This book presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. It is intended to provide a unique compendium of current and emerging machine learning paradigms for healthcare informatics, reflecting the diversity, complexity, and depth and breadth of this multi-disciplinary area.

This edited book presents point of view and the work being undertaken by active researchers in the domain of IOT and its applications with societal impact. The book is useful to other researchers for the understanding of the research domain and different points of views expressed by the experts in their contributed chapters. The contributions are from both industry and academia; hence, it provides a rich source of both theoretical and practical work going on in the research domain of IOT.

This edited book is a collection of chapters invited and presented by experts at 10th industry symposium held during 9-12 January 2020 in conjunction with 16th edition of ICDCIT. The book covers topics, like machine learning and its applications, statistical learning, neural network learning, knowledge acquisition and learning, knowledge intensive learning, machine learning and information retrieval, machine learning for web navigation and mining, learning through mobile data mining, text and multimedia mining through machine learning, distributed and parallel learning algorithms and applications, feature extraction and classification, theories and models for plausible reasoning, computational learning theory, cognitive modelling and hybrid learning algorithms.

This edited book is a collection of chapters invited and presented by experts at 10th industry symposium held during 9-12 January 2020 in conjunction with 16th edition of ICDCIT. The book covers topics, like machine learning and its applications, statistical learning, neural network learning, knowledge acquisition and learning, knowledge intensive learning, machine learning and information retrieval, machine learning for web navigation and mining, learning through mobile data mining, text and multimedia mining through machine learning, distributed and parallel learning algorithms and applications, feature extraction and classification, theories and models for plausible reasoning, computational learning theory, cognitive modelling and hybrid learning algorithms.

This edited book presents point of view and the work being undertaken by active researchers in the domain of IOT and its applications with societal impact. The book is useful to other researchers for the understanding of the research domain and different points of views expressed by the experts in their contributed chapters. The contributions are from both industry and academia; hence, it provides a rich source of both theoretical and practical work going on in the research domain of IOT.