https://ebookmass.com/product/machine-intelligence-big-data-
Instant digital products (PDF, ePub, MOBI) ready for you
Download now and discover formats that fit your needs...
Machine Learning, Big Data, and IoT for Medical Informatics Pardeep Kumar
https://ebookmass.com/product/machine-learning-big-data-and-iot-formedical-informatics-pardeep-kumar/
ebookmass.com
Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics Sunil Kuma Dhal
https://ebookmass.com/product/big-data-analytics-and-machineintelligence-in-biomedical-and-health-informatics-sunil-kuma-dhal/
ebookmass.com
BIG DATA ANALYTICS: Introduction to Hadoop, Spark, and Machine-Learning Raj Kamal
https://ebookmass.com/product/big-data-analytics-introduction-tohadoop-spark-and-machine-learning-raj-kamal/
ebookmass.com
Advanced Data Analytics Using Python : With Architectural Patterns, Text and Image Classification, and Optimization Techniques 2nd Edition Sayan Mukhopadhyay
https://ebookmass.com/product/advanced-data-analytics-using-pythonwith-architectural-patterns-text-and-image-classification-andoptimization-techniques-2nd-edition-sayan-mukhopadhyay/ ebookmass.com
R.K. Pathria
https://ebookmass.com/product/statistical-mechanics-fourth-editioninstructors-manual-r-k-pathria/
ebookmass.com
(eBook PDF) Disputed Moral Issues: A Reader 5th Edition
https://ebookmass.com/product/ebook-pdf-disputed-moral-issues-areader-5th-edition/
ebookmass.com
Circuit
Theory 3rd Edition A Nagoor Kani
https://ebookmass.com/product/circuit-theory-3rd-edition-a-nagoorkani/
ebookmass.com
Social Work Practice and Psychopharmacology: A Person in Environment Approach 3rd Edition, (Ebook PDF)
https://ebookmass.com/product/social-work-practice-andpsychopharmacology-a-person-in-environment-approach-3rd-edition-ebookpdf/
ebookmass.com
The Theory of (Not Quite) Everything Kara Gnodde
https://ebookmass.com/product/the-theory-of-not-quite-everything-karagnodde/
ebookmass.com
and Practice
https://ebookmass.com/product/qualitative-research-evaluation-methodsintegrating-theory-and-practice/
ebookmass.com
Machine Intelligence, Big Data Analytics, and IoT in Image Processing
Scrivener Publishing
100 Cummings Center, Suite 541J Beverly, MA 01915-6106
Machine Intelligence, Big Data Analytics, and IoT in Image Processing Practical Applications
Edited by Ashok Kumar
Chitkara University Institute of Engineering & Technology, Chitkara University, Punjab, India
Megha Bhushan
School of Computing, DIT University, Dehradun, Uttarakhand, India
José A. Galindo
Department of Computer Languages and Systems, University of Seville, Spain
Lalit Garg
Computer Information Systems, University of Malta, Malta and
Yu-Chen Hu
Dept. of Computer Science and Information Management, Providence University, Tai Chung, Taiwan
This edition first published 2023 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA © 2023 Scrivener Publishing LLC
For more information about Scrivener publications please visit www.scrivenerpublishing.com.
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
Wiley Global Headquarters
111 River Street, Hoboken, NJ 07030, USA
For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.
Limit of Liability/Disclaimer of Warranty
While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials, or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read.
Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-86504-9
Cover image: Pixabay.Com
Cover design by Russell Richardson
Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines
3
6.4
6.5.3
7.3.1
7.3.1.3
7.3.3.3
7.3.3.4
7.3.3.5
7.3.3.6
7.4
8 A Status Quo of Machine Learning Algorithms in Smart Agricultural Systems Employing IoT-Based WSN: Trends, Challenges and Futuristic Competences
Abhishek Bhola, Suraj Srivastava, Ajit Noonia, Bhisham Sharma and Sushil Kumar Narang
8.1 Introduction
8.2 Types of Wireless
8.3 Application of Machine Learning Algorithms for Smart Decision Making
8.4 ML and WSN-Based
8.5
8.6
Part III: Smart City and Villages
9
Huma Naz, Sachin Ahuja, Rahul Nijhawan and Neelu Jyothi Ahuja
9.1
9.2
9.3
Anurag Sinha, N. K. Singh, Ayushman Srivastava, Sagorika Sen and Samarth
Balasundaram, Cailassame Nedunchezhian, Mathiazhagan Arumugam and Vinoth Asaikannu
Preface
The concepts of machine intelligence, big data analytics and the Internet of Things (IoT) continue to improve our lives through various cutting-edge applications such as disease detection in real time, crop yield prediction, smart parking and so forth. The transformative effects of these technologies are life-changing because they play an important role in demystifying smart healthcare, plant pathology, and smart city/village planning, design and development. This book presents a cross-disciplinary perspective on the practical applications of machine intelligence, big data analytics and IoT by compiling cutting-edge research and insights from researchers, academicians and practitioners worldwide. It identifies and discusses various advanced technologies, such as artificial intelligence, machine learning, IoT, image processing, network security, cloud computing and sensors, to provide effective solutions to the lifestyle challenges faced by humankind. These practical innovative applications may include navigation systems for road transportation, IoT- and WSN-based smart agriculture, plant pathology through deep learning, cancer detection from medical images and smart home solutions. Moreover, cloud computing has made it possible to access these real-life applications remotely over the internet. The primary concern of this book is to equip those new to this field of application, as well as those with more advanced knowledge related to practical application development, exploit the inherent features of machine intelligence, big data analytics and IoT. For instance, how to harness these advanced technologies to develop practical applications such as drone-based surveillance, smart transportation, healthcare, smart farming solutions, and robotics for automation.
