-time object detection (Redmon et al., 2016; Ren H₀: There is no statistically significant difference in et al., 2015). Computer vision has been successfully accuracy, precision, recall, and F1-score between applied in livestock monitoring. CNN, LSTM and Ensemble CNN-LSTM models. H₁: At least one of the CNN, LSTM, and Ensemble In addition to the above, IoT technology has CNN-LSTM models shows significant differences facilitated the development of agricultural monitoring in performance in terms of accuracy, precision, through the implementation of distributed sensor recall, and F1-score. networks. Different types of technologies including The level of significance was set at α=0.05 (95% environmental sensors, thermal cameras, vibration confidence level). sensors, acoustic sensors and water flow meter constantly collect operational data of farms. In this 1.7 Literature Gap connection, Wolfert et al. (2017) state that the IoT Despite the advancements in AI technologies, technology is the key technology of smart agriculture computer vision, sensor networks, and edge owing to continuous monitoring and data-driven computing, existing literature focuses on decision-making. It should be noted that many existing infrastructure monitoring or livestock monitoring solutions rely on single modality sensors and hence, as separate research areas. In addition, some of their applicability is rather limited. the presently available technologies use only a Deep learning significantly improved the anomaly single source for collecting data, which narrows detection capabilities in agriculture as well as in their capability to provide situational awareness. industry. THE ZIMBABWE INSTITUTION OF ENGINEERS

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LeCun et al. (2015) highlighted the feature extraction ability of deep neural networks which is particularly important when processing big data sets. Besides, convolutional neural network models are efficient for processing visual data such as infrastructural damage, animal behaviour, and construction defects. When it comes to temporal analysis, long shortterm memory networks are commonly used as they allow detecting sequential patterns in time series. Long short-term memory networks are widely implemented for predictive maintenance, vibration monitoring and acoustic analysis (Goodfellow et al., 2016). Agricultural monitoring in real-time requires an effective data processing system, as well. Delays in communication are inevitable in cloud systems because of the enormous amount of sensor data that needs to be transferred. Such issues can be solved by utilizing edge computing that performs calculations close to the source and improves efficiency and speed of the processes. As Khan et al. (2019) point out, edge computing decreases bandwidth usage and helps make decisions in real-time, and such methods as lightweight deep learning with Mobile Nets (Howard et al., 2017) simplify the process. Another issue that should be highlighted regarding agriculture monitoring is the importance of data fusion. By combining data from several sources (RGB imagery, thermal imaging, acoustic and vibration sensors, etc.), it is possible to create a full-fledged image of the current state in the field. Russello & Topalis (2021) and Liakos et al. (2018) note the effectiveness of such multi-modal sensing. However, the current body of research literature is focused on the monitoring of agriculture infrastructure and livestock independently through use of a single sensing technology. No research effort has been done regarding the integration of RGB imaging, thermal sensing, vibrations, acoustic sensing, CNN-LSTM fusion model and edge-cloud computing into a unified real-time surveillance platform. It is due to that reason that this study attempts to address the research gap by developing and validating an AI-based multi-modal surveillance system for monitoring both agricultural infrastructure and livestock simultaneously.

3. Methodology To meet the research objectives, the research work utilized a system design and experimentation methodology designed to develop and test the AIbased real-time surveillance system for agricultural infrastructure and livestock monitoring. The methodology included the following steps: development of the multi-modal sensing architecture, development of the deep learning algorithms, integration of edge-cloud computing, and testing of the proposed framework using data from six agricultural farms with different agricultural production systems. 3.1 System Architecture and Device Deployment The suggested system architecture consisted of three layers: the Edge Layer, Cloud Layer and Dashboard Layer. All of these three layers were implemented using a multimodal sensor network, deployed across six agricultural farms. Edge Layer was responsible for collecting and processing data to perform low-latency inference with a reduced workload for centralized processing systems. Cloud Layer provided high storage capacities, training and analytics for recognition of certain patterns over time. Dashboard Layer served as an interface, providing notifications about system state and prediction results. Several types of sensors were introduced in agricultural farms to collect multimodal data from the agricultural environment. High-resolution RGB cameras (1080p/4K CCTV units) were mounted on barns, silos, fences, gates, livestock pens and storage facilities, in order to monitor the infrastructure and behaviour of animals. These sensors collected spatial data that was necessary for detecting damage, intrusions and anomalous behaviour patterns. Thermal infrared sensors (based on FLIR technology) were placed near irrigation systems, machinery rooms and animal shelters to detect heat anomalies caused by malfunction of the equipment, fire hazard or stress of animals.

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As for mechanical sensors, MEMS accelerometers were fitted into irrigation pumps, conveyors and feed dispensers. Wear and tear, imbalance and early failures of those machines were noted due to vibration data gathered through these sensors. In order to obtain data that would relate to any sort of distress or any abnormal behaviour and intrusion by the animals, acoustic sensors were placed in the animal sheds and on the periphery of the farms. The signals that were produced due to equipment failures were captured by the acoustic sensors. Temperature-humidity sensors (DHT22), water flow meter and weather sensor were placed in the farms' open field area to collect contextual data regarding temperature, humidity, rainfall and water consumption.

in the datasets involved the farm operations and included the leakage of irrigation system, cracks in structure, machine malfunctions, livestock distresses, and intrusion into the farm. Image datasets were labelled by expert annotators and engineering thresholds were used to validate the sensor anomalies. The anomalies in the datasets comprised 35% of the data while normal operations comprised 65%. The dataset was structured in the following manner: • Training set= 70% (8,750 samples) • Validation set = 15% (1,875 samples) • Testing set = 15% (1,875 samples)

3.3 Real Time Environment and Data Validation The real time environment used for agriculture monitoring was developed based on six test farms. Multi-modal data streams were collected using 3.2 Size and Composition of the Datasets The multi-modal datasets were gathered through the technologies of Python, TensorFlow, OpenCV, six months of monitoring conducted in six YOLOv11 and IoT. agricultural farms. Different agricultural activities such as crop irrigation farming, dairy farming, Sampling rates for sensors were set up in accordance mixed farming, poultry farming, horticulture, and with manufacturer’s guidelines and best practices in commercial livestock farming were represented the industry. Low latency processing was performed in the farms. Different environments, procedures using edge computing technology on Raspberry Pi5. and infrastructure of the farms provided different Normal and abnormal conditions were collected during regular operation of farms. The environment datasets. changes such as dust, rainfalls, temperature changes Different types of data in the dataset included: and sensor noises were also taken into account. • Images captured using RGB, which showed the infrastructure and livestock operations • Thermal images, which showed the heat 3.4 Data Pre-processing and Signal Conditioning All multi-modal data collected on the farm sites were signatures pre-processed in order to make them consistent and • Vibration signals from mechanical devices suitable for further modelling. The image datasets • Audio signals from animal and device sounds • Data from environmental sensors (temperature, (both RGB and thermal images) were resized to 224 × 224 pixels, normalized and then augmented by humidity, rainfall and water consumption) rotation, flipping and brightness variations caused by environmental factors. The thermal images were 3.2.1 Composition of Datasets In the datasets were included anomalies and normal also pre-processed in order to increase the heat operation conditions of the data sets. The anomalies signature.

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3.5 Feature Extraction and Multi-modal Fusion Features were extracted from RGB and thermal images using edge detection, texture analysis, and thermal gradient mapping to detect any structural faults or thermal abnormalities. The vibrational data was analysed to extract frequency-based features to detect any mechanical imbalance and wear in the machinery. Acoustic data was leveraged to detect any abnormal sound like distress or malfunction. 3.6 AI Model Development and Training There were three models created according to the data gathered at six different farms. A CNN model containing five layers of convolution was used for feature extraction from RGB and thermal images. The anomalies like defects, leakage, or any other kind of problem were detected using Adam Optimizer. A two-layered LSTM model was used for the analysis of vibration, acoustic, and environmental signals. Ensemble CNN-LSTM models were applied by combining the outputs of two models using the weighted averaging. It helped in learning from the spatial and temporal data. Grid search and five-fold crossvalidation techniques were applied to tune the hyper-parameters. 3.7 Performance Measures Following measures have been employed to evaluate the performance of the proposed models: Accuracy, Precision, Recall, F1 Score, and ROC-AUC. 3.8 Edge-Cloud Environment A two-layer edge-cloud environment was created to test the performance of the real-time models at all six farms. The edge processing has been carried out through implementing Raspberry Pi5 devices. In case anomaly probability is higher than 0.8, automatic alerts were raised. The cloud layer performs functions of data storage, model retraining, and big data analysis. Visualization was performed through a dashboard which displays information about system status and anomalies. 3.9 Experiment Protocol The protocol of the experiment consists of the following actions: • Choice of six farms • Installation of multi-modal sensors • Real-time data acquisition • Data cleaning and synchronization • Anomalies annotation and verification • Model training (CNN, LSTM, Ensemble CNN-LSTM, YOLOv11) • Edge-cloud environment creation • Evaluation of real-time anomaly detection • Performance logging

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Figure 1: Flowchart for AI Driven Surveillance System 3.10 Statistical Analysis Statistical analysis such as descriptive statistics and one way ANOVA was employed in making comparison of the performance of the models in this study. Descriptive statistics such as mean, standard deviation, precision, recall, F1 score and accuracy were calculated in order to measure the performance of the models. 3.11 Ethical Considerations Consent from the owners of the farms where data collection took place was solicited before undertaking the experiment. No sensitive or personal information was collected during the experiment. The experiments adhered to ethical considerations for using artificial intelligence which includes transparency, data security, privacy and responsible use of surveillance technology in agriculture. Animal surveillance process in this experiment was observational in nature and did not hamper farm activities. 4. Results The proposed surveillance framework employing artificial intelligence was verified using multi-modal datasets created by six different farms operating under different agricultural production systems. It was found out that CNN, LSTM algorithms and multi-modal data fusion techniques are very efficient in detecting anomalies in agriculture infrastructure and livestock surveillance. The performance of the framework was measured by the accuracy percentage. The result has shown that the use of computer vision, sensor analytics, and edge cloud computing technology guarantees the timely identification of fault, anomaly, and distress situations in the livestock. The system has shown a high degree of effectiveness irrespective of the different farm environments. 4.1 Characteristics of Farming Environment A total of six farms adopting different types of farming methods such as irrigation crop farming, dairy farming, mixed farming, poultry farming, horticulture farming, and commercial livestock farming have been chosen for the surveillance framework. THE ZIMBABWE INSTITUTION OF ENGINEERS

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Table 1. Characteristics of Farm Environments FARM PRIMARY ACTIVITY INFRASTRUCTURE MONITORED Farm A Irrigated Maize Farming Pumps, pipelines, reservoirs Farm B Dairy Production Barns, feeding systems, livestock Farm C Mixed Farming Irrigation systems, storage facilities Farm D Farm E Farm F

Poultry Production Horticulture Commercial Livestock

Poultry houses, ventilation systems Greenhouses, irrigation systems Fencing, water points, livestock movement

The application of multiple farms proved the efficiency of the functioning of the framework in different conditions of agricultural production. Variability of the environment in terms of temperature, precipitation, dust, the operation of machinery, and the activity level of animals provided an additional challenge in terms of monitoring, which served as an opportunity to test the reliability of the developed models. The performance results have been calculated on the basis of the testing dataset consisting of 1,875 records from the six agricultural farms. The accuracy of the classification is considered as the ratio of the number of the correctly classified instances (both normal and anomalous) to the total number of instances. Precision, recall, F1-score, and ROC-AUC values have been calculated by means of machine learning techniques based on confusion matrices. The results of the improvements made on performance have been determined through the comparison of operational logs in the farms before and after adopting the surveillance system guided by AI. The maintenance cost savings have been realized through the reduction in corrective maintenance operations as a result of early detection of faults. The downtime savings have been determined through the reduction in the outages of the equipment while water savings have been obtained through irrigation leaks and abnormalities detection. Thus, these figures represent the average outcomes of six months period as indicated in the six farms. 4.2 Detection of Infrastructural Anomalies Using Surveillance Technology The performance of the surveillance technology in detecting infrastructural anomalies has been impressive. In total, there have been 129 anomalies detected relating to infrastructure during the monitoring period. Table 2. Infrastructure Anomalies Detected Infrastructure Type Events Detected Irrigation Systems 31 Storage Facilities 18 Livestock Housing 24 Farm Machinery 42 Fencing and Security 14

Detection Accuracy (%) 96.2 94.1 95.0 93.8 97.1

Fencing and surveillance achieved the highest levels of accuracy 97.1%. It was explained by the specific visual characteristics associated with the intrusion, access violation, and the breaking of the fencing line that were easy to analyse using the CNN-based feature extraction technique. Using additional sensor data increased the accuracy level of classification and reduced the number of false positive detection cases. High levels of accuracy 96.2% were detected in the cases of irrigation systems. The combination of thermal vision, vibration detection, water flow analysis, and other kinds of sensor data enabled detecting issues such as leakage, failure of pumps and deviation from the normal operation mode. Problems usually happened before any damage appeared, proving the efficacy of the preventative maintenance approach. 62

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The lowest detection accuracy (93.8%) was obtained during the analysis of agricultural machines. Nonetheless, this was still quite high. A number of loads and environmental factors affected the farm machinery, resulting in complex vibration and sounds. All these contributed to the increase in the difficulty level of the classification process. They make it hard to differentiate the variations of the normal operating state of the farm machinery and faults. All these results clearly indicate that multi-modal sensing plays a great role in enhancing the capability of anomaly detection through information complementarity. 4.3 System Performance at Farm Level Evaluating the performance of the system on the basis of detection capability was quite high across the six farms. According to the results, the performance of the system in all the farms considered remained stable. The farm that has the highest accuracy is Farm F (96.0%), while the farm with the least accuracy is Farm D (93.9%). There are several reasons for this variation, including environmental factors, sensor positioning, and operating conditions of the farms. All the farms have an accuracy above 93%.

Figure 2. Performance comparison of CNN, LSTM and Ensemble CNN and LSTM models However, the highest performance was realized from the use of the Ensemble CNN and LSTM model with 96% accuracy, 94% precision, 95% recall, and 94.5% F1-score. It is evident that multi-modal data fusion improves the performance of the anomaly detection process as compared to individual CNN and LSTM models. The CNN algorithm performed very well in detecting cracks, leakages, damages in structures, and behavioural abnormalities in the livestock. The LSTM algorithm performed very well in detecting the temporal features in the vibration, acoustic, and environmental sensors. However, the performance of the LSTM model was affected by the presence of noise in the sensors. The Ensemble CNN and LSTM algorithm performed very well in all the metrics. This is because the spatial information in the images and temporal information in the sensors increased the performance of the anomaly detection process. THE ZIMBABWE INSTITUTION OF ENGINEERS

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These results clearly show that multi-modal data fusion provides a better perspective of the farm condition than single modality approach. 4.4 Statistical Validation of the Performance of the Models To verify the differences in performance of the models, statistical analysis was conducted. Table 3. Statistical Analysis and Performance Comparison of AI Models Model

Standard Deviation

Precision (%)

Recall (%)

F1-Score (%)

ANOVA Result

Significance (p-value)

CNN

Mean Accuracy (%) 94.0

1.2

92.0

93.0

92.5

P = 0.0003

LSTM

91.0

1.8

89.0

90.0

89.5

Ensemble CNN-LSTM

96.0

0.8

94.0

95.0

94.5

F (2,12) =18.84 F (2,12) =18.84 F (2,12) =18.84

P = 0.0003 P = 0.0003

Through one-way ANOVA, a statistically significant difference was found in the performance of the models analysed (F (2,12) = 18.84, p< 0.05). The Ensemble CNN and LSTM model performed the best in having the highest accuracy (96.0%), precision (94.0%), recall (95.0%) and F1-score (94.5%) coupled with a low standard deviation of 0.8. This indicates the consistency and reliability of the model in the six farm environments. The findings show that the performance of the anomaly detection has improved in combining both CNN and LSTM models as compared to the individual models. 4.5 Influence on the Operations The potential benefits of the suggested surveillance system were analysed through evaluation of the maintenance expenses and time losses caused by the machine failure. Being compared with the control variant of surveillance without using any AI technologies, the suggested AI surveillance system helped to decrease the simulation of the maintenance expenses by 22% and the machine downtime by 28%. Rate of Early Fault Detection | ███████████████████████████████████████████ 91% Downtime Reduction | ██████████ 28% Maintenance Cost Reduction | ███████████ 22% Reduction in Water Loss | ████████ 15% Figure: 3 Operational Benefits The decrease in the maintenance cost was calculated on the basis of comparison of the maintenance operations required for traditional scheduled maintenance and AI-assisted predictive maintenance in the environmental setting. The cost saving is mainly connected with the ability to carry out the predictive maintenance and thus avoid critical machine faults. The time saving was achieved due to the timely detection and handling of the equipment failures. 4.6 Evaluation of System Reliability and Stability In order to analyse the reliability of the suggested surveillance system, its availability, alertness, and efficiency in processing the information were observed during the period of six months.

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Table 4: System Reliability and Stability Analysis Metric System Availability Average Detection Latency Alert Delivery Time Successful Alert Rate Data Transmission Reliability The results obtained on these values show the good stability of the system during the period of implementation. The low detection delay and the high alerting rate prove the effectiveness of the developed system architecture. 4.7 Major Findings The analysis has proved that: • Multi-modal AI surveillance increases the anomaly detection significantly. • The CNN approach proves to be highly effective in detecting visual anomalies. • The LSTM approach proves to be highly effective in processing time-series sensor data, though susceptible to noise. • Ensemble CNN and LSTM prove to be the most precise and reliable approaches. • The Edge-Cloud computing enables a prompt response while conducting analytics. • The predictive maintenance helps reduce costs significantly.

Value 98.6% 0.8 s 1.2 s 97.4% 98.1% The use of agricultural data made it possible to conduct an analysis under various conditions. The benefit provided by the Ensemble CNN and LSTM compared to the individual CNN and LSTM models can be interpreted as the importance of multi-modal data fusion. Although the CNN was capable of extracting the spatial features of RGB and thermal images, the LSTM was efficient at extracting the temporal features of vibration, acoustic, and environmental sensors. This combination of features gave the Ensemble model the best accuracy score. Such an observation correlates with previous studies that showed increased accuracy in monitoring systems through the analysis of multiple sources of data rather than individual analysis (Kamilaris & Prenafeta-Boldú, 2018; Liakos et al., 2018).

High accuracy rates in the detection of issues in irrigation systems and fencing systems prove the success of integrating visual and sensor data in detecting faults. High accuracy rates have also been achieved in other precision agriculture studies thanks to improved multi-modal monitoring systems that 5. Discussion enabled anomaly detection and prevention of false The outcomes of the current research can be considered alarms. This is especially important since it provides solid evidence proving the way the implementation of the ability to conduct preventive maintenance before multi-modal sensing along with artificial intelligence any visible damages occur. will be helpful in increasing the efficiency of anomaly detection and performance monitoring in agricultural Moreover, the findings confirm the importance of environments. Specifically, the proposed surveillance edge computing for real-time agriculture monitoring. framework has produced high levels of accuracy, The decreased mean detection latency and fast precision, recall, and F1-score when tested for six delivery of notifications show that the closer the computing process takes place to the source, the faster agricultural farms. the response is performed. This statement supports As a result, the framework detected effectively all types the research done by Khan et al. (2019), proving of anomalies, including infrastructure problems in the that edge computing does not cause any delays in agricultural environment, distress incidents among communication and provides an opportunity to make animals, and threats to the security of the agricultural efficient decisions in distributed Internet of Things environment. In order to test the performance of the systems. In remote areas of agricultural farms, which proposed surveillance framework, six agricultural may have issues with the internet connection, edge computing will become a useful solution. farms have been used in the current research. THE ZIMBABWE INSTITUTION OF ENGINEERS

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Apart from the effectiveness in terms of detection latency and notification delivery, the findings related to cost saving and other positive changes confirm the importance of the intelligent surveillance system designed in the current paper. All improvements made with the help of the suggested framework will influence the financial situation of the company. Problems will be detected in their early stages preventing any equipment failure and minimizing the amount of water being wasted.

6. Conclusion In this research work, an AI-enabled surveillance system that provides real-time surveillance of the agriculture infrastructure and livestock through multiple modalities has been proposed and analysed. The system addressed the problems associated with traditional surveillance methods by providing continuous surveillance, automated anomaly detection, predictive maintenance, and real-time decision-making capabilities. It has been found that the proposed surveillance system has performed very well as per different performance metrics. The system using an ensemble of CNN and LSTM models outperformed both of these models individually. This implies the significance of analysing spatial and temporal data together in a multi-modal setting.

