Artificial Intelligence Theory, Models, and Applications

go Cover Half Title Title Page Copyright Page Contents Preface Acknowledgments Editors Contributors 1. Artificial Intelligence: A Complete Insight 1.1 Introduction 1.2 Artificial Intelligence: What and Why? 1.3 History of AI 1.3.1 Turing Test 1.4 Foundations of AI 1.4.1 Logic and Reasoning 1.4.2 Pattern Recognition 1.4.3 Cognitive Science 1.4.4 Heuristics 1.4.5 Philosophy 1.4.6 Mathematics 1.4.7 Psychology 1.4.8 Linguistics 1.5 The AI Environment 1.6 Application Domains of AI 1.6.1 Gaming 1.6.2 Education 1.6.3 Healthcare 1.6.4 Agriculture 1.6.5 Entertainment 1.6.6 Manufacturing 1.6.7 Banking and Insurance 1.6.8 Automobiles 1.7 AI Tools 1.8 Challenges in AI 1.8.1 Loss of Self-Thinking 1.8.2 Bias in the Design of Artificial Intelligence 1.8.3 Limitation on Data 1.8.4 Threat to Manpower 1.8.5 Lifestyle Changes 1.8.6 Security Threats 1.9 Future Prospects of AI 1.9.1 Applicability 1.9.2 Dynamism in AI Model 1.9.3 Economic Feasibility 1.9.4 User Training 1.10 Summary References 2. Artificial Intelligence and Gender 2.1 What Is Artificial Intelligence? 2.2 What Is Machine Learning? 2.2.1 Supervised Learning 2.2.2 Unsupervised Learning 2.2.3 Reinforcement Learning 2.3 What Is Deep Learning? 2.4 Artificial Intelligence Enterprise Applications 2.5 Artificial Intelligence and Gender 2.5.1 Artificial Intelligence Is Gender-Biased, But Why? 2.5.2 Limited Data for Training 2.5.3 Workplace Bias 2.5.4 Indifferent Approach to the "Female Genius" 2.5.5 Artificial Intelligence-Based Harassment 2.5.6 Art Reflects Life 2.5.7 Virtual Agents 2.5.8 Who Will Artificial Intelligence Replace? 2.5.9 India Is No Different 2.5.10 Steps Artificial Intelligence Teams Should Take to Avoid Gender Bias 2.6 Concluding Thoughts References 3. Artificial Intelligence in Environmental Management 3.1 Current Work in AI for Environment 3.1.1 Organizations and Their Initiatives 3.1.2 General Framework for AI in Environmental Management 3.2 AI for Cleaner Air - Smart Pollution Control 3.2.1 Current Challenges 3.2.2 Potential AI Applications 3.2.3 Sample Case Study 3.3 AI for Water Preservation - Smart Water Management 3.3.1 Current Challenges 3.3.2 Potential AI Applications 3.3.3 Sample Case Study 3.4 AI for Better Agriculture - Smart Farming 3.4.1 Current Challenges 3.4.2 Potential AI Applications 3.4.3 Sample Case Study 3.5 AI for Better e-Waste Management - Smart Monitoring/Control 3.5.1 Current Challenges 3.5.2 Potential AI Applications 3.5.3 Sample Case Study 3.6 AI for Climate Control - Smart Energy Optimization 3.6.1 Current Challenges 3.6.2 Potential AI Applications 3.6.3 Sample Case Study 3.7 Risks and Rewards of AI in Environmental Management References 4. Artificial Intelligence in Medical Imaging Objectives 4.1 Introduction to Medical Imaging 4.2 Applying Artificial Intelligence (AI) in Medical Imaging 4.2.1 Computer-Aided Detection (CAD) 4.2.2 Principles of Computer-Aided Image Analysis in Medical Imaging 4.2.3 Machine Learning (ML) and Deep Learning (DL) 4.2.4 Content-Based Image Retrieval (CBIR) 4.2.5 Radiomics and Radiogenomics 4.3 AI in Various Medical Imaging