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How Machine Learning is Innovating Today's World

A Concise Technical Guide

Edited by Arindam Dey, Sukanta Nayak, Ranjan Kumar and Sachi Nandan Mohanty
Copyright: 2024   |   Status: Published
ISBN: 978139214112  |  Hardcover  |  
528 pages
Price: $225 USD
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One Line Description
Provides a comprehensive understanding of the latest advancements
and practical applications of machine learning techniques.

Audience
The book will be extremely useful to professionals, post-graduate research scholars, policymakers, corporate managers, and anyone with technical interests looking to understand how machine learning and artificial intelligence can benefit their work.

Description
Machine learning (ML), a branch of artificial intelligence, has gained tremendous momentum in recent years, revolutionizing the way we analyze data, make predictions, and solve complex problems. As researchers and practitioners in the field, the editors of this book recognize the importance of disseminating knowledge and fostering collaboration to further advance this dynamic discipline. “How Machine Learning is Innovating Today’s World” is a timely book and presents a diverse collection of 25 chapters that delve into the remarkable ways that ML is transforming various fields and industries.
It provides a comprehensive understanding of the practical applications of ML techniques. The wide range of topics include:

An analysis of various tokenization techniques and the sequence-to-sequence model in natural language processing
Explores the evaluation of English language readability using ML models
A detailed study of text analysis for information retrieval through natural language processing
The application of reinforcement learning approaches to supply chain management The performance analysis of converting algorithms to source code using natural language processing in Java
Presents an alternate approach to solving differential equations utilizing artificial neural networks with optimization techniques
A comparative study of different techniques of text-to-SQL query conversion t
The classification of livestock diseases using ML algorithms
ML in image enhancement techniques
The efficient leader selection for inter-cluster flying ad-hoc networks
A comprehensive survey of applications powered by GPT-3 and DALL-E Recommender systems’ domain of application
Reviews mood detection, emoji generation, and classification using tokenization and CNN
Variations of the exam scheduling problem using graph coloring
The intersection of software engineering and machine learning applications Explores ML strategies for indeterminate information systems in complex bipolar neutrosophic environments
ML applications in healthcare, in battery management systems, and the rise of AI-generated news videos
How to enhance resource management in precision farming through AI-based irrigation optimization

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Author / Editor Details
Arindam Dey, PhD, is an associate professor at the School of Computer Science, VIT-AP University, India. He has published more than 50 research articles in national and international peer-reviewed journals. Dr. Dey has 14 years of teaching and research experience in the areas of optimization and genetic algorithms.

Sukanta Nayak, PhD, is an assistant professor in the Department of Mathematics, School of Advanced Sciences (SAS) at VIT-AP University, Amaravati, Andhra Pradesh, India. He completed his doctoral research at NIT Rourkela, has authored three books, and published numerous research articles in international journals.

Ranjan Kumar, PhD, is an assistant professor in the Department of Mathematics, School of Advanced Sciences (SAS) at VIT-AP University, Amaravati, Andhra Pradesh, India. He has numerous peer-reviewed research articles to his name and is the recipient of numerous awards and titles including an Honorary Professorship from Cypress International Institute University, Texas, USA.

Sachi Nandan Mohanty, PhD, is in the School of Computer Science and Engineering (SCOPE) at VIT-AP University, Amaravati, Andhra, Pradesh, India. He has edited 25 books and published 60 international journals of international repute. His research areas include data mining, big data analysis, cognitive science, fuzzy decision-making, brain-computer interface, cognition, and computational intelligence. In 2015, he was awarded the first prize of the Best Thesis Award by the Computer Society of India.

