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kumar m machine learning iot intelligent systems 2022

Type:: E-books
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Size:: 23.9 MB
Uploaded On:: Oct. 18, 2021, 8:34 a.m.
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Info Hash:: 88804D1A2495646C1891013F9787E5894B480907

Textbook in PDF format 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. The book presents an overview of the different algorithms by focusing on the advantages, disadvantages and applications of each algorithm in the field of Machine learning and IOT. The book provides machine learning (ML) techniques to address both intelligence and configurability to various IoT devices. The book also reports the challenges and the future directions in the IoT and machine learning. This book comes with an energy-efficient cross layer model and energy-related routing metric combination to prolong the lifetime of low power IoT networks. This book deals with Machine Learning which is subset of AI that uses computational statistics to find a mathematical model describing Input and Output Data. Machine Learning techniques have been successfully involved in a various applications including assistance in medical diagnosis and analyzing disease based on clinical and laboratory symptoms with appropriate data to give more efficient result for diagnosing disease. Though these new skills are prodigious, they result in numerous challenges including resource constraints of IoT devices, poor interoperability, heterogeneity of IoT system and several privacy and security vulnerabilities. They also expose severe IoT security challenges. Further, traditional security approaches against the most prominent attacks are insufficient. Therefore, enabling the IoT devices to learn and adapt to various threats dynamically and addressing them proactively need immediate attention. In this regard, machine learning (ML) techniques are employed to address both intelligence and reconfigurability to various IoT devices. 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. A Study on Feature Extraction and Classification Techniques for Melanoma Detection Machine Learning Based Microstrip Antenna Design in Wireless Communications LCL-T Filter Based Analysis of Two Stage Single Phase Grid Connected Module with Intelligent FANN Controllers Motion Vector Analysis Using Machine Learning Models to Identify Lung Damages for COVID-19 Patients Enhanced Effective Generative Adversarial Networks Based LRSD and SP Learned Dictionaries with Amplifying CS Deep Learning Based Parkinson’s Disease Prediction System Non-Uniform Data Reduction Technique with Edge Preservation to Improve Diagnostic Visualization of Medical Images A Critical Study on Genetically Engineered Bioweapons and Computer-Based Techniques as Counter Measure An Automated Hybrid Transfer Learning System for Detection and Segmentation of Tumor in MRI Brain Images with UNet and VGG-19 Network Deep Learning-Computer Aided Melanoma Detection Using Transfer Learning Development of an Agent-Based Interactive Tutoring System for Online Teaching in School using Classter Fusion of Datamining and Artificial Intelligence in Prediction of Hazardous Road Accidents

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kumar m machine learning iot intelligent systems 2022

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