Multimedia Data Processing and Computing
1st Edition
Published on 28. November 2023
196 pages
978-1-000-99649-4 (ISBN)
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.
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.
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Illustrations
21 Tables, black and white; 9 Line drawings, color; 2 Line drawings, black and white; 57 Halftones, color; 6 Halftones, black and white; 66 Illustrations, color; 8 Illustrations, black and white
ISBN-13
978-1-000-99649-4 (9781000996494)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Suman Swarnkar | J. P. Patra | Tien Anh Tran
Multimedia Data Processing and Computing
Book
09/2025
1st Edition
CRC Press
€73.20
Shipment within 10-20 days
Suman Swarnkar | J. P. Patra | Tien Anh Tran
Multimedia Data Processing and Computing
Book
11/2023
1st Edition
CRC Press
€126.90
Shipment within 10-20 days
Persons
Dr. Suman Kumar Swarnkar received a Ph.D. (CSE) degree in 2021 from Kalinga University, Nayaraipur. He received his M.Tech. (CSE) degree in 2015 from the Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India.He is currently associated with Chhatrapati Shivaji Institute of Technology, Durg, India as an Assistant Professor in Computer Science & Engineering Department.
Dr J P Patra is a Professor at Shri Shankaracharya Institute of Professional Management and Technology, Raipur, under Chhattisgarh Swami Vivekanand Technical University, Bhilai, India. He has more than 17 years of experience in research, teaching in the areas of Artificial Intelligence, Analysis and Design of Algorithms, Cryptography, and Network Security.
Dr. Tien Anh Tran, is an Assistant Professor at Department of Marine Engineering, Vietnam Maritime University, Haiphong City, Vietnam. He graduated B.Eng. and M.Sc in Marine Engineering from Vietnam Maritime University, Haiphong City, Vietnam. He received the Ph.D. degree at Wuhan University of Technology, Wuhan City, People's Republic of China in 2018.
Dr. Bharat Bhushan is an Assistant Professor of Department of Computer Science and Engineering (CSE) at School of Engineering and Technology, Sharda University, Greater Noida, India. He received his Undergraduate Degree (B-Tech in Computer Science and Engineering) with Distinction in 2012, he received his Postgraduate Degree (M-Tech in Information Security) with Distinction in 2015 and Doctorate Degree (PhD Computer Science and Engineering) in 2021 from Birla Institute of Technology, Mesra, India. He has earned numerous international certifications such as CCNA, MCTS, MCITP, RHCE and CCNP.
Dr. Santosh Biswas completed B.Tech in Computer Science and Engineering from NIT Durgapur in the year 2001. Following that he received the degree of MS (by Research) and PhD from IIT Kharagpur in the year 2004 and 2008, respectively. After that he is working as a faculty member in the Department of Computer Science and Engineering, IIT Guwahati for seven years, where he is currently an associate professor. His research interests are VLSI testing, Embedded Systems, Fault Tolerance and Network Security. Dr. Biswas has revived several awards namely, Young Engineer Award by Center for Education Growth and Research (CEGR) 2014 for contribution to Teaching and Education, IEI young engineer award 2013-14, Microsoft outstanding young faculty award 2008-09, Infineon India Best Master's Thesis sward 2014 etc. Contribution to Research and Higher education.
Dr J P Patra is a Professor at Shri Shankaracharya Institute of Professional Management and Technology, Raipur, under Chhattisgarh Swami Vivekanand Technical University, Bhilai, India. He has more than 17 years of experience in research, teaching in the areas of Artificial Intelligence, Analysis and Design of Algorithms, Cryptography, and Network Security.
Dr. Tien Anh Tran, is an Assistant Professor at Department of Marine Engineering, Vietnam Maritime University, Haiphong City, Vietnam. He graduated B.Eng. and M.Sc in Marine Engineering from Vietnam Maritime University, Haiphong City, Vietnam. He received the Ph.D. degree at Wuhan University of Technology, Wuhan City, People's Republic of China in 2018.
Dr. Bharat Bhushan is an Assistant Professor of Department of Computer Science and Engineering (CSE) at School of Engineering and Technology, Sharda University, Greater Noida, India. He received his Undergraduate Degree (B-Tech in Computer Science and Engineering) with Distinction in 2012, he received his Postgraduate Degree (M-Tech in Information Security) with Distinction in 2015 and Doctorate Degree (PhD Computer Science and Engineering) in 2021 from Birla Institute of Technology, Mesra, India. He has earned numerous international certifications such as CCNA, MCTS, MCITP, RHCE and CCNP.
Dr. Santosh Biswas completed B.Tech in Computer Science and Engineering from NIT Durgapur in the year 2001. Following that he received the degree of MS (by Research) and PhD from IIT Kharagpur in the year 2004 and 2008, respectively. After that he is working as a faculty member in the Department of Computer Science and Engineering, IIT Guwahati for seven years, where he is currently an associate professor. His research interests are VLSI testing, Embedded Systems, Fault Tolerance and Network Security. Dr. Biswas has revived several awards namely, Young Engineer Award by Center for Education Growth and Research (CEGR) 2014 for contribution to Teaching and Education, IEI young engineer award 2013-14, Microsoft outstanding young faculty award 2008-09, Infineon India Best Master's Thesis sward 2014 etc. Contribution to Research and Higher education.
Content
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
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
