Machine Learning and Deep Learning Driven Techniques for Multimodal Data Security in the Internet of Multimedia Things
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Home > Computing and Information Technology Books > Computer Science Books > Artificial intelligence > Machine learning > Machine Learning and Deep Learning Driven Techniques for Multimodal Data Security in the Internet of Multimedia Things: (Security)
Machine Learning and Deep Learning Driven Techniques for Multimodal Data Security in the Internet of Multimedia Things: (Security)

Machine Learning and Deep Learning Driven Techniques for Multimodal Data Security in the Internet of Multimedia Things: (Security)


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About the Book

We are living in an AI and data-driven era. A huge volume of data is generated from all sectors including smart cities, intelligent transportation systems, smart healthcare, education, smart agriculture and smart industrial processes. This information comprises multiple modalities such as textual data, images, sound, gestures and genetic sequences, which are generated through various sources.

The internet of multimedia things (IoMT) is one of the key contributors to the collection, analysis and management of voluminous multimodal data. The dominating features of IoMTs are well-timed delivery of data and dependability, but they require high levels of memory and computational power, which requires higher bandwidth and more power. Therefore, they require rigorous quality of service (QoS) and efficient, reliable and secure network frameworks.

The objective of this book is to explore machine learning (ML) and deep learning (DL) techniques for securing data with multiple modalities in a wide range of data-centric smart applications in the IoMT framework. In all data-centric application domains where on-time availability of data and reliability are the major concerns, IoMT is contributing significantly. The authors present the challenges faced to organize and process multimodal data in data-centric applications, several types of data modalities, and innovative IoMT frameworks. A particular focus is placed on the role of ML and DL techniques in securing multi-modal data for real-time monitoring in smart environment applications.

Machine Learning and Deep Learning Driven Techniques for Multimodal Data Security in the Internet of Multimedia Things caters to the needs of AI, big data and IoT advanced students, academic and industry researchers, engineers, security experts, data analysts, and AI and data-centric application developers.



Table of Contents:
  • Chapter 1: Introduction to Multimodal Data
  • Chapter 2: Role of Multimedia Systems and tools in Diversified Domains
  • Chapter 3: Internet of Multimedia Things: Key Role, Framework, and Security Issues
  • Chapter 4: Challenges in the Adoption of ML and DL Techniques for Securing Multimodal Data
  • Chapter 5: Analysis and Management of Multimodal Data: Big Data Approach
  • Chapter 6: Security Issues and Mechanisms in Multimodal Data
  • Chapter 7: ML and DL Techniques to Detect Cyber Attacks on Multimodal Databases
  • Chapter 8: Privacy-Preserving ML to Safeguard Multimodal Data: Internet of Multimedia Things Framework
  • Chapter 9: Deep Learning with Blockchain to Deploy Secure Multimodal Smart City Applications
  • Chapter 10: Secure ML and DL Systems for Intelligent Traffic Monitoring and Management
  • Chapter 11: Security of Multimodal Healthcare Data: Importance, Techniques, and Challenges
  • Chapter 12: ML Facilitated Encrypted Smart Homes Systems in the Internet of Multimedia Environment
  • Chapter 13: Machine Learning with Blockchain for Securing Agriculture-based Applications
  • Chapter 14: Leveraging ML and DL for Safeguarding Multimodal Data in Industrial IoT Environments
  • Chapter 15: Safeguarding Educational Multimodal Data in the IoT with ML and DL


About the Author :
Sita Rani is a postdoctoral research scientist at the University of South Florida, USA, and also serves at Guru Nanak Dev Engineering College, Ludhiana, India. With over 23 years of academic and research experience, she has been recognised among Stanford University's Top 2% Scientists (2024, 2025). She has received the ISTE Best Teacher Award (2020) and the International Young Scientist Award (2021). Her work includes SCIE/Scopus publications, patents, books, and contributions to AI, data science, healthcare, and sustainability. Sachin Kumar is an associate professor and researcher in the Akian College of Science and Engineering at the American University of Armenia (AUA), Yerevan, Armenia since July 2024, and is currently serving as vice chair of IEEE Armenia subsection. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE), a life member of the International Association of Engineers (IAENG), and a member of the Association for Computing Machinery (ACM).


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Product Details
  • ISBN-13: 9781837241361
  • Publisher: Institution of Engineering and Technology
  • Publisher Imprint: Institution of Engineering and Technology
  • Language: English
  • ISBN-10: 1837241368
  • Publisher Date: 01 Sep 2026
  • Binding: Digital download and online
  • Series Title: Security


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Machine Learning and Deep Learning Driven Techniques for Multimodal Data Security in the Internet of Multimedia Things: (Security)
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