Lakhmi C. Jain

Valentina