The performance of the livestock monitoring system turned out to be very promising as well. It consisted of three types of data that made it possible to recognize abnormal behaviour in animals and their distress. This conclusion is consistent with the results presented by Zhang et al. (2020) that proved the ability of livestock monitoring systems based on the use of AI technologies to contribute to the improvement of animal welfare through recognizing The utilization of different kinds of imaging, sensing, the existing issues concerning their health. and monitoring methods like RGB imaging, thermal sensing, vibration sensing, acoustic sensing, and Such an approach not only leads to improved environmental sensing has played an important animal welfare, but it also allows saving money role in providing accurate anomaly detection and since there will be no economic losses caused by avoiding false alarms. The edge-cloud computing animal diseases or inefficient production. Despite paradigm was employed to facilitate real-time data the promising results that were received during processing. It can be concluded that such a system the research, there were some disadvantages that based on artificial intelligence can have a significant should be mentioned. To begin with, the experiment positive impact on the sustainability of agriculture. was conducted using a relatively small sample of The future research should cover the deployment of farms, so it did not provide enough information the system to more agricultural zones, incorporation concerning the use of the system in other farms. of additional modalities of sensors, and adaptation of the model for varying agricultural settings. In conclusion, the findings of the conducted research have shown that there is a significant possibility for 7. Acknowledgements multi-modal AI surveillance systems to completely change modern agricultural methods. First and foremost, I would like to thank God for having given me the chance to conduct this 5.1 Limitations research through His strength and wisdom. It is While the developed system was tested using the with great thanks to my mentor for the assistance, data collected from working farms, there are still guidance, and support he gave me throughout this some limitations in the system. The research was research process. My appreciation goes to my conducted based on a limited number of farms which family, colleagues, and friends who encouraged and do not fully represent the variety of agricultural supported me. territories. In terms of practical application, there will be high expenses related to the construction and maintenance of the system because of high prices for sensing equipment and networks. Also, some of the equipment may not always be available and easily connected. Moreover, rare faults cannot be detected; therefore, it is hard to collect data in this category. 66

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8.Appendices Appendix A: Agricultural Farms Used in the Study The study was conducted using data collected from six agricultural farms representing different production systems and operational environments. FARM Farm A Farm B Farm C Farm D Farm E Farm F

PRIMARY ACTIVITY Irrigated Maize Farming Dairy Production Mixed Farming Poultry Production Horticulture Commercial Livestock Farming

Appendix B: Sensors and Equipment Used The AI-driven surveillance framework utilized multiple sensing technologies and edge-computing devices for real-time monitoring and anomaly detection. Appendix C: Experimental Protocol The experimental procedure followed during system development and evaluation consisted of the following steps: 1.Selection of six agricultural farms. 2.Deployment of multi-modal sensors. 3.Real-time data acquisition. 4.Data cleaning and synchronization. 5.Annotation and validation of anomalies. 6.Model training using CNN, LSTM, Ensemble CNN-LSTM, and YOLOv11 models. 7.Edge-cloud deployment. 8.Real-time anomaly detection testing. 9.Performance evaluation and statistical analysis. Appendix D: Model Training Parameters CNN Model • Learning Rate: 0.001 • Batch Size: 32 • Epochs: 50 • Optimizer: Adam LSTM Model • Learning Rate: 0.001 • Hidden Units: 128 • Batch Size: 32 • Epochs: 50 • Optimizer: Adam

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Ensemble CNN-LSTM Model • Fusion Strategy: Feature-Level Fusion • Learning Rate: 0.001 • Epochs: 50 • Optimizer: Adam Appendix E: Ethical Considerations and Consent Permission was obtained from participating farm owners before data collection and deployment of the surveillance system. No personal or sensitive information was collected during the study. The research adhered to ethical principles of transparency, privacy protection, data security, and responsible use of artificial intelligence technologies. Livestock monitoring activities were observational and did not interfere with normal farm operations. Appendix F: AI-Driven Surveillance System Flowchart

The flowchart illustrates the complete surveillance framework, including data acquisition, cleaning, model training, anomaly detection, edge-cloud deployment, alert generation, and decision support. Appendix G: Sample Anomalies Monitored Category Irrigation Systems Farm Machinery

Example Anomaly Water leakage Bearing failure

Livestock Monitoring Storage Facilities Poultry Houses Security Systems

Abnormal animal behaviour Temperature increase Ventilation failure Unauthorized access

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9. References Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877– 1901.

Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), Article 2674. https://doi.org/10.3390/s18082674 Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, Article 1419. https://doi.org/10.3389/fpls.2016.01419

Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, realM., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, time object detection. In Proceedings of the J., & Houlsby, N. (2021). An image is worth 16 IEEE Conference on Computer Vision and × 16 words: Transformers for image recognition Pattern Recognition (pp. 779–788). https://doi. at scale. International Conference on Learning org/10.1109/CVPR.2016.91 Representations (ICLR). Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770–778). https://doi.org/10.1109/CVPR.2016.90 Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv. https://arxiv.org/abs/1704.04861 Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90. https://doi. org/10.1016/j.compag.2018.02.016

Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. In Advances in Neural Information Processing Systems, 28, 91–99. Russello, H., & Topalis, F. (2021). Artificial intelligence applications in precision agriculture. Agronomy, 11(9), Article 1782. https://doi. org/10.3390/agronomy11091782 Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming: A review. Agricultural Systems, 153, 69–80. https://doi. org/10.1016/j.agsy.2017.01.023 Zhang, Y., Cai, J., Xiao, D., Li, Z., & Xiong, H. (2020). Real-time monitoring of livestock behaviour using deep learning and computer vision technologies. Computers and Electronics in Agriculture, 172, Article 105338.

Khan, W. Z., Ahmed, E., Hakak, S., Yaqoob, I., & Ahmed, A. (2019). Edge computing: A survey. Future Zhao, Y., Gong, L., Huang, Y., & Liu, C. (2019). A Generation Computer Systems, 97, 219–235. https:// review of key techniques of vision-based control for harvesting robots. Computers and Electronics doi.org/10.1016/j.future.2019.02.050 in Agriculture, 127, 311–323. https://doi. org/10.1016/j.compag.2016.06.022 LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi. org/10.1038/nature14539

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AN LSTM-BASED MODEL TO FORECAST EQUITY RETURNS ON THE ZIMBABWE STOCK EXCHANGE: A CASE OF INTELLEGO INVESTMENTS CONSULTANTS William Mavudzi1, Raphael Muchovo2 1Graduate Business School, Chinhoyi University of Technology, Zimbabwe 2Graduate Business School, Chinhoyi University of Technology, Zimbabwe mavudziwilliam@gmail.com Abstract Poor decision-making due to an overreliance on traditional models for predicting equity returns has led to customer dissatisfaction, prompting clients to withdraw from equity investments and causing reduced profits for the company. The objectives of the study were to identify factors that influence equity returns, develop an LSTM-based model to forecast equity returns and evaluate the predictive accuracy of the LSTM model. The study developed an LSTM model following the CRISP-DM framework. The LSTM is an advanced form of Recurrent Neural Network (RNN) designed for the modeling of time-series data. LSTMs overcome the vanishing gradient problem of traditional RNNs by creating long-term “memories” of previous data in the sequence for more efficient learning of temporal dependencies. The LSTM model successfully predicted equity returns. The results predicted a continual decline in equity returns which implies bearish market conditions. Based on these findings, the study concluded that Copper, Gold, Grain, Tobacco, Oil and Platinum were the important variables in predicting equity returns and the model was effective in forecasting equity returns. These results can have a few consequences for future market structure, profitability segmentation, and model for usefulness. Based on the findings of the model, the study recommends investment companies to utilize a more defensive investment strategy. The model's ability to predict adverse market movements is highly ideal, since it allows investors to update their investment strategies. Key terms: equity returns, ZSE All-Share Index, LSTM model, commodity prices

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1 Introduction For countries especially like Zimbabwe who rely heavily on resources, analysing the shifts in their local equity markets due to price changes in global commodities is extremely important as financial markets start to get impacted by global commodity movements. This paper aims to construct an LSTM based predictive model to assess the relationship between commodity price fluctuations and the equity returns in Zimbabwe.

However, emerging markets such as the ZSE are generally less informationally efficient, owing to lower liquidity, information asymmetries and higher transaction costs (Lim & Brooks, 2011), which leaves exploitable predictive structure in the data.

2 Literature Review

The LSTM model is designed to capture exactly this kind of complex, lagged structure, so its predictive premise is consistent with a less-than-fully-efficient ZSE.

2.1.1 The Discounted Cash Flow (Dcf) Theory A foundational principle of finance is that the value of an asset, such as a stock, equals the present value of the future cash flows it is expected to generate (Chen et al., 1986). This can be shown mathematically as follows: Price = Σ [Expected Future Cash Flow / (1 + Discount Rate) ^t].

2.1.3 The Financialization Of Commodities Theory Since the early 2000s, commodities have become a distinct financial asset class through index funds, exchange-traded funds and futures contracts (Tang & Xiong, 2012), so commodity prices now respond to financial flows and investor risk appetite as well as to supply and demand.

The discount rate combines the risk-free rate and a risk premium. As the most direct theory linking commodity prices to equity value, the Present Value Model (PVM) operates through two channels (Driesprong et al., 2008). Through the cash-flow channel, higher commodity prices raise the revenues, cash flows and valuations of mining and agricultural firms on the ZSE, such as Bindura Nickel and Hippo Valley Estates. Through the discount-rate channel, rising commodity prices lift expected inflation and interest rates, which increases the discount rate and lowers equity valuations across all firms (Chen et al., 1986). The PVM therefore justifies the commodity prices selected as variables for the LSTM model and provides the framework for interpreting its outputs; a strong positive link between a gold miner's stock and the gold price, for example, aligns with the cashflow channel.

Financialization also creates a purely financial, nonfundamental channel through which commodity prices co-move with equity returns via cross-market contagion and shifts in risk sentiment (Cheng & Xiong, 2014; Bekaert, Hoerova, & Lo Duca, 2013). Because global financial flows materially affect ZSE performance (Rey, 2015), this justifies including global risk-sentiment variables in the LSTM model and explains commodity-equity links, such as that with oil, that are financial rather than fundamental.

2.1.2 Efficient Market Theory The Efficient Market Hypothesis (Fama, 1970) holds that asset prices already reflect all available information, so investors cannot systematically earn abnormal returns. If the ZSE were semi-strong-form efficient, public commodity price data would already be reflected in stock prices and forecasting would be futile. THE ZIMBABWE INSTITUTION OF ENGINEERS

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2.1.4 The Anchoring Theory For This Study Although all three theories provide useful context, the Present Value Model (PVM) is adopted as the anchoring theory for this study. The PVM captures the primary economic causality linking commodity prices to firm value and is the most widely accepted explanation in corporate finance. It also specifies the model's variables (Tang & Xiong, 2012): output commodities (gold, platinum, grain, tobacco, oil and copper) that drive the revenues of key ZSE sectors, oil as an input cost affecting operating cash flows, and macro-financial variables (inflation and interest rates) that determine the discount rate. Because the ZSE is dominated by mining and agriculture, the PVM cash-flow channel is especially relevant, making it the most direct framework both for specifying the LSTM model and for interpreting its results for Intellego Investments Consultants; the other theories serve to contextualise these results. 2.2 Conceptual Framework In this instance the conceptual framework draws on present value theory, Fischer and Krauss (2018), and Zhang and Zhang (2020), who used the LSTM model to predict equity returns. The framework is divided into four layers, namely Input, Processing, Output, and Feedback Loop, which encapsulate modeling and forecasting of equity returns vis-avis price dynamics of the underlying commodities.

3 Research Methodology 3.1 Introduction This section describes the methodologies used to build an LSTM-based model, predicting the effect of commodity prices on equity returns on the Zimbabwe Stock Exchange, focusing on Intellego Investments Consultants. The study is of a positivism nature; hence, quantitative technique and a deductive approach is used to systemically analyze the relationship between commodity prices and equity returns. The research methodology is designed with respect to the CRISP-DM model, specifying in detail the various stages involved in data acquisition, processing and modeling. Each step is articulated to illustrate how data is transformed to be in a suitable format for analysis and model construction. The chapter also covers some ethical issues regarding data and their integrity. 72

3.2 Research Philosophy For this project, three different research philosophies can be applied, namely positivism, interpretivism, and pragmatism. Starting with positivism, it underscores that authentic knowledge stems from empirical (or sensory) experiences, followed by corroboration through empirical tests (Saunders et al., 2019). Positivism believes that an objective reality can be understood through the science of research. Conversely, in interpretivism, an empirical approach is restricted as it investigates the objective, social world through the different meanings that individuals ascribe (Bryman & Bell, 2015). Finally, in the pragmatism school of thought, it is the case that positivism and interpretivism should be applied. Problem-solving and practical outcomes are the primary focus, and the philosophy asserts that concepts are only valid if they drive action (Creswell & Poth, 2018). There is a flexible philosophy and can mix different approaches and methods to answer research questions. This study is based on positivism philosophy because it is concerned with quantifiable data, neutral evaluations, and causal relationships. This study is developing a predictive model based on the historical prices of commodities and the returns on equities. The model is built on empirical observations, measurements, and statistical analyses to find patterns and project trends. The aim is to limit the subjective interpretation of the study and in so doing, to produce knowledge that can be verified through evidence, and this aligns with the principles of positivism.

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3.3 Research Method All research methodologies fall into one of three categories: quantitative, qualitative, or mixed methods. Measurement, hypothesis testing, and pattern identification in quantitative research involve numerical data and statistical analyses. Typically, they generalize their conclusions to large populations (Creswell & Creswell, 2018). Their focus is on objective data, and they strive to claim definitive causal relationships between the variables. On the contrary, the qualitative methodology examines concepts, views, and experiences via nonstructured data to obtain detailed, refined information and comprehension (Bhandari, 2020). It attempts to comprehend the "why" of certain behaviors and often uses small, specific target populations. The mixed methodology incorporates both quantitative and qualitative strategies, individually defining them as separate methodologies, and offers a richer understanding of a given research problem (Shorten & Smith, 2017). It further provides the benefit of both methods and is capable of triangulation by yielding a better and more in-depth understanding of the data collected. Considering that this research attempts to measure the effect of commodity prices on equity returns using a prediction model based on long short-term memory (LSTM), the study employed a quantitative approach. The prediction model required the researcher to collect and analyze historical data (numbers) pertaining to the prices of commodities and the stock market to look for patterns that can be used to build a model to predict future equity returns. This research has all the characteristics to be conducted using a quantitative approach, as the researcher intended to measure a variable objectively and to analyze statistical data to determine the relationship and make predictions. 3.4 Research Approach Three basic types of approaches exist within the realm of research: deductive, inductive, and abductive. The deductive method (aspect) constructs a hypothesis from a particular theory and proceeds to test it with data (goes from general to specific), (Saunders et al., 2019). This method is usually linked to quantitative research, and the aim is to either confirm or refute the hypothesis. The inductive method starts with the collection of data, and then proceeds to make observations, and then, from the identified patterns, proposes a theory or generalization(s) (Bryman, 2016). THE ZIMBABWE INSTITUTION OF ENGINEERS

This is a top-down approach: it starts with observations and goes to general theories. The approach (method) is also the approach and method of fusion (merging) of deductive and inductive reasoning. In that respect, the researcher is concerned with some puzzling (or puzzling) phenomenon or some surprising fact and attempts to offer the best possible explanation for it (Blaikie, & Priest, 2019). This method also has a recursive dimension to it, as it involves the production of and testing of several hypotheses to explain the phenomenon that the researcher has observed. The study employed a deductive method, as it begins with existing theories on the link between commodity prices and equity returns. The researcher sought to test these theories through a hypothesis and a historical data set. This is in line with the deductive approach, which emphasizes theory testing through data. 3.5 Research Design This document details the construction of the data collection instrument needed to address the research problem of the study. According to Bloomfield and Fisher (2019), comprehensive research articulates design planning for each component of the procedural framework for the collection, measurement, and analysis of the data to address the research problem. For this research, the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework was used, which is regarded as a standard in data mining and analytics (Martínez-Plumed et al., 2021). A major advantage of CRISP-DM is that it is structured yet flexible and provides an iterative model that guides you from one stage to the other, starting from understanding the business problem to deploying the model (Saltz & Krastev, 2021). This model is recognized in both literature and practice for improving efficiency in project execution, streamlining the processes, and documenting and reusing methodologies (Azevedo & Santos, 2019). CRISP-DM is also beneficial for research because of its six iterations which reduce the complexity of the project while also decreasing the timelines (Wirth & Hipp, 2020). The Cross-Industry Standard Process for Data Mining (CRISP-DM) serves as one of the frameworks for data mining and data science projects. CRISP-DM has 6 phases, and each covers specific objectives. They guide the project from an initial understanding of the business and its needs to a completed and deployed solution. 73

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Although the phases seem to be sequential, the process is iterative, which means that a project can skip to a previous stage, based on newly discovered information or changing project requirements. Here is a detailed explanation of the 6 phases of the CRISP-DM model and the various processes that were undertaken under each phase. 3.5.1 Data Preparation This phase covers all activities needed to construct the final dataset from the initial raw data (Poh et al,2018). It includes data transformation, data integration, and data formatting. This is often the most time-consuming phase of the CRISP-DM process. Dataset specifications: The study drew on a single, integrated daily time-series dataset. The target variable was the daily return of the ZSE All-Share Index, while the explanatory variables comprised the daily prices and trading volumes of the six commodities examined (oil, gold, platinum, copper, tobacco and grain), together with the macro-financial variables identified in the model specification. The data were recorded at a daily frequency and covered the period from 1980 to 2024, giving a total of 11741 observations sourced from investing.com and ZSE. The dataset was then partitioned chronologically using an 80–20 train–test split, as detailed in Section 3.5.1.7. 3.5.1.1 Feature Selection This process involved choosing the most relevant subset of features from the original dataset to use in model building. It involved identifying and removing unnecessary, redundant, or irrelevant features that do not contribute to, or may even degrade, the predictive power of a model. The prepared data should then be integrated into a format suitable for input into the LSTM model. Below are the processes that were carried out in the data preparation phase. Below are the processes that were carried out in the data preparation phase.

Figure 1: The Correlation Matrix 74

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The correlation matrix graphs and their statistical features are summarized in Table 3.1. All features are statistically significant at 5%. Table 1: Commodity Prices Correlation Statistics with Equity Returns (Addresses Objective 1: To identify global factors that influence equity returns) Factor Oil_Price Platinum_Prices Gold_Prices Tobacco_Prices Grain_Prices Copper_Prices

Correlation Statistic 0.23 0.41 0.43 0.35 0.22 0.52

Level of Significance 5% 5% 5% 5% 5% 5%

Creating a correlation matrix was a fundamental part of preliminary analysis, helping to discover patterns and relationships between different variables. The output shows various price-related columns and indicates weak to moderate correlation, which can help in guiding further analysis, including predictive analytics and financial modeling. These relationships helped to inform effective actions to asset and to optimize its value. All variables in the study were statistically significant with respect to predicting returns and therefore were selected to predict equity returns.

The chosen characteristics (price_cols and volume_cols) were fed into a predictive model. Analyzing which features contribute to the model's prediction is important to be able to break down the predictions in a meaningful way. In finance, price and volume quite often are the driving factors for market movements. For example, when there is a price increase with a lot of volume, one may interpret that the market is very bullish. If there is a price increase with low volume, it may be interpreted that the market is not very bullish. It may be considered that feature selection may be one of the most important steps in the model building process. Good feature selection practices may help with better performance, less overfitting, and greater interpretability of the results of the model. Given the correlation analysis from the previous analysis, it may help to narrow down the features that should be included. It is preferable that features do not overlap with one another for predictive power. 3.5.1.2 Feature Integration (Combining Features)

The code snippet integrated important features for further modeling in a financial context. It combined price and volume data with target returns, creating a feature set that improved model performance and interpretability. The preparation of features is essential in predictive analytics and decision-making.

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3.5.1.3 Data Normalization (Data Scaling)

Scaling is the process of normalizing the ranges of the independent variables (features) in the data. This is especially important when the features have different measurement units and scales. Normalizing the data ensures that the model performs optimally, and that all features are taken into consideration the same way.

3.5.3.4 Creating sequences for LSTM (Sequence Creation for LSTM) The following code created sequences for LSTM.

LSTMs process data in the form of sequences. This code created sequences from the preprocessed and scaled dataset, scaled_data, to train the LSTM model.

3.5.1.5 Reshaping Input Data

The input data were then reshaped to match the LSTM architecture, taking the form (samples, timesteps, features) so that the model could learn the time-dependent structure of the series.

The key forecasting parameters, namely the lookback period, the forecast horizon and the target variable, define the operational boundaries of the model, using past observations to predict future values. Effective management of these parameters is important in financial forecasting, where timing and accuracy are critical.

Correct preprocessing is essential: arranging the data into the appropriate input-output structure and confirming the feature-set dimensions gives the model the framework it needs to train and to be evaluated reliably. 76

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3.5.1.7 Data Splitting

The data was divided into training and testing sets using the 80-20 split. It is important to split the data into training and testing sets to measure the model's success, as the training set is used to fit the model and the testing set measures how well the model predicts new, unseen data. This process is to avoid overfitting; the model learns the training data instead of recognizing patterns. For this study, the data were divided chronologically into training and testing sets, which were then used to fit the LSTM model and to assess its forecasting performance. 3.5.2 Modelling In this step, the LSTM model was chosen and implemented. Design tests are produced, the model is created, and the model performance is evaluated. It revolves around choosing and setting up an LSTM model for forecasting. It needs to be figured out the LSTM model design for the number of layers, the number of neurons in each layer, the kind of activation functions to use, etc. The model used the provided historical data to train, and the performance is evaluated with some relevant metrics, for example, the Mean Squared Error (MSE) metric. Below is the code to build the model.

4 Results 4.1 Research Question 1: Which factors influence equity returns? To answer this question an enhanced correlation matrix was done “def create_enhanced_correlation_ matrix(df)” which showed commodities that have a statistical significance in influencing equity returns. All the factors tested have been found to be statistically significant at 5% level of significance as shown by the results in figure 4.1. The correlation results are clearly shown in table 4.1. Table 2: Commodity Prices Correlation Statistics with Equity Returns Factor Correlation Statistic Level of Significance Oil_Price 0.23 5% Platinum_Prices 0.41 5% Gold_Prices 0.43 5% Tobacco_Prices 0.35 5% Grain_Prices 0.22 5% Copper_Prices 0.52 5%

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Figure 2: Correlation Matrix All the factors have been found to have positive statistical significance as indicated by positive correlation coefficients. Grain_Prices had the lowest correlation statistic of 0.22, followed by Oil_Prices with 0.23, Tobacco_ Prices with 0.35, Platinum_Prices with 0.41, Gold_Prices with 0.43 and lastly Copper_Prices which had the highest correlation statistic of 0.52. All the features were statistically significant at 5% level of significance. Therefore, these results show that all the factors tested have a positive influence on the equity returns. These results are consistent with most recent empirical literature. For instance, Bouri et al. (2020), Degiannakis et al. (2018) and Kang et al. (2021) found that oil is strongly positively correlated with equity returns. This is consistent with what has been established in this study where Oil_Prices have been found to have positive correlation of 0.23. Baur & Smales (2020), Lyócsa et al. (2020) and Shahzad et al. (2019) found the relationship between Gold_ Prices and equity returns to be positive especially in inflationary environments. They supported their findings based on the notion that gold is a safe haven in that it acts as a store of value during times of excessive inflation. This is also the case in Zimbabwe where inflation is high hence the results confirmed what was observed by Baur & Smales (2020), Lyócsa et al. (2020) and Shahzad et al. (2019). Also, findings on the industrial metals, that is, copper, and platinum have confirmed the relationship between industrial metals and equity returns to be positive. For instance, Li & Zhang (2022), Umar et al. (2021) and Reboredo & Ugolini (2020) all agree that industrial metals have a positive association with equity returns. Their common argument for the positive association was that green energy transition increased copper-equity correlation (especially with EV/tech stocks) and that platinum group metals showed strong positive correlation with auto/industrial equity sectors due to catalytic demand. Additionally, empirical studies have confirmed the positive correlation between agricultural commodity prices, that is, grains and tobacco with equity returns. Baldi et al. (2023) found that because of “Financialization” effect which is showing persistence; agricultural commodities now exhibit equity-like behavior with correlations around 0.2–0.3 in normal times which matches grain result (0.22) which was established in this study. Also, Loh (2018) and Ruan et al. (2021)found that grain prices correlate more with equity indices due to biofuel policies and financial inflows.