Modalities 4.4 AI in Computed Tomography 4.4.1 CT Reconstruction Algorithms: From Concept to Clinical Necessity 4.4.2 Importance of AI-Based Detection in CT 4.4.3 Present and Future Developments 4.5 AI in Mammography 4.5.1 Limitations of Human Observers 4.5.2 Computer Vision (CV) and AI 4.5.3 Detection of Microcalcifications and Breast Masses 4.5.4 Present Status and Future Directions 4.6 AI in Magnetic Resonance Imaging (MRI) 4.6.1 Developments of AI in MRI 4.6.2 Future Directions 4.7 AI in Medical Ultrasound(US) 4.7.1 DL Architectures 4.7.2 Applications of DL in Medical US Image Analysis 4.7.3 Future Perspectives 4.8 AI in Nuclear Medicine Imaging 4.8.1 Define a Radiomic Diagnostic Algorithm 4.8.2 Applications of AI in Nuclear Medicine 4.8.3 Future Scenarios 4.9 Salient Features of AI in Medical Imaging 4.9.1 Opportunities and Applications 4.9.2 Challenges 4.9.3 Pitfalls 4.9.4 Guidelines for Success 4.9.5 Regulatory and Ethical Issues References 5. Artificial Intelligence (AI): Improving Customer Experience (CX) Objective 5.1 Introduction to Artificial Intelligence (AI) 5.1.1 What Is AI? - The Basics 5.2 Customer Experience (CX) and the Use of AI 5.2.1 Customer Journey 5.2.2 Customer Touchpoints 5.2.3 Customer Journey Mapping and Touchpoints 5.3 Customer Expectations from CX 5.4 Customer Journeys and the Use of Artificial Intelligence 5.4.1 Need Identification/Awareness Creation 5.4.1.1 Use of Social Media in Awareness Creation 5.4.2 Consideration/Searching Suitable Options 5.4.3 Purchase Decision 5.4.4 Retention/In-Life Support 5.4.5 Loyalty 5.5 Conclusion 5.6 Future of AI 5.6.1 Future of AI in Customer Experience 5.6.2 Future of AI across Verticals References 6. Artificial Intelligence in Radiotherapy Objectives 6.1 Introduction 6.2 Importance of Artificial Intelligence (AI) in Radiotherapy 6.3 AI Tools for Automated Treatment Planning (ATP) 6.3.1 Present ATP Techniques 6.3.2 AI Applications, Advancements, and Research Guidance in ATP 6.3.3 AI Challenges in ATP 6.4 AI in Intensity Modulated Radiotherapy (IMRT) 6.4.1 AI for IMRT Dose Estimation 6.4.2 AI for IMRT Planning Support 6.4.3 AI for Modeling IMRT Outcome and Plan Deliverability 6.4.4 AI for Auto-Segmentation of OAR in IMRT 6.4.5 Future Directions 6.5 AI in Brachytherapy 6.6 AI in Radiotherapy Quality Assurance 6.6.1 Developments in ML towards Quality Assurance 6.6.2 Applications of ML Models for Quality Assurance in Radiotherapy 6.6.3 Quality Assurance of ML Algorithms in Radiotherapy 6.6.4 Challenges Associated with AI for Quality Assurance in RT 6.6.5 Future Directions to Improve AI-Based Quality Assurance in RT 6.7 AI in Radiation Biology 6.8 AI in Radiation Protection/Safety 6.8.1 Motivations to Develop AI-Based Systems for Radiation Protection 6.8.2 Problems Associated with AI-Based Systems for Radiation Protection 6.8.3 Benefits and Future Directions 6.9 Radiomics in Radiotherapy 6.9.1 Radiomics Objectives and Workflow 6.9.2 Influence of Radiomics in RT 6.9.3 Challenges for Medical Physicists 6.9.4 Future Directions 6.10 AI Considerations for RT Curriculum Development References 7. Artificial Intelligence in Systems Biology: