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Submit a ProposalSupervised and Unsupervised Data Engineering for Multimedia Data
 | Edited by Suman Kumar Swarnkar, J. P. Patra, Sapna Singh Kshatri, Yogesh Kumar Rathore, and Tien Anh Tran Series: Advances in Data Engineering and Machine Learning Copyright: 2024 | Status: Published ISBN: 9781119786344 | Hardcover | 323 pages Price: $225 USD  |
One Line Description
Explore the cutting-edge realms of data engineering in multimedia with Supervised and Unsupervised Data Engineering for Multimedia Data, where expert contributors delve into innovative methodologies, offering invaluable insights to empower both novices and seasoned professionals in mastering the art of manipulating multimedia data with precision and efficiency.
Audience
Data scientists, computer scientists, researchers, educators, and professionals engaged in the fields of artificial intelligence, multimedia data analysis, and data engineering
Data scientists, computer scientists, researchers, educators, and professionals engaged in the fields of artificial intelligence, multimedia data analysis, and data engineering
Description
Supervised and Unsupervised Data Engineering for Multimedia Data presents a groundbreaking exploration into the intricacies of handling multimedia data through the lenses of both supervised and unsupervised data engineering. Authored by a team of accomplished experts in the field, this comprehensive volume serves as a go to resource for data scientists, computer scientists, and researchers seeking a profound understanding of cutting edge methodologies.
The book seamlessly integrates theoretical foundations with practical applications, offering a cohesive framework for navigating the complexities of multimedia data. Readers will delve into a spectrum of topics, including artificial intelligence, machine learning, and data analysis, all tailored to the challenges and opportunities presented by multimedia datasets. From foundational principles to advanced techniques, each chapter provides valuable insights, making this book an essential guide for academia and industry professionals alike. Whether you are a seasoned practitioner or a newcomer to the field, Supervised and Unsupervised Data Engineering for Multimedia Data illuminates the path toward mastery in manipulating and extracting meaningful insights from multimedia data in the modern age.
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Supervised and Unsupervised Data Engineering for Multimedia Data presents a groundbreaking exploration into the intricacies of handling multimedia data through the lenses of both supervised and unsupervised data engineering. Authored by a team of accomplished experts in the field, this comprehensive volume serves as a go to resource for data scientists, computer scientists, and researchers seeking a profound understanding of cutting edge methodologies.
The book seamlessly integrates theoretical foundations with practical applications, offering a cohesive framework for navigating the complexities of multimedia data. Readers will delve into a spectrum of topics, including artificial intelligence, machine learning, and data analysis, all tailored to the challenges and opportunities presented by multimedia datasets. From foundational principles to advanced techniques, each chapter provides valuable insights, making this book an essential guide for academia and industry professionals alike. Whether you are a seasoned practitioner or a newcomer to the field, Supervised and Unsupervised Data Engineering for Multimedia Data illuminates the path toward mastery in manipulating and extracting meaningful insights from multimedia data in the modern age.
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Author / Editor Details
Suman Kumar Swanrkar holds a PhD in computer science and engineering and combines over two years of industry experience with over eight years as an assistant professor.
Suman Kumar Swanrkar holds a PhD in computer science and engineering and combines over two years of industry experience with over eight years as an assistant professor.
J. P. Patra, PhD, a seasoned professor, boasts more than 17 years of research and teaching in artificial intelligence, algorithms, cryptography, and network security, with numerous patents and contributions to reputable publishers.
Sapna Singh Kshatri, PhD, serves as an assistant professor specializing in artificial intelligence and machine learning, having authored several books and received multiple awards.
Yogesh Kumar Rathore, an assistant professor with 16 years of experience, has published over 40 research papers in conferences and journals, showcasing a comprehensive understanding of computer science and engineering.