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Table of Contents
Preface
Part 1: Natural Language Processing (NLP) Applications
1. A Comprehensive Analysis of Various Tokenization Techniques and Sequence-to-Sequence Model in Natural Language Processing
Kuldeep Vayadande, Ashutosh M. Kulkarni, Gitanjali Bhimrao Yadav, R. Kumar and Aparna R. Sawant
1.1 Introduction
1.2 Literature Survey
1.3 Sequence-to-Sequence Models
1.3.1 Convolutional Seq2Seq Models
1.3.2 Pointer Generator Model
1.3.3 Attention-Based Model
1.4 Comparison Table
1.5 Comparison Graphs
1.6 Research Gap Identified
1.7 Conclusion
References
2. A Review on Text Analysis Using NLP
Kuldeep Vayadande, Preeti A. Bailke, Lokesh Sheshrao Khedekar, R. Kumar and Varsha R. Dange
2.1 Introduction
2.2 Literature Review
2.3 Comparison Table of Previous Techniques
2.4 Comparison Graphs
2.5 Research Gap
2.6 Conclusion
References
3. Text Generation & Classification in NLP: A Review
Kuldeep Vayadande, Dattatray Raghunath Kale, Jagannath Nalavade, R. Kumar and Hanmant D. Magar
3.1 Introduction
3.2 Literature Survey
3.3 Comparison Table of Previous Techniques
3.3.1 Sentiment Analysis
3.3.2 Translation
3.3.3 Tokenization Based on Noisy Texts
3.3.4 Question Answer Model
3.4 Research Gap
3.5 Conclusion
References
4. Book Genre Prediction Using NLP: A Review
Kuldeep Vayadande, Preeti Bailke, Ashutosh M. Kulkarni, R. Kumar and Ajit B. Patil
4.1 Introduction
4.2 Literature Survey
4.3 Comparison Table
4.4 Research Gap Identified
4.5 Future Scope
4.6 Conclusion
References
5. Mood Detection Using Tokenization: A Review
Kuldeep Vayadande, Preeti A. Bailke, Lokesh Sheshrao Khedekar, R. Kumar and Varsha R. Dange
5.1 Introduction
5.2 Literature Survey
5.3 Comparison Table of Previous Techniques
5.4 Graphs
5.5 Research Gap
5.6 Conclusion
References
6. Converting Pseudo Code to Code: A Review
Kuldeep Vayadande, Preeti A. Bailke, Anita Bapu Dombale, Varsha R. Dange and Ashutosh M. Kulkarni
6.1 Introduction
6.2 Literature Review
6.3 Comparison Table
6.4 Graphs of Comparison Done
6.5 Research Gap Identified
6.6 Conclusion
References
Part 2: Machine Learning Applications in Specific Domains
7. Evaluating the Readability of English Language Using Machine Learning Models
Shiplu Das, Abhishikta Bhattacharjee, Gargi Chakraborty and Debarun Joardar
7.1 Introduction
7.2 Contribution in this Chapter
7.3 Research Gap
7.4 Literature Review
7.5 Proposed Model
7.6 Model Analysis with Result and Discussion
7.7 Conclusion
References
8. Machine Learning in Maximizing Cotton Yield with Special Reference to Fertilizer Selection
G. Hannah Grace and Nivetha Martin
8.1 Introduction
8.2 Literature Review
8.3 Materials and Methods
8.3.1 Problem Definition
8.3.2 Objectives
8.3.3 Data Collection
8.3.4 Data Preprocessing
8.3.5 Steps Involved in Combined Decision-Making Approach Using Machine Learning Algorithms
8.4 Application to the Fertilizer Selection Problem
8.5 Conclusion and Future Suggestions
References
9. Machine Learning Approaches to Catalysis
Sachidananda Nayak and Selvakumar Karuthapandi
9.1 Introduction
9.2 Chem-Workflow
9.3 ML Basic Concepts
9.4 ML Models in Catalysis
9.5 ML in Structure–Activity Prediction
9.6 Conclusion and Future Works
References
10. Classification of Livestock Diseases Using Machine Learning Algorithms
G. Hannah Grace, Nivetha Martin, I. Pradeepa and N. Angel
10.1 Introduction
10.2 Literature Review
10.3 Materials and Methods
10.3.1 Definition of the Problem
10.3.2 Objectives
10.3.3 Data Collection
10.3.4 Data Preprocessing
10.3.5 Steps Involved in Supervised Learning Classifiers
10.4 Application of the Supervised Classifiers in Disease Classification
10.5 Results and Conclusion
References
11. Image Enhancement Techniques to Modify an Image with Machine Learning Application
Shiplu Das, Sohini Sen, Debarun Joardar and Gargi Chakraborty
11.1 Introduction
11.2 Literature Review