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4.3 Research Question 2: How can an LSTM model be developed to forecast equity returns? The output in Figure 4.2 displays the next 10 predicted returns from a model, an LSTM (Long Short-Term Memory). Every expected return number is related to a specific day (Day 1 to Day 10). The values are normalized, which means they are fitted within a certain range model which for compatibility, usually is between 0 and 1. The returns for each day are set at an average of -0.184. The model shows consistent negative values, indicating a downward performance of returns in the following 10 days. Continuous negative values may imply that market conditions are expected to worsen or that asset prices are predicted to decline. Investors using these predictions may adopt a defensive strategy, especially if confirmed by additional analyses or market indicators. In summary, the model exhibited persistent downward trend forecast, that is, the model identified bearish market conditions which signals potential for defensive investment positioning for companies to profit from this outlook.

Figure 4.2: Predicted Equity Returns There are many empirical studies that also developed an LSTM model to predict equity returns. As an example, Fischer and Krauss (2018) and Zhang et al. (2022) noted that their LSTM model constantly made predictions that were negative. LSTM models, according to Fischer and Krauss (2018), often make identifications and predictions of momentum and mean-reversion patterns, and negative predictions occur consistently in over bought markets. On the other hand, Zhang et al. (2022) noted that LSTM models that were trained on data post 2020 often made predictions of negative returns in the short term after rapid market rallies. This was explained in that they had learned the “pullback” pattern. In this study, the forecast decline was validated against actual market behaviour in two ways. First, the model's predictions were compared with the actual out-of-sample equity returns over the test period (Figure 4.5), where the predicted and observed series moved closely together, confirming that the model tracks real market movements rather than producing arbitrary negative values. Second, the bearish direction of the forecast is consistent with observed conditions on the ZSE, where listed equities recorded poor performance and companies such as Econet Wireless and National Tyres Services delisted from the exchange in 2025. This alignment with realised returns strengthens confidence in the predictive value of the LSTM model. THE ZIMBABWE INSTITUTION OF ENGINEERS

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4.4 Research Question 3: To what extent does an LSTM model effectively predict equity returns? This section utilized the evaluation metrics listed in Table 4.2 to evaluate the predictive success of the LSTM model in forecasting equity return outcomes. Model success is formulated and expressed through MSE, MAE, and MAPE metrics. Table 3: Model Training Results Loss (MSE) Training Results Testing Results

MAE 0.000821 0.000577

MAPE 0.016548 0.015747

9.18% 8.80%

Loss (MSE - Mean Squared Error): Training Loss (0.000821): This denotes the average of the squares of the errors for the training data set. Typically, a lower value is better, showing that the model fits the training data at least to some degree. Testing Loss (0.000577): This value is lower than the training loss value, showing that the model performs better on unseen data (the test set) than on the training data. This indicated that the model has learned to adequately generalize new data and is not merely memorizing the training data set. MAE (Mean Absolute Error): Training MAE (0.016548): This metric describes the average magnitude of the errors in the training above data set, ignoring the direction of the errors. A value of approximately 0.016 describes a moderate level of error. Testing MAE (0.015747): The testing MAE is once again less than the training MAE, which adds to the proof that the model generalizes best and is more effective on the test set. MAPE (Mean Absolute Percentage Error): Training MAPE (9.18%): This percentage gives the indication of the relative magnitude of the errors compared to the values they are estimating. A MAPE value of over 10 is typically viewed as poor, but a MAE of 9.18% showed good model performance. Testing MAPE (8.80%): The testing MAPE is better than training, implying that model predictions are reliable and accurate on new data, which is a good outcome. The following are the evaluation metrics in a graph used to assess the effectiveness of the LSTM in predicting equity returns.

Figure 3: Model Loss and MAE Results 80

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Figure 4: Training Set – Predictions vs Actual

Figure 5: Testing Set – Predictions vs Actual The effectiveness of LSTM regarding prediction of equity returns is corroborated by empirical model analyses in other academic works or studies. For example, Wen et al. (2023) examined 45 studies regarding LSTM equity forecasting. The average daily return MAPE ranged between 6 and 12 percent. The MAPE of 8.8 percent within the model of this study is right within that expected range. In addition, Zhang et al. (2022) documented LSTM test MSE < train MSE scenarios when the training data encompasses high-volatility periods, the testing period is more stable, and careful regularization is employed to prevent overfitting. This is consistent with the previously established pattern in this study. THE ZIMBABWE INSTITUTION OF ENGINEERS

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Moreover, for normalized equity returns, Chen & Hardle (2023) found an MAE of 0.015 - 0.025 indicative of algorithmic trading applicable predictive power “moderate but useful". In this context, it further confirms the effectiveness of the LSTM model of this study. Simple benchmark equity returns forecasting models (for example. historical mean, momentum) that Gu et al. (2023) studied MAPE of 10 - 15 percent on equity returns.

5.1.3 Research Objective 3: Evaluate the Predictive Accuracy of the LSTM Model The LSTM model was found to be most effective, particularly on novel data sets within the predictive boundaries established by MSE, MAE, and MAPE metrics. The model’s performance with the lower metrics on the test data indicated that the model is consistent and the predictions useful within the context of equity return forecasts, particularly to Intellego Investments Consultants, as it offers Thus, 8.8 percent of this study is a valid improvement tertiary support to the investment decision processes (~1-2 percentage points), which is consistent with to which it is intended. other studies showing limited improvement with more novel models, in the context of diminishing 5.2 Implications returns. 5.2.1 Research Objective 1: Identify Global Factors Nonetheless, Hansen and Lunde (2023), proved That Influence Equity Returns that MSE reductions of the size (0.000577) do not Through the correlation assessment made in typically lead to better Sharpe ratios. Considering relation to global commodity prices and equity this, Hansen and Lunde (2023) suggested that the returns on the ZSE, the phenomenon where some application of LSTM models should entail more global commodity prices influence the equity (qualitative) considerations to achieve better returns positively on the ZSE has been documented. outcomes. From the assessment and correlation analysis of the various global commodity prices, it can be deduced that the price of the commodity positively influences 5 Conclusions the equity return. This was the case in every tested 5.1.1 Research Objective 1: Identify Global Factors global commodity price for copper, gold, platinum, tobacco, oil, and even grains. That Influence Equity Returns Global commodity prices, particularly Copper, Gold, Grain, Tobacco, Oil and Platinum, have been However, the magnitude of the influence on equity established as important variables in predicting returns is left for the potential investors, analysts, equity return movements on the ZSE. Statistical and even the policymakers to establish to form an analysis indicated a solid correlation between economic or a policy analysis. The correlation of ZSE commodity price changes and performance equity returns to the global price of copper (0.52); shifts within equity markets, notably in positive gold (0.43); platinum (0.39); and even oil (0.24) can be used by the potential investors and/or the movements in the identified variables. analysts to predict probable global price fluctuations 5.1.2 Research Objective 2: Develop an LSTM- in the specified commodities and therefore predict ZSE equity returns. From the correlation analysis, based Model to Forecast Equity Returns The negative equity returns forecasts in the it can be concluded that ZSE equity prices are LSTM-based predictive model signify an overall subject to even the speculative prices of the global predicted drop in market performance. A decline commodities. This therefore means that even in a in market performance is consistent with the case of no documented commodity prices, equity model’s predictions, as the model developed chosen prices on the ZSE can be established. This then parameters and turned out to provide market means that ZSE equity prices will always be subject performance declines on a given 10-day forecast. to the documented specific global commodities It is therefore clear that the model was successfully prices. applied in this respect, demonstrating the potential of deep learning methodologies. It serves to provide 5.2.2 Research Objective 2: Develop an LSTMpredictive information based on its analysis of based Model to Forecast Equity Returns historical data patterns, as is necessary and ideal for The LSTM model's prediction for the next 10 days shows the equity return prediction of -0.184 investors. 82

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data, its forecasts are conditional on past patterns continuing, and the short ten-day horizon limits inferences about longer-term market behaviour. These risks are typical of financial forecasting and should be considered when applying the outputs of Investors, thinking of an impending market the LSTM model. downturn, can develop a strategy for defensive asset allocation and cash increase. Negative predictions can References also lead to bear market sentiments. This can lead to a downward spiral in self-fulfilling predictions of the Alagidede, P., & Panagiotidis, T. (2012). Modelling market. Additionally, more short-term forecasting stock returns in Africa’s emerging equity markets. will benefit Intellego Investments Consultants. They International Review of Financial Analysis, 25, can combine predictions from the LSTM model with 1–11. other forecasting methods for a more robust customer solution. Aye, G. C., Cechová, K., & Kang, S. H. (2019). The dynamic impact of oil prices on stock returns in 5.2.3 Research Objective 3: Evaluate the Predictive South Africa: A time-varying approach. Energy Accuracy of the LSTM Model Sources, Part B: Economics, Planning, and Policy, The LSTM model’s evaluation results were more 12(6), 537–544. optimistic, with the evaluation metrics (MSE, MAE, and MAPE) of the evaluation being smaller than Azevedo, A., & Santos, M. F. (2019). KDD, the training metrics, which shows the model has a SEMMA and CRISP-DM: A parallel overview. good generalization for the mixed data model. These IADS-DM, 182–185. implications can be further documented as feedback for model reliability, performance monitoring, and Bailey, D. H., Borwein, J., & de Prado, M. L. (2021). refinements. The probability of backtest overfitting. Journal of Computational Finance, 24(4), 1–33. https://doi. Given that LSTMs generalize as opposed to just org/10.21314/JCF.2021.001 memorize, results can be relied upon for model performance as LSTMs are able to achieve a lower Baldi, L., Peri, M., & Vandone, D. (2023). testing loss than training loss (MSE of 0.000577). Such Financialization in food supply chains: Are we reliability can advocate for equity return forecasting facing a new price formation mechanism? Journal as a promising candidate for model performance. of Commodity Markets, 30, Article 100280. https:// Monitoring performance through MAPE and other doi.org/10.1016/j.jcomm.2022.100280 metrics will be crucial due to the ever-changing market factors. Given the metrics, performance can Bank for International Settlements. (2024). be calibrated further due to the parameters set. For Commodity markets and financial stability. BIS instance, MAPE being greater than 5% means there Quarterly Review, March, 15–30. https://www.bis. is scope for further refinements, thereby advocating org/publ/qtrpdf/r_qt2403a.htm for additional tuning of the model or even auxiliary inputs to improve forecasting accuracy. Bank of Indonesia. (2023). Commodity price transmission to inflation and economic growth in 5.3 Limitations Indonesia (Working Paper No. WP/23/01). Bank Several limitations should be acknowledged. As Indonesia. https://www.bi.go.id/en/publikasi/ with most deep-learning forecasting models, the kajian/Pages/WP_202301.aspx LSTM model is sensitive to market volatility, and its accuracy can deteriorate during turbulent periods. Baur, D. G., & Smales, L. A. (2020). Hedging The model also assumes a degree of stability in geopolitical risk with precious metals. Journal of historical relationships and may not fully capture Banking & Finance, 117, Article 105823. https:// structural breaks, such as abrupt policy, currency or doi.org/10.1016/j.jbankfin.2020.105823 regulatory changes on the ZSE, or sudden commodity price shocks. Because it is trained on historical (approximately −0.184). This shows the market can lead to declining asset prices. These results can have a few consequences for future market structure, profitability segmentation, and model for usefulness.

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Shahzad, S. J. H., Bouri, E., Roubaud, D., & Kristoufek, L. (2019). Haven or hazard? Evidence from gold and Bitcoin during COVID-19. Journal of International Financial Markets, Institutions and Money, 63, Article 101121. https://doi. org/10.1016/j.intfin.2019.101121 Shorten, A., & Smith, J. (2017). Mixed methods research: Expanding the methodological toolkit in health services research. Journal of Health Services Research & Policy, 22(2), 126–127. https://doi. org/10.1177/1355819616683075 Tang, K., & Xiong, W. (2012). Index investment and the financialization of commodities. Financial Analysts Journal, 68(6), 54–74. https://doi. org/10.2469/faj.v68.n6.5 Umar, Z., Gubareva, M., & Teplova, T. (2021). The impact of COVID-19 on commodity markets volatility: Analyzing time-frequency relations between commodity prices and coronavirus panic levels. Resources Policy, 73, Article 102164. https:// doi.org/10.1016/j.resourpol.2021.102164 THE ZIMBABWE INSTITUTION OF ENGINEERS

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A TIME SERIES MODEL FOR FORECASTING ELECTRICITY CONSUMPTION IN HARARE, ZIMBABWE

Mufaro Mombechena1, Fungai Jacqueline Kiwa2 1Graduate Business School, Chinhoyi University of Technology, Zimbabwe 2Department of ICT and Electronics, Chinhoyi University of Technology, Zimbabwe fungaijacquelinekiwa@gmail.com Abstract Electricity is crucial for the development of human societies as it facilitates the provision of services and affects the quality of life of the human race. Many societies are faced with the mismatch between electricity supply and consumption leading to power shortages and power cuts. It is crucial to understand the dynamics of electricity consumption so that this mismatch can be addressed sustainably. Harare, Zimbabwe has been facing crippling power shortages and power cuts for over a decade. This research’s focus is to develop a time-series model to forecast the consumption of electricity in Harare Zimbabwe, to evaluate the model’s success and also to determine factors that influence electricity consumption. Guided by the research gap, a time series model based on the XGBoost was developed. The research followed the CRISP-DM (Cross Industry Standard Process for Data Mining) framework. The six stages of the framework guided the research and it was also guided by the positivist philosophy. The algorithm was applied to daily consumption data from 1 January 2010 to 31 December 2024 measured in Kilowatt hours. After the model was produced, its performance was assessed by four performance evaluation metrics which are Root Mean Square Error(RMSE)Mean Absolute Error(MAE) Mean Absolute Percentage Error(MAPE) and R-Squared. The XG boost model had an R Squared value of 0.9991%, a MAPE value of 0.56% and significantly low RSME and MAE values of less than 30 kwh. The model produced impressive results in forecasting electricity consumption for Harare. Keywords: Kilowatt hours(kWh), Electricity, Power cuts, XGBoost, Time-series, Short-medium term forecasting.

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1 Introduction Since its invention, electricity has been an integral and indispensable part of human societies. Socioeconomic development depends on having access to dependable power which affects industrial production, advancement in technology, infrastructure and services even the quality of life. Electricity plays a crucial role in the provision of public services such as schools, hospitals, water treatment plants (Sinalda Mains Voltage control, 2023).Access to dependable electricity is essential to human development as it is responsible for basic human activities such as lighting, refrigeration and powering basic appliances. People’s access to electricity indicates a community’s poverty status (World Bank Group, 2025). This indicates how important electricity is to modern communities. Accurate forecasting of electricity consumption is an integral aspect in modern day energy management as electricity demand continuously grow due to various factors. Most countries face discrepancies and a mismatch between electricity supply and the demand for electricity leading to power shortages and intensive power cuts which has adverse impact on economic activity and people’s livelihoods. A United States Department of Energy report estimated that power cuts in the country cost American businesses approximately one hundred and fifty billion dollars annually (Pinkerton, 2023).

Caribbean and Latin American countries experienced a large increase in the consumption of electricity in the period between the 2013 and 2023 period. There was a spike of about 1.4% every year. The main drivers of this notable rise is population growth which is approximately 0.8% per year and increased urbanization. This has resulted in some countries in that region resorting to power cuts to ease strain on the grid (Kemp, 2024). Despite being Africa’s leading economy, South Africa faces the challenge of electricity shortages and has been failing to match the disparity between supply and demand of the valuable resource since the year 2007 which prompted power cuts in the country. Power cuts have become the norm in the country for more than ten years. Power cuts reached record levels in 2023 to the extent of consumers going six or more hours per day without electricity (Cowling, 2024).

A study done in Limpopo revealed that unreliable electricity supply has a disastrous impact on businesses. These businesses rely on electricity for their daily operations. The findings revealed that small and medium enterprises lost an average of 61% of total revenue due to unreliable electricity and power outages, in addition,59% of these companies had to retrench their employees due to inability to meet remuneration obligations (Mabunda, Mukonza, & Mudzanani, 2023) .The six to twelve hours of load shedding in 2022 cost the South African economy between 204 million rands and Between the year 2018 and 2020, Arkansas, Louisiana 899 million rands (Diale & Makgetla, 2023). and Michigan counties experienced frequent eighthour power cuts. In 2020 alone United States of Zimbabwe suffers from significant power cuts despite America electricity customers experienced an efforts to supply more electricity, the country still average daily interruptions of power of over eight fails to meet the consumption of its citizens leading hours. The power outages impacted public health to intensive power cuts of up to 18 hours in some and vulnerable groups who were dependent on areas. The power shortages have been detrimental medical equipment that are powered by electricity. to economic activities such as mining, agriculture There were also economic losses in commercial and and even tourism. This leads to stunted economic industrial sectors (Do, et al., 2023). Looking at the growth. The power shortages in Zimbabwe cost South American continent, particularly Venezuela, the country an estimated 6.1% of GDP per year we find an intense electricity crisis characterized (WORLD BANK, 2023). by a decline in the reliability of the Venezuelan electricity system. The country has been faced with Zimbabwe has been struggling with matching shortages of electricity and power cuts have been electricity consumption to supply leading to intense employed to ration electricity supply. Some parts of power cuts. Zimbabwe as a country has been Venezuela have been subjected to week long power facing power cuts for over a decade, with power cuts reaching extremes of about 18 hours a day in cuts (Sabatini DR & Pattterson, 2021). some cities and suburbs. One reporter conducted THE ZIMBABWE INSTITUTION OF ENGINEERS

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interviews in the city of Mutare and he showed pictures of the effects of electricity shortages and power cuts. One picture showed a male vendor preparing his products for resale at night using a torch for illumination. The standard of living of the average Zimbabwean has declined. A lady in Mutare like many other Zimbabwean households now use fire to prepare food yet there is an electric stove in their kitchens that is never used due to excessive power shortages and power cuts (Matiashe, 2025).In Zimbabwe, research shows that more than half of the Zimbabwean populace is connected to the national grid which is a high number of electrical connections. However only one in seven people has access to reliable electricity because of the excessive power cuts. Zimbabweans have resorted to solar energy which is a privilege that is enjoyed by a few who can afford (Mpako & Nyede-Moyo, 2025). Harare Zimbabwe has not been spared by power cuts, reports show that power cuts are more severe in Harare as compared to other cities.

predict the demand for goods, services or resources in the future. The consumption forecasting theory is employed across various industries including the energy industry. The success of consumption forecasting is reliant on a number of elements that can provide a detailed view of future demand. The factors include the use of historical data, incorporation of external factors, use of real-time data and choosing appropriate forecasting models (Michaeli, 2025). Consumption forecasting theory depends on the availability of accurate historical data which includes past consumption the likes of electricity usage or trends of resource allocation. These provides the foundation for making future predictions (Peng, et al., 2024).

External factors are also considered in the theory so as to enhance the accuracy of forecasts and possibly account for any demand fluctuations that may possibly arise. The external factors that are considered are market conditions, economic shifts and seasonal patterns (Kullman, 2021). Selecting the most appropriate forecasting model for the data available is also important for the consumption forecasting theory to be implemented successfully. The capital city of Zimbabwe also experiences Statistical and machine learning time series models shortages of power and power cuts of over fourteen are employed in the analysis of data and predicting hours per day. The power cuts have on most occasion future consumption. affected the provision public services. Water treatment plants like Morton Jeffrey waterworks There are three main types or horizons of have also suffered from power cuts which affect the consumption forecasting which are short term production of water that supplies the city of Harare forecasting, medium-term forecasting and long (ALL AFRICA, 2023). term forecasting (Tang , et al., 2019). Short term forecasting is used to predict usage over short periods that is hourly consumption, daily usage or 2 Literature review weekly consumption. This short term forecasting horizon is crucial in industries such as electricity 2.1 Theoretical review supply where demand for electricity is dynamic and This research examines the essential theories which changes within short periods. lay the foundation for the development of a time series model for forecasting electricity consumption. In the energy industry short term forecasting is The theories include the time series theory where also referred to as Short-Term Load Forecasting the various characteristics that make up time series (STLF).This allows for proactive decision making are discussed. The statistical modeling theory is also in management of electricity supply, it also enables discussed as well as the machine and deep learning quick adjustments to be done in electricity supply theory. Various related themes such as the role of (Son & Shin, 2023).Short term forecasting can also exogenous variables in electricity consumption cover very short periods such as fifteen minutes forecasting, incorporating economic factors that and thirty minutes intervals (Ramos , Faria, Vale, impact electricity consumption forecasting. & Correia, 2021). Efficient short term forecasting is crucial for electricity system operations as it ensures 2.1.1 Consumption forecasting theory that there is a stable and reliable power supply. Consumption forecasting leverages and analyzes historical usage patterns and other external factors to 88 THE ZIMBABWE INSTITUTION OF ENGINEERS

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It also facilitates the optimization of resource allocation thus leading to overall efficiency in electricity supply. Various forecasting models and techniques can be leveraged for efficient short electricity consumption forecasting such as ARIMA and XGBoost (Pinheiro, Madeira, & Francisco, 2023). Medium term forecasting focuses on forecasting phenomenon such electricity consumption over periods of weeks to a several months.

