SUPERVISED AND UNSUPERVISED DATA ENGINEERING FOR MULTIMEDIA DATA / edited by Suman Kumar Swarnkar, JP Patra, Sapna Singh Kshatri, Yogesh Kumar Rathore and Tien Anh Tran.

Format:

Book

Author/Creator:

Swarnkar, Suman Kumar.

Contributor:

Swarnkar, Suman, editor.

Patra, J. P., editor.

Kshatri, Sapna Singh, 1986- editor.

Rathore, Yogesh Kumar, editor.

Tran, Tien Anh, editor.

Series:

Advances in data engineering and machine learning.

Advances in data engineering and machine learning

Language:

English

Subjects (All):

Software engineering.

Multimedia systems.

Physical Description:

1 online resource

Edition:

1st ed.

Place of Publication:

Hoboken, NJ : John Wiley & Sons, Inc., 2024.

Newark : John Wiley & Sons, Incorporated, 2024.

Summary:

SUPERVISED and UNSUPERVISED DATA ENGINEERING for MULTIMEDIA DATA 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. 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're 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.

Contents:

Cover

Title Page

Copyright Page

Dedication Page

Book Description

Contents

List of Figures

List of Tables

Preface

Chapter 1 SLRRT: Sign Language Recognition in Real Time

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

Chapter 2 Unsupervised/Supervised Feature Extraction and Feature Selection for Multimedia Data: (Feature extraction with feature selection for Image Forgery Detection)

2.1 Introduction

2.2 Problem Definition

2.3 Proposed Methodology

2.4 Experimentation and Results

2.5 Feature Selection &amp

Pre-Trained CNN Models Description

2.6 Bat ELM Optimization Results

Conclusion

Declarations

Consent for Publicaton

Conflict of Interest

Acknowledgement

Chapter 3 Multimedia Data in Healthcare System

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

Chapter 4 Automotive Vehicle Data Security Service in IoT Using ACO Algorithm

Introduction

Literature Survey

System Design

Result and Discussion

Chapter 5 Unsupervised/Supervised Algorithms for Multimedia Data in Smart Agriculture

5.1 Introduction

5.2 Background.

5.3 Applications of Machine Learning Algorithms in Agriculture

Chapter 6 Secure Medical Image Transmission Using 2-D Tent Cascade Logistic Map

6.1 Introduction

6.2 Medical Image Encryption Using 2D Tent and Logistic Chaotic Function

6.3 Simulation Results and Discussion

6.4 Conclusion

Chapter 7 Personalized Multi-User-Based Movie and Video Recommender System: A Deep Learning Perspective

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

Chapter 8 Sensory Perception of Haptic Rendering in Surgical Simulation

Methodology

Background Related Work

Application

Case Study

Result

Chapter 9 Multimedia Data in Modern Education

Introduction to Multimedia

Traditional Learning Approaches

Applications of Multimedia in Education

Chapter 10 Assessment of Adjusted and Normalized Mutual Information Variants for Band Selection in Hyperspectral Imagery

Test Datasets

Statistical Accuracy Investigations

Results and Discussion

Chapter 11 A Python-Based Machine Learning Classification Approach for Healthcare Applications

Discussion

Chapter 12 Supervised and Unsupervised Learning Techniques for Biometric Systems

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 Editors

Index

EULA.

Notes:

OCLC-licensed vendor bibliographic record.

Description based on publisher supplied metadata and other sources.

ISBN:

9781119786436

1119786436

9781119786443

1119786444

9781119786429

1119786428

OCLC:

1428898882