Neural Networks, Machine Learning, and Image Processing

SECTION I Mathematical Modeling and Neural Network' Mathematical EssenceChapter 1 Mathematical Modeling on Thermoregulation in Sarcopenia1.1. Introduction 1.2. Discretization 1.3. Modeling and Simulation of Basal Metabolic Rate and Skin Layers Thickness 1.4. Mathematical Model and Boundary Conditions 1.5. Solution of the Model 1.6. Numerical Results and discussion 1.7. Conclusion References Chapter 2 Multi-objective University Course Scheduling for Uncertainly Generated Courses2.1 Introduction 2.2 Literature review 2.3 Formulation of problem2.4 Methodology2.5 Numerical Example2.6 Result and Discussion 2.7 Conclusion References Chapter 3 MChCNN : A Deep Learning Approach to Detect Text based Hate Speech3.1. Introduction Background and Driving Forces3.2. Related Work 3.3. Experiment and Results 3.4. Conclusion References Chapter 4 PSO Based PFC Cuk Converter fed BLDC Motor Drive for Automotive Applications4.1. Introduction 4.2. Operation of Cuk converter fed BLDC motor drive system 4.3. Controller Operation 4.4. Result and Discussion 4.5. Conclusion References Chapter 5 Optimize Feature Selection for Condition based monitoring of Cylindrical bearing using Wavelet transform and ANN5.1. Introduction 5.2. Methodology5.3. Data Preparation 5.4. Result and Discussion 5.5. Conclusion References Chapter 6 SafeShop - An integrated system for safe pickup of items during COVID-196.1. Introduction 6.2. Literature Survey 6.3. Methodology 6.4. Result and Discussion 6.5. Conclusion References Chapter 7 Solution of First Order Fuzzy Differential Equation using Numerical Method7.1. Introduction 7.2. Preliminaries7.3. Methodology7.4. Illustration7.5. ConclusionReferences SECTION II Simulations in Machine Learning and Image ProcessingChapter 8 Multi-layer Encryption Algorithm for Data Integrity in Cloud Computing8.1. Introduction 8.2. Related works 8.3. Algorithm description 8.4. Simulation and performance analysis 8.5. Conclusion and Future Work References Chapter 9 Anomaly detection using class of supervised and unsupervised learning algorithms9. 1. Introduction 9.2. Adaptive threshold and regression techniques for anomaly detection 9.3. Unsupervised Learning techniques for anomaly detect ...