11.3 Image Enhancement Techniques for Betterment of the Images
11.4 Proposed Image Enhancement Techniques
11.5 Conclusion
References
12. Software Engineering in Machine Learning Applications: A Comprehensive Study
Kuldeep Vayadande, Komal Sunil Munde, Amol A. Bhosle, Aparna R. Sawant and Ashutosh M. Kulkarni
12.1 Introduction
12.2 Related Works
12.3 Comparison Table
12.4 Graph of Comparison
12.5 Machine Learning in Software Engineering
12.6 Conclusion
References
13. Machine Learning Applications in Battery Management System
Ponnaganti Chandana and Ameet Chavan
13.1 Introduction
13.2 Battery Management System (BMS)
13.2.1 Key Parameters of Battery Management System
13.2.1.1 Voltage
13.2.1.2 Temperature
13.2.1.3 State of Charge
13.2.1.4 State of Health
13.2.1.5 State of Function
13.3 Estimation of Battery SOC and SOH
13.3.1 Methods of Estimating SOC
13.3.1.1 Coulomb Counting Method
13.3.1.2 Open Circuit Voltage (OCV) Method
13.3.1.3 Kalman Filtering Method
13.3.1.4 Artificial Neural Network (ANN) Method
13.3.1.5 Fuzzy #
13.3.1.6 Extended Kalman Filtering Method
13.3.1.7 Gray Box Modeling Method
13.3.1.8 Support Vector Machine (SVM) Method
13.3.1.9 Model Predictive Control Method
13.3.1.10 Adaptive Observer Method
13.3.1.11 Impedance-Based Method
13.3.1.12 Gray Prediction Method
13.3.2 Methods of Estimating SOH
13.3.2.1 Capacity Fade Model
13.3.2.2 Electrochemical Impedance Spectroscopy (EIS) Method
13.3.2.3 Voltage Relaxation Method
13.3.2.4 Fuzzy Logic Method
13.3.2.5 Particle Filter Method
13.3.2.6 Artificial Neural Network (ANN) Method
13.3.2.7 Support Vector Machine (SVM) Method
13.3.2.8 Gray Box Modeling Method
13.3.2.9 Kalman Filtering Method
13.3.2.10 Multi-Model Approach
13.4 Cell Balancing Mechanism for BMS
13.5 Industrial Applications
13.5.1 Industrial Applications of Machine Learning in Battery Management System
13.5.2 Machine Learning Algorithms That Are Used for Industrial Applications in Battery Management System
13.5.3 Steps Involved in Machine Learning Approach in BMS Applications
13.5.4 Applications of Different ML Algorithms in BMS
13.5.4.1 Artificial Neural Networks (ANNs)
13.5.4.2 Decision Trees
13.5.4.3 Support Vector Machines (SVMs)
13.5.4.4 Random Forest
13.5.4.5 Gaussian Process
13.6 Case Studies of ML-Based BMS Applications in Industry
13.6.1 Machine Learning Approach to Predict SOH of Li-Ion Batteries
13.6.2 Anomaly Detection in Battery Management System Using Machine Learning
13.6.3 Optimization of Battery Life Cycle Using Machine Learning
13.6.4 Prediction of Remaining Useful Life Using Machine Learning
13.6.5 Fault Diagnosis of Battery Management System Using Machine Learning
13.6.6 Battery Parameter Estimation Using Machine Learning
13.6.7 Optimization of Battery Charging Using Machine Learning
13.6.8 ML Approach to Estimate State of Charge
13.6.9 Battery Capacity Estimation Using ML Approach
13.6.10 Anomaly Detection in Batteries Using Machine Learning
13.6.11 ML-Based BMS for Li-Ion Batteries
13.6.12 Battery Management System Based on Deep Learning for Electric Vehicles
13.6.13 A Review of ML Approaches for BMS
13.6.14 Battery Management Systems Using Machine Learning Techniques
13.6.15 Machine Learning for Lithium-Ion Battery Management: Challenges and Opportunities
13.6.16 An ML-Based BMS for Hybrid EVs
13.6.17 Battery Management System for EVs Using ML Techniques
13.6.18 A Hybrid BMS Using Machine Learning Techniques
13.7 Challenges
13.8 Conclusion
References
14. ML Applications in Healthcare
Farooq Shaik, Rajesh Yelchurri, Noman Aasif Gudur and Jatindra Kumar Dash
14.1 Introduction
14.1.1 Supervised Learning
14.1.2 Unsupervised Learning
14.1.3 Semi-Supervised Learning
14.1.4 Reinforcement Learning
14.2 Applications of Machine Learning in Health Sciences
14.2.1 Diagnosis and Prediction of Disease
14.2.1.1 Predicting Thyroid Disease
14.2.1.2 Predicting Cardiovascular Disease
14.2.1.3 Predicting Cancer
