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NS GYRO Guide ABOUT COMPANY NS SOLVERLAB Technologies Pvt. Ltd. www.solverlab.com

NS SOLVERLAB Technologies Pvt. Ltd. is Research & Development firm dedicated for innovative excellence driven by Passion, Creativity and Willpower. Team of core R&D Engineers, Mathematicians, IT specialists developing state of art technology for solving real time problems in Life Sciences, Engineering, Industry, Business, Environmental sciences, Social sciences. One can never solve the problem on the level it was created. Our technology will provide you the platform to cross these level barriers and solve it. ABOUT PRODUCT NS GYRO NS GYRO is an artificial intelligence based software, engineered for data mining and pattern recognition. It provides advanced hybrid intelligent platform where various machine learning algorithms undergo smart fusion to come out with better solutions integrating the advantages and giving a way out to drawbacks, of participant solvers It explores the newer dimensions for the user in a user-friendly environment to analyze and solve their problems.


Data Mining – Data mining is a very popular tool for business intelligence, science and engineering. It is an artificial intelligence based approach to extract the patterns of the data. On a broader level it can be divided into: Supervised Machine Learning – Classification: In this category already known data column (class) is used into the data samples for training and predictive model is devised. This model is then used to predict class output for the unknown sample. UnSupervised Machine Learning – Clustering: In this category known data column (class) is not used into the data samples for training. Here groups and structures in the data are discovered and clusters are formed. This clustering model can be used to group the unknown samples which share the similar attribute values.

FEATURES

1. Hybrid Intelligent System with Well Organized 3 Phase Setup 2. Interactivity & Interpretation 3. Multidimensional Analysis 4. Data Wizard Support 5. Project Workspace Management


2.1.1 Hybrid Intelligent System

HYBRID INTELLIGENT SYSTEM WITH WELL ORGANIZED 3 PHASE SETUP

Supervised Learning

Artificial Neural Network with Artificial Immune System

Artificial Neural Network with Genetic Algorithms

Artificial Neural Network - Backpropagation

Gene Expression Programming

Genetic Programming

UnSupervised Learning

Self Organizing Map (SOM)

2.1.1.1 Supervised Machine Learning

Uses Various Machine Learning Algorithms in Smart Fusion to produce better solutions


TrainingPhase

– Search Solutions

Validation Phase – Validate Solutions Service Phase

– Use Solutions


2.1.1.2 UnSupervised Machine Learning

Uses Clustering Algorithms to produce better solutions Training-Clustering Phase – Search Clusters Validation Phase – Validate Clusters Service Phase – Use Clusters


2.1.2 Interactivity and Interpretation

INTERACTIVITY & INTERPRETATION

1. Linear Solution View 2. Graphical Solution View 1. 3. Pattern Graph 2. 4. Pattern Grid

2.1.2.1 Linear Solution View LINEAR SOLUTION VIEW


2.1.2.2 Graphical Solution View

GRAPHICAL SOLUTION VIEW


2.1.2.3 Pattern Graph PATTERN GRAPH


2.1.2.4 Pattern Grid PATTERN GRID


2.1.3 Multidimensional Analysis

MULTIDIMENSIONAL ANALYSIS

1. 2-Dimensional Analysis 2. 3- Dimensional Analysis

2.1.3.1 2-Dimensional Analysis

2 – DIMENSIONAL ANALYSIS


2.1.3.2 3-Dimensional Analysis Support

3 – DIMENSIONAL ANALYSIS SUPPORT

1. 3D Points 2. 3D Bars 3. 3D Bubbles 4. Mesh Solid Surface 5. Mesh Frame Surface 6. Mesh Plane Surface 7. Triangulated Surface 8. Grid Surface 2.1.3.2.1 3D Points

3D POINTS


2.1.3.2.2 3D Bars

3D BARS


2.1.3.2.3 3D Bubbles


3D BUBBLES


2.1.3.2.4 Mesh Solid Surface


MESH SOLID SURFACE


2.1.3.2.5 Mesh Frame Surface


MESH FRAME SURFACE


2.1.3.2.6 Mesh Plane Surface


MESH PLANE SURFACE


2.1.3.2.7 Triangulated Surface


TRIANGULATED SURFACE


Contents ABOUT COMPANY .................................................................................................................................................................................................................................................................................................................................................. 1 ABOUT PRODUCT ................................................................................................................................................................................................................................................................................................................................................... 1 FEATURES ............................................................................................................................................................................................................................................................................................................................................................ 2 2.1.1 Hybrid Intelligent System ............................................................................................................................................................................................................................................................................................................................ 3 HYBRID INTELLIGENT SYSTEM WITH WELL ORGANIZED 3 PHASE SETUP ............................................................................................................................................................................................................................................. 3 Supervised Learning ......................................................................................................................................................................................................................................................................................................................................... 3 Artificial Neural Network with Artificial Immune System .................................................................................................................................................................................................................................................................................... 3 Artificial Neural Network with Genetic Algorithms ............................................................................................................................................................................................................................................................................................... 3 Artificial Neural Network - Backpropagation ......................................................................................................................................................................................................................................................................................................... 3 Gene Expression Programming ............................................................................................................................................................................................................................................................................................................................... 3 Genetic Programming .............................................................................................................................................................................................................................................................................................................................................. 3 UnSupervised Learning .................................................................................................................................................................................................................................................................................................................................... 3 Self Organizing Map (SOM) ..................................................................................................................................................................................................................................................................................................................................... 3 Uses Various Machine Learning Algorithms in Smart Fusion to produce better solutions ............................................................................................................................................................................................................................................. 3 TrainingPhase

