Machine Learning in Multimedia
Unlocking the Power of Visual and Auditory Intelligence
1st Edition
Published on 10. December 2024
170 pages
978-1-040-22642-1 (ISBN)
Description
This book explores the interdisciplinary nature of machine learning in multimedia, highlighting its intersections with fields such as computer vision, natural language processing, and audio signal processing.
Machine Learning in Multimedia: Unlocking the Power of Visual and Auditory Intelligence serves as a comprehensive guide to navigating this exciting terrain where artificial intelligence meets the rich tapestry of visual and auditory data. At its core, this book seeks to unravel the mysteries and unveil the potential of machine learning in the realm of multimedia. Whether it's enhancing user experiences in virtual environments, revolutionizing medical diagnostics, or shaping the future of entertainment, the impact of machine learning in multimedia is profound and far-reaching. The journey begins with a thorough exploration of the foundational principles of machine learning, providing readers with a solid understanding of algorithms, models, and techniques tailored specifically for multimedia data. Through clear explanations and illustrative examples, readers will gain insights into how machine learning algorithms can be trained to extract meaningful patterns and insights from diverse forms of multimedia content. Moving beyond theory, this book delves into practical implementations and real-world applications of machine learning in multimedia. Through a series of case studies and examples, readers will witness firsthand how machine learning algorithms are transforming industries and reshaping the way we interact with multimedia content. Whether it's improving image recognition accuracy in autonomous vehicles, enabling personalized recommendations in streaming platforms, or enhancing speech recognition systems for better accessibility, the possibilities are limitless.
This book will be helpful to computer science, data science, and artificial intelligence researchers, students, and professionals looking to unlock the full potential of visual and auditory intelligence through the power of machine learning.
Machine Learning in Multimedia: Unlocking the Power of Visual and Auditory Intelligence serves as a comprehensive guide to navigating this exciting terrain where artificial intelligence meets the rich tapestry of visual and auditory data. At its core, this book seeks to unravel the mysteries and unveil the potential of machine learning in the realm of multimedia. Whether it's enhancing user experiences in virtual environments, revolutionizing medical diagnostics, or shaping the future of entertainment, the impact of machine learning in multimedia is profound and far-reaching. The journey begins with a thorough exploration of the foundational principles of machine learning, providing readers with a solid understanding of algorithms, models, and techniques tailored specifically for multimedia data. Through clear explanations and illustrative examples, readers will gain insights into how machine learning algorithms can be trained to extract meaningful patterns and insights from diverse forms of multimedia content. Moving beyond theory, this book delves into practical implementations and real-world applications of machine learning in multimedia. Through a series of case studies and examples, readers will witness firsthand how machine learning algorithms are transforming industries and reshaping the way we interact with multimedia content. Whether it's improving image recognition accuracy in autonomous vehicles, enabling personalized recommendations in streaming platforms, or enhancing speech recognition systems for better accessibility, the possibilities are limitless.
This book will be helpful to computer science, data science, and artificial intelligence researchers, students, and professionals looking to unlock the full potential of visual and auditory intelligence through the power of machine learning.
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
26 Tables, black and white; 61 Line drawings, black and white; 15 Halftones, black and white; 76 Illustrations, black and white
ISBN-13
978-1-040-22642-1 (9781040226421)
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 Kumar Swarnkar | Annu Sharma | J. Somasekar
Machine Learning in Multimedia
Unlocking the Power of Visual and Auditory Intelligence
Book
approx. 06/2026
1st Edition
CRC Press
€69.00
Not yet published
Suman Kumar Swarnkar | Annu Sharma | J. Somasekar
Machine Learning in Multimedia
Unlocking the Power of Visual and Auditory Intelligence
Book
12/2024
1st Edition
CRC Press
€109.60
Shipment within 10-20 days
Samuel C. Sugarman
Machine Learning in Multimedia
Unlocking the Power of Visual and Auditory Intelligence
Book
12/2024
CRC Press
Unfortunately, price unknown
Available (delivery time upon request)
Person
Suman Kumar Swanrkar received a Ph.D. (CSE) degree in 2021 from Kalinga University, Nayaraipur, Chhattisgarh. He received an M.Tech. (CSE) degree in 2015 from the Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India. He has 12+ years of experience in Educational Institutes as an Assistant Professor. Currently associated with Shri Shankaracharya Institute of Professional Management & Technology, Raipur as an Assistant Professor in Computer Science & Engineering Department. He has Guided 10+ MTech Scholars. He has published and granted an Indian/Australian patent, some are waiting for a grant. He has authored and co-authored more than 50 journal articles including WOS & Scopus papers and presented research papers in 10 international conferences. He has completed many FDP, Training, webinars & workshops and also completed the 2-Weeks comprehensive online Patent Information Course. Proficiency in handling the Teaching, Research as well as administrative activities. He has contributed massive literature in the fields of Intelligent Data Analysis, Nature-Inspired Computing, Machine Learning and Soft Computing.
Annu Sharma is an Associate Professor in the Department of Computer Applications at RajaRajeswari College of Engineering. She holds a Master's degree in Computer Science and Applications from the Department of Computer Science and Applications, University of Jammu, J&K, and a Ph.D. from the Department of Computer Science, Gurukul Kangri University, Haridwar, Uttrakhand. She has more than 20 years of teaching experience at the Master's and Bachelor's levels, including working Executives. Before joining RRCE, she had worked with Bangalore University, IMT Faridabad, Haryana, Central University of Jammu, J&K, and Arya College Ludhiana, Punjab. Her research interest include Biometrics, Image Processing, Bioinformatics, IOT, Cyber Security, and Machine Learning. She has publications in various Scopus-indexed reputed International Journals and leading International Conferences.
J. Somasekar received a Ph.D. degree in CSE from JNTUA, Andhra Pradesh, and M.Tech. degree from the National Institute of Technology Karnataka (NITK), Surathkal. He is currently working as a Professor of CSE Department, JAIN (Deemed-to-be University), Bangalore and Post-doctoral Researcher at University of South Florida, USA. As a resource person, he has delivered 195 Technical talks for FDPs, Workshops, and Webinars in 13 states of the country. He got an All India Rank of 43 in the GATE exam. He has 16 years of experience in teaching and 6 years of experience in research. He has published more than 35 research articles in leading journals indexed in SCI & SCOPUS, conference proceedings, and 3 international textbook chapters. He is guiding five CSE Ph.D. research scholars. His research interest includes Image processing, Data Science, Machine Learning, Big Data Analytics, and ML for Cyber Security.
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, 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. For the three consecutive years (2021 to 2023), Stanford University (USA) listed Dr. Bharat Bhushan in the top 2% scientists list. He earned numerous international certifications such as CCNA, MCTS, MCITP, RHCE and CCNP. He has published more than 150 research papers in various renowned International Conferences and SCI indexed journals. He has contributed with more than 50 book chapters in various books and has edited 30 books from the most famed publishers. He is a series editor of 2 prestigious Scopus Indexed Book Series named CMIA (Computational Methods for Industrial Applications) and FGIS (Future Generation Information System) published by CRC Press, Taylor and Francis, USA. He has served as Keynote Speaker (resource person) in numerous reputed faculty development programs and international conferences held in different countries including India, Iraq, Morocco, China, Belgium and Bangladesh. He has served as a Reviewer/Editorial Board Member for several reputed international journals. In the past, he worked as an assistant professor at HMR Institute of Technology and Management, New Delhi and Network Engineer in HCL Infosystems Ltd., Noida.
Annu Sharma is an Associate Professor in the Department of Computer Applications at RajaRajeswari College of Engineering. She holds a Master's degree in Computer Science and Applications from the Department of Computer Science and Applications, University of Jammu, J&K, and a Ph.D. from the Department of Computer Science, Gurukul Kangri University, Haridwar, Uttrakhand. She has more than 20 years of teaching experience at the Master's and Bachelor's levels, including working Executives. Before joining RRCE, she had worked with Bangalore University, IMT Faridabad, Haryana, Central University of Jammu, J&K, and Arya College Ludhiana, Punjab. Her research interest include Biometrics, Image Processing, Bioinformatics, IOT, Cyber Security, and Machine Learning. She has publications in various Scopus-indexed reputed International Journals and leading International Conferences.
J. Somasekar received a Ph.D. degree in CSE from JNTUA, Andhra Pradesh, and M.Tech. degree from the National Institute of Technology Karnataka (NITK), Surathkal. He is currently working as a Professor of CSE Department, JAIN (Deemed-to-be University), Bangalore and Post-doctoral Researcher at University of South Florida, USA. As a resource person, he has delivered 195 Technical talks for FDPs, Workshops, and Webinars in 13 states of the country. He got an All India Rank of 43 in the GATE exam. He has 16 years of experience in teaching and 6 years of experience in research. He has published more than 35 research articles in leading journals indexed in SCI & SCOPUS, conference proceedings, and 3 international textbook chapters. He is guiding five CSE Ph.D. research scholars. His research interest includes Image processing, Data Science, Machine Learning, Big Data Analytics, and ML for Cyber Security.
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, 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. For the three consecutive years (2021 to 2023), Stanford University (USA) listed Dr. Bharat Bhushan in the top 2% scientists list. He earned numerous international certifications such as CCNA, MCTS, MCITP, RHCE and CCNP. He has published more than 150 research papers in various renowned International Conferences and SCI indexed journals. He has contributed with more than 50 book chapters in various books and has edited 30 books from the most famed publishers. He is a series editor of 2 prestigious Scopus Indexed Book Series named CMIA (Computational Methods for Industrial Applications) and FGIS (Future Generation Information System) published by CRC Press, Taylor and Francis, USA. He has served as Keynote Speaker (resource person) in numerous reputed faculty development programs and international conferences held in different countries including India, Iraq, Morocco, China, Belgium and Bangladesh. He has served as a Reviewer/Editorial Board Member for several reputed international journals. In the past, he worked as an assistant professor at HMR Institute of Technology and Management, New Delhi and Network Engineer in HCL Infosystems Ltd., Noida.
Content
1. Machine Learning Techniques for Accurate Prediction and Detection of Chronic Diseases 2. A Novel Approach to Multimedia Malware Detection using Bi-LSTM and Attention Mechanisms 3. Exploring Machine Learning Applications for Enhancing Security and Privacy in Multimedia IoT: A Comprehensive Review 4. Advanced Machine Learning Strategies for Road Object Detection in Multimedia Environments 5. A Multimedia-Driven Machine Learning Approach for Mastitis Detection in Dairy Cattle 6. Music Genre Classification using Long Short-Term Memory (LSTM) Networks: Analyzing Audio Spectrograms for Enhanced Multimedia Understanding 7. Deep Learning-Based Image Recognition for Autonomous Vehicles: Enhancing Safety and Efficiency 8. Identification of Heart Disease Risk in Early Ages with Bagging Techniques 9. EEG-based Emotion Recognition using SVM Classifier 10. Mortality Prediction of Neonatal due to Jaundice Using Machine Learning 11. ML Techniques Implementation for Heart Prediction in Healthcare 12. Analyzing the Performance of ML Classification Algorithms for Stroke Prediction
