Pushpendu Kar – författare

Interoperability in IoT for Smart Systems discusses the different facets of interoperability issues among the IoT devices and their solutions, the scalability issues in an IoT network, and provides solutions for plug-n-play of new devices with the existing IoT system. It also addresses the possible usage of interoperable and plug-n-play IoT networks in different systems to make them smarter. Aimed at researchers and graduate students in computer science, computer engineering, computer networks, electronics engineering, this book

Interoperability in IoT for Smart Systems discusses the different facets of interoperability issues among the IoT devices and their solutions, the scalability issues in an IoT network, and provides solutions for plug-n-play of new devices with the existing IoT system. It also addresses the possible usage of interoperable and plug-n-play IoT networks in different systems to make them smarter. Aimed at researchers and graduate students in computer science, computer engineering, computer networks, electronics engineering, this book

Recommender Systems: A Multi-Disciplinary Approach presents a multi-disciplinary approach for the development of recommender systems. It explains different types of pertinent algorithms with their comparative analysis and their role for different applications. This book explains the big data behind recommender systems, the marketing benefits, how to make good decision support systems, the role of machine learning and artificial networks, and the statistical models with two case studies. It shows how to design attack resistant and trust-centric recommender systems for applications dealing with sensitive data.

Features of this book:

This book is aimed at researchers and graduate students in computer science, electronics and communication engineering, mathematical science, and data science.

Recommender Systems: A Multi-Disciplinary Approach presents a multi-disciplinary approach for the development of recommender systems. It explains different types of pertinent algorithms with their comparative analysis and their role for different applications. This book explains the big data behind recommender systems, the marketing benefits, how to make good decision support systems, the role of machine learning and artificial networks, and the statistical models with two case studies. It shows how to design attack resistant and trust-centric recommender systems for applications dealing with sensitive data.

Features of this book:

This book is aimed at researchers and graduate students in computer science, electronics and communication engineering, mathematical science, and data science.

The book includes a thorough examination of the many types of algorithms for recommender systems, as well as a comparative analysis of them. It addresses the problem of dealing with the large amounts of data generated by the recommender system. The book also includes two case studies on recommender system applications in healthcare monitoring and military surveillance. It demonstrates how to create attack-resistant and trust-centric recommender systems for sensitive data applications. This book provides a solid foundation for designing recommender systems for use in healthcare and defense.