Opportunities in Agriculture, Biomedicine, and Healthcare Objectives 7.1 Introduction to Artificial Intelligence (AI) 7.2 AI Methodologies and Algorithm for Systems Biology 7.2.1 Machine Learning (ML) 7.2.2 ML Algorithm Applied in Systems Biology 7.2.3 Computational Neural Networks 7.2.4 Pros and Cons of Artificial Neural Network (ANN) 7.3 Applications of Artificial Intelligence (AI) in Agriculture, Biomedicine, and Healthcare 7.3.1 AI in Agriculture 7.3.2 AI in Biomedicine 7.3.3 AI in DNA Expression Profiling 7.3.4 AI for Identifying Exonic Regions 7.3.5 AI in Identifying Variants/Mutations from Genetic Data 7.3.6 AI Workflow Method for Genomic Analysis 7.3.7 AI in Structure Prediction 7.3.8 AI in Phylogeny 7.3.9 AI in Healthcare 7.4 Case Studies on AI in Systems Biology 7.4.1 AI Technologies in Systems Biology towards Pharmacogenomics 7.4.2 AI in Systems Biology for Cancer Cure 7.4.3 Applications of AI for COVID-19 Pandemic 7.5 Future Challenges in Artificial Intelligence Acknowledgments References 8. Artificial Intelligence Applications in Genetic Disease/Syndrome Diagnosis Objectives 8.1 Introduction 8.2 Milestones 8.3 Algorithms 8.4 Artificial Intelligence in the Diagnosis of Genetic Diseases 8.4.1 Cancer 8.4.2 Diabetes 8.5 Artificial Intelligence in the Diagnosis of Syndromes 8.6 Artificial Intelligence in the Diagnosis of Psychiatric Disorders 8.6.1 Depression 8.6.2 Alzheimer's Disease 8.6.3 Autism Spectrum Disorder 8.6.4 Anxiety 8.6.5 Parkinson's Disease 8.7 Artificial Intelligence in Other Disease Diagnosis 8.7.1 Infectious Disease 8.7.2 Lung and Brain Disease 8.8 Food and Drug Administration Approval and Guidelines 8.9 Conclusion References 9. Artificial Intelligence in Disease Diagnosis via Smartphone Applications 9.1 Introduction 9.2 Smartphone Applications and ML Algorithms in Disease Diagnosis 9.2.1 Diagnosis of Diseases by Using Smartphone Applications 9.2.1.1 Smartphone App for Noninvasive Detection of Anemia 9.2.1.2 Mobile Touch Screen Typing in the Detection of Motor Impairment of Parkinson's Disease 9.2.1.3 Screening Services for Cancer on Android Smartphones 9.2.1.4 Detection of Cardiovascular Disease Using Smartphone Mechanocardiography 9.2.1.5 Mobile-Enabled Expert System for Diagnosis of Tuberculosis in Real Time 9.2.1.6 Smartphone-Based Pathogen Detection of Urinary Sepsis 9.2.1.7 Detecting Acute Otitis Media Using a Mobile App 9.2.1.8 Diagnosis of Covert Hepatic Encephalopathy via Encephal App, a Smartphone-Based Stroop Test 9.2.1.9 Mobile-Based Nutrition and Child Health Monitoring 9.2.1.9.1 Other Mobile Apps in Healthcare 9.2.2 Machine Learning Technology in the Diagnosis of Various Diseases 9.2.2.1 Heart Disease 9.2.2.2 Diabetes Disease 9.2.2.3 Liver Disease 9.2.2.4 Dengue Disease 9.2.2.5 Hepatitis Disease 9.2.2.6 Genetic Disorders (Acromegaly) from Facial Photographs 9.3 Conclusion Acknowledgment References 10. Artificial Intelligence in Agriculture 10.1 Introduction to Artificial Intelligence 10.2 Agriculture - Never Die Business Until Humans Exist 10.3 Need for AI in Agriculture 10.4 Emerging Agricultural Technologies 10.4.1 Soil and Water Sensors 10.4.2 Weather Tracking 