This book is a significant addition to the body of knowledge on practical applications emerging from machine intelligence, big data analytics and IoT. The chapters deal with specific areas of applications of these technologies. This deliberate choice of covering a diversity of fields was to emphasize the applications of these technologies in almost every contemporary aspect of real life to assist working in different sectors by understanding
Preface
and exploiting the strategic opportunities offered by these technologies. A summary of the main ideas of the work presented in each of the chapters follows:
– Chapter 1 is based on the models used to diagnose Alzheimer’s disease (AD). These models utilize CaffeNet, GoogLeNet, VGGNet-16, VGGNet-19, DenseNet with varying depths, Inception-V4, AlexNet, ResNet-18, ResNet-152, or even ensemble transfer-learning, that are pre-trained on generalized images for AD classification to achieve better performance as compared to training a model from scratch.
– Chapter 2 describes how to detect cancerous lung nodules from a lung CT scan image given as input and how to classify the lung cancer along with its severity. A novel deep learning method is used to detect the location of cancerous lung nodules.
– Chapter 3 outlines a classifier used to divide the liver and CT images into normal and abnormal categories based on the main features in terms of shape, texture, and feature statistics. It includes four stages: preprocessing, fuzzy clustering, feature extraction and classification. Furthermore, the grey-level co-occurrence matrix (GLCM) method is used to extract the features.
– Chapter 4 provides some of the major emerging digital technologies which have transformed the lives of individuals by making their future dependent upon the resilience of these technologies. It also highlights some of the major challenges related to these technologies with their suitable implications.
– Chapter 5 describes a model based on ResNet architecture in deep learning to help farmers detect plant leaf diseases at an early stage in order to take precautionary measures against them.
– Chapter 6 discusses an IoT-based smart irrigation system to assist farmers in precision agriculture for increasing crop yield. It uses multiple sensor metrics to help anticipate conditions for irrigation planning by predicting soil moisture, temperature, and humidity.
– Chapter 7 presents a hybrid model for wheat crop yield prediction using machine learning (ML) approaches, namely k-nearest neighbors (KNN), naïve Bayes, artificial neural network, logistic regression, support vector machine and linear discriminant analysis. The model works in two stages: the first stage uses a feature selection strategy to find the best features for wheat crops, and the second stage uses ML to estimate crop yield based on these best features.
– Chapter 8 discusses wireless sensor network (WSN)-based techniques used for smart agriculture and applications of ML for smart decision-making.
– Chapter 9 provides an insight into the applications of data preprocessing techniques and their effects on information retrieval. It covers the major issues that need to be dealt with before beginning any data analysis process.
– Chapter 10 focuses on the security for the latest paradigm shift in cloud and distributed computing. It delineates various risk parameters in the cloud environment and provides some novel methods to be adopted for cloud data security.
– Chapter 11 talks about the internet of drone things (IoDT), its applications in the modern world, research opportunities, and current challenges to be dealt with. Furthermore, it discusses new age inventions, security issues, and attacks that frequently occur in the IoDT.
– Chapter 12 presents an artificial intelligence-based gesture recognition system for the prediction of Indian sign language in real time. It covers different experiments using two-dimensional convolutional neural network-based classification to convert images into text.
– Chapter 13 sets forth applications, challenges, and future developments in the field of intelligent transportation systems (ITS) in India. It explains ITS and evaluates their feasibility in India.
– Chapter 14 provides a survey of evolutionary techniques used in navigation to create opportunities for analysts and researchers seeking to understand the broad pattern of different algorithms used in the navigation system.
– Chapter 15 examines the IoT-based vehicle parking system in Indian cities. Additionally, it discusses vehicle parking and its basic requirements, various technologies incorporated in modern parking systems, different sensors utilized in parking facilities, and the advantages of IoT-based vehicle parking systems in detail.