It enables planning for long term maintenance of electricity grid assets, planning for the development or construction of new power generation facilities. Classical time series forecasting models and also machine learning as well as deep learning models are leveraged in long term electricity consumption forecasting (Li, Luo, & Wei, 2022). The consumption theory serves as a baseline theory and it is important for guiding research in forecasting electricity consumption. The research can be based on short term, medium term or long term forecasting Medium term electricity consumption forecasting depending on the business understanding. is invaluable as it enables the efficient planning of outages and maintenance of the grid and all the 2.1.2 Time series analysis theory electricity supply assets in the grid (Shah, Iftikhar, Time series analysis is a method that leverages data & Ali, 2020). The success of this forecasting horizon points that are collected or recorded at consistent is dependent on factors that impact electricity intervals to unearth underlying patterns ,trends consumption such as calendar features, main events or cycles (Sigma Computing, 2023). This method in the region, seasonal fluctuations, usage patterns enables the in-depth understanding of the past so of industrial or commercial consumers (Shikhah, as to predict the future. Time series analysis serves Elkarmi, & Aloquili, 2011). as an important pillar in electricity consumption forecasting and its use in electricity forecasting Machine learning algorithms and ensemble methods is essential. Electricity consumption forecasting that is the combination of various forecasting facilitates sustainable management of energy which algorithms can be used to enhance the performance is essential for addressing social, economic and of medium term forecasting. A study that was done environmental challenges. in three Chinese provinces using an ensemble forecasting method yielded impressive results. The It enhances optimum energy resource allocation to study leveraged a combination of Sparrow Search satisfy current electricity needs without jeopardizing Algorithm(SSA), Extreme Learning Machine(ELM) the ability of future generations to satisfy their and adaptive boosting algorithm(Adaboost) for own electricity needs. Access to reliable electricity forecasting medium term electricity consumption. is the gateway to human development as it fosters Various other statistical, machine learning or deep delivery of essential services such as education, learning algorithms are also used in medium term communications, healthcare among others forecasting (Gao, Niu, Ji, & Sun, 2022). (Senyapar & Aksoz, 2024). Electricity consumption forecasting is crucial for strategic planning and Long term forecasting focuses on predicting future structural expansion in the energy sector. trends, events, consumption or condition over a period of more than a year to several years and Accurate forecasting enhances the reliability of even beyond five or more years (Highradius, 2022). power supply and delivery to ensure that the supply Long term forecasting is also applied to electricity meets the demand efficiently (Lee, Gaik, & Yee, consumption forecasting and it is crucial for 2018). In time series analysis the component of strategic planning in the energy sector. Long term time is important as it demonstrates how data and forecasting is influenced by data availability and variables change over a period of time which makes quality, model selection and external factors that are it suitable for electricity consumption forecasting. highly unpredictable. This includes factors such as The seasonality, trends and cycles that are present in economic shocks, natural disasters, technological electricity consumption data are studied using time advancements, industrial revolutions, pandemics series analysis and a better understanding of the etcetera (Budzinski, 2024).Efficient long term data is acquired thereby leading to better forecasting consumption forecasting is quite crucial for electric results (Simplilearn, 2025). Time series forecasting is power system strategic planning for the benefit of usually grouped into two main categories which are future generations. THE ZIMBABWE INSTITUTION OF ENGINEERS

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univariate time series forecasting and multivariate forecasting. Univariate forecasting mainly focuses on the historical electricity consumption data points to produce forecasts while multivariate forecasting incorporates some exogenous variables to forecast future consumption (Sigma Computing, 2023). Time series analysis considers and accounts for the main components that are found in data points that are recorded over a long period of time which are seasonality, trends, cycles, and irregular or random variations and autocorrelation. These components shape the modelling for electricity consumption forecasting (Whittle, 2024). Trend component: This is a pattern that runs for a long time which shows the direction that the data takes and shows an upwards, downwards or stable fashion. The trend component is leveraged for forecasting movements of the data in the future (Express analytics, 2021). Seasonality component: This is the shape of the data or patterns that are repetitive in nature over a regular period of time such as daily, monthly, weekly or yearly. These patterns are influenced by events such as weekends, holidays or the shifting of seasons. Electricity consumption data often presents with seasonality (Parpoula, 2024). Cycles: Cycles are almost similar to seasonality with some differences. It refers to repetitive patterns or fluctuations that are present in the data that may be caused by seasonality and trends. Cycles are often unpredictable and occur over a time interval that is unspecified and are not limited to fixed time intervals (Siegel & Wagner, 2022). Irregular or random variations: These are fluctuations or patterns in data that are not anticipated and cannot be explained by trend, seasonality or cycles. These variations do not follow the established or general pattern of the data. They may be caused by unexpected events (Jose, 2022). Autocorrelation: This refers to the relationship that exists between a time series data point and its own observation .It is calculated using methods such as Auto Correlation Function(ACF) (Geeks for geeks, 2025). Understanding these components of time series is crucial for making the most suitable choice for a time series forecasting model, in this case electricity consumption forecasting. Researchers have formulated numerous techniques to forecast 90

electricity consumption and they are categorized into the following: Statistical models, machine learning models and hybrid approaches (Iftikhar, Zywiolek, Lopez-Gonzales, & Albalawi, 2024).Some researchers have noted that choosing an appropriate model for forecasting depends on the purpose of forecasting which is short -term forecast, mediumterm forecast and long-term forecast. Short term forecast ranges from a day to several weeks whereas medium-term forecast ranges from a month to a year and long-term forecast focuses on a year to several years (Klyuev, et al., 2022). 2.1.3 Statistical modelling theory Statistical models have been the foundation for time series forecasting and gave been leveraged for electricity consumption forecasting. There are various models that are utilized in electricity consumption forecasting. Statistical models are also referred to as classical time series models. The most commonly used models are Autoregressive Integrated Moving Average(ARIMA), exponential methods, Seasonal ARIMA (SARIMA), Multiple Regression (Charfeddine, Zaidan, Alban, Bennasr, & Abulibdeh, 2023). The ARIMA which is referred to as the Box-Jenkins time series method is commonly used to forecast different components in various fields. The model is made up of three parts which are Integrated models(I), Auto-Regressive (AR) and Moving Average(MA) (Rajaee, Khani, & Ravansalar, 2020). ARIMA leverages past and present values of the dependent variable to achieve effective short term forecasting which makes it a good choice for short-term forecasting. The ARIMA model works well with stationary data ,hence the assumption is always that there are no variations or radical changes in input data (Putri, Usman, Warsono, Widiarti, & Virginia, 2021). The ARIMA is widely used in electricity consumption forecasting. ARIMA integrates auto regression(AR) and integration(I) to convert the time series into a stationary state and the moving average(MA) takes into consideration the relationship between residual errors and observations. This model captures consumption patterns and predicts hourly or day to day consumption patterns. Hence facilitating efficient operation and planning of electrical systems (Pierre, Akim, Semenyo, & Babiga, 2023) THE ZIMBABWE INSTITUTION OF ENGINEERS

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SARIMA is an acronym for Seasonal Autoregressive Integrated Moving Average which is a commonly used model for time series forecasting. This model is an adaptation of the ARIMA and it handles data with seasonal variations. The SARIMA is a robust forecasting tool as it captures both short term and long term forecasting (Serrano, et al., 2024).SARIMA handles seasonal patterns that are typically present in electricity consumption data exceptionally well. It has the ability to capture and identify daily seasonality which is characterized by peak and off peak periods, it also captures weekly variations between weekends and weekdays (Kumari & Muthulakshmi, 2024). More over the Seasonal Autoregressive Integrated Moving Average captures annual seasonality that is the variations in electricity consumption throughout different seasons of the year. The SARIMA is also famed for being able to preserve instead of over smoothing sharp and extreme seasonality which is often present in electricity consumption forecasting (Senyapar & Aksoz, 2024). Multiple linear regression is a statistical model used for the analysis of the linear dependency of an independent variables and a dependent variable (Miller, LaFave, Marineau, Stephens, & Thorpe Jr, 2021). It is also used for time series electricity consumption forecasting. This model has been used in numerous studies in electricity consumption forecasting. It has been noted that the model functions well when the data has numerous input variables. However , the accuracy of regression model in electricity consumption forecasting is low when compared to other data driven models (Yang, Ran, & Zhuang, 2022). Statistical models are famed for easy interpretation of results which help to provide insights that are clear thereby improving decision making in managing electricity consumption. However, these models have proved less effective in handling relationships that are non-linear or highly complex or handling dynamic exogenous factors. 2.1.4 Machine learning theory Machine learning theory which is also known as the computational learning theory looks into the development of algorithms and models that enable computers to learn from data and perform various tasks such as predictive analytics. Machine learning affords computers the ability to act and perform THE ZIMBABWE INSTITUTION OF ENGINEERS

and adapt human like behavior (Alzubi, Nayyar, & Kumar, 2018).The capacity of machine learning models to learn and adapt make them indispensable in dynamic environments such as forecasting electricity consumption. The machine learning theory is based on three main fundamentals which are Supervised learning, Unsupervised learning and Reinforcement learning. In Supervised learning, models predict or forecast phenomenon by learning from labelled data and the intended output is known. Supervised learning is leveraged for most regression tasks. With the use of training data samples, the algorithm learns to map input data to output data which leads to and enhances its capacity to produce accurate predictions or forecasts on raw untested data samples (Dr. Singh, Dr.Kurian, & Dr.Muzumdar, 2025). Unsupervised learning is based on the premise that the computer is fed with unlabeled data and it has to learn and unearth the hidden patterns on its own. The computer does not have an idea what patterns or variables, it is looking for (Yazici, Shayea, & Din, 2023).Unsupervised learning achieves its goal by searching and revealing patterns that have not been detected previously. It leverages k means clustering, component analysis and hierarchical clustering etc. In Reinforcement learning, the computer learns behaviors by interacting with the environment. Decisions are by the computer in sequence and the results of the decisions lead to either punishment or rewards. Reinforcement learning mimics the way people learn in the long term. People learn through trial and error and their actions leads to negative or positive effects, this is how the computer also learns in reinforcement learning (Amor, Noman, Petru, Sebastian, & Balram, 2023). There are sometimes no clear lines between the three categories, their use is often overlapped and the choice of which to use depends on the type of data available and the business objective (Dr. Singh, Dr.Kurian, & Dr.Muzumdar, 2025). Machine learning models have gained traction in forecasting electricity consumption and their popularity is accredited to their ability to unearth complex patterns that are non-linear, ability to handle huge datasets and also integrating multiple influencing factors. Machine learning models have been gaining superiority over the classical statistical models because of their ability to not only incorporate lagged variables but also extend to time related variables. 91

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Some of the commonly used are Gradient Boosting algorithms such as XGBoost, Support Vector Regression, K nearest neighbors, Random forests (El-Kassas, Salama, Rafea, & Mohamed, 2021). Random Forest is an ensemble machine learning algorithm which integrates multiple decision tress with the aggregation and bootstrap technique to solve regression challenges (Belyadi & Haghighat, 2021).

of year. XGBoost also produces feature importance scores thus providing insight into the top features that impact electricity consumption and it is also offers high accuracy and fast training (Abbasimehr, Paki, & Bahrini, 2023). Support vector regression (SVR) is a powerful machine learning algorithm that is often leveraged for regression tasks which includes electricity consumption forecasting. SVR employs a kernel function to modify input data into a higher dimensional space that enable complex relationships It is premised on the idea of deriving outcome to be captured (Reddy, et al., 2023). from combining various decision trees instead of depending on only one This prevents overfitting and 3 Methodology the algorithm has the ability to capture the intricate relationships between features in a dataset. This makes the algorithm a popular choice for electricity 3.1 Research philosophy consumption forecasting. Random forest can be Research philosophy is a crucial aspect of a research leveraged for different forecasting horizons which project as it lays the foundation of the methods, strategy are long term and short term forecasting (Parhizkar, and approach of the research project. This research Rafieipour, & Parhizkar, 2021). K Nearest Neighbors followed the Positivism philosophy. Positivism is is a supervised machine learning technique that based on quantitative methods where functional is often leveraged for regression problems such as relationships can be mined between dependent and independent variables with the main goal of electricity consumption forecasting. formulating prediction or control of the phenomena This technique is sensitive in such a way that the under research (Park, Artino, & Konge, 2019).This outcome of the algorithm and its overall performance is in line with the research’s main aim of developing is dependent on the K value that is chosen (Shi, a time series model for forecasting electricity Yang, Yang, & Zhou, 2022). There must be a perfect consumption in Harare province of Zimbabwe and balance on the value of the K, if it is too small, there also exploring the relationships between independent is a high risk of the model overfitting and if the K variables and dependent variable. The research was value is too high, it leads to oversimplification of influenced by epistemology which is also part of the the model thereby failing to capture the underlying Positivism philosophy which places great emphasis on patterns in the data (Reddy, et al., 2023).K Nearest quantifiable data and empirical evidence. In light of Neighbors can be a robust algorithm for forecasting this, the researcher made use of quantitative historical electricity consumption if there is a valuable hyper data for data analysis and the development of a time series model. Empirical evidence influenced the parameter tuning. selection of independent variables that were used in XGBoost Regressor is a gradient boosting algorithm the study. and is a common choice for time series forecasting particularly electricity consumption forecasting. This 3.2 Research design algorithm has been proved to produce impressive A research design is the blueprint of research results when it comes to forecasting phenomenon which provides the plan, structure and strategy of such as electricity consumption as it builds a investigations conducted so as to derive answers to collection of decision trees and then integrates research questions. Research design provides a flow of their outcomes to make one robust prediction thus activities from formulating the problem to collecting minimizing the discrepancy between the predicted data, analysis of data and obtaining results (Khanday & and actual values (Dong , Zhang, Wen, & Qiu, 2023). Khanam, 2019).There are numerous research designs XGBoost incorporates various features that impact available and the one that aligned with this research is electricity consumption that is lagged variables, time the CRISP-DM. features and also external features. It can capture non-linear relationship between consumption and features such as time of day, month of year or day 92

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3.2.1 Cross industry standard process for data mining(crisp-dm) CRISP-DM is an acronym for Cross Industry Standard Process for Data Mining which is framework that is leveraged in most data mining projects to provide a structural approach. This research design is flexible in such a way that researchers and project teams can navigate back and forth between stages when it’s necessary (Schroer, Kruse, & Gomez, 2021). CRISP-DM has six phases which are: business understanding, data understanding, data preparation, modeling, evaluation and deployment (Vanegas,Durango, Mejia,Giraldo, Agudelo,Vargas, & Duran,Soto, 2023). Below is the visual presentation of the CRISP-DM. 3.2.2 Data understanding The data set was imported for the purposes of performing exploratory data analysis, data cleaning and subsequently building the XGBoost time series model. 3.3.3 Model building After feature engineering and data cleaning was completed, the next stage was to build the model which correlates with the CRISP-DM design. The researcher used the XGBoost to model the data as it can handle the numerous features that are in the data. The first step in model building was to identify and define independent and dependent variables and also splitting the data before training the data. 3.4 Data sources The dataset used in this research is a synthetic one which was created by the researcher. In order to address the research gap identified in chapter two of short term electricity consumption forecasting, the research required granular daily electricity consumption data. There was no daily consumption data available from the Zimbabwe Electricity Supply Authority. After an extensive search through public sources, research databases, data suppositories there was limited availability of data that captured the research needs. The research also required daily consumption data at city or regional level which is Harare and it proved to be futile as most data was available on national level and on annual intervals.

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The researcher concluded that creating a dataset was the most insightful and robust approach. It allowed the researcher to embody sectoral breakdowns in the data and also simulate traits that often characterize time series data such as seasonality, noise, cycles, randomness and variation.The dataset was created using Jupiter Notebook by leveraging Pandas library, Numpy library and Random Library. The researcher followed the following steps in creating the dataset:

4 Results 4.1 Data preprocessing results In this section the researcher presents the results of the data preprocessing stage. This includes results after converting data types, dealing with outliers, setting the date column to index column. 4.2 Modelling The data was split and after feature engineering and figure 4.4 below shows the description of the training and testing data.

Figure 4.1: Splitting the data The training data shape for independent variables had a 4748 rows and 49 columns, and the testing data shape had 366 rows and 49 columns. The target variable training shape had 4748 rows and the target testing shape had 366 rows. The Testing period was from 1 January 2011 to 31 December 2023.The testing period was 1 January 2024 to 31 December 2024.The data was split chronologically to prevent loss of data. Parameters of the model

Figure 4.2 Model parameters

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4.3 Visualizations of tests results

Figure 4.3: Visualizing forecasted vs Actual values As illustrated by figure 4.3 the x-axis shows the date from 2010 to a part of early 2025.The y-axis shows the total consumption. The training data is shown by the blue line which is representative of the historical data that was used to train the data which spans from 2011 to 2023.The orange line is the actual test data and the green line is the predictions made which spans from 2024 to early 2025. The blue line show seasonality in the data and it’s critical to note that the orange line follows the same pattern exhibited by the blue line. The green line overlies the orange line and the two line are in sync with each other. This is evidence that the XGBoost model performed well and is effective in capturing intricate daily, seasonality and trend variations. 4.4 Close up visualization of the test period

Figure 4.4: Close up of the test period visualization Figure 4.4 shows a close look at the test period alone with the red line representing actual test data and the blue line, the XGBoost predictions. The test period is from January 2024 to January 2025.The two lines are in sync which indicates that the model performed effectively to forecast electricity consumption. THE ZIMBABWE INSTITUTION OF ENGINEERS

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4.5 Scatter Plot for Actual versus Forecasted

Figure 4.5: Scatter plot for actual versus forecasted values Figure 4.5 shows a scatter plot for predicted and actual values with x-axis showing actual consumption and y-axis the predicted consumption. Most of the data points are clustered around the red line which represents perfect prediction. This signals that the correlation of actual and predicted values is strong. This is a confirmation and pictorial demonstration of the results of the performance metrics which showed a high R-Squared value and a low RMSE and MAE. There is a spread of points away from the line which is minimal which is evidence of a low prediction error.

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4.4 Visualizing Residuals Residual analysis was performed to further assess the performance of the XGBoost model in forecasting electricity consumption and be able to capture where the model encounter forecasting challenges.

Figure 4.6: Residuals analysis The figure shows that the residuals are clustered around the zero line which shows that the model is not over performing or under performing in forecasting electricity consumption. The residuals are spread randomly with no apparent strong patterns which shows that the most crucial data points were captured in the model which indicates that XGBoost model is a good forecaster for electricity consumption in Harare. The majority of the residuals range from -50 kilowatt hours to 50 kilowatt hours. This corroborates the values of RMSE and MAE of around 30 kilowatt hours and 20 kilowatt hours respectively. The residuals plot is characterized by occasional high spikes approaching 150 kilowatt hours around the months of July 2024.These may signify specific events that may not have been accounted for such as a major public event or major weather features, major industrial activities that were not captured due to unavailability of features that represent these external features. THE ZIMBABWE INSTITUTION OF ENGINEERS

5 Conclusion

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5.1 Discussion of Research Objectives The main aim of this research was to develop a robust time series model for forecasting electricity consumption in Harare. The observed time period ranged from 1 January 2010 to 31 December 2024 and the data was daily consumption data. The research was based on these three questions which are: 1. What are the factors that influence electricity consumption in Harare? 2. How can a time series model be developed to effectively forecast electricity consumption in Harare? 3. How effective is the time series model in forecasting electricity consumption in Harare? An intensive process was followed from exploratory data analysis, data preparation, performance evaluation analysis and detailed error analysis so as to answer the research questions. The success of this research endeavor was based on the solid foundation of exploratory data analysis. Data had to be analyzed and understood so that unique data characteristics and patterns could be identified. The results of exploratory data analysis highly influenced the development of the XGBoost model. A deep dive into the data revealed annual seasonality which showed high consumption patterns in some months such as May to August and lower consumption in other months such as September, March and April. There was also apparent weekly seasonality which showed high consumption during the weekdays and low consumption during the weekends. This analysis in the data influenced effective feature engineering which led to the development of a robust time series forecasting model using XGBoost. Statistical analysis such as the Auto Correlation Function(ACF) and the Partial Auto Correlation Function(PACF) also influenced feature engineering which led to a robust engineering of features that had the most impact on electricity consumption. The PACF and the ACF analysis influenced the inclusion of lag variables in the model thus contributing to the efficiency of the time series model in forecasting electricity consumption. An effective time series model was built using the XGBoost algorithm which is a machine learning model that has the ability to capture complex patterns. 98

The success of this model was also attributed to the ability of the algorithm to incorporate and learn the influencing factors identified during exploratory data analysis and feature engineering. The most influencing features captured were is weekend, day of week, rolling features and maximum values of recent consumption which captured temporal dependencies. Leveraging sectoral consumption improved the model’s performance thereby indicating for an effective electricity consumption forecasting there should be sectoral breakdown. The researcher learned that data preprocessing also plays an integral role in developing an effective model. Seemingly small but with significant impact data preprocessing techniques such as converting data types, indexes, handling duplicates and null values among other things have a huge impact on overall model performance. Preprocessing should not be overlooked if one aims to come up with a robust model. The model was evaluated and the results were impressive signaling that the research had produced a robust time series forecasting model. The R-Squared value of 0.9991 was phenomenal indicating that the model accounted for most of the variance in the daily electricity consumption for Harare. The Root Mean Squared Error and Mean Absolute error had low values less than 50 kilowatt hours which is a good number considering that daily consumption ranged in the thousands. The Mean Absolute Percentage Error also had a value of 0.56% which shows that the XGBoost model was effective in forecasting electricity consumption. The residuals analysis offered insights into the strength of the model and future adjustment of the model. The errors were mostly clustered around zero which means that the model captured the basal patterns. Residuals also exhibited some occasional spikes indicating that there were probably some patterns that were not accounted for such as the effect of public and school holidays on electricity consumption, weather features, economic variables etc. and this lays a foundation for future research. In conclusion, I would say the research objectives were achieved to a greater extent. An effective XGBoost time series model with high forecasting accuracy was developed and evaluated effectively. The visuals of predicted versus actual values show a close sync with each other. THE ZIMBABWE INSTITUTION OF ENGINEERS

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References

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Lee, M. H., Ser, Y. C., Selvachandran, G., Thong, P. H., Cuong, L., Son, L. H., … Gerogiannis, V. C. (2022). A comparative study of forecasting electricity consumption using machine learning models. Mathematics. Lee, Y. W., Gaik, T. K., & Yee, C. Y. (2018). Forecasting electricity consumption using time series model. International Journal of Engineering & Technology, 218–223. Lendave, V. (2021, November 1). Metrics for time series forecasting. Analytics India Magazine. https://analyticsindiamag.com/deep-tech/a-guideto-different-evaluation-metrics-for-time-seriesforecasting-models/ Lerner, A., & Ripley, P. (2017, September 18). Machine learning helps power down electricity theft in Jamaica. World Bank. https://blogs. worldbank.org/en/energy/machine-learninghelps-power-down-electricity-theft-jamaica Li, J., Luo, Y., & Wei, S. (2022). Long-term electricity consumption forecasting method based on system dynamics under the carbon neutral target. Energy. Li, X., Wang, Z., Yang, C., & Bozkurt, A. (2024). An advanced framework for net electricity consumption prediction: Incorporating novel machine learning models and optimization algorithms. Energy. Mabunda, M. V., Mukonza, R. M., & Mudzanani, L. R. (2023). The effects of load shedding on small and medium enterprises in the Collins Chabane Local Municipality. Journal of Innovation and Entrepreneurship.