10.4.3 Satellite Imaging Agriculture 10.4.4 Automation Systems 10.4.5 RFID Technology 10.5 Potential Agricultural Domains for Modernization 10.5.1 AI in Crop Monitoring 10.5.2 AI in Seed Germination 10.5.3 AI in Soil Management 10.5.4 AI in Crop Productivity 10.5.5 AI in Price Forecasting of Agricultural Products 10.5.6 AI in Pest and Weed Management 10.5.7 AI in Agricultural Land Utilization 10.5.8 AI in Fertilizer Optimization 10.5.9 AI in Irrigation Management 10.6 Can AI Transform Agricultural Scenario? 10.7 Summary References 11. Artificial Intelligence-Based Ubiquitous Smart Learning Educational Environments 11.1 Introduction 11.2 Need and Considering the Foundations 11.3 Framework for Ubiquitous Smart Learning 11.4 Role and Advantage of Using Artificial Intelligence 11.4.1 Advantage of Artificial Intelligence 11.5 Conclusion References 12. Artificial Intelligence in Assessment and Evaluation of Program Outcomes/Program Specific Outcomes 12.1 Introduction 12.2 Assessment and Evaluation 12.3 Data Set Attributes and Implementation Platform 12.4 Evaluation Using Machine Learning Models 12.4.1 Statistical Summary 12.4.2 Data Visualization 12.4.3 Model Creation and Estimation 12.4.4 Build Models 12.4.5 Select Best Model 12.4.6 Make Predictions 12.4.7 Evaluate Predictions 12.5 Observations 12.6 Conclusion References 13. Artificial Intelligence-Based Assistive Technology 13.1 Introduction 13.2 Overview of AI on AT 13.2.1 What Is Artificial Intelligence? 13.2.2 How AI Is Changing the World 13.2.3 How Can Artificial Intelligence Be Used in AT? 13.3 A Transformative Impact of AI on AT 13.4 Extensive AT Applications Based on AI 13.5 AI Experience and AT for Disabled People in India 13.6 AI-Powered Technology for an Inclusive World 13.6.1 Applications Based on Vision Areas 13.6.2 Applications Based on Voice Areas 13.7 Research Perceptive Over AI Influence on AT 13.7.1 Vision-Based Applications 13.7.2 Voice-Based Applications 13.8 AI Implementation on Assistive Technologies - Pragmatic Approach 13.9 Conclusion References 14. Machine Learning Objectives 14.1 Introduction 14.1.1 Why Should Machines Learn? 14.1.2 The Importance of Machine Learning 14.2 What Is Machine Learning? 14.2.1 The Simple Side of ML 14.2.2 The Technical Side of ML 14.2.2.1 Gathering Data 14.2.2.2 Data Preparation 14.2.2.3 Choose a Model 14.2.2.4 Training 14.2.2.5 Evaluation 14.2.2.6 Parameter Tuning 14.2.2.7 Using the Model 14.2.3 Summing Up 14.3 Types of Machine Learning 14.3.1 Supervised Learning 14.3.2 Unsupervised Learning 14.3.3 Reinforcement Learning 14.4 Machine Learning Algorithms 14.4.1 Linear Regression 14.4.2 Logistic Regression 14.4.3 Decision Tree 14.4.4 Support Vector Machine (SVM) 14.4.5 Naïve Bayes 14.4.6 K-Nearest Neighbor (K-NN) 14.4.7 K-Means 14.4.8 Random Forest 14.4.9 Dimensionality Reduction Algorithms 14.4.10 Gradient Boosting Algorithms 14.5 Tools Available for Machine Learning 14.5.1 Programming Languages 14.5.1.1 Python 14.5.1.2 R 14.5.1.3 Scala 14.5.1.4 Julia 14.5.1.5 Java 14.5.2 Frameworks 14.5.2.1 Tensor Flow 14.5.2.2 PyTorch 14.5.2.3 Spark ML Library 14.5.2.4 CAFFE (Convolutional Architecture for Fast