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

• Autore: Dey Arindam
• Pubblicazione: Newark : , : John Wiley & Sons, Incorporated, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (477 pages)
• Disciplina: 006.3/1
• Soggetto topico: Machine learning
• Altri autori: NayakSukanta KumarRanjan MohantySachi Nandan
• Nota di contenuto: 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.
• Sommario/riassunto: 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.
• Titolo autorizzato: How Machine Learning Is Innovating Today's World
• ISBN: 1-394-21416-2; 1-394-21415-4
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910877044903321
• Lo trovi qui: Univ. Federico II