Description
This book focuses on different applications of multimedia with supervised and unsupervised data engineering in the modern world. It includes AI-based soft computing and machine techniques in the field of medical diagnosis, biometrics, networking, manufacturing, data science, automation in electronics industries, and many more relevant fields.
Multimedia Data Processing and Computing provides a complete introduction to machine learning concepts, as well as practical guidance on how to use machine learning tools and techniques in real-world data engineering situations. It is divided into three sections. In this book on multimedia data engineering and machine learning, the reader will learn how to prepare inputs, interpret outputs, appraise discoveries, and employ algorithmic strategies that are at the heart of successful data mining. The chapters focus on the use of various machine learning algorithms, neural net- work algorithms, evolutionary techniques, fuzzy logic techniques, and deep learning techniques through projects, so that the reader can easily understand not only the concept of different algorithms but also the real-world implementation of the algorithms using IoT devices. The authors bring together concepts, ideas, paradigms, tools, methodologies, and strategies that span both supervised and unsupervised engineering, with a particular emphasis on multimedia data engineering. The authors also emphasize the need for developing a foundation of machine learning expertise in order to deal with a variety of real-world case studies in a variety of sectors such as biological communication systems, healthcare, security, finance, and economics, among others. Finally, the book also presents real-world case studies from machine learning ecosystems to demonstrate the necessary machine learning skills to become a successful practitioner.
The primary users for the book include undergraduate and postgraduate students, researchers, academicians, specialists, and practitioners in computer science and engineering.
Table of Contents
Chapter 1. A Review On Despeckling Of Earth Surface Visuals Captured By Synthetic Aperture Radar
Chapter 2. Emotion Recognition Using Multimodal Fusion Models: A Review
Chapter 3. Comparison of CNN-based features with gradient features for Tomato plant leaf disease detection
Chapter 4. Delay Sensitive and Energy Efficient Approach for Improving Longevity of Wireless Sensor Network
Chapter 5. Detecting Lumpy Skin Disease using Deep Learning Techniques
Chapter 6. Forest Fire Detection using Nine-Layer Deep Convolutional Neural Network
Chapter 7. Identification of the Features of Vehicle using CNN
Chapter 8. Plant Leaf Disease Detection Using Supervised Machine Learning Algorithm
Chapter 9. Smart Scholarship Registration Platform using RPA Technology
Chapter 10. Data Processing Methodologies and a Serverless Approach to Solar Data Analytics
Chapter 11. A Discussion with Illustrations on World changing ChatGPT- an Open AI Tool
Chapter 12. A Discussion with Illustrations on World changing ChatGPT- an Open AI Tool
Chapter 13. Advancing Early Cancer Detection with Machine Learning: A Comprehensive Review of Methods and Applications