– Chapter 16 discusses a secure data transmission and key exchange for ensuring the confidentiality of data. Also, a lightweight authentication mechanism for ensuring the integrity and confidentiality of data shared over an unsecured network is presented.
– Chapter 17 delineates machine learning models in the prediction of strength parameters of fiber-reinforced polymer (FRP) wrapped reinforced concrete (RC) beams. It provides a summary of machine learning models in the estimation of bond strength between FRP and concrete surface, and shear and flexural strength of FRP wrapped RC beams.
– Chapter 18 describes existing AI-based studies for forecasting the indoor air quality of buildings and the future of AI-based indoor air quality forecasting. It provides an overview of the important role of machine learning models in the prediction of indoor pollutant concentrations to develop warning systems which help to affect the occupant’s health positively.
This book was edited by a team of academicians and experts. It is our hope that readers will draw several benefits from both the theoretical and practical aspects covered in the book to enhance their own practice or research.
The Editors
Dr. Ashok Kumar Phagwara, India
Dr. Megha Bhushan Dehradun, India
Dr. José Galindo Seville, Spain
Dr. Lalit Garg Valetta, Malta
Dr. Yu-Chen Hu
Tai Chung, Taiwan
January 2023
1 Deep Learning Techniques Using Transfer Learning for Classification of Alzheimer’s Disease
Monika Sethi1, Sachin Ahuja2* and Puneet Bawa1
1Chitkara University Institute of Engineering & Technology, Chitkara University, Punjab, India
2ED-Engineering at Chandigarh University, Punjab, India
Abstract
Alzheimer’s disease (AD) is a severe disorder in which brain cells degenerate, increasing memory loss with treatment choices for AD symptoms varying based on the disease’s stage, and as the disease progresses, individuals at certain phases undergo specific healthcare. The majority of existing studies make predictions based on a single data modality either they utilize magnetic resonance imaging (MRI)/positron emission tomography (PET)/diffusion tensor imaging (DTI) or the combination of these modalities. However, a thorough understanding of AD staging assessment can be achieved by integrating these data modalities and performance could be further enhanced using a combination of two or more modalities. However, deep learning techniques trained the network from scratch, which has the following drawbacks: (a) demands an enormous quantity of labeled training dataset that could be a problem for the medical field where physicians annotate the data, further it could be very expensive, (b) requires a huge amount of computational resources. (c) These models also require tedious and careful adjustments of numerous hyper-parameters, which results to under or overfitting and, in turn, to degraded performance. (d) With a limited medical training data set, the cost function might get stuck in a local-minima problem. In this chapter, a study is done based on the models used for AD diagnosis. Many researchers finetuned their networks instead of scratch training and utilized CaffeNet, GoogleNet, VGGNet-16, VGGNet-19, DenseNet with varying depths, Inception-V4, AlexNet, ResNet-18, ResNet-152, or even ensemble transfer-learning models pretrained on generalized images for AD classification performed better.
*Corresponding author: ed.engineering@cumail.in
Ashok Kumar, Megha Bhushan, José A. Galindo, Lalit Garg and Yu-Chen Hu (eds.) Machine Intelligence, Big Data Analytics, and IoT in Image Processing: Practical Applications, (3–22) © 2023 Scrivener Publishing LLC
Keywords: Alzheimer disease, transfer learning, deep learning, parameter optimization
1.1 Introduction
In the United States, AD is the most widespread neurodegenerative condition and the sixth major cause of fatalities. The global disease burden of AD is expected to exceed $2 trillion by 2030, requiring preventative care [1]. Despite the tremendous study and advancements in clinical practice, nearly half of AD patients are correctly identified for anatomy and progression of the disease based on medical indicators. The existence of neurofibrillary tangles and amyloid plaques in histology is the most definitive evidence for AD. Consequently, the presence of plaque is not associated with the beginning of AD, but rather with sensory and neuron damage. Dr. Alois Alzheimer (a psychiatrist and neuropsychologist) was the origin for the naming of this disease, who studied the brain of a 51-year-old woman who died of severe cognitive impairment in 1906 [2]. Dr. Alois investigated her brain and discovered clumps, which were actually the accumulation of proteins in and around the neurons, resulting in their loss. The key characteristics for identifying or confirming the existence of the illness are shrinkage of the hippocampus and cerebral cortex, as well as growth of the ventricles. The hippocampus is essential in learning and memory, in addition to acting as a connection between the central nervous system of the body’s organs. AD eventually destroys the portion of the brain that controls heart and respiratory activity, resulting in death [3].