Feature Embedding) 14.5.2.5 scikit-learn 14.5.2.6 Amazon Machine Learning 14.5.3 Databases 14.5.3.1 MySQL 14.5.3.2 PostgreSQL 14.5.3.3 MongoDB 14.5.3.4 MLDB 14.5.3.5 Spark with Hadoop HDFS 14.5.3.6 Apache Cassandra 14.5.3.7 Microsoft SQL Server 14.5.4 Deployment Tools 14.5.4.1 Github 14.5.4.2 PyCharm Community Edition 14.5.4.3 Pytest 14.5.4.4 CircleCi 14.5.4.5 Heroku 14.5.4.6 MLFlow 14.6 Application Areas of Machine Learning 14.6.1 Everyday Life 14.6.1.1 Virtual Personal Assistants 14.6.1.2 Personalized Shopping 14.6.1.3 Weather Forecasts 14.6.1.4 Google Services 14.6.1.5 Spam Detection 14.6.1.6 Credit Card Fraud Detection 14.6.2 Healthcare 14.6.2.1 Personalized Medicine/Treatment 14.6.2.2 Medical Imaging and Diagnostics 14.6.2.3 Identification of Diseases and Diagnosis 14.6.2.4 Drug Discovery and Development 14.6.2.5 Smart Health Records 14.6.2.6 Treatment and Prediction of Disease 14.6.3 Agriculture 14.6.3.1 Crop Management 14.6.3.2 Livestock Management 14.6.3.3 Plant Breeding 14.6.3.4 Soil Management 14.6.3.5 Agriculture Robot 14.6.4 Transportation 14.6.4.1 Driverless Cars 14.6.4.2 Predictions While Commuting 14.6.4.3 Video Surveillance 14.6.4.4 Transportation Services 14.6.4.5 Interactive Journey 14.6.5 Education 14.6.5.1 Customized Learning Experience 14.6.5.2 Increasing Efficiency of Education 14.6.5.3 Predictive Analytics 14.6.5.4 Personalized Learning 14.6.6 Urbanization 14.6.6.1 Automatic Classification of Buildings and Structures 14.6.6.2 Urban Population Modeling 14.6.6.3 Other Applications 14.6.7 Social Media Services 14.6.7.1 Personalized Feeds 14.6.7.2 Email Spam and Malware Filtering 14.6.7.3 Online Customer Support 14.6.7.4 Search Engine Result Refining 14.6.8 Financial World 14.6.8.1 Online Fraud Detection 14.6.8.2 Robo-Advisory 14.6.8.3 Customer Service 14.6.8.4 Risk Management 14.6.8.5 Marketing Strategy 14.6.8.6 Network Security 14.7 Conclusion References 15. Machine Learning in Human Resource Management Objectives 15.1 Introduction 15.2 How Machine Learning Helps Human Resource Management 15.3 Machine Learning Concepts 15.3.1 Supervised Learning 15.3.1.1 Naïve Bayes Classifier (Generative Learning Model) 15.3.1.2 K-Nearest Neighbor 15.3.1.3 Support Vector Machine (SVM) 15.3.1.4 Logistic Regression (Predictive Learning Model) 15.3.1.5 Decision Trees 15.3.1.6 Random Forest 15.3.2 Unsupervised Learning 15.3.2.1 Exclusive (Partitioning) 15.3.2.2 Overlapping 15.3.3 Reinforcement Learning 15.4 Applications of Machine Learning in HRM 15.4.1 Recruitment 15.4.2 Applicant Tracking and Assessment 15.4.3 Attracting Talent 15.4.4 Attrition Detection 15.4.5 Machine Learning Technologies to Optimize Staffing 15.4.6 Talent Management 15.4.7 Individual Skill Management 15.4.8 Chatbox for FAQs of Employees 15.4.9 Clustering for Strategic Management of HRM 15.5 Case Studies 15.5.1 Case Study 1: Competency-Based Recruitment Process 15.5.1.1 True Positive 15.5.1.2 True Negative 15.5.1.3 False Positive 15.5.1.4 False Negative 15.5.1.5 Recall 15.5.1.6 Precision 15.5.1.7 F1-Measure 15.5.1.8 Accuracy 15.5.2 Case Study 2: Prediction of Employee Attrition 15.5.3 Case Study 3: Performance Assessment for