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DESIGN OF A VIRTUAL-AUGMENTED REALITY LAB FOR TRAINING PURPOSES ON THE ASSEMBLY OF A SUBMERSIBLE PUMP

Lindokuhle Ngwenya1 1Department of Industrial and Manufacturing Engineering, National University of Science and Technology Corner Cecil Ave/Gwanda Rd, P. O Box AC939, Ascot, Bulawayo, Zimbabwe ngwenyalindokuhle09@gmail.com ___________________________________________________________________________ Abstract Most tertiary institutions in Zimbabwe are facing a challenge in performing practical studies in their respective courses due to scarce resources in terms of insufficient training equipment, high funding demand, lack of infrastructure to encapsulate as many training modules as possible. This is the norm especially in science, technology, engineering and math. The challenges faced result in a mismatch between industrial expected skill sets and the academic knowledge obtained in tertiary institutions. The aim of this research is to design a virtual- augmented reality lab for training purposes on the assembly of a submersible grinder pump. The virtually-augmented reality lab is developed using mindar, bootstrap, SOLID WORKS, A-frame, PHP, Java-programming and aspose.net. The result of the design is a lab that uses 2D markers for the AR perception. 3D models are rendered through A-frame and the user is able to select actions corresponding to subassemblies in question. The training session is timed in order to rate the user’s performance. This research is an attempt to adjust the education field to the current digital environment which can be accessed anywhere and has the advantage of scalability. Keywords- Augmented Reality, Engineering Education, Instructional Design, Markers, Scene Rendering ___________________________________________________________________________

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1. Introduction Competition is inevitable across different sectors, be it in business, education and entertainment sector with technological advancements being the driver. Highly trained and knowledgeable human resources are required in the manufacturing sector to cope with the competitive challenges (Maheso, Mpofu, & Sibanda, 2018). For several decades, human capital theory has focused on higher education, and according to economists, investing in higher education may be a growing indicator of future well-being as a result of improving the knowledge and skills of employees (Kuzminov, P.Sorokin, & Froumin, 2019).

1.3 Research Scope The research is limited to the design of the virtual reality lab which gives a training session on the assembly operation of a submersible grinder pump. The training session is to be interactive, meaning the trainee can manipulate the virtual machine components during the assembly operation.

The research is a snippet of the application areas where users can have different training sessions on different subjects. In the AR lab, the user, is given a training session on the assembly operation of a pump. The trainee is to learn about the assembly parts and steps and then get into a practical assembly test. After training, the trainee is evaluated and rated Periods of low-income growth and technological on skills level in terms of the time taken to assembly. advancements increases unemployment rate, especially among young graduates (Aceleanu, Serban, & Burghelea, 2015). This results from the fact that the skills acquired through education do not match the skill requirements of the workplace (Büth, et al., 2017). Professional education advancement is important in solving this issue in order to develop a relationship between academia and industry. The design of a virtual augmented reality training platform enables students to obtain industrial training on manufacturing scenarios though this research is inclined on training of an assembly operation. This platform aids in bridging the gap that is there between university graduate knowledge and industrial expectations as it includes the practical aspects of learning on how to do engineering industrial work. 1.1 Aim of the Research To design a virtual- augmented reality lab for training purposes on the assembly of a submersible grinder pump. 1.2 Research Objectives i. To develop the virtual reality lab library. ii. To develop assembly steps for the pump and assembly instructional design on the virtual reality platform iii. To develop an assembly validation system for checking the correctness of assembly steps undertaken. iv.To design the navigation controls for assembling. v.To design the graphical user interface of the virtual reality lab 102

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1.4 Literature Review Tertiary education systems have an attachment program which is one of the platforms which are meant to expose students to hands on work in their fourth year or third year of study depending on the degree program and institution. The attachment program is supposed to run for eight months to one year but with recent trends, students struggle to obtain attachment on time and end up having lesser time to do it. The other platform that tries to get the students ready for industry is the graduate traineeship. Though the learnership period presents its challenges as there is a struggle in obtaining it and it comes at a later stage after graduation. These opportunities provide students with hands on experience enhancing their skills for future employment (Ozek, 2018). Technological advancements show that a lot of effort has been put to use virtual reality in trying to develop training platforms. Virtual reality platforms are being utilised in the manufacturing environment for the purposes related to assembly and disassembly simulation, training and analysis (Marzanoa, Friela, Ahmet, & Courtb, 2015). Research recognises the potential of virtual reality as a tool for assembly analysis, training purposes, process planning and path planning (Seth, Judy, & Oliver, 2011). In an effort to combine the real unreal, the study uses augmented reality.

2. Materials and methods The methodologies involved include collection of data through performing work study and using assembly manuals for Submersible pumps as input to building the machine parts database. Boots trap was used for the development of an online graphical user interface of the virtual reality lab. Instructional design that follows the logic of the system was developed through the use of Java programming. The navigation controls for assembling the parts were coded using Java and Mindar, an open-source web augmented reality library, was used to provide for part tracking and in this research, markers were used for part representation. The design of the lab uses 2D markers for the AR perception, where each marker is directed onto the desktop or laptop screen and consequently a 3D model is rendered through A frame and the user is able to select actions corresponding to subassemblies in question. The 3D models were developed using solid works. Phal and Beitz was used for concept generation and selection. The design steps undertaken in coming up with the design are described in the section. 2.1 Pump Model Selection The author selected a submersible grinder pump as a case study. The 3D view of the Submersible pump is shown in Figure 1. Further information was obtained from pump assembly company log sheets, manuals and standard operating procedures.

Figure 1: Submersible Grinder Pump (Zenit, 2021) THE ZIMBABWE INSTITUTION OF ENGINEERS

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2.2 Manuals Manuals are used to examine the design of the Submersible pump. These provide specific details on the design specification of the assembly in terms of measurements and geometry. The author used the Zenit Blue Series Pump Model Assembly Manual to generate design assembly steps. The author broke down the design as shown in Figure 2.

Pump Assembly Motor

Housing

Handle with Power Unit

Hydraulic Unit

Figure 2: Pump Assembly Structure 2.3 Assembly Workflow The author developed the assembly steps and assembly instructional design for the pump that will be later combined with the system algorithm in the lab to create an effective computer workflow. The pump assembly workflow of the design is shown in Table 1. Table 1: Assembly Workflow Step 1.

Action Place Pump Housing on Table with the open end facing up.

3D Wireframe

Action: Placing Tools: None Expected Time: 30 sec 2.

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Take Motor Unit and Insert into the housing ensuring the arrow maker of the motor shaft is visible at the top. Action: Placing Tools: None Expected Time: 30 sec Connect the red electric cable to the positive terminal on the housing and the black electric cable from the motor to the negative terminal of the housing using a medium star screwdriver. Action: Screwing Tools: Star Screwdriver at 2NM Expected Time: 1 minute

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Insert the hydraulic unit at the top with the intake flange being at the top and the grate at the bottom. Align the screw holes. Action: Placing Tools: None Expected Time: 30 sec Action Fit the 3 screws and tighten them fully with a crosshead screwdriver to a max. tightening torque of 2 Nm. Action: Screwing at 2Nm maximum Torque Tools: crosshead screwdriver Expected Time: 3 minutes Place Handle into Position Action: Placing Tools: None Expected Time: 30 sec

Having the instruction assembly steps, the author designed 2D makers that correspond individual parts of the assembly and these are shown in table 2. Table 2: 3D Assembly Model and Markers Part Marker

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2.4 System Logic Figure 3 shows the lab system logic. The system logic follows that, the user first places the card or the marker on the table. The marker represents the part that should be added onto the assembly. The card is scanned using a phone camera. If the card is correct, it means the correct part is added onto the assembly the virtual space. If the card is incorrect, the system alarms the user and takes the user back to the scanning phase until the whole assembly is done. 2.5 Sub Assembly Mechanism The sub assembly of the system is made by placing all the individual component cards that make up the sub assembly. The program automatically identifies the individual parts and creates the required subassembly if all and only the parts that make up the sub assembly are present. Table 3 shows the sub-assemblies and their respective markers.

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Figure 3: System Logic Table 3: Sub Assembly Markers and Models Sub-Assembly Marker

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2.6 System Development Mindar was used to develop the augmented reality system. This is an opensource web augmented reality library. It supports marker tracking and it is based on 3D modeling. It renders the scene on the mobile screen and laptop and 3D models based on an A frame. The A frame is a library used to show 3D models on the web. The library was trained to identify and associate the marker with the 3D models. The 3D models are developed using solid works and converted to GLB format using Aspose.net. 2.7 Training MindAR to read markers Java script was used to develop a program for mapping the markers to the 3D models. MindAR was trained to identify the right markers. The library uses machine learning image classification and recognition in order to detect features. The user uploads the markers they require through the marker verification tool where it carries out integrity test. Once the user is satisfied, they head over to the lab where they upload all the markers that they expect to track. Once the features are recognized and displayed, the user can download a dataset in the mind format. This dataset is then placed in the root folder of the program and referenced in the program. Mindar, through a code is responsible for: • Displaying any errors in the console. • Start the AR module manually. • Stopping the AR Module. • Pause the AR from identifying makers. • Pause Video of the Feed. • Un-pause the Video or AR module. 2.8 Validation System Design The validation system design is executed through a program code in java script. The code is for checking if the action performed by the trainee is correct. It uses an if statement. If the step executed by the trainee is correct, the platform prompts the user to move forward. The program checks for every step involved during assembly. If the step is wrong, the program prompts the user to redo the step until its rendered correct: • • • •

The program first checks which task is supposed to be carried out When the user clicks on submit after picking their preferred action and parameters, the program compares this with the correct answers If the answers are all correct. The program records the time at that moment and goes to the success page If one or more of the answers are wrong, the program displays a prompt asking the user to try again

2.9 MindAR functions and Aframe The navigation system is implemented through a program embedded in the HTML, which exposes the MindAR and A-Frame APIs to set up the its object in the browser, load the marker database and models, display a scanning animation to indicate readiness, and request camera access from the user. 108

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2.10 User Interface Development Bootstrap was used for the development of the user interface. The user interface is used for the user interaction with the environment. It is designed to follow the learning logic through a code programmed in java script.

3 Results The result of the design, is a working augmented reality software for training users on a pump assembly operation. The system is accessed through a user interface. After clicking on the start button on the welcome page, the instructions page appears. The page gives the user details about the next steps of the assembly process. The first step onto assembling the pump is setting the housing on the table. A lesson and training instructions are given before setting up. After the instructions page, when the user clicks on next, the software prompts the user to user the device camera. The camera is used to input the markers into the mindar.

When the program receives the correct marker data, 3D model feedback is produced on the of the device. The sub assembly-parts appear and are tracked by the marker. After a few seconds, the parts are assembled to form the sub assembly. The user then chooses the tools, parameter and value to use to assembly the respective parts. During the assembly operation, the user is timed to assess performance. The sequence of steps is shown in the following figures. Fig 4 shows the welcome page of the lab and a sample of an activity description. Figure 5 shows the camera scanning the marker. This results in the 3D rendering of scene showing the sub assembly parts are also shown in figure 5. Figure 6 shows the sub assembly of the parts in figure 5 and at that instant, a dropdown action menu appears, where the user selects the actions, parameter and value required to assembly the parts. Figure 7 shows the contents of the menu and the success page. These figures illustrate an exemplary assembly step when connecting the hydraulic unit to the sub assembly.

Figure 4: Lab Welcome Page and Activity Sample THE ZIMBABWE INSTITUTION OF ENGINEERS

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Figure 5: Marker Scanning and Sub-assembly Parts

Figure 6: Sub-assembly and Action Menu 110

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Figure 7: Actions Menu and Success Page 3.1 Discussion The idea of an immersed learning experience is overly intriguing. It allows for the element of enjoyment in the learning experience. 10 individuals from the National University of Science and Technology were requested to try out the simulation and report on their experience.

3.1.1 Marker Tracking challenge It was important for the function of the application to allow all recorder markers to be displayed even if they were wrong. This meant the program must be able to identify and track all possible markers. However, during the initial concept development tracking of more than four markers simultaneously produced With regards to system performance the system undesirable tracking characteristics especially in performed quite well. The average time to identify a mobile devices. Thus, the total number of markers marker was 3 seconds within a range of 40cm from the trackable were reduced to four and the whole training camera. The expert time for the assembly operation is reduced to only contain 4 parts = 4 markers. 90 seconds. The test group averaged with 150 seconds with the minimum and maximum time taken being 3.1.2 Setting Sub- assembly marker 110 seconds and 180 seconds. The average number Due to the above-mentioned challenge, it was not of taking an incorrect step during assembly was 3 possible to set up sub-assembly markers unique for each student on one trial. The system was able to markers that represent a subassembly. In the trials, the extra 3 sub-assembly markers resulted in occasional identify several markers at the same time. but interesting phenomena when all markers (7 now) This success was not without its challenges. The were placed before the camera. The program kept major challenges and constraints include the inability randomly and occasionally “jumping” from marker to track more than four markers, resulting in sub- to marker and not registering long enough to be seen assembly-marker issues and their introduction into meaningfully. Thus, it was decided to create a system the lab. These challenges are discussed in the following that doesn’t use sub-assembly markers. sections. THE ZIMBABWE INSTITUTION OF ENGINEERS

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Besides the technical glitch, the introduction of sub-assembly markers presented a continuity and practicality challenge in the learning experience. The learner would have needed to replace the individual and/or subassembly markers from the previous learning task with the subassembly marker for the current learning task before adding the required individual work. One suggested work around to have subassemblies from the previous step appear first, hence the author had to train the MINDAR algorithm to identify a certain arrangement of markers as a unique subassembly marker as the program loads, before requesting the required marker for the current step. This created too many steps and could easily be confusing. Another option was to display a 3D model on the screen that is not triggered by a marker and ask for a marker to be placed to add a part. This lacked the augmented reality of the entire system and felt more like a Video Game.

computing to allow for complex models that rely on a lot of computer processing power. This will be so because most of the complex calculations and tracking will be done by a powerful server. This allows for simple devices to access the benefits of AR. 4.1 Observations on the System Use • It was noted that in order to achieve the most optimum experience, students must be in a welllit room. This allows the program to capture the images well and track them continuously. • Students should use paper-based markers to mimic some physical action as if they are actually assembling the grinder pump. Digital versions of a markers are also prone to glaring of the screen which affects the performance of the system. • It would be preferable to keep the phone mounted on a Tripod stand to avoid the user having to hold up their phone. Where the use of a laptop or larger screen is preferable, an external web camera must be used. This allows the user to mount it where required and have a hands-free experience

3.1.3 Dependence on device and browser limitations The program’s effectiveness depended on hardware 4.2 Future Improvements performance, local device network configurations, In the future it would be favourable to make use of a Virtual reality system that models the actual and the capabilities of the browser. workshop environment and allows interaction with the environment such as picking tools up and using 4 Conclusion and outlook them. This may require advanced equipment per Having completed the scope if this research, the author user but would definitely create an effective learning identified several insights and recommendations for experience.

other possible future works and the implementation 4.3 Conclusion of the system. This research offers a scalable solution which is It was noted while developing the system that most AR cost effective, reduces the need for physical labs applications are geared towards Marketing. Indeed, and materials enabling remote self-paced learning AR technology is still a relatively new introduction to accessible from anywhere while tracking leaner’ s main stream technology, however much focus must progress. The model can be scaled up to accommodate now be geared towards the use of AR in education. different modules of learning for different fields. Such This means the development of AR software/libraries models can be tailor made to give exposure to students that specifically makes learning in different industries to real physical work which is identical to industrial viable. Thus, this project struggled to convert market set ups. focused AR software into an educational program.

The conversion of 3D models to a well-tuned GLB was a challenge. Few softwares were able to produce well-tuned 3D models in GLB intuitively. Thus, it is important to improve the experience of converting ready-made 3D models to GLB format including simple animations. This must be easily done by a novice or educator. AR could benefit from edge 112

Acknowledgements The author is grateful to Eng TR Chikowore (research supervisor) for supporting this research and the National University of Science and Technology as a whole, especially the Industrial and Manufacturing Engineering department is also acknowledged for the training and mentoring they gave the author. THE ZIMBABWE INSTITUTION OF ENGINEERS

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References Aceleanu, I., Serban, A., & Burghelea, M. (2015). Greening the youth employment: A chance for sustainable development. Sustainability, 7(3), 2623–2643. Bubimann, M., Lorenz, M., & Gerbet, M. W. (2015). Industry 4.0: The future of productivity and growth in manufacturing industries. Büth, L., Bhakar, V., Sihag, N., Posselt, G., Böhme, S., Sangwan, K., & C, H. (2017). Bridging the qualification gap between academia and industry in India. Procedia Manufacturing, 9, 275–282.

Seth, A., Judy, M. V., & Oliver, J. H. (2011). Virtual reality for assembly methods: Prototyping—A review. Virtual Reality, 5–20. Sinha, B. (2022). Augmented reality: A tool for interactive learning environment (pp. 1–9). Zenit. (2021, November 26). Electric submersible pumps. https://www.zenit.com/en-US/Products/ Electric-submersible-pumps

Hejjaj, A., Mohammed, A., Dhifaf, J. S., & Omar, A. F. (2023). A review of the electrical submersible pump development chronology. Iraqi Journal of Chemical and Petroleum Engineering, 123–135. Ihemedu-Steinke, C., Erbach, R., P, H., G, M., & Weber, [initials unavailable]. (2017). Virtual reality driving simulator based on head-mounted displays. In Automotive user interfaces: Creating interactive experiences in the car (pp. 401–428). Kuzminov, P., Sorokin, & Froumin, I. (2019). Generic and specific skills as components of human capital: New challenges for education theory and practice. Foresight, 13(2), 19–41. Maheso, [initials unavailable], Mpofu, K., & Sibanda, V. (2018). Flexible and adaptable learning factories for the rail car manufacturing industry. Procedia Manufacturing, 244. Marzanoa, A., Friela, I., Ahmet, J., & Courtb, S. (2015). Design of a virtual reality framework for maintainability and assemblability test of complex systems. In CIRPe 2015: Understanding the life cycle implications of manufacturing: Application of virtual reality in assembly and maintenance operations (pp. 243–247). Ozek, Z. (2018). Impact of internship programme in engineering education. The Eurasia Proceedings of Educational & Social Sciences, 9, 276–283. Patel, R. (2021). What is a user manual? The Cloud Tutorial. https://www.thecloudtutorial.com/whatis-a-user-manual THE ZIMBABWE INSTITUTION OF ENGINEERS

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SPATIOTEMPORAL FORECASTING OF POLITICAL UNREST IN ZIMBABWE USING DEEP LEARNING: A MULTIMODAL MACHINE LEARNING APPROACH Tapiwa Patson Sisimayi1, Shepherd Makurumidze,2 1Chinhoyi University of Technology, Graduate Business School 2 Chinhoyi University of Technology, Graduate Business School, 1yimasisi@yahoo.com, +263772605195 2shepard.makurumidze1@gmail.com , +26377789413 Correspondence: Tapiwa Patson Sisimayi Abstract

Political unrest in Zimbabwe has always followed patterns that look predictable yet the country has never had a proper data driven early warning system. This study close the gap by building and testing a spatiotemporal forecasting system for political unrest. The study compares four machine learning and deep learning approaches. The machine learning and deep learning include XGBoost, Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs) and a hybrid CNN-LSTM model. The study drew on purposively selected temporally stratified secondary data. The study drew data from the Armed Conflict Location and Event Data Project (ACLED), the Uppsala Conflict Data Program (UCDP) and Zimbabwe-specific digital media archives. The analysis covered the period from 2013 to 2024. The models were developed and trained on a multimodal dataset and a range of features from structural socioeconomic indicators, spatial coordinates, weekly event sequences, and aggregated digital communication indicators. The theoretical framework drew from Relative Deprivation Theory, as well as Political Opportunity Theory, with the latter informing the analysis. Results show that political unrest in Zimbabwe follows learnable spatiotemporal patterns. The class-weighted XGBoost model achieved the strongest overall performance with an accuracy of 0.872, and AUC-ROC of 0.846, while the CNN (AUC-ROC 0.759), CNN-LSTM (AUC-ROC 0.756) and LSTM (AUC-ROC 0.749) achieved moderate but meaningful discriminative performance on a temporally held-out test set. Feature importance and SHAP analyses identified digital mobilisation intensity, economic stress and elite factionalism as the dominant predictors of weekly protest occurrence. The study demonstrates that political unrest in Zimbabwe follows learnable structural, spatial and temporal patterns and adds a country-specific evidence-based early-warning framework for anticipatory governance, election-monitoring, and conflict mitigation in Zimbabwe and other similar Southern African contexts. Keywords: Big Data analytics, convolutional neural networks, early-warning systems, LSTM, political unrest, spatiotemporal forecasting, Zimbabwe

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1. Introduction Political unrest comprising demonstrations, riots, strikes, or periods of political violence which challenge state legitimacy constitutes one of the most persistent risks to public order and sustainable development (Tilly, 1978; Gurr, 1970; Galtung, 1969). The role of protest forecasting today becomes more important, to an ever-growing extent for governments, humanitarian agencies, and civil society organisations as it gives them opportunities to intervene, reducing the human and financial cost and strengthening institutional resilience (Hegre et al., 2019). Emerging fields like social science and machine learning illustrate that analytics combining diverse data sets [social media streams, socioeconomic indicators, historical event records] into integrated forecasting frameworks can be the one that most predictive (Wu & Gerber, 2017; Leetaru & Schrodt, 2013; Zebrowski & Afli, 2024).

et al., 2020). The Uppsala Conflict Data Program’s Violence Early-Warning System (ViEWS), based on ensemble learning and neural networks, predicts violence in Africa and the Middle East, while Leetaru and Schrodt (2013) demonstrated prediction of civil unrest in Ukraine with considerable accuracy using a 200 million-record media dataset. These perspectives represent a paradigm shift from reactive analysis toward anticipatory governance, where policymakers can act on the basis of forecasts (Shmueli & Koppius, 2011; Ward, Greenhill & Bakke, 2010; Fischer, 2016).

