How Machine Learning is Innovating Today's World

Name: How Machine Learning is Innovating Today's World | A Concise Technical Guide
Brand: Wiley
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A Concise Technical Guide

Arindam Dey Sukanta Nayak Ranjan Kumar Sachi Nandan Mohanty(Herausgeber*in)

Wiley (Verlag)

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Erschienen am 6. Juni 2024

480 Seiten

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Inhalt

Cover
Series Page
Title Page
Copyright Page
Contents
Preface
Part 1: Natural Language Processing (NLP) Applications
Chapter 1 A Comprehensive Analysis of Various Tokenization Techniques and Sequence-to-Sequence Model in Natural Language Processing
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
Chapter 2 A Review on Text Analysis Using NLP
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
Chapter 3 Text Generation & Classification in NLP: A Review
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
Chapter 4 Book Genre Prediction Using NLP: A Review
4.1 Introduction
4.2 Literature Survey
4.3 Comparison Table
4.4 Research Gap Identified
4.5 Future Scope
4.6 Conclusion
References
Chapter 5 Mood Detection Using Tokenization: A Review
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
Chapter 6 Converting Pseudo Code to Code: A Review
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
Chapter 7 Evaluating the Readability of English Language Using Machine Learning Models
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
Chapter 8 Machine Learning in Maximizing Cotton Yield with Special Reference to Fertilizer Selection
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
Chapter 9 Machine Learning Approaches to Catalysis
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
Chapter 10 Classification of Livestock Diseases Using Machine Learning Algorithms
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
Chapter 11 Image Enhancement Techniques to Modify an Image with Machine Learning Application
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
Chapter 12 Software Engineering in Machine Learning Applications: A Comprehensive Study
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
Chapter 13 Machine Learning Applications in Battery Management System
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
Chapter 14 ML Applications in Healthcare
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
Chapter 15 Enhancing Resource Management in Precision Farming through AI-Based Irrigation Optimization
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
Chapter 16 An In-Depth Review on Machine Learning Infusion in an Agricultural Production System
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
Chapter 17 Reinforcement Learning Approach in Supply Chain Management: A Review
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
Chapter 18 Alternate Approach to Solve Differential Equations Using Artificial Neural Network with Optimization Technique
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
Chapter 19 GPT-3- and DALL-E-Powered Applications: A Complete Survey
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
Chapter 20 New Variation of Exam Scheduling Problem Using Graph Coloring
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
Chapter 21 A Comparative Study of Different Techniques of Text-to-SQL Query Converter
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
Chapter 22 Trust-Based Leader Election in Flying Ad-Hoc Network
22.1 Introduction
22.2 Related Work
22.3 Discussion of the Proposed Methodology and Results
22.4 Conclusion
References
Chapter 23 A Survey on Domain of Application of Recommender System
23.1 Introduction
23.2 Background
23.3 Study of Recommender Systems
23.4 Conclusion
References
Chapter 24 New Approach on M/M/c/K Queueing Models via Single Valued Linguistic Neutrosophic Numbers and Perceptionization Using a Non-Linear Programming Technique
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 (a,ß,?)-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 a-Cuts in ? (N(x,y))
24.3.6.2 Upper and Lower Boundaries of the ß -CUTS in ? (N(x,y))
24.3.6.3 Upper and Lower Boundaries of the ? -CUTS in ? (N(x,y))
Conclusion
References
Chapter 25 The Rise of AI-Generated News Videos: A Detailed Review
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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