14.2.1.4 Predicting Diabetes
14.2.1.5 Predicting Alzheimer’s
14.2.2 Drug Development and Discovery
14.2.3 Clinical Decision Support (CDS)
14.2.4 Medical Image Examination
14.2.5 Monitoring of Health and Wearable Technology
14.2.6 Telemedicine and Remote Patient Monitoring
14.2.7 Chatbots and Virtual Medical Assistants
14.3 Why Machine Learning is Crucial in Healthcare
14.4 Challenges and Opportunities
14.5 Conclusion
References
15. Enhancing Resource Management in Precision Farming through AI-Based
Irrigation Optimization
Salina Adinarayana, Matha Govinda Raju, Durga Prasad Srirangam, Devee Siva Prasad, Munaganuri Ravi Kumar and Sai babu veesam
15.1 Introduction to Precision Farming
15.1.1 Definition of Precision Farming
15.1.2 Importance of Precision Farming in Agriculture
15.2 Role of Artificial Intelligence (AI) in Precision Farming
15.2.1 Influence of AI in Precision Farming
15.2.2 Challenges and Limitations of AI in Precision Farming
15.3 Data Collection and Sensing for Precision Farming
15.3.1 Remote Sensing Techniques
15.3.2 Satellite Imagery Analysis
15.3.3 Unmanned Aerial Vehicles (UAVs) for Data Collection
15.3.4 Internet of Things (IoT) Sensors
15.3.5 Data Preprocessing and Integration
15.4 Crop Monitoring and Management
15.4.1 Crop Yield Prediction
15.4.2 Disease Detection and Diagnosis
15.4.3 Nutrient Management and Fertilizer Optimization
15.5 Precision Planting and Seeding
15.5.1 Variable Rate Planting
15.5.2 GPS and Auto-Steering Systems
15.5.3 Seed Singulation and Metering
15.5.4 Plant Health Monitoring and Care
15.6 Harvesting and Yield Estimation
15.6.1 Yield Estimation Models
15.6.2 Real-Time Crop Monitoring During Harvest
15.7 Data Analytics and Machine Learning
15.7.1 Predictive Analytics for Crop Yield
15.7.2 Machine Learning Algorithms for Precision Farming
15.7.3 Big Data Analytics in Precision Farming
15.8 Integration of AI with Other Technologies
15.8.1 AI and Blockchain in Supply Chain Management
15.8.2 AI and Drones in Precision Farming
15.8.3 AI and Robotics Collaboration
15.9 Case Studies and Success Stories
15.10 Challenges and Future Trends
15.11 Conclusion
References
16. An In-Depth Review on Machine Learning Infusion in an Agricultural Production System
Sarthak Dash, Sugyanta Priyadarshini and Sukanya Priyadarshini
16.1 Background Study
16.2 Research Methodology
16.2.1 Planning the Review
16.2.2 Search String
16.2.3 Selection Criteria
16.2.4 Conduction of Review
16.3 Results and Discussion
16.3.1 Crop Yield Prediction
16.3.2 Crop Disease Management
16.3.3 Water Management
16.3.4 Soil Management
16.3.5 Weather Forecasting
16.4 Conclusion
References
Part 3: Artificial Intelligence and Optimization Techniques
17. Reinforcement Learning Approach in Supply Chain Management: A Review
Rajkanwar Singh, Pratik Mandal and Sukanta Nayak
17.1 Introduction
17.2 Literature Review
17.2.1 Challenges
17.2.2 Advantages of Using ML Techniques in SCM
17.2.3 Limitations of Using ML Techniques in SCM
17.2.4 Effectiveness of ML Techniques in Handling Various SCM Activities
17.3 Methodology
17.4 Reinforcement Learning in Supply Chain Management
17.4.1 RL and Its Application in SCM
17.4.2 Benefits of RL in SCM
17.5 Adoption of Reinforcement Learning in Supply Chain Management
17.5.1 Technical Barriers
17.5.2 Organizational Barriers
17.5.3 Cultural Barriers
17.5.4 Economic Barriers
17.6 Alternatives to Reinforcement Learning in Supply Chain Management
17.7 Conclusion
References
18. Alternate Approach to Solve Differential Equations Using Artificial Neural
Network with Optimization Technique
Ramanan R., Sukanta Nayak and Arun Kumar Gupta
18.1 Introduction
18.2 Artificial Neural Network
18.2.1 Architecture
18.2.2 Neuron Architecture
18.2.2.1 Single and Multilayer Neural Networks
18.2.2.2 Feedforward Neural Network
18.2.2.3 Feedback Neural Network
18.2.3 Different Training Process
18.2.3.1 Supervised Training