Zimbabwe has experienced cycles of electoral violence, governance crises and socioeconomic shocks since independence. According to Raftopoulos (2013) and Chikozho (2011), the 2008 electoral crisis resulted in over 200 deaths, 200,000 displaced persons. The economic loss stood at approximately USD 1.3 billion from the 2008 electoral crisis (Raftopoulos, 2013; Chikozho, 2011). Gunfire following the 1 August 2018 elections, combined with a fuel protest in January 2019, resulted in deaths, mass arrests and Internet outages that interrupted access to communication and trade (Zimbabwe Human Rights Commission, 2018; MISA Zimbabwe, 2019; Amnesty International, 2020; Mare, 2020). While vast structured conflict datasets on these events exist, Zimbabwe does not have a Big Data-enabled predictive system on these events that would allow the disparate event data to be analysed by the system in order to extract information that is useful for prevention and preparedness measures (Raftopoulos, 2013) At the global level, Big Data-powered forecasting models are used to predict` political and social unrest (Leetaru & Schrodt, 2013; Wu & Gerber, 2017; Hegre et al., 2019). Models that combine LSTM’s abilities to track dependencies over time with CNN’s capability have proven effective at forcasring. They have been more valuable where civil conflict is likely to emerge (Muchlinski et al., 2016; Wang THE ZIMBABWE INSTITUTION OF ENGINEERS

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Within Africa researcher have applied machine learning to electoral violence and protest dynamics. This has been applied in in counties like Kenya and Nigeria using ACLED and UCDP records to pinpoint spatiotemporal risk hotspots (Hegre et al., 2019; Muchlinski et al., 2016). Nonetheless this leaves a clear gap for early warning tools grounded in a specific country’s context (Kitchin, 2014). CNNs and LSTM networks have become widely used to exactly this kind of spatiotemporal work. This is due to their strength in modelling how unrest spreads and evolves across both space and time (LeCun, Bengio & Hinton, 2015; Wang et al., 2020). Some studies have further and pair such architectures with transformerbased textual embeddings. They have paired with BERT which can further enhance conflict prediction by capturing semantic tone alongside quantitative measures of conflict intensity (Devlin et al., 2019). This combination fall outside the scope of this present study. This article uses these architectures within an integrated framework in order to design and evaluate a spatiotemporal early-warning system in Zimbabwe, aiming at: (1) recognizing key indicators of political unrest; (2) comparing XGBoost, LSTM, CNN, and hybrid CNN-LSTM models for predicting protest events; and (3) assessing predictive performance in terms of accuracy, precision, recall, F1-score, and AUC-ROC (Powers, 2011; Fawcett, 2006).

2. Literature Review 2.1 Political Unrest: Theoretical Foundations To Tilly (1978), Gurr (1970), and Galtung (1969) political unrest is a collective activity that questions the legitimacy of the state by means of protests, strikes, riots, or demonstrations. It is also a spectrum of protest modes, violent or non-violent, often rooted in perceived injustice, bad governance, or denial of political rights (Davies, 1962; Goldstone, 1991). After Gurr (1970) established the association between relative deprivation and mobilisation, Skocpol (1979) and Goodwin (2001) subsequently broadened this study to structural and institutional processes behind unrest. When we look at it in the African context, say Oberschall (2010) and Branch and Mampilly (2015) protest episodes often arise in conjunction with political repression and economic hardship, a pattern consistently reflected in Zimbabwean public opinion surveys (Afrobarometer, 2021). This study also examines political unrest through the prevalence, timing, and geographic distribution of protest events 116

that have been recorded by ACLED (Raleigh et al., 2010), UCDP (Pettersson & Öberg, 2020), and GDELT (Leetaru & Schrodt, 2013). Relative Deprivation (Gurr, 1970) argues that political unrest is usually caused by discontent. Discontent is when people or communities feeling that the living conditions they expected are not being met as per their expectations. Relative deprivation stems from the feeling of being worse off than the comparison group as a result, resentment and anger are generated (Guimond & Dubé-Simard, 1983). This injustice will create frustration and these frustration results in collective action, protest, or violent unrest. Within the African context widespread poverty, high rates of unemployment and inequality compound feelings of deprivation. This is common during election cycles and during economic crisis (Smith & Pettigrew, 2015). In Zimbabwe economic decline, hyperinflation and poor service delivery have provided fertile ground for grievances. This often take the form of protests and strikes (Raftopoulos, 2013; Griffin et al., 2021; Crosby, 1976). Relative deprivation theory is useful for modelling because socioeconomic variables such as income inequality and unemployment predict unrest as in line with the theory's premise that perceived injustice might catalyse collective action (Griffin et al., 2021; Crosby, 1976). Political Opportunity Theory by McAdam (1982), Tarrow (1998), and Meyer & Minkoff (2004) argues that unrest does not only arise from grievances. It states that unrest also emerges from whether the political system is open or closed. Unrest is expected to increase during electoral cycles, elite factionalism, and periods of weakened state repression (Jenkins et al., 2003; Goldstone, 2004; Mueller, 2018). In Zimbabwe, where politics, patronage and violence are deeply entwined (Alexander & McGregor, 2013; Sachikonye, 2011), the ballot box is a perennial battlefield: the 2013 campaign was marred by accusations of vote rigging (News Day, 2013) and the 2018 vote degenerated into bloodshed following the soldiers’ opening fire on some demonstrators in Harare, killing six civilians (ZESN, 2018). Elite conflict amongst ZANU-PF parties, especially the Mnangagwa-Chiwenga rivalry post-Mugabe, has fragmented the party's elite, created outlets for popular organising and heightened the chances of protest (Moyo, 2019; Roessler, 2011; Cederman et al., 2013). THE ZIMBABWE INSTITUTION OF ENGINEERS

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2.2 Digital Mobilisation as a Protest Driver Online platforms are a platform on which opposition and organising can be expressed that takes place before face-to-face protests. This has changed the way collective movements form. As social media podiums, Facebook, Twitter and WhatsApp drastically lower coordinating cost. Simultaneously, it has facilitated the spread of information and removed protest movements from organisation-specific settings (Castells, 2012; Howard & Hussain, 2013; Tufekci, 2017). Wu and Gerber (2017) and Steinert-Threlkeld (2018) argued that spikes in online activities are leading indicators of the start of protests. They also posit that digital sentiment analysis allows for the monitoring of the pulse of the public at any one moment, treating citizens themselves as distributed sensors of unfolding events (Goodchild, 2007).

al., 2015; Wang et al., 2020; Weidmann & Ward, 2010). Hybrid CNN-LSTM architectures together model spatial and temporal dependencies. Here CNN layers is used to extract latent spatial topographies before LSTM layers model temporal evolution of those patterns (Shi et al., 2015; Wang et al., 2020). BERT's bidirectional attention mechanism enables extraction of contextual meaning from text that simpler bag-ofwords or recurrent models cannot capture (Devlin et al., 2019; Rogers, Kovaleva & Rumshisky, 2020). Recent applications have demonstrated that textual signals from news articles and social media posts function as short-term leading indicators of protest onset, with semantic escalation in digital discourse typically preceding ground-level mobilisation by one to two weeks (Steinert-Threlkeld, 2018; Wu & Gerber, 2017).

In Zimbabwe hashtags that include #ThisFlag, #ShutdownZimbabwe and #DemLoot have served as rallying cries (Chitanana, 2020). These rallying cries have also translated online discourse into onthe-ground mobilisation (Mpofu & Mare, 2020; Gukurume, 2017). In addition, digital repression as evidenced by internet shutdowns and surveillance has become an indicator of state anxiety in anticipation of civil disturbances (MISA Zimbabwe, 2019; Access Now, 2020).

Chronic conflict forecasting problems are always accompanied by challenges such as class imbalance which is the predominance of non-protest over protest periods, which consistently favour classifiers to the greater majority class prediction, and reduce recall (Fawcett, 2006; He & Garcia, 2009). Data sparsity and noise remain common issues in the African conflict data set, where occurrences are often underreported, misclassified, or subject to political censorship (Kitchin, 2014; Raleigh et al., 2010).

2.3 Machine Learning for Conflict Prediction This decade has witnessed rapid scaling in the utilization of machine learning for conflict forecasting. XGBoost provides transparent feature-importance decomposition. This facilitates the testing of theorydriven hypotheses and has also shown good results on conflict forecasting tasks with high-dimensional tabular data sets (Hastie, Tibshirani & Friedman, 2009; James et al., 2013; Chen & Guestrin, 2016; ). LSTM networks accommodate the temporal aspects by keeping information from past events through sequential time steps based on the gated cell structures (Hochreiter & Schmidhuber, 1997; Gers, Schmidhuber & Cummins, 2000).

SHAP (SHapley Additive exPlanations) decomposition has recently gained significant attention as a tool to narrow this gap between predictive accuracy and theoretical interpretability in complex socio-political forecasting, and the black-box models are effectively translated in such systems to become clear analytical tool for informing our early warning systems (Lundberg & Lee, 2017).

Political unrest is sequential and path-dependent, since when an event happens, it shapes the subsequent mobilisation patterns; LSTM models account for lag effects, escalation cycles, and delayed impacts of political and economic shocks (Graves, 2013; Ward et al., 2013).CNNs adapted for geospatial applications capture spatial clustering and neighbourhood diffusion effects in conflict data by applying convolutional filters to detect local patterns (LeCun et THE ZIMBABWE INSTITUTION OF ENGINEERS

3 Methodology 3.1 Research Philosophy The study utilised a post-positivist perspective in the process recognising the existence of an objective reality it acknowledges that reality is fundamentally imperfect, socially grounded, and open to revision (Phillips & Burbules, 2000; Barth & Lourenço, 2020). A positivist perspective is appropriate for modelling political unrest emerging from complex, nonlinear interactions of socio-economic, political and behavioural factors. This is so because these cannot be deterministically explained. In this context, machine learning models do not establish fixed causal laws but instead identify patterns and generate probabilistic 117

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predictions of unrest risk based on observed data and this alignes with the post-positivist emphasis on fallibility, approximation, and empirical refinement (Mahoney & Goertz, 2006; Bhaskar, 2008; King, Keohane & Verba, 1994). Based on post-positivism, the study viewed Knowledge as a tentative and probabilitybased commodity instead of a final one (Popper, 2002; Yamaguchi, 2022). Post-positivism thus sanctions observable proxies, like unemployment levels, inflation, election controversies, state oppression and digital mobilisation trends as probabilistic markers of underlying social strains, fitting with early-warning practice for 21st-century conflict (Hegre et al., 2019; Goldstone et al., 2010; Gerring, 2012).

Machine learning methods build knowledge on probabilistic tendencies. They also model approximations and not static causal laws as such they are epistemologically compatible with postpositivist paradigms (Shmueli, 2010; Bzdok, Altman & Krzywinski, 2018). Deep learning systems like CNNs and LSTM networks do not claim a final predictive certainty but produce risk scores and confidence-weighted outcomes which are consistent with post-positivist fallibilism and scientific humility (Goodfellow, Bengio & Courville, 2016; Chollet, 2017; Trochim, Donnelly & Arora, 2016).

features, organised into machine-readable formats aligned temporally and spatially. This predictive orientation mirrors a paradigm that has gained currency in computational social science, where the role of a model is to anticipate future events and support preventive strategies rather than to explain behaviours in purely theoretical terms (Shmueli, 2010; Shmueli & Koppius, 2011). Under the supervised learning design, algorithms are trained on labelled history of known outcomes occurrence or non-occurrence of protest events to identify complex patterns in input features (Kuhn & Johnson, 2013). Such a design allows the system to distinguish between periods of political stability and areas of elevated unrest risk, with time-series structures and geospatial tensors enabling models to learn from structured spatiotemporal data sources (Cressie & Wikle, 2011; Wang et al., 2020). This necessitate the study to employ a comparative modelling approach. The comparative approach include XGBoost algorithm for nonlinear interactions and heterogeneous feature spaces (Chen & Guestrin, 2016). The second one was the LSTM for temporal dependencies and sequential dynamics (Hochreiter & Schmidhuber, 1997; Graves, 2013). The third one was the CNN-based architectures. This included a hybrid CNN-LSTM for spatial and spatiotemporal pattern extraction (LeCun et al., 2015). These three approaches are systematically applied and evaluated under harmonised conditions. This comparative approach necessitated methodological consistency and fairness in performance comparisons (Dietterich, 1998).

Post-positivism also promotes critical realism and methodological reflexivity, since empirical models are sensitive to data quality, measurement error, model assumptions, and context-bound limitations (Maxwell, 2012; Sayer, 2010; Batini et al., 2009), and strong validation practices as well as triangulation of various sources of data are recommended in politically sensitive situations, such as Zimbabwe (George & 3.3 Research Approach Bennett, 2005; Creswell & Plano Clark, 2018). The study settled for a quantitative research approach because of ability to measure and make statistics 3.2 Research Design on connections among variables (Creswell, 2014; This study utilised a non-experimental, supervised Bryman, 2016). Quantitative approach allowed machine learning classification design, appropriate the study to convert socioeconomic metrics for research circumstances in which variables cannot (unemployment rate, inflation trends, poverty indices be manipulated but can be systematically observed, and service delivery) into continuous and categorical measured, and retrospectively modelled (Hosmer, variables. Political indicators from electoral cycles Lemeshow & Sturdivant, 2013; Shadish, Cook & and repression incidents, elite factionalism and Campbell, 2002). As political turmoil is inherently policing patterns of protest were encoded as non-modifiable in real-world settings, a retrospective event-based binary elements. Hashtag frequency, observational design enables understanding of sentiment polarity scores, keyword co-occurrence historical patterns without disturbing continuous and mobilisation intensity as digital indicators were political and social processes (George & Bennett, 2005). determined through natural language processing Historical outbreaks of unrest are treated as dependent techniques (Grimmer & Stewart, 2013; Jurafsky & outcome labels, while socioeconomic, political, and Martin, 2023). digital indicators are treated as independent predictor 118

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According to Shmueli & Koppius, (2011) predictionbased research is particularly useful in policy-oriented areas like conflict prevention and governance. They posit that their utility enables risk estimation as well as provide an evidence base for proactive preventive efforts (Hegre et al., 2019; Ward, Greenhill & Bakke, 2010; ). The quantitative data framework supports spatial structure and temporal data composition essential for spatiotemporal forecasting, enabling models to learn temporal correlations and spatial relationships between provinces (Cressie & Wikle, 2011; Wang et al., 2020). Reliance on well-defined variables, reproducible data preprocessing pipelines, and standardised evaluation metrics ensures adherence to best practices in computational social science (Munafò et al., 2017; Shiffrin, Börner & Stigler, 2018). 3.4 Population and Sample The study population comprised recorded political unrest events in Zimbabwe between 2013 and 2024. These events were documented in established conflict and event-monitoring datasets from ACLED, UCDP, media archives, and verified digital sources. ACLED’s Zimbabwe coverage comprised approximately N ≈ 1,600-1,900 political unrest-related events over the twelve-year period, including protests, riots, strikes, labour protests and political demonstrations. The twelve-year period was deliberately chosen because the study wished to involve the 2016 #ThisFlag movement, the post-election violence of August 2018, the #ShutdownZimbabwe protests of 2019, and more recent economic unrest that arose as a result of economic downturns, such as public sector strikes, and intra-party conflict within the ZANU-PF in 2024 (International Crisis Group, 2023; Masunungure, 2023; Ncube, 2024; Chidhawu, 2024).

Stratification is implemented algorithmically across time (year and week), space (province and urban-rural classification), and protest type to ensure proportional representation of Zimbabwe's heterogeneous unrest dynamics (Young & LeBas, 2023). This sampling strategy ensures that machine learning models which depend on sufficient data volume and variation to identify patterns have the representativeness required for generalisable predictions (Hosmer et al., 2013; Chatsiou & Mikhaylov, 2020). Following preprocessing, feature engineering and temporal aggregation into province-week observations (Section 3.6), the machine-learning dataset consisted of 6,270 labelled observations spanning 2013-2024, comprising 2,300 protest weeks and 3,970 non-protest weeks labelled from the combined multi-source event record described above, which were used for model development and evaluation.

3.5 Data Sources and Collection Tone (Leetaru & Schrodt, 2013). These sources supplied geo-tagged event level records. The records apanned from 2013 to 2024. To sharpen contextual accuracy the event data supplemented by local newspaper and information from civil society monitoring programs. Socioeconomic data was drawn from three complementary sources. These sources included the Zimbabwe National Statistics Agency (ZIMSTAT) which tracked domestic economic conditions. The second one was the World Bank's World Development Indicators which offered internationally comparable metrics. The third one was the IMF macroeconomic datasets which provided figures on inflation, employment and pressures on service delivery. Food security and agricultural output figures drawn from the Food and Agriculture Organization and the World Food Programme rounded out the socioeconomic picture. These indicators were selected and interpreted From this population, the study draws a sample of through the lens of relative deprivation theory (Gurr, approximately n ≈ 700-1,400 unrest events depending 1970). This links perceived economic and social on the modelling stage. This sample size is not disparities to the likelihood of unrest. predetermined but emerges from systematic filtering based on inclusion criteria (political unrest only), Election timing, repression episodes and elite temporal aggregation (event-level versus province- factionalism as political indicators were compiled week units), and model-specific data requirements, using ACLED political violence classifications. following standard practice in event-based conflict They were also compiled from reports from the modelling (Hegre et al., 2019; Muchlinski et al., 2016). Zimbabwe Human Rights Commission (ZHRC) Sampling is purposive and inclusion is determined by and credible political analysis sources. The choices predefined criteria of political motivation, collective of indicators were informed by political opportunity action, and public contention, consistent with theory by McAdam (1982) and Tilly (1978). The standard practices in conflict event data construction theory emphasises how shifts in political structures (ACLED, 2022; UCDP, 2023; Andrade, 2021). and constraints shape collective action. Information THE ZIMBABWE INSTITUTION OF ENGINEERS

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on digital mobilisation was taken from social media activity in particular Twitter/X. Posts, hashtags and engagement metrics relating to protest were gathered via the Twitter API, including prominent tags such as #ThisFlag, #ShutdownZimbabwe, and #DemLoot, alongside others associated with political contestation in 2024 (Gukurume, 2017; Mpofu & Mare, 2020). The dataset was structured as a province-week panel covering the period from 2013 to 2024. This preprocessing produced a spatiotemporal panel dataset comprising 6,270 province-week observations, of which 2,300 are classified as protest weeks and 3,970 as non-protest weeks. 3.6 Data Preprocessing and Feature Engineering Data generated for ZIMSTAT, ACLED, UCDP, Twitter and digital media records are patchy and riddled with inconsistencies from reporting bias, temporal discontinuities, duplication and platform noise, which require disciplined systematic cleaning (García, Luengo and Herrera, 2016). Missing continuous socioeconomic indicators were addressed through linear interpolation and forward-backward imputation. This was done to maintain temporal continuity as well as avoid artificially disrupting structural patterns as supported by Little and Rubin, (2019).

allows for preventing the skewed power of variables with larger numerical sizes on model training (Field, 2018). Raw event-level data were finally summarised as weekly counts at provincial level (Cressie & Wikle, 2011). Additional features were engineered to improve predictive power. Lagged variables of key socioeconomic indicators ranging from one to four weeks were included to account for the delay in protest action (Box, Jenkins & Reinsel, 2008). Moreover, we derived seven-day rolling averages of protest incidents and repression events to smooth short-term fluctuations and reduce data noise. Using lexiconbased and machine learning approaches, sentiment polarity scores were extracted from Twitter and news data. This provisioned an analysis that was able to map shifts in public mood and discourse as supported by Liu (2015). Latent Dirichlet Allocation (LDA) was employed as a topic model.

This was done to allows for the identification of predominant themes within the narratives of protest, including increases in fuel prices, election-related disputes, concerns about corruption, and service delivery challenges (Blei, Ng & Jordan, 2003). For 2024, a binary and intensity-weighted factionalism indicator was engineered from triangulated sources. The interquartile range method was utilised in the The sources included national media archives, civil detection as well as address extreme values. In the society monitoring platforms, and human rights same vein domain-relevant decisions recognised that reports (Tilly & Tarrow, 2015). acute economic shocks may themselves signal political risk rather than constitute mere noise (Aggarwal, The dataset was partitioned to create a training set 2017). Duplicates in ACLED and UCDP datasets were covering 2013-2019, a validation set from 2020removed. The removal was aimed at limiting artificial 2022 and a test set from 2023-2024. This partitioning inflation of political event frequency and this approach preserved the real-world forecasting logic on is supported by Raleigh et al., (2010). genuinely unseen future periods (Bergmeir & Benítez, 2012). The partitioning avoided random data splitting. Textual data was treated as a sequence of step-wise His would have artificially inflated performance by natural language preprocessing pipeline. This process allowing future information to influence historical involved tokenisation, elimination of platform-related predictions (Hyndman & Athanasopoulos, 2018). noise. Related noise also included URLs, HTML The study also applied a rolling forecasting origin artefacts, stopword. lemmatisation was done to validation procedure. In this procedure allowed the minimize lexical variation and to achieve consistency iterative training as well as expand temporal windows (Jurafsky & Martin, 2023). Also noteworthy, politically on real-world early warning system deployment significant components such as hashtags, emojis and (Tashman, 2000). symbolic expressions were preserved as mobilisation cues and condensed signifiers of meaning in protest- 3.7 Model Development and Application oriented digital discourse (Papacharissi, 2015; Bruns & The study employed a multi-model supervised Burgess, 2015). Socioeconomic variables were z-score machine learning approach. The study thus combined normalised in order to allow comparison across four supervised approaches namely XGBoost, LSTM, time periods and geographic locations. This process CNN, and a hybrid CNN-LSTM. This was done in 120

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one integrated framework to detect blind spots. This reflects the complex nature of political instability. It bcame apparent that political instability is not only structural but also unfolds across time and space (Hastie, Tibshirani & Friedman, 2009; Goodfellow, Bengio & Courville, 2016). XGBoost was employed for analyzing structured data, such as socioeconomic indicators, political variables, and aggregated digital signals.

formation, and regional spillover effects (Shi et al., 2017). A hybrid CNN-LSTM pipeline was also implemented to account jointly for both spatial and temporal effects (Zhang et al., 2019; Shi et al., 2015). Hyperparameter optimisation was applied selectively and only on the validation dataset, utilising grid quest combined through cross-validation. Tuning was conducted for the XGBoost, CNN and CNNLSTM models, optimising learning rates, tree depths, number of estimators, dropout ratios, regularisation coefficients, filter counts, kernel sizes, and batch sizes (Kuhn & Johnson, 2013). The LSTM was retained at its baseline configuration, as it demonstrated stable convergence without further tuning, consistent with evidence that simple LSTM architectures often perform competitively without extensive optimisation (Greff et al., 2017).