Educational Institution 15.6 Summary References 16. Machine Learning Models in Product Development and Its Statistical Evaluation 16.1 Introduction 16.2 Methodology for Model Buildings 16.2.1 Prepare the Data 16.2.2 Perform Data Analysis 16.3 Product Development 16.4 Smart Manufacturing 16.5 Quality Control Aspects 16.6 Machine Learning Methods 16.6.1 Regression Analysis 16.6.2 Classification 16.6.3 Clustering 16.6.3.1 Supervised Clustering 16.6.3.2 Unsupervised Clustering 16.6.3.3 Semi-Supervised Clustering 16.7 Statistical Measures 16.8 Algorithm for Data Analytics 16.9 Real-Time Applications 16.10 Results 16.11 Conclusion Acknowledgments References 17. Influence of Artificial Intelligence in Clinical and Genomic Diagnostics 17.1 Artificial Intelligence 17.1.1 Branches of Computer Science 17.1.2 Applications of Artificial Intelligence 17.2 Machine Learning 17.2.1 Approaches of Machine Learning 17.3 Influence of Artificial Intelligence in Machine Learning 17.3.1 Classification of AI 17.3.2 Utilization of AI 17.4 Medical Machine Learning 17.4.1 Computer Techniques Used in Medical ML 17.4.2 Methods for Calculating Biological Sequence Patterns 17.5 Influence of AI and ML in Clinical and Genomic Diagnostics 17.5.1 Classification of AI Used in Medical Data 17.5.1.1 Computer Vision 17.5.1.2 Time Series Analysis 17.5.1.3 Automatic Speech Recognition 17.5.1.4 Natural Language Processing 17.5.2 AI in Clinical Genomics 17.5.2.1 Variant Calling 17.5.2.2 Genome Annotation and Variant Classification 17.5.2.3 Coding Variants Classification 17.5.2.4 Non-Coding Variants Classification 17.5.2.5 Phenotype-Genotype Mapping 17.5.2.6 Genetic Diagnosis 17.5.2.7 Electronic Health Record (EHR) to Genetic Diagnosis 17.5.2.8 Genotype-Phenotype Predictions 17.6 Conclusion References 18. Applications of Machine Learning in Economic Data Analysis and Policy Management 18.1 Goals of Economic Policies 18.1.1 Areas under Consideration for Economic Policy Creation 18.2 Current Methods and Thought Process to Analyze, Measure, and Modify Policies 18.3 Overview of Common Machine Learning Algorithms, Tools, and Frameworks 18.4 Applicability of Advanced Machine Learning Methods in Economics 18.4.1 Broad Areas for ML Adoption 18.4.2 Challenges in Economic Analysis and Advantages of Machine Learning 18.4.3 Major Areas of Concern for Leveraging ML in Economics 18.5 Machine Learning Studies Undertaken by Economists, Institutions, and Regulators 18.6 Conclusion References 19. Industry 4.0: Machine Learning in Video Indexing 19.1 Introduction 19.2 Importance of Video Indexing 19.3 Video Structure Analysis 19.4 How Data Mining and Machine Learning Help Video Indexing 19.5 Analysis of Machine Learning Concepts for Video Indexing 19.5.1 Supervised Learning 19.5.1.1 Naive Bayes Model 19.5.1.2 Decision Trees 19.5.1.3 Linear Regression 19.5.1.4 Random Forest 19.5.1.5 Support Vector Machine (SVM) 19.5.1.6 Ensemble Method 19.5.2 Unsupervised Learning 19.5.2.1 K-Means Clustering 19.5.2.2 Association Rules 19.5.3 Reinforcement Learning 19.6 Applications of Machine Learning Approach for Video Indexing 19.6.1 News Classification 19.6.2 Video Surveillance 