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

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

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 solv...

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Detalles Bibliográficos
Autor principal: Dey, Arindam (-)
Otros Autores: Nayak, Sukanta, Kumar, Ranjan, Mohanty, Sachi Nandan
Formato: Libro electrónico
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated 2024.
Edición:1st ed
Materias:
Machine learning.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009840475706719
Tabla de Contenidos:
  • 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 &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
  • 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.

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