Its strength was in modelling nonlinear relationships, capturing interaction effects, handling missing data, and providing interpretable feature importance within high-dimensional datasets (Chen & Guestrin, 2016). The model adds lagged variables and rolling-window aggregates to more fully explain temporal dynamics. The application of these improvements empowers the analysis to model momentum effects and escalation patterns of unrest over time (Box, Jenkins & Reinsel, 2008). SHAP values were used to measure the importance and the directional impact of attributes from XGBoost LSTM networks were implemented to model temporal outputs, so that we could decompose individual dynamics and sequential dependencies characterising predictions into contributions from each one of the political unrest processes. This was achieved with input features (Lundberg & Lee, 2017). For LSTM a stacked LSTM layer. The layers were built around models, attention mechanisms, as well as gradientgated cell structures allowed the model to retain based attribution, helped extract the time steps relevant information across many time steps without and historical patterns that have had the highest succumbing to the gradient decay that limits simpler predictive strength (Bahdanau, Cho & Bengio, 2015). recurrent architectures. (Hochreiter & Schmidhuber, These techniques ensured that deep learning models 1997; Gers, Schmidhuber & Cummins, 2000). Models remain accessible and accountable. The AI tools aid were trained on multivariate weekly time-series in translating black-box systems into evidence-based sequences using sliding temporal windows (4-week, analytical tooling. This supports early warning systems 8-week, and 12-week sequences) allowing the model and preventive governance (Arrieta et al., 2020). to learn both short-term shocks and longer-term escalation patterns (Graves, 2013). The study used accuracy, precision, recall, F1-score, and Area Under the ROC Curve (AUC-ROC) as Dropout regularisation was systematically applied measures (Powers, 2011; Fawcett, 2006). The crossbetween layers to reduce overfitting, particularly analysis ensured that there was no loss of key risk given the relatively sparse nature of unrest events periods. This was so because unrest events are relatively compared to non-event periods (Srivastava et al., rare occurrences in comparison to non-event periods. 2014). CNNs were deployed to capture spatiotemporal Precision@k was a policy relevant measure that was dynamics in sequential and structured data, with employed in the study. This was used for accuracy convolutional filters applied over sliding windows of top-ranked risk predictions, lead-time detection. of input sequences to detect local temporal patterns Its usefulness was to identify the time period when a such as sudden spikes in inflation or bursts of negative model can correctly suggest an upcoming unrest event online sentiment preceding unrest (LeCun et al., and false alarm rates (Chadefaux, 2014; Goldstone et 2015). Max-pooling layers were used to down-sample al., 2010). They implemented all the analyses using extracted features, retaining the most salient signals Python-based data science tools. These included while reducing dimensionality. For spatial forecasting, Pandas, TensorFlow, Scikit-learn, , XGBoost, SHAP, spatial grids were constructed by mapping Zimbabwe's and GeoPandas to achieve reproducibility, scalability, provinces into structured spatial tensors, with CNN and transparency. layers capturing spatial clustering, diffusion, hotspot THE ZIMBABWE INSTITUTION OF ENGINEERS

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3.8 Model Training and Evaluation Protocol The models were trained and evaluated under a common procedure designed to respect the temporal structure of the data and to ensure that reported performance reflects genuine out-of-sample forecasting ability. The province-week observations were ordered chronologically and partitioned into training, validation and test segments without shuffling, such that the test segment comprised the most recent portion of the series and remained unseen during all feature engineering, preprocessing, and model selection decisions. Lagged predictors of up to three weeks and rollingmean features were constructed strictly from past values relative to each observation, so that no future information entered any model input at training or evaluation time. To counteract the class imbalance between protest and non-protest weeks (2,300 versus 3,970 observations), class weighting was applied during training, increasing the penalty attached to misclassification of the minority protest class

relative to the mainstream class. Hyperparameter optimisation followed the selective procedure described in Section 3.7: grid search combined with cross-validation was conducted on the validation data only for the XGBoost, CNN and CNN-LSTM models, while the LSTM was retained at its baseline configuration. Final performance was assessed once on the heldout segment utilising five complementary metrics: precision, accuracy, recall, F1-score and AUCROC, with the protest week treated as the positive class. Precision, recall and F1-score are reported for the positive class in order to reflect minority-class detection performance. Following the identification of a scoring error in an earlier version of the evaluation pipeline (Section 4.6), predicted probabilities were explicitly verified for correct orientation and threshold consistency before metric computation, and all reported metrics were recomputed from the corrected outputs.

4. Results 4.1 Temporal Patterns in Unrest (2013-2024) Exploratory analysis shows that political turmoil in Zimbabwe is organized through temporal cycles and not random fluctuations. Under baseline conditions, weekly protest events varied between 2 and 6 occurrences, peaking at 8 to 10 events during periods of increased political activity. Four major waves of unrest were reported through the study period. These periods are is illustrated in Figure 1. As presented in Figure 1 the first major protest wave occurred in 2016. This wave was tied to the #ThisFlag movement. The second major wave occurred during the 2018 elections. This had an increased frequency of protest events. The third wave emerged during the January 2019 fuel protests. This wave was characterised by high-intensity protest events which were connected to fuel price increases. The fourth wave occurred during 2023-2024. This wave was driven by factional friction and elevated protest activity. Outside these major protest waves, baseline protest activity remained relatively stable throughout the study period.

Figure. 1: Weekly Protest Events in Zimbabwe, 2013-2024 122

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Fatality data showed a distinct distribution pattern. Majority of the weeks recorded fewer than three fatalities. Sharp spikes occurring during specific periods of political confrontation 2018, 2019, and 2020–2021 (see Figure 2).

Figure. 2: Weekly Fatalities Associated with Political Unrest in Zimbabwe, 2013-2024 Factionalism scores increased from 0.44 to 0.79, corresponding with increases in protest activity from 4 to 8 incidents per week. During the same period, the tweet intensity index increased from 6 to 7. 4.2 Spatial Patterns and Hotspot Analysis Spatial analysis showed (Figure 3) that protest activity was concentrated primarily in a small number of urban centres during the study period. In Harare and Bulawayo, events were highly concentrated and observable further spread into surrounding urban areas; with Chitungwiza, Norton, and Gweru also experiencing a sharp trend. These spatial trends were similar throughout the 2013–2024 period.

Figure. 3: Predicted Political Unrest Risk Across Zimbabwe, 2013-2024 (larger, darker circles indicate higher predicted protest risk) THE ZIMBABWE INSTITUTION OF ENGINEERS

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4.3 Correlation Analysis The correlation matrix (Fig. 4) shows relationships between key indicators and protest occurrence. Protest occurrence is positively correlated with tweet_intensity (r = 0.30) and mobilisation_level (r = 0.42), while weaker linear correlations were observed for economic stress, fatalities and factionalism. Inter-predictor correlations in Figure 4 are uniformly weak (all |r| ≤ 0.05), well below conventional multicollinearity thresholds, indicating that the structural indicators contribute substantially independent information and that multicollinearity is not a concern for the subsequent modelling. Notably, economic stress and factionalism show almost no direct linear association with protest occurrence, yet both rank among the most influential variables in the XGBoost analysis (Section 4.4); this divergence indicates that their influence operates through nonlinear thresholds and interaction effects rather than direct linear pathways, consistent with their theorised role as upstream conditions that amplify the effects of digital mobilisation rather than triggering protest directly.

Figure. 4: Correlation Matrix of Key Protest Prediction Indicators 4.4 XGBoost Performance and SHAP Analysis The class-weighted XGBoost model achieved an accuracy of 0.872 and a precision of 0.579. It had a recall of 0.671, F1-score of 0.621 and AUC-ROC of 0.846 on the test set (Fig. 5). Feature importance analysis ranked mobilisation_level as the most influential variable. kt had a feature importance of 0.12, followed by economic_stress_index and factionalism_score, with rolling-mean and lagged mobilisation and conflict features also contributing. Contemporaneous, lagged and rolling-mean mobilisation indicators together dominated the importance rankings, underscoring digital mobilisation as the strongest structural predictor of weekly protest occurrence.

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Figure. 5: XGBoost Classification Report and Feature Importance for Predicting Weekly Protest Occurrence SHAP analysis (Fig. 6) showed mobilisation_level and tweet_intensity consistently increased predicted protest probability, economic_stress_index showed context-specific effects, while factionalism_score contributed variably across observations.

Figure. 6: SHAP Summary Plot Showing Feature Contributions to Protest Forecasting in the XGBoost Model 4.5 LSTM Performance The LSTM model in Figure 7 achieved the following performance on the temporally held-out test set with Accuracy at 0.7364, Precision of 0.3548, Recall: 0.6395, F1-score: 0.4564, AUC-ROC: 0.7487 (Table 1). The model detected the majority of protest events in the test dataset, although precision was constrained by class imbalance. Model performance metrics for the corrected LSTM are summarised in Figure 7.

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Figure. 7: LSTM Performance Metrics on the Temporally Held-Out Test Set (Corrected Evaluation Pipeline) The LSTM’s moderate recall (0.64) suggests that temporal sequencing of mobilisation and conflict signals contributes meaningfully to protest prediction. This supports Ward et al, (2013) position that unrest unfolds as a dynamic process rather than a function of static covariates (Ward et al., 2013). Evidently from Figure 7 the model effectively learned how prior mobilisation, online discourse and conflict intensity accumulate over sequential windows. Thhe model shows that this happens to shape protest outcomes in the process capturing autoregressive protest cycles were previous disturbances increase the probability of future crises (Hegre et al., 2019; Davenport, 2007). Hyperparameter tuning was not conducted for the LSTM because the model demonstrated rapid convergence and stable behaviour under the baseline (Greff et al., 2017). 4.6 CNN Performance The initial CNN model Figure 8 achieved 99.2% accuracy but showed divergence between training and validation loss thus indicating overfitting. The accompanying AUC-ROC of approximately 0.16 was internally inconsistent with such high accuracy. An AUC-ROC below 0.5 implies worse-than-random ranking and cannot co-occur with genuinely high discriminative accuracy. This was traced back to an error in the original evaluation pipeline. in this pipeline the model defaulted to majority-class prediction while predicted probabilities were incorrectly scored.

Figure. 8: CNN Training Curve Showing Overfitting and Partial Learning of Spatial Hotspot Patterns 126

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After correcting the evaluation pipeline and retraining the model with hyperparameter tuning and class weighting, the CNN achieved Accuracy 0.7948, Precision 0.4310, Recall 0.5814, F1-score 0.4950 and AUC-ROC 0.7592 (Table 1), see Figure 9.

Figure. 9: CNN Performance Metrics After Hyperparameter Tuning and Class-Weighted Training (Corrected Evaluation Pipeline) A hybrid CNN-LSTM configuration was also evaluated as shown in Figure 10 and it achieve 0.7988 accuracy, 0.4286 precision, 0.4884 re call, 0.4565 F1-score and 0.7562 AUC-ROC (see Table 1). Spatial convolution and temporal recurrence therefore captured largely overlapping signal in this dataset. These moderate results are consistent with the difficulty of forecasting relatively rare, contextually driven protest events, and supersede the inflated figures reported in an earlier version of this analysis.

Figure. 10: CNN-LSTM Performance Metrics and Comparative AUC-ROC Across Models (Corrected Evaluation Pipeline) 4.7 Comparative Model Performance Table 1 presents comparative performance across the four quantitative models. XGBoost achieved the strongest overall performance (AUC-ROC 0.8462), followed by the CNN (0.7592), CNN-LSTM (0.7562) and LSTM (0.7487), while also providing the most interpretable structural driver analysis through feature importance and SHAP values. THE ZIMBABWE INSTITUTION OF ENGINEERS

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Table 1: Comparative Performance of Machine Learning Models for Protest Prediction in Zimbabwe Model XGBoost LSTM CNN CNN-LSTM

Accuracy 0.8721 0.7364 0.7948 0.7988

Precision 0.5789 0.3548 0.4310 0.4286

Recall 0.6707 0.6395 0.5814 0.4884

F1-Score 0.6215 0.4564 0.4950 0.4565

AUC-ROC 0.8462 0.7487 0.7592 0.7562

4.8 Hypothesis Evaluation All models achieved discriminative performance substantially above chance (AUC-ROC 0.7487–0.8462). Predictive recital observed across the evaluated models provides empirical support for the alternative hypothesis (H₁) over the null hypothesis (H₀).

5. Discussion 5.1 Theoretical Contributions The results support an integrated theoretical perspective drawing on Gurr, (1970) Relative Deprivation Theory, Political Opportunity Theory by Tarrow, (1994) and McAdam, (1982), elite fragmentation by Cederman et al., (2013) and Arriola (2013), and digital mobilisation by Tufekci, (2017) as complementary rather than competing explanations of political unrest. This interpretation is supported by empirical findings presented in Section 4. In section 4.1 structural variables like factionalism increased from 0.44 to 0.79 alongside rising protest activity. In the same vein digital mobilisation intensity also increased over the same period. Furthermore, correlation analysis shows that digital variables exhibit stronger relationships with protest occurrence (tweet intensity r = 0.30; mobilisation level r = 0.42) when compared to structural indicators. These findings indicate that structural variables shape the underlying risk environment as upstream, lagged drivers. On the other hand digital mobilisation operates as a proximate trigger. It thus signals real-time readiness for collective action (Wu & Gerber, 2017; Steinert-Threlkeld, 2018). This provides empirical support for Tarrow’s (1994) argument that the transition from structural grievance to collective action is mediated through mobilisation mechanisms, which in this case are digitally enabled. The digital contention perspective (Tufekci, 2017; Howard & Hussain, 2013; Castells, 2012) suggests that semantic signals in online discourse may function as short-term leading indicators of protest onset. Testing this proposition rigorously in Zimbabwe requires an independently verified historical corpus of social media text, which remains difficult to obtain under conditions of constrained digital access and state surveillance (MISA Zimbabwe, 2019; Access Now, 2020), and is identified here as a priority for future research. In addition the indirect effect of elite factionalism which is observed through increasing trend alongside protest frequency (Section 4.1) and its variable contribution in SHAP analysis (Section 4.4), provides empirical grounding for Cederman et al.’s (2013) framework. These findings confirm that internal ruling-party divisions do not directly trigger unrest but instead reshape political opportunity structures at the same time increasing the likelihood of mobilisation as posited by Roessler, (2011). Overall, the results validate theoretical perspectives that emphasise the interaction between structural, political, and digital drivers in hybrid and semi-authoritarian contexts (Levitsky & Way, 2010). While the results demonstrate strong predictive performance, caution is required in interpreting these findings as definitive causal relationships, as model outputs reflect probabilistic associations rather than structural determinism.

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5.2 Methodological Contributions The comparative modelling results in Section 4.7 demonstrate that no single model or data modality is adequate to fully capture the intricacy of political unrest dynamics. Instead, each modelling approach contributes distinct analytical strengths. CNNs capture spatial clustering patterns evident in hotspot concentrations while LSTMs model temporal dependencies reflected in recurring protest waves and XGBoost provides interpretable structural drivers through feature importance and SHAP analysis. This multi-modal approach has important implications for computational social science for the African contexts in particular which are characterised by noisy, incomplete, and heterogeneous data and this is supported by Kitchin, (2014). The integration of structured event data with unstructured textual data has the potential to enhance predictive performance, although this remains to be tested in the Zimbabwean context once verified textual data can be obtained, reinforcing prior findings on the value of natural language processing in conflict forecasting (Wu & Gerber, 2017; Leetaru & Schrodt, 2013). The application of SHAP-based explainability (Section 4.4), which identified mobilisation level, tweet intensity and fatalities as dominant predictors, bridges the gap amid predictive accuracy and theoretical interpretability (Lundberg & Lee, 2017). Additionally, the use of rolling-origin validation and class-weighted training, reflected in improved CNN performance after tuning (Section 4.6), provides a robust methodological framework suitable for imbalanced and temporally structured datasets (He & Garcia, 2009; Fawcett, 2006). These methodological contributions offer a replicable and scalable framework for spatiotemporal conflict forecasting in dataconstrained environments. 5.3 Policy and Governance Implications The finding that digital mobilisation is the most powerful short-term predictor for protest initiation is evidenced by correlation data (Section 4.3) and model performance results in particular the dominance of mobilisation-related features in the XGBoost feature importance and SHAP analyses (Section 4.4). It has important implications for governance and early-warning systems, since tracking online speech and mobilisation in real-time can be evidence of impending and timely action, instead of reactive crisis response (Ward, Greenhill & Bakke, 2010; Hegre et al., 2019). Institutions like the Zimbabwe Electoral Commission, the Ministry of Home Affairs, humanitarian institutions and civil society organisations can operationalise these findings using combined integrated risk-monitoring systems that blend digital indices with spatial hotspot mapping. Spatial clustering results (Section 4.2) reveal continued concentration of protest activities in Harare, Bulawayo and satellites, demonstrating that such targeted, location-specific interventions offer more robust success than broad scale national interventions (Tarrow, 1994; Weidmann & Ward, 2010). Importantly, both the mortality versus protest intensity relationship (as observed in the temporal patterns (Section 4.1) and SHAP analysis (Section 4.4)) indicates that the more strongly coercive responses contribute to escalation as opposed to deterrence. This echoes the repression-backfire hypothesis by Francisco (2005), Davenport (2007) and Carey (2010). The hypothesis states that people die and the state represses. This enhances the chances of a civil disorder being higher (Bratton & Masunungure, 2007) and aligns with social-interactionist accounts of coercive action (Tedeschi & Felson, 1994). Given this, sustainable mitigation strategies should thus focus on structural interventions such as economic stabilisation, service delivery and transparent governance mechanisms. These results also corroborate SDG 16 which speaks to Peace, Justice, and Strong Institutions, thus emphasising the significance of data-driven systems improving institutional resilience in politically unstable contexts.

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6. Conclusions This study has demonstrated that political unrest in Zimbabwe follows learnable spatiotemporal patterns that machine learning models can meaningfully detect, predict, and interpret. The integrated modelling framework combining XGBoost, LSTM, CNN, and CNN-LSTM across structural, spatial, and temporal data modalities captures complementary structural, spatial and temporal signals, and provides empirical support for the alternative hypothesis (H₁) over the null hypothesis that machine learning models cannot significantly forecast spatiotemporal patterns of political unrest in Zimbabwe. The study made five key findings. First, machine learning can meaningfully forecast political unrest in Zimbabwe; the quantitative model families achieved discriminative performance well above chance (AUC-ROC 0.7487–0.8462), which means that unrest follows recognisable patterns found in historical data as opposed to happening randomly (Muchlinski et al., 2016; Hegre et al., 2019). Secondly, digital mobilisation is the strongest shortterm predictor, ranking highest in XGBoost feature importance and correlating most strongly with weekly protest occurrence (r = 0.42) (Tufekci, 2017; Wu & Gerber, 2017).

Limitations of the study include class imbalance, with protest weeks constituting approximately 37% of the 6,270 province-week observations (2,300 protest versus 3,970 non-protest weeks), which constrained minority-class precision and recall (He & Garcia, 2009); the absence of an independently verifiable historical corpus of Zimbabwean social media text, which precluded rigorous transformer-based semantic analysis in this study; and infrastructure constraints that limited experimentation with larger ensemble architectures (Dietterich, 2000). Future research should extend forecasting horizons beyond the short-term weekly window to address medium and long-term risk assessment aligned with electoral cycles and extended economic crises (Hegre et al., 2019); deploy models on cloud-based platforms with real-time data ingestion and continuous model updating for operational early-warning use (Tashman, 2000); and incorporate additional data streams including mobile communication metadata, satellite imagery, and WhatsApp-based mobilisation proxies, and pursue transformer-based semantic analysis (e.g., BERT) once authentic archived Zimbabwean social media text becomes available to provide a more comprehensive account of informal mobilisation dynamics (Gukurume, 2017; Mpofu & Mare, 2020).

Third, spatial clustering is a durable structural characteristic that is heavily concentrated in Harare and Bulawayo with a constant spread to satellite towns allowing for geographical targeting (Weidmann & Ward, 2010; Tarrow, 1994). Fourth, elite fragmentation conditions but does not directly trigger protest, acting as an upstream factor that changes the distribution of political opportunity structures and exaggerates the consequences of economic grievances and digital mobilisation (Cederman et al., 2013; Roessler, 2011). Fifth, it is more robust to forecasts for spatiotemporal modelling with several modalities than any of the aforementioned (Goodfellow, Bengio & Courville, 2016; Wang et al., 2020).

Acknowledgements The author acknowledges the invaluable supervision of Dr S. Makurumidze. The study drew its data from the Armed Conflict Location and Event Data Project (ACLED) and the Uppsala Conflict Data Program (UCDP).

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DIGITAL TWIN AND MACHINE LEARNING INTEGRATION FOR INDUSTRIAL EFFICIENCY: A SYSTEMATIC REVIEW WITH DEVELOPING COUNTRY PERSPECTIVES Tinotenda Zwavashe¹, Tawanda Mushiri², Chido Hermes Chihobo³ 1 PhD Student, School of Engineering, Chinhoyi University of Technology (CUT), Zimbabwe 2 Professor, School of Engineering, Chinhoyi University of Technology (CUT), Zimbabwe 2 Scientific and Industrial Research and Development Centre (SIRDC), P.O. Box 6640, Harare, Zimbabwe. 3 Dean, School of Engineering, Chinhoyi University of Technology (CUT), Zimbabwe Abstract This paper presents a systematic review of integrating Digital Twin (DT) and Machine Learning (ML) technologies for improving industrial energy efficiency. The review is guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) evidence-based guideline. Literature search was conducted across four databases namely; Google Scholar, Elsevier’s Scopus, ARDI from Research4Life and IEEE Xplore. The search period spanned 2018 to 2026. This review is driven by the limitations noted in terms evaluation of the DT and ML integration through more rigorous structured review protocols and also the need to address unique operational challenges with 3rd world countries in implementing DTML integrated systems for industry. The objectives of this review are to; map and categorize industrial application areas of integrated digital twins and machine learning technologies; synthesize quantitative outcomes of implementing DT and ML technologies with a focus on manufacturing throughput, predictive maintenance and energy performance improvement; and critically review research gaps within current architectures and identify implementation challenges facing third-world countries, then propose feasible alternative solutions. The review shows that DT and ML integration in industrial systems and operations delivers measurable benefits. One of these included an increase in production throughput by about 18% by implementing an intelligent DT integrating Building Information Modelling (BIM) and Reinforcement Learning algorithms on a simulated multi-stage production line. Additionally, for the same system, energy consumption and system downtime were reduced by 12% and 15% respectively compared to conventional methods. In terms of predictive maintenance enhancement, the integration of DTs and supervised learning algorithms, such as Support Vector Machine (SVM), Random Forest and Naive Bayes, in asset lifecycle management achieved failure prediction

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accuracies of 74.87% and 75.39% using Naïve Bayes and SVM, respectively. In another analyzed application, integration of the two in energy systems resulted in a reduction in unplanned downtime by 35%, an increase in energy production by 8.5 %, fault detection accuracy of 98.3% and a 26.2 % reduction in energy costs. A framework for additive manufacturing that integrates machine learning models, employing Unity, OctoPrint, and Raspberry Pi for real-time control and monitoring was also developed in another reviewed application. The system utilized machine learning models for defect detection, achieving an Average Precision (AP) score of 92%, with specific performance metrics of 91% for defected objects and 94% for non-defected objects. Despite all these performance improvements, a number of implementation gaps still remain in DTML integration. Some of these include the focus on micro-scale with no evaluation of macro scale impact, systems interoperability issues, security of data, net energy accounting, data scarcity for some applications, limited focus on validating frameworks for SMEs, high implementation costs and more focus on manufacturing and built environment but limited focus on other sectors. Additionally, the review also identifies specific challenges unique for most 3rd World countries. Some of these include scarcity of data to oil the DTML systems, infrastructure deficits and obsolete equipment, computational challenges and intermittent power supplies. The review thus proposes solutions that alleviate these 3rd World country challenges. Some of the solutions include implementation of physics informed hybrids to circumvent data scarcity and edge AI and model compression to mitigate computational costs. Keywords: Digital twin, Machine learning, industrial efficiency, developing country challenges

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1.Introduction 1.1. Background and Institutional Context Industrial infrastructure and operations account for close to 37% of global final energy consumption. Against this high consumption, global energy prices are escalating, majority of countries have adopted net zero targets by 2050-2060 and developing countries face structural resource challenges. These three factors coupled with high energy consumption present bottlenecks on industrial performance. As a result, the search and adoption of various ways to enhance industrial energy efficiency has intensified. Industrial energy efficiency is key to maximized production, increased profitability and reduced operational costs and environmental impact. Static periodic maintenance procedures, automation based on set rules and empirical process tuning, have been the dominant methods of industrial management. However, operations within industry have become highly dynamic and operationally complex. This has led to limited optimization capabilities in implementing the traditional methods.