18.2.3.2 Unsupervised Training
18.2.4 Learning Process
18.2.5 Activation Function in ANN
18.2.5.1 Unipolar Sigmoid Function
18.2.5.2 Bipolar Sigmoid Function
18.3 Backpropagation Algorithm
18.4 Solving Differential Equation Using ANN
18.4.1 Structure of Multi-Layer ANN
18.4.2 General Formula for Solving ODE
18.4.3 Formulation for nth-Order Initial Value Problem (IVP)
18.4.4 Case Study: Solving First-Order Linear Differential Equation
18.4.4.1 Algorithm
18.4.4.2 Example
18.4.4.3 Second Approach
18.4.4.4 Algorithm
18.4.4.5 Example
18.4.4.6 Comparison between First and Second Approaches
18.5 System Identification Using ANN
18.5.1 Problem Structure for System Identification
18.5.2 Analysis and Modelling
18.5.3 ANN Training for SI
18.5.4 Results and Discussion
18.6 Conclusion
References
19. GPT-3- and DALL-E-Powered Applications: A Complete Survey
Kuldeep Vayadande, Chaitanya B. Pednekar, Priya Anup Khune, Vinay Sudhir Prabhavalkar and Varsha R. Dange
19.1 Introduction
19.2 Understanding GPT-3
19.3 Understanding DALL-E
19.4 Applications Powered By GPT-3 and DALL-E
19.4.1 Copy.ai
19.4.2 OpenAI GPT-3 Playground
19.4.3 GPT-3 Sandbox
19.4.4 AI Dungeon
19.5 Challenges and Open Issues
19.6 Future Directions
19.7 Conclusion
References
20. New Variation of Exam Scheduling Problem Using Graph Coloring
Angshu Kumar Sinha, Soumyadip Laha, Debarghya Adhikari, Anjan Koner and Neha Deora
20.1 Introduction
20.1.1 Review of Previous Work
20.1.2 Application
20.1.3 Main Result
20.1.4 Organization of the Paper
20.2 Notations and Preliminaries
20.3 Description of Algorithm
20.3.1 The Algorithm
20.3.2 Illustration of the Algorithm
20.3.2.1 Problem of Scheduling the Examination
20.3.3 Algorithm in Python
20.3.4 Algorithm in C++
20.3.5 Algorithm in C
20.4 Time Complexity of the Algorithm
20.5 Concluding Remarks
20.6 Acknowledgments
References
Part 4: Emerging Topics in Machine Learning
21. A Comparative Study of Different Techniques of Text-to-SQL Query Converter
Kuldeep Vayadande, Preeti A. Bailke, Vikas Janu Nandeshwar, R. Kumar and Varsha R. Dange
21.1 Introduction
21.2 Literature Survey
21.3 Comparison Table of Previous Techniques
21.4 Comparison Graphs
21.5 Research Gap
21.6 Conclusion
References
22. Trust-Based Leader Election in Flying Ad-Hoc Network
Joydeep Kundu, Sahabul Alam and Sukanta Oraw
22.1 Introduction
22.2 Related Work
22.3 Discussion of the Proposed Methodology and Results
22.4 Conclusion
References
23. A Survey on Domain of Application of Recommender System
Sudipto Dhar
23.1 Introduction
23.2 Background
23.3 Study of Recommender Systems
23.4 Conclusion
References
24. New Approach on M/M/c/K Queueing Models via Single Valued Linguistic Neutrosophic Numbers and Perceptionization Using a Non-Linear Programming Technique
Antony Crispin Sweety C. and Vennila B.
24.1 Introduction
24.2 Neutrosophic M/M/C/K Queue
24.3 Perceptionization of the NM/NM/c/K Queuing Model Using a Non-Linear Programming Technique
24.3.1 Classic M/M/c/K Model
24.3.2 Neutrosophic M/M/c/K Queue
24.3.3 Performance Measures
24.3.4 Neutrosophic Extension Principle
24.3.4.1 (α,β,γ)-Cut of Set Neutrosophic Numbers
24.3.5 Non-Linear Programming (NLP)
24.3.6 Parametric Non-Linear Programming Technique
24.3.6.1 Upper and Lower Boundaries of the α-Cuts in θ((x,y))
24.3.6.2 Upper and Lower Boundaries of the β-CUTS in θ ((x,y))
24.3.6.3 Upper and Lower Boundaries of the γ-CUTS in θ ((x,y))
Conclusion
References
25. The Rise of AI-Generated News Videos: A Detailed Review
Kuldeep Vayadande, Mustansir Bohri, Mohit Chawala, Ashutosh M. Kulkarni and Asif Mursal
25.1 Introduction
25.2 Web Scraping
25.3 Image Searching
25.4 News Authentication
25.5 Scripting for Video
25.6 Audio Generation
25.7 Mapping Text and Images
25.8 AI-Avatar Generation
25.9 Video Generation
25.10 Thumbnail Creation
25.11 Conclusion
References
Index

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