19.6.3 Speech Recognition 19.6.4 Services of Social Media 19.6.5 Medical Services 19.6.6 Age/Gender Identification 19.6.7 Information Retrieval 19.6.8 Language Identification 19.6.9 Robot Control 19.7 Case Studies 19.8 Summary References 20. A Risk-Based Ensemble Classifier for Breast Cancer Diagnosis 20.1 Introduction 20.2 Related Works 20.2.1 Problem Statement 20.3 Background 20.3.1 K-Nearest Neighbor 20.3.2 Naïve Bayes Classifier 20.3.3 Isotonic Separation 20.3.4 Random Forest 20.3.5 Support Vector Machine 20.3.6 Linear Discriminant Analysis 20.3.7 Proposed Risk-Based Ensemble Classifier 20.4 Experimental Analysis 20.4.1 Data Sets 20.4.2 Experimental Setup 20.4.3 Statistical Analysis 20.4.4 Findings 20.5 Conclusion References 21. Linear Algebra for Machine Learning 21.1 Introduction 21.2 Linear Algebra - Basics and Motivations 21.2.1 Vectors 21.2.2 Vector Space 21.2.3 Vector Subspace 21.2.4 Span 21.2.5 Basis 21.2.6 Linear Mapping 21.2.7 Matrix 21.2.8 Matrix Representation of Linear Mappings 21.2.9 Transformation Matrix 21.2.10 Determinant 21.2.11 Eigenvalue 21.2.12 Rank 21.2.13 Diagonal Matrix 21.2.14 Diagonalizable 21.3 Matrix Decompositions 21.3.1 The LU Decomposition 21.3.2 The QR Decomposition 21.3.3 The Cholesky Decomposition 21.3.4 The Eigenvalue Decomposition 21.3.5 The Singular Value Decomposition (SVD) 21.3.5.1 Geometric Interpretation of SVD 21.3.5.2 Application: Data Compression Using SVD 21.3.5.3 Dimensionality Reduction 21.3.5.4 Principal Component Analysis (PCA) 21.4 Linear Regression 21.4.1 The Least Squares Method 21.4.2 Linear Algebra Solution to Least Squares Problem 21.5 Linear Algebra in Machine Learning 21.5.1 Shining in Machine Learning Components 21.5.2 Enhancing Machine Learning Algorithms Acknowledgments References 22. Identification of Lichen Plants and Butterflies Using Image Processing and Neural Networks in Cloud Computing 22.1 Introduction 22.2 Objectives of the Present Study 22.3 Background Information 22.3.1 About Lichens 22.3.2 About Butterflies 22.3.3 Image Processing Techniques in Lichen and Butterfly Identification 22.3.4 Image Processing Techniques in Cloud Computing 22.4 Methodology 22.5 Observations 22.5.1 Pre-Processing of Lichen Images 22.5.2 Segmentation of Pre-Processed Lichen and Butterfly Images 22.5.3 Classification and Prediction of Lichen and Butterfly Species 22.5.4 Identification of Lichen and Butterfly Images 22.5.5 Implementation of Artificial Neural Networks (ANNs) 22.5.6 Grayscale Sub-Images of Lichens and Butterfly Extracted from Input Images 22.5.7 Cloud Computing Techniques 22.6 Conclusion Acknowledgments References 23. Artificial Neural Network for Decision Making Objectives 23.1 Introduction 23.2 Components of ANN 23.3 Structure with Explanation 23.3.1 Network Architectures 23.3.2 Feedforward Neural Networks 23.3.3 Backpropagation Training Algorithm 23.4 Types of Learning 23.4.1 Supervised Learning (SL) 23.4.2 Unsupervised Learning (UL) 23.4.3 Reinforcement Learning (RL) 23.5 Application 23.5.1 Performance Analysis Using Real Data 23.5.2 Results and Discussion 23.5.2.1 Results 23.5.2.2 Discussion 23.6 Conclusion References Index