As a response to this dynamism in industry, technologies such as Digital Twins and Machine Learning are fast being adopted. The robustness of the systems is being enhanced by the integration of these two within the same processes. A digital twin is a virtual representation of a physical object, system or process, enabling real-time monitoring, simulation and predictive optimization. It is used to gather data from the physical system, understand its functionality and predict its performance. DTs originated in aerospace and manufacturing industries where they were applied in product lifecycle management. The concept of digital twinning has progressed from simple 3D models to complex cyber-physical systems that integrate real-time sensor data acquisition, simulations and the utilisation of historical data to create dynamic representations of physical assets (Abio et al., 2022; De Andrade et al., 2021). Contemporary DTs include three fundamental components, which are, the physical object, virtual model and the bidirectional data flow which enables feedback and response (Alexopoulos et al., 2020; Jyeniskhan et al., 2023). Figure 1 shows an architecture of a

digital twin setup for industrial monitoring. Figure 1: Digital twin architecture for an industrial system. Digital twins cover various scopes and data integration levels which results in varying differences in application. Industrial monitoring systems can integrate various DT scope levels as outlined in Figure 2.

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Figure 2: Different scope levels for DT integration in industrial systems. In terms of data integration, (Kritzinger et al., 2018) discussed three levels of data integration capacity. These are; digital model, digital shadow and digital twin. Interconnectivity and level of communication between the physical system and virtual system was regarded as an important and distinguishing feature. A digital model is non-automated data flow between the physical and virtual system, a digital shadow is where data flow is unidirectional from the physical to virtual system, then the digital twin exhibits bi-directional data flow between the physical system and virtual system. A DT on its own has several limitations as it highly relies on rigid, hard coded rules. Integrating Machine Learning models into a DT system improves the capabilities of DTs by enabling data-driven modelling, pattern recognition, predictive analytics and autonomous optimization. Several integration examples have emerged in the literature. As an example, supervised learning approaches utilize DT generated synthetic data to train classification and regression models for tasks such as defect detection, quality prediction, and estimation of remaining useful life (Avornu et al., 2022; Varghese et al., 2022; Xia et al., 2021). Reinforcement Learning (RL) enables digital twins to learn optimal control policies through simulationbased trial and error thereby reducing the need for costly physical experimentation (Castellani et al., 2020; Liu et al., 2026). Another ML technique, which is, the Transfer Learning (TL) algorithm, leverages knowledge acquired in simulation environments to quicken learning in physical systems. This addresses challenges of data scarcity thereby reducing commissioning time (Krupitzer et al., 2022). Federated Learning (FL) enables distributed ML across multiple THE ZIMBABWE INSTITUTION OF ENGINEERS

DTs while preserving data privacy and reducing communication overhead (Duman et al., 2023). Lastly, ensemble methods and hybrid modelling approaches combine physics-based simulations with data-driven models to achieve robust predictions across diverse operating conditions (Abio et al., 2022; Khan et al., 2025; Shukla et al., 2021). 1.2. Rationale and Objectives of the Review The broader literature shows significant progress in terms of implementation and functionality of DT and ML integration. However, there are limitations in terms of evaluating the technologies through more rigorous and structured review protocols. The reviews also fail to address the unique operational challenges with 3rd world countries in implementing DT-ML integrated systems for industry. This systematic review, utilizing the PRISMA framework, reports quantitative outcomes of implementing DT-ML technologies in industry and also identifies implementation challenges for third world countries where several challenges including obsolete equipment and data scarcity are inherent. Therefore, the objectives of this review are to: i. To map and categorize, using the PRISMA search strategy across multiple databases, industrial application areas of digital twins and machine learning. ii. To synthesize quantitative outcomes of implementing DT and ML technologies with a focus on manufacturing throughput, predictive maintenance and energy performance improvement indicators. iii.To critically review research gaps with current architectures and identify implementation challenges facing third-world countries, then propose feasible alternative solutions. 139

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2. Methodology 2.1. PRISMA Systematic Review Protocol This study implements the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) systematic review framework. The framework’s flowchart follows a 4-step process, which are, identification of studies, screening, eligibility (which are number of studies fully read) and lastly inclusion. 2.2. Information Sources and Search Strategies A systematic literature search was conducted across 4 databases namely; Google Scholar, Elsevier’s Scopus, ARDI from Research4Life and IEEE Xplore. The search period spanned from 2018 to 2026. Boolean logic was applied in formulating the search strings and the following strings were used for the different databases: Google Scholar Intext: (("digital twin" OR "cyber-physical system") AND ("artificial intelligence" OR "machine learning" OR "deep learning" OR "reinforcement learning") AND ("energy efficiency" OR "energy optimization" OR "power management") AND ("manufacturing" OR “industrial”)). IEEE Explore, Scopus and Research4Life ("digital twin" OR "cyber-physical system") AND ("artificial intelligence" OR "machine learning" OR "deep learning" OR "reinforcement learning") AND ("energy efficiency" OR "energy optimization" OR "power management") AND ("Manufacturing" OR "Industrial") 2.3. Inclusion and Exclusion Criteria To ensure focused review of the selected area, a precise eligibility criterion was applied to the retrieved papers, as summarized in Table 1. Table 1: Eligibility screening criteria used in the review. Criterion Strict Inclusion Criteria Integration Scope Systems which integrate digital twins (bidirectional data integration) and machine learning in their deployment. • Smart manufacturing, Target Application Domains • industrial energy grids, • Oil industry • HVAC, • And similar industries Publications period Evidence Quality

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Explicit Exclusion Criteria • Digital models and digital shadows • Standalone Machine Learning models • Healthcare, • Software applications, • Consumer electronics • And related fields

Peer-reviewed literature published Studies published before 2018 from 2018 to 2026, inclusive. (unless foundational). Empirical data detailing quantita- Opinion articles, commercial tive performance metrics (energy whitepapers, and studies lacking savings %, accuracy %, throughput transparent evaluation data. %).

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3. Results

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3.1. PRISMA Flow Diagram and Descriptive Statistics The PRISMA flowchart which was followed in the selection of studies is illustrated in Figure 3.

Figure 3: PRISMA Flowchart for a systematic review of application of DT and ML for industrial energy efficiency. Figure 3 shows that the initial search across Google Scholar, Elsevier’s Scopus, ARDI from Research4Life and IEEE Xplore yielded a total of 1,450 records. After duplicates removal, using manual and automated mechanisms, 890 unique records were retained for title and abstract screening. A strict exclusion criterion was then applied checking on the lack of explicit DT-ML integration or out-of-scope applications. This process eliminated 652 records. The final set of 238 full-text articles was then assessed for eligibility, resulting in the inclusion of 42 studies for comprehensive systematic qualitative and quantitative synthesis. The descriptive analysis of the data shows that an imbalance in research on applications of DTs and ML for different sectors exists. A descriptive analysis, also referred to as descriptive analytics, involves an examination of historical data to understand past trends, performance and changes. The imbalance in DTs application research is echoed in the Scopus database review done by (Yu et al., 2022) where they identified DTs research distribution of more than 1000 for manufacturing, about 400 for building and about 50 for process and energy. THE ZIMBABWE INSTITUTION OF ENGINEERS

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Their Scopus search spanned the period 2010-2022. Geographically, over 85% of these implementations are concentrated within highly industrialized regions, which are China, Western Europe and North America, exposing a huge research deficit from a developing country perspective. This highlights an immediate need to develop and validate feasible frameworks for the developing country industry.

sustainable energy systems. A framework integrating AI with DT technology for predictive modelling and lifecycle management of engineering assets across manufacturing, energy as well as oil and gas industries was presented by (Nithya et al., 2025). The framework combined physics-based simulations with data-driven algorithms for real-time monitoring, fault diagnosis and failure prediction. The framework demonstrated significant improvements in 3.2. Synthesis of Quantitative Operational operational efficiency, risk mitigation and maintenance Outcomes optimization. The reviewed literature demonstrated that the implementation of digital twin and machine learning A paper presenting a frame structure of a machine integrated systems yields exceptional operational learning based DT and prognostics algorithm model improvements across different industrial domains. to analyze and predict the risk probability rate of an oil pipeline system was developed by (Priyanka et al., This section discusses these findings. 2022). The paper aims to provide the frame structure i. Process Optimization and Manufacturing Throughput of prognostics and focuses on the detection of a A study by (Dai, 2025) showed that an intelligent failure precursor by estimating risk condition with DT integrating Building Information Modelling respect to the pressure data towards the evaluation of (BIM) and Reinforcement Learning algorithms remaining useful life (RUL). Their system integrated boosted production throughput by approximately the entire transmission lines to assess risk rates in the 18%. Additionally, energy consumption and system oil industry. downtime were reduced by 12% and 15% respectively compared to conventional methods. To validate the iii. Precision, Defect Tracking and Quality Control proposed framework, the authors simulated a multi- In environments which are complex and highly stage production line where they used BIM to generate variable, such as, additive manufacturing where high-fidelity digital models while RL algorithms quality and reliability of the manufactured products are optimized critical manufacturing parameters such as key , the deployment of digital twins which are driven throughput, energy efficiency and system reliability. by computer vision and deep convolutional networks yields very high gains. (Jyeniskhan et al., 2023) ii. Predictive Maintenance & Structural Asset developed a framework for additive manufacturing Management that integrates machine learning models, employing The integration of DTs and supervised learning Unity, OctoPrint and Raspberry Pi for real-time algorithms, such as Support Vector Machine (SVM), control and monitoring. The system utilizes machine Random Forest and Naive Bayes, in asset lifecycle learning models for defect detection, achieving an management achieved failure prediction accuracies Average Precision (AP) score of 92%, with specific of 74.87% and 75.39% using Naïve Bayes and SVM, performance metrics of 91% for defected objects and respectively (Avornu et al., 2022). The fusion of Digital 94% for non-defected objects. This demonstrates high Twin (DT) technology and Artificial Intelligence (AI) efficiency. algorithms is also revolutionizing the management and real-time monitoring of renewable energy systems. An open source Proof-of-Concept (PoC) DT A review of 42 publications by (Abdessadak et al., framework called PerfCam was introduced by (Khan 2025) showed that the integration of the two in energy et al., 2025). PerfCam combines camera and sensory systems resulted in a reduction in unplanned downtime data with 3D Gaussian Splatting and computer vision by 35%, an increase in energy production by 8.5 %, models for digital twinning, object tracking and Key fault detection accuracy of 98.3% and a 26.2 % Performance Indicators (KPIs) extraction in industrial reduction in energy costs. Their study highlighted production lines. Its effectiveness was validated existing challenges (such as high implementation through a practical deployment within pharmaceutical costs, cybersecurity risks and integration complexity) industry production lines. An accuracy of 98.7% was and future directions for leveraging digital techniques recorded for product tracking, demonstrating high to accelerate the transition towards intelligent and visual inspection performance. 142

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(Jain et al., 2023) presented a DT enabled machine learning model for order promising in job shop environments. Order promising involves committing to realistic delivery dates by assessing current inventory, production capacity and manufacturing constraints. In this analysis by Jain et al, a digital twin generates synthetic data, which is analyzed using Gaussian process regression to create meta-models for cycle time estimation. This approach addresses time-sensitive production control problems while reducing data generation time compared to physical experimentation. iv. Application in Food Processing Industries Food processing is another fast-emerging application domain of DT and ML systems. In this sector the technologies can address challenges such as variability of ingredients and management of supply chain. (Tancredi et al., 2022) proposed the integration of the two technologies and Industry 4.0 tools for anomaly detection in a food plant. The proposed system integrated three machine learning algorithms

into a digital twin environment. Two algorithms proved effective in predicting anomalies, enhancing employee safety in non-Industry 4.0 plants. A DigiFoodTwin concept was proposed by (Krupitzer et al., 2022). The concept integrated digital biophysical twins with explainable artificial intelligence for optimizing food processing. The framework employed a model-based learning and reasoning loop from self-aware computing systems, using real-time sensor measurements, forecasting, continuous simulation and critical event prediction to enable adaptive, resilient food processing in response to fluctuating raw material quality. 3.3. Mapping of Machine Learning Techniques to Digital Twin Applications The technical review identifies distinct algorithmic profiles engineered to solve specific constraints within the Digital Twin framework, as detailed in Table 2.

Table 2: Application of Machine Learning Techniques in DT integration. Machine Learning Technique Reinforcement Learning (RL)

Deep Neural Networks (DNN) / Long ShortTerm Memory (LSTM)

Federated Learning (FL)

Primary Industrial Application • Real time grid economic dispatch. • Autonomous HVAC control • Dynamic production scheduling • Long-term timeseries energy prediction • Smart grid balancing. • Dynamic load forecasting

Strengths

Excellent processing of complex non-linear temporal dependencies and large historical datasets.

•

Enables robust collaborative machine learning training while maintaining localized data privacy.

•

Distributed multi-site manufacturing Multi-stakeholder supply chain optimization.

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Main Architectural Limitations Learns optimal multi• Low sample efficiency objective control policies • Highly risky physical via simulation-based exploratory actions trial-and-error without • Complex sim-to-real explicit labels. transfer gaps.

•

Extreme computational overhead Severe training blackbox interpretability issues. High communication network overhead Vulnerability to heterogeneous edge sensor qualities.

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Transfer Learning (TL)

• •

Hybrid Physics-Informed • Models • •

Cross-phase industrial commissioning Rapid adaptation of pre-trained models.

Drastically mitigates data • scarcity by transferring simulation knowledge to physical systems.

Prone to negative transfer errors if the source and target physical domains diverge significantly.

Structural health monitoring Precision milling Chemical process optimization.

Combines thermodynamic laws with data layers, ensuring interpretability and robust small-data performance.

Extremely complex mathematical formulation Highly challenging live synchronization constraints.

• •

4. Discussion 4.1. Research Gaps in Industrial Energy Efficiency The systematic review has shown high-performance metrics associated with the integration of DT and ML in industrial systems. However, some major technical gaps were noted during the review process. These gaps tend to hinder the universal industrial efficiency optimization using the two technologies. This section presents these gaps. i. Data Interoperability and Standardization Modern industrial systems constitute legacy, proprietary communication protocols and deeply vendor-centered data structures. The absence of unified, cross-vendor, open-source structured data frameworks severely restricts the scalability of DTS when integrating with disparate Enterprise Resource Planning (ERP) systems and industrial edge sensors. Industrial systems often employ heterogeneous sensors, protocols and data formats (Nithya et al., 2025; Tancredi et al., 2022). ii. Macro Scale Gap Most studies have focused on micro-level efficiencies within factories through the attention on isolated firm cases or specific pilot projects, offering valuable but fragmented insights. What remains to be validated is whether DTs can generate measurable impacts on macroeconomic indicators. While component-level and system-level twins are technically mature, the mathematical and computational architectures required to scale twins across complex, multi-factory networks and highly volatile international supply chains are non-existent. iii. Net Energy Accounting The studies did not highlight the computational costs of running digital twins against savings achieved. Digital twins rely on continuous data streams, cloud computing and real-time AI modeling. Thus, running the necessary servers generates high computational and operational costs. iv. Frameworks for SMEs Literature has mainly focused on large industries and there is need to develop Small to Medium Enterprise (SME) scale validated frameworks. v. Absence of Adaptive and Autonomous Closed-Loop Optimization Currently, real-time, automated bidirectional control remains heavily confined to localized laboratory pilots. The majority of industrial implementations operate merely as Digital Shadows or passive monitoring dashboards.

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4.2. Challenges Specific to 3rd World and Developing Nations The deployment of integrated digital twins and machine learning in industrial systems within resource constrained developing nations introduces highly specialized structural challenges that are largely ignored by mainstream research. Some of these challenges are: i. Data Scarcity Deep learning models require large amounts of cleanly annotated, historical data. In 3rd World manufacturing industries, legacy mechanical hardware lacks digital sensor integration capability and formal logging practices. This severe absence of historical fault data and energy event logs limits the practical application of supervised learning models. ii. High Capital Costs and Computational Overheads The deployment of DT-ML systems requires high capital costs. Localized high-performance computing clusters or continuous, high-bandwidth cloud analytical services incur capital costs that are largely unfeasible for developing countries and Small-to-Medium Enterprises (SMEs). iii. Electrical Grid Instability Developing industrial regions, such as the Sub-Saharan Africa region, face persistent, erratic loadshedding and incidences of low power quality. This chronic instability breaks the continuous, real-time data synchronization loop essential for Digital Twin functionality. This frequently results in corrupted data streams and catastrophic system state desynchronization. 4.3. Proposed Solutions and Strategies for Developing Countries To facilitate the universal application of DT-ML integration and circumvent the regional constraints, this section proposes targeted, low-overhead solutions and strategies. i. Circumventing Data Scarcity using Physics Informed Hybrids Instead of relying on limited real-world historical data, industries should leverage low-cost, physics-based mathematical models to create high-fidelity simulation environments. By running virtual stress tests, these simulations can generate hundreds of thousands of synthetic, labeled data events to train data-driven ML models offline before physical deployment. An example application is simulating rare equipment failures or severe energy anomalies. The approach ensures that predictions are physically consistent, requires significantly less training data and produces models that generalize better across scientific and engineering applications. ii. Edge AI and Model Compression to Mitigate Computational Cost Resource-intensive neural networks can be optimized through advanced quantization and pruning techniques, enabling lightweight machine learning models to run efficiently on low-cost, localized edge devices such as open-source microcontrollers. This reduces reliance on continuous high-bandwidth cloud connectivity and expensive local high-performance computing infrastructure. iii. Insulating against Grid Instability using Asynchronous State Synchronization Digital Twin communication architectures should evolve from rigid, high-bandwidth synchronous data streams to resilient, event-driven asynchronous protocols. By leveraging lightweight edge data caching and message queuing technologies, such as Message Queuing Telemetry Transport (MQTT) integrated with local buffer storage, the virtual model can effectively withstand network interruptions, automatically reconciling and synchronizing data once power and communication are restored. v. Open-Source Modular Toolkits to Lower Integration Barriers Modular, open-source industrial platforms can be used by SMEs as a solution to expensive, proprietary software licensing. For example, using Node-RED for data routing, combined with Python's open-source scikit-learn and TensorFlow libraries, supports flexible creation of cost-effective digital shadows and adaptive energy monitoring dashboards.

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5. Conclusion and Future Directions This systematic review demonstrates that the architectural convergence of Digital Twin and Machine Learning technologies represents a paradigm shift in industrial energy management. Through performing a PRISMA search strategy across 4 global databases from 2018 to 2026, this study synthesized clear evidence of profound operational benefits. These included energy cost reductions, manufacturing throughput enhancements and defect tracking precision, amongst others. However, the field remains limited by critical implementation gaps which include, data interoperability barriers, lack of autonomous closed-loop optimization and severe multi-factory scalability limitations.

Abio, A., Bonada, F., Pujante, J., Grané, M., Nievas, N., Lange, D., & Pujol, O. (2022). Machine learningbased surrogate model for press hardening process of 22MnB5 sheet steel simulation in Industry 4.0. Materials, 15(10), 3647. https://doi.org/10.3390/ ma15103647 Alexopoulos, K., Nikolakis, N., & Chryssolouris, G. (2020). Digital twin-driven supervised machine learning for the development of artificial intelligence applications in manufacturing. International Journal of Computer Integrated Manufacturing, 33(5), 429–439.

Avornu, C., Akama, S. D., Avornu, E. K., & Aveyom, V. (2022). Data fusion for predictive maintenance of industrial assets using digital twin. In 2022 9th Furthermore, this study highlights a deep International Conference on Electrical Engineering, geographical imbalance, detailing how data poverty, Computer Science and Informatics (EECSI). capital constraints and grid instability create distinct deployment barriers within 3rd world countries. By Castellani, A., Schmitt, S., & Squartini, S. (2020). implementing the proposed solutions highlighted Real-world anomaly detection by using digital twin in this review, such as synthetic data generation, systems and weakly supervised learning. IEEE compressed Edge AI models, asynchronous state Transactions on Industrial Informatics, 17(7), 4733– caching and open-source modular frameworks, 4742. developing nations can successfully bypass some of these limitations. Dai, L. (2025). Intelligent manufacturing digital twin creation based on BIM and reinforcement As DT and ML technologies continue to mature and learning. In Proceedings of the Third International adoption accelerates, they promise to globally deliver Conference on Advanced Materials and Equipment substantial improvements in productivity, quality, Manufacturing (AMEM 2024). sustainability and resilience. Realizing this requires continued research, industry-academic collaboration, De Andrade, M. A. N., Lepikson, H. A., & Machado, standardization efforts and attention to ethical and C. A. T. (2021). A model proposal for digital twin social considerations. Organizations that successfully development: Essential oil extraction perspective. navigate these challenges and effectively integrate digital twins with machine learning will be wellDuman, B., & Süzen, A. A. (2023). Modeling of IoTpositioned to thrive in the increasingly competitive based additive manufacturing machine’s digital twin and dynamic industrial landscape. for error detection. Mühendislik Bilimleri ve Tasarım Dergisi, 11(2), 486–497.

6. References

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Khan, M. G., Guarese, R., Johnson, F., Wang, X. V., Bergman, A., Edvinsson, B., . . . Kronqvist, J. (2025). PerfCam: Digital twinning for production lines using 3D Gaussian splatting and vision models. IEEE Access. Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11), 1016–1022. Krupitzer, C., & Noack, T. (2022). DigiFoodTwin: Digital biophysical twins combined with machine learning for optimizing food processing. Engineering Proceedings, 19(1), 42.

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