How Machine Learning Is Innovating Today's World : A Concise Technical Guide.

Format:

Book

Author/Creator:

Dey, Arindam.

Contributor:

Nayak, Sukanta.

Kumar, Ranjan.

Mohanty, Sachi Nandan.

Language:

English

Subjects (All):

Machine learning.

Physical Description:

1 online resource (477 pages)

Edition:

1st ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2024.

Summary:

Provides a comprehensive understanding of the latest advancements and practical applications of machine learning techniques. 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 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. 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.

Contents:

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

Chapter 3 Text Generation &amp

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

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

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

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

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

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

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

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

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

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

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

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

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.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9781394214167

1394214162

9781394214150

1394214154

OCLC:

1438916451