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Table of Contents
List of Figures
List of Tables
Preface
1. SLRRT: Sign Language Recognition in Real Time
Monika Lamba and Geetika Munjal
1.1 Introduction
1.2 Literature Survey
1.3 Model for Sign Recognition Language
1.4 Experimentation
1.5 Methodology
1.6 Experimentation Results
1.7 Conclusion
Future Scope
References
2. Unsupervised/ Supervised Feature Extraction and Feature Selection for Multimedia Data (Feature extraction with feature selection for Image Forgery Detection)
Arun Anoop M., Karthikeyan P. and S. Poonkuntran
2.1 Introduction
2.2 Problem Definition
2.3 Proposed Methodology
2.4 Experimentation and Results
2.5 Feature Selection & Pre-Trained CNN Models Description
2.6 Bat ELM Optimization Results
Conclusion
Declarations
Consent for Publicaton
Conflict of Interest
Acknowledgement
References
3. Multimedia Data in Healthcare System
Sarita, Pallavi Pandey and Yogita Yashveer Raghav
3.1 Introduction
3.2 Recent Trends in Multimedia Marketing
3.3 Challenges in Multimedia
3.4 Opportunities in Multimedia
3.5 Data Visualization in Healthcare
3.6 Machine Learning and its Types
3.7 Health Monitoring and Management System Using Machine Learning Techniques
3.8 Health Monitoring Using K-Prototype Clustering Methods
3.9 AI-Based Robotics in E-Healthcare Applications Based on Multimedia Data
3.10 Future of AI in Health Care
3.11 Emerging Trends in Multimedia Systems
3.12 Discussion
References
4. Automotive Vehicle Data Security Service in IoT Using ACO Algorithm
Sivanantham, K. and Blessington Praveen, P.
Introduction
Literature Survey
System Design
Result and Discussion
Conclusion
References
5. Unsupervised/Supervised Algorithms for Multimedia Data in Smart Agriculture
Reena Thakur, Parul Bhanarkar and Uma Patel Thakur
5.1 Introduction
5.2 Background
5.3 Applications of Machine Learning Algorithms in Agriculture
References
6. Secure Medical Image Transmission Using 2-D Tent Cascade Logistic Map
L. R. Jonisha Miriam, A. Lenin Fred, S. N. Kumar, Ajay Kumar H., I. Christina Jane, Parasuraman Padmanabhan and Balázs Gulyás
6.1 Introduction
6.2 Medical Image Encryption Using 2D Tent and Logistic Chaotic Function
6.3 Simulation Results and Discussion
6.4 Conclusion
Acknowledgement
References
7. Personalized Multi-User-Based Movie and Video Recommender System: A Deep Learning Perspective
Jayaramu H. K., Suman Kumar Maji and Hussein Yahia
7.1 Introduction
7.2 Literature Survey on Video and Movie Recommender Systems
7.3 Feature-Based Solutions for Movie and Video Recommender Systems
7.4 Fusing: EF – (Early Fusion) and LF – (Late Fusion)
7.5 Experimental Setup
7.6 Conclusions
References
8. Sensory Perception of Haptic Rendering in Surgical Simulation
Rachit Sachdeva, Eva Kaushik, Sarthak Katyal, Krishan Kant Choudhary and Rohit Kaushik
Introduction
Methodology
Background Related Work
Application
Case Study
Future Scope
Result
Conclusion
Acknowledgement
References
9. Multimedia Data in Modern Education
Roopashree, Praveen Kumar M., Pavanalaxmi, Prameela N. S. and Mehnaz Fathima
Introduction to Multimedia
Traditional Learning Approaches
Applications of Multimedia in Education
Conclusion
References
10. Assessment of Adjusted and Normalized Mutual Information Variants for Band Selection in Hyperspectral Imagery
Bhagyashree Chopade, Divyesh Varade, Vikas Gupta and Divyesh Varade
Introduction
Test Datasets
Methodology
Statistical Accuracy Investigations
Results and Discussion
Conclusion
References
11. A Python-Based Machine Learning Classification Approach for Healthcare Applications
Vishal Sharma
Introduction
Methodology
Discussion
References
12. Supervised and Unsupervised Learning Techniques for Biometric Systems
Pallavi Pandey, Yogita Yashveer Raghav, Sarita Gulia, Sagar Aggarwal and Nitin Kumar
Introduction
Various Biometric Techniques
Major Biometric-Based Problems from a Security Perspective
Supervised Learning Methods for Biometric System
Unsupervised Learning Methods for Biometric System
Conclusion
References