– Search Solutions.................................................................................................................................................................................................................................................................................................................. 4

Validation Phase – Validate Solutions ............................................................................................................................................................................................................................................................................................................... 4 Service Phase

– Use Solutions ..................................................................................................................................................................................................................................................................................................................... 4

Uses Clustering Algorithms to produce better solutions..............................................................................................................................................................................................................................................................................................12 2.1.2 Interactivity and Interpretation ..................................................................................................................................................................................................................................................................................................................16 INTERACTIVITY & INTERPRETATION ..............................................................................................................................................................................................................................................................................................................16 GRAPHICAL SOLUTION VIEW ............................................................................................................................................................................................................................................................................................................................20 PATTERN GRAPH ...............................................................................................................................................................................................................................................................................................................................................24


.................................25


2.1.3 Multidimensional Analysis..........................................................................................................................................................................................................................................................................................................................30 MULTIDIMENSIONAL ANALYSIS .......................................................................................................................................................................................................................................................................................................................30 2 – DIMENSIONAL ANALYSIS .............................................................................................................................................................................................................................................................................................................................30 3 – DIMENSIONAL ANALYSIS SUPPORT ...........................................................................................................................................................................................................................................................................................................46 3D BUBBLES............................................................................................................................................................................................................................................................................................................................................................71


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.....................................74


.....................................76


.....................................78


MESH SOLID SURFACE ........................................................................................................................................................................................................................................................................................................................................80


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..........................................................................................................................83 MESH FRAME SURFACE ......................................................................................................................................................................................................................................................................................................................................84


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.....................................87


..........................................................................................................................88


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MESH PLANE SURFACE .......................................................................................................................................................................................................................................................................................................................................91


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TRIANGULATED SURFACE ..................................................................................................................................................................................................................................................................................................................................97


.....................................98


GRID SURFACE ....................................................................................................................................................................................................................................................................................................................................................150

........................................................................................................................150 MULTIVARIATE ANALYSIS ................................................................................................................................................................................................................................................................................................................................151


...................................152


...................................154


...................................156


2.1.4 Data Wizard Support ................................................................................................................................................................................................................................................................................................................................157 DATA WIZARD SUPPORT ...................................................................................................................................................................................................................................................................................................................................157 SUPPORTED DATA INPUT MODES ...................................................................................................................................................................................................................................................................................................................157 SMART DATA MANAGEMENT ...........................................................................................................................................................................................................................................................................................................................158 DATA IMPORT ......................................................................................................................................................................................................................................................................................................................................................160

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................................................................................................................................................................................................................................................162 DATA REPOSITORY .............................................................................................................................................................................................................................................................................................................................................163


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........................................................164 DATA FORMAT TOOLS........................................................................................................................................................................................................................................................................................................................................165 FILTER DATA........................................................................................................................................................................................................................................................................................................................................................166


.....................................................................................................................................................................................................................................................166 CATEGORICAL TO NUMERIC DATA CONVERTION WIZARD ......................................................................................................................................................................................................................................................................168


.................................................................................................................................................................................................................................................................................................................168

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........................................................173 2.1.5 Project Workspace Management .............................................................................................................................................................................................................................................................................................................176 PROJECT WORKSPACE MANAGEMENT .........................................................................................................................................................................................................................................................................................................176


...................................177


...................................179


...................................181


...................................183


2.1.3.2.8 Grid Surface


GRID SURFACE


2.1.3.3 Multivariate Analysis

MULTIVARIATE ANALYSIS


2.1.4 Data Wizard Support

DATA WIZARD SUPPORT

3. 1. Supported Data Input Modes 4. 2. Smart Data Management 5. 6. 3. Export Used Data

2.1.4.1 Supported Data Input Modes

SUPPORTED DATA INPUT MODES

1. MS SQL Database

2. EXCEL Database


3. MS Access Database

4. TXT File Data Separated By Any Delimiter

2.1.4.2 Smart Data Management

SMART DATA MANAGEMENT

1. Data Import 2. Data Repository 3. Data Format Tools


2.1.4.2.1 Data Import


DATA IMPORT


2.1.4.2.2 Data Repository


DATA REPOSITORY


2.1.4.2.3 Data Format Tools


DATA FORMAT TOOLS

5. 1. Filter Data 6. 2. Normalize Data 7. 3. Numeric Conversion Wizard 2.1.4.2.3.1 Filter Data


FILTER DATA

2.1.4.2.3.2 Normalize Data

NORMALIZE DATA


2.1.4.2.3.3 Categorical To Numeric Data Conversion Wizard


CATEGORICAL TO NUMERIC DATA CONVERTION WIZARD


2.1.4.3 Export Used Data EXPORT USED DATA View Data in Excel Export Data to Excel View Data in TxT File Export Data to Txt File


2.1.5 Project Workspace Management

PROJECT WORKSPACE MANAGEMENT


2.1.5.1 Save Data Used in Workspace SAVE DATA USED IN WORKSPACE Data used in the project will be saved in the same directory where workspace file (.NSLAB) is saved. Training Data saved as Workspace Name followed by "TrainingData.txt" Validation Data saved as Workspace Name followed by "ValidationData.txt" Service Data saved as Workspace Name followed by "ServiceData.txt"

NS GYRO  

NS GYRO is an artificial intelligence based software, engineered for data mining and pattern recognition. It provides advanced hybrid intell...

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