Unfortunately, AD does not yet have a definitive cure [4]. Instead, the objective is to reduce the illness’s development, treat suffering, manage learning disabilities, and enhance the quality of life. Clinical trials, on the other hand, can significantly slow down the progression of psychiatric disorders if the diagnosis is made early. Whereas more psychological therapies and, eventually, prevention or even a cure are essential (long-term) goals, early diagnosis may result in improved treatment outcomes benefits for diseased. Except in a few cases where genetic abnormalities may be identified, the precise cause of AD is still obscure.
The assessment of empirical biomarkers is necessary for the early treatment of disease [5]. A number of noninvasive neuroimaging approaches, including computed tomography (CT) scans, both structural and functional MRI and PET, have been explored for the prediction of AD. To produce cross sectional pictures of the bones, blood arteries, and soft tissues within the body, computer processing is used to integrate a succession of
X-ray images recorded from different angle defined on your body. Plain X-rays do not give as much detail as CT scan imaging. An MRI scan employs a powerful magnet and radio waves to see at structures beneath the brain, according to the National Institute of Health. MRI scans are used by healthcare physicians to examine a variety of diseases, from damaged ligaments to cancer. To see and evaluate changes in cellular metabolism, PET is a functional imaging method, which thus employs radioactive additives termed as radiotracers.
Radiologists and clinicians, who are medical experts, analyze medical imaging data [6]. As a result of the probable tiredness of human specialists while evaluating images manually, a computer-assisted approach has proven to be beneficial for researchers as well as physicians. However, machine learning (ML) approaches are helping to improve the issue. Medical image analysis tasks need the use of ML to discover or learn useful features that characterize the correlations or patterns present in data. Since relevant or task-related characteristics are often created by human specialists on the basis of their domain expertise, it might be difficult for nonexperts to use ML techniques for their own research in the traditional manner. A number of projects are now working on the problem of learning sparse representations from training samples or pre-set dictionaries. Since then, there are attempts to generate sparse representations based on predefined dictionaries, which might be learned from training dataset. As a result of the concept of parsimony, sparse representation is used in many scientific fields. A sparsely inducing penalization and feature learning technique has been shown to be effective in medical image analysis when it comes to determining feature representation and selection [7]. Though data with a shallow architecture are still found to have meaningful patterns or regularities, techniques such as sparse representation or dictionary learning are still limited in their ability to represent them. Feature engineering has been incorporated into a learning phase in deep learning (DL), though, overcoming this issue [8]. Instead of manually extracting features, DL takes simply a collection of data with little preparation, if required, and then learns the valuable interpretations in an automatic method. Due to this shift in responsibility for feature extraction and selection, even nonexperts in ML may now use DL effectively for their own research work, especially in the medical field for imaging analysis [9].
However, DL is afflicted by data dependency, one of the most significant problems. Comparatively to standard ML approaches, DL relies on a significant quantity of training data in order to discover hidden patterns in data. There is an interesting relationship between the size of the model in terms of the numbers of layers and the volume of information required.
Transfer learning (TL) eliminates the dependency of a huge amount of data requirement, which inspires us to utilize this to combat the problem of inadequate training data. This concept is driven by the idea that people may strategically utilize past knowledge to solve new problems or accomplish desirable results. The fundamental reasoning underlying this idea in ML was presented during a Neural Information Processing Systems (NIPS-95) symposium on “Learning to Learn,” which emphasized the need of lifelong ML approaches that store and apply previously acquired information [10]. TL approaches have recently shown results in a variety of practical applications. In Verma et al. [11], researchers utilized TL methods to transfer text data across domains. For fixing natural language processing issues, structural correspondence learning was presented by an author in Nalavade et al. [12]. Researchers employed several Convolutional Neural Network (CNN)-based TL models to detect AD [13].
This chapter presents the results of several TL techniques employed by previous researchers to identify AD.
1.2 Transfer Learning Techniques
TL is an ML research subject that focuses on retaining information received while addressing the problem and adapting it to some other but similar issue. As an instance, knowledge acquired when learning to identify trucks may be used while aiming to classify other four-wheeler vehicles. In CNN, this may be implemented in one of two ways: either the weights of all CNN layers are coupled to some other CNN layer with classification Layer output, as well as just utilizing “off-the-shelf CNN features,” whereby CNN serves like a generalized feature extractor to be analyzed later.
Several domains of knowledge engineering, such as classifier, prediction, and segmentation, have already experienced significant results using ML and data mining techniques [14]. Many ML techniques, however, operate successfully with an assumption that training test data are collected from the same dimensional region and variance. Most statistical models must still be redesigned from beginning when the population varies, employing new received data for training. In several practical applications, recollecting the necessary training data and rebuilding the models is either too expensive or not feasible. It would be extremely beneficial if researchers could reduce the need for the time and efforts associated with acquiring training samples. Transferring information or learning across problem contexts would be advantageous in such scenarios.
