Meenu Gupta

Gopal Chaudhary

Victor Hugo C. de Albuquerque

Focusing on the challenges, directions, and future predictions with the role of 5G in smart healthcare monitoring, this book offers the fundamental concepts and analyses on the methods to apply Internet of Things (IoT) in monitoring devices for diagnosing and transferring data. It also discusses self-managing to help providers improve their patients' healthcare experience. Smart Healthcare Monitoring Using IoT with 5G: Challenges, Directions, and Future Predictions illustrates user-focused wearable devices such as Fitbit health monitors and smartwatches by which consumers can self-manage and self-monitor their own health. The book covers new points of security and privacy concerns, with the expectation of IoT devices gaining more popularity within the next ten years. Case studies depicting applications and best practices as well as future predictions of smart healthcare monitoring by way of a 5G network are also included. Interested readers of this book include anyone working or involved in research in the field of smart healthcare, such as healthcare specialists, computer science engineers, electronics engineers, and pharmaceutical practitioners.

Challenges, Directions, and Future Predictions

Smart Healthcare Monitoring Using IoT with 5G

Manju Khari

Smriti Srivastava

Ruben Gonzalez Crespo

Parul Arora

This book provides readers with a comprehensive and recent exposition in deep learning and its multidisciplinary applications, with a concentration on advances of deep learning architectures. The book discusses various artificial intelligence (AI) techniques based on deep learning architecture with applications in natural language processing, semantic knowledge, forecasting and many more. The authors shed light on various applications that can benefit from the use of deep learning in pattern recognition, person re-identification in surveillance videos, action recognition in videos, image and video captioning. The book also highlights how deep learning concepts can be interwoven with more modern concepts to yield applications in multidisciplinary fields. Presents a comprehensive look at deep learning and its multidisciplinary applications, concentrating on advances of deep learning architectures; Includes a survey of deep learning problems and solutions, identifying the main open issues, innovations and latest technologies; Shows industrial deep learning in practice with examples/cases, efforts, challenges, and strategic approaches.

Concepts and Real-Time Applications of Deep Learning

Dharmendra Saini

Vedika Gupta

"This book provides a thorough understanding of the integration of computational intelligence with information retrieval including content-based image retrieval using intelligent techniques, hybrid computational intelligence for pattern recognition, intelligent innovative systems, and protecting and analysing big data on cloud platforms. This book aims to investigate how computational intelligence frameworks are going to improve information retrieval systems. The emerging and promising state-of-the-art of human-computer interaction is the motivation behind this book"--

Computational Intelligence for Information Retrieval

Vanita Jain

M. Cengiz Taplamacioglu

M. S. Agarwal

This book gathers the best papers presented at the International Conference on Data Sciences, Security and Applications (ICDSSA 2019), organized by Bharati Vidyapeeth’s College of Engineering, New Delhi, India, on 7–8 March 2019. The respective contributions present original research work, essential information, techniques and applications in the fields of data mining, artificial intelligence and computational intelligence. They also discuss machine learning in business intelligence and big data analytics, soft computing, security, cloud computing and the latest trends.

Proceedings of ICDSSA 2019

Advances in Data Sciences, Security and Applications

Gopal Chaudhary

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