Chapter 1 A Study on Feature Extraction and Classification Techniques for Melanoma Detection
Chapter 2 Machine Learning based Microstrip Antenna Design in Wireless Communications
Chapter 3 LCL-T Filter Based Analysis of Two Stage Single Phase Grid Connected Module with Intelligent FANN Controllers
Chapter 4 Motion Vector Analysis Using Machine Learning Models to Identify Lung Damages for COVID-19 Patients
Chapter 5 Enhanced Effective Generative Adversarial Networks Based LRSD and SP Learned Dictionaries with Amplifying CS
Chapter 6 Deep Learning Based Parkinson’s Disease Prediction System
Chapter 7 Non-Uniform Data Reduction Technique with Edge Preservation to Improve Diagnostic Visualization of Medical Images
Chapter 8 A Critical Study on Genetically Engineered Bioweapons and Computer-Based Techniques as Counter Measure
Chapter 9 An Automated Hybrid Transfer Learning system for Detection and Segmentation of Tumor in MRI Brain Images with UNet and VGG-19 Network
Chapter 10 Deep Learning-Computer Aided Melanoma Detection Using Transfer Learning
Chapter 11 Development of an Agent-based Interactive Tutoring System for Online Teaching in School using Classter
Chapter 12 Fusion of Datamining and Artificial Intelligence in Prediction of Hazardous Road Accidents

The fusion of AI and IoT enables the systems to be predictive, prescriptive, and autonomous, and this convergence has evolved the nature of emerging applications from being assisted to augmented, and ultimately to autonomous intelligence. This book discusses algorithmic applications in the field of machine learning and IoT with pertinent applications. It further discusses challenges and future directions in the machine learning area and develops understanding of its role in technology, in terms of IoT security issues. Pertinent applications described include speech recognition, medical diagnosis, optimizations, predictions, and security aspects. Features: Focuses on algorithmic and practical parts of the artificial intelligence approaches in IoT applications. Discusses supervised and unsupervised machine learning for IoT data and devices. Presents an overview of the different algorithms related to Machine learning and IoT. Covers practical case studies on industrial and smart home automation. Includes implementation of AI from case studies in personal and industrial IoT. This book aims at Researchers and Graduate students in Computer Engineering, Networking Communications, Information Science Engineering, and Electrical Engineering