About the Authors
Index
Back to Top
List of Figures
List of Tables
Preface
1. SLRRT: Sign Language Recognition in Real Time
Monika Lamba and Geetika Munjal
1.1 Introduction
1.2 Literature Survey
1.3 Model for Sign Recognition Language
1.4 Experimentation
1.5 Methodology
1.6 Experimentation Results
1.7 Conclusion
Future Scope
References
2. Unsupervised/ Supervised Feature Extraction and Feature Selection for Multimedia Data (Feature extraction with feature selection for Image Forgery Detection)
Arun Anoop M., Karthikeyan P. and S. Poonkuntran
2.1 Introduction
2.2 Problem Definition
2.3 Proposed Methodology
2.4 Experimentation and Results
2.5 Feature Selection & Pre-Trained CNN Models Description
2.6 Bat ELM Optimization Results
Conclusion
Declarations
Consent for Publicaton
Conflict of Interest
Acknowledgement
References
3. Multimedia Data in Healthcare System
Sarita, Pallavi Pandey and Yogita Yashveer Raghav
3.1 Introduction
3.2 Recent Trends in Multimedia Marketing
3.3 Challenges in Multimedia
3.4 Opportunities in Multimedia
3.5 Data Visualization in Healthcare
3.6 Machine Learning and its Types
3.7 Health Monitoring and Management System Using Machine Learning Techniques
3.8 Health Monitoring Using K-Prototype Clustering Methods
3.9 AI-Based Robotics in E-Healthcare Applications Based on Multimedia Data
3.10 Future of AI in Health Care
3.11 Emerging Trends in Multimedia Systems
3.12 Discussion
References
4. Automotive Vehicle Data Security Service in IoT Using ACO Algorithm
Sivanantham, K. and Blessington Praveen, P.
Introduction
Literature Survey
System Design
Result and Discussion
Conclusion
References
5. Unsupervised/Supervised Algorithms for Multimedia Data in Smart Agriculture
Reena Thakur, Parul Bhanarkar and Uma Patel Thakur
5.1 Introduction
5.2 Background
5.3 Applications of Machine Learning Algorithms in Agriculture
References
6. Secure Medical Image Transmission Using 2-D Tent Cascade Logistic Map
L. R. Jonisha Miriam, A. Lenin Fred, S. N. Kumar, Ajay Kumar H., I. Christina Jane, Parasuraman Padmanabhan and Balázs Gulyás
6.1 Introduction
6.2 Medical Image Encryption Using 2D Tent and Logistic Chaotic Function
6.3 Simulation Results and Discussion
6.4 Conclusion
Acknowledgement
References
7. Personalized Multi-User-Based Movie and Video Recommender System: A Deep Learning Perspective
Jayaramu H. K., Suman Kumar Maji and Hussein Yahia
7.1 Introduction
7.2 Literature Survey on Video and Movie Recommender Systems
7.3 Feature-Based Solutions for Movie and Video Recommender Systems
7.4 Fusing: EF – (Early Fusion) and LF – (Late Fusion)
7.5 Experimental Setup
7.6 Conclusions
References
8. Sensory Perception of Haptic Rendering in Surgical Simulation
Rachit Sachdeva, Eva Kaushik, Sarthak Katyal, Krishan Kant Choudhary and Rohit Kaushik
Introduction
Methodology
Background Related Work
Application
Case Study
Future Scope
Result
Conclusion
Acknowledgement
References
9. Multimedia Data in Modern Education
Roopashree, Praveen Kumar M., Pavanalaxmi, Prameela N. S. and Mehnaz Fathima
Introduction to Multimedia
Traditional Learning Approaches
Applications of Multimedia in Education
Conclusion
References
10. Assessment of Adjusted and Normalized Mutual Information Variants for Band Selection in Hyperspectral Imagery
Bhagyashree Chopade, Divyesh Varade, Vikas Gupta and Divyesh Varade
Introduction
Test Datasets
Methodology
Statistical Accuracy Investigations
Results and Discussion
Conclusion
References
11. A Python-Based Machine Learning Classification Approach for Healthcare Applications
Vishal Sharma
Introduction
Methodology
Discussion
References
12. Supervised and Unsupervised Learning Techniques for Biometric Systems
Pallavi Pandey, Yogita Yashveer Raghav, Sarita Gulia, Sagar Aggarwal and Nitin Kumar
Introduction
Various Biometric Techniques
Major Biometric-Based Problems from a Security Perspective
Supervised Learning Methods for Biometric System
Unsupervised Learning Methods for Biometric System
Conclusion
References
About the Authors
Index
Back to Top