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

Rani, Sita; Kumar, Sachin

Couverture rigide

ISBN 10 : 183724135X ISBN 13 : 9781837241354

Editeur : Institution of Engineering and Technology, 2026

Synopsis

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.

A propos du livre

À propos des auteurs

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).

Détails de l'édition

• Éditeur: Institution of Engineering and Technology
• Date d'édition: 2026
• Langue: anglais
• ISBN 10: 183724135X
• ISBN 13: 9781837241354
• Reliure: Relié
• Nombre de pages: 256
• Coordonnées du fabricant: non disponible
• Personne responsable: GPSR Kontakt; gpsr@libri.de; ; Europaallee 1; Bad Hersfeld; 36244; Allemagne