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

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.

Sita Rani is an assistant professor at Guru Nanak Dev Engineering College, Ludhiana, India, and a postdoctoral research scientist at the University of South Florida, USA. She earned her PhD in Computer Science and Engineering from I.K. Gujral Punjab Technical University, India, in 2018 and completed her postdoctoral research at South Ural State University, Russia (2022-23). She also holds a PG certificate in Data Science and Machine Learning from IIT Roorkee. With over 23 years of academic and research experience, she has been recognized 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). He is an associate editor of various indexed journals published by the IET, Wiley, Elsevier, and Nature. His research interests include AI and its applications to smart environments, Internet of Things, natural language processing, and cognitive sciences. He is the author of 90+ scientific articles published in peer-reviewed journals indexed in Web of Science (WoS) and Scopus. He earned his PhD degree in Data Mining from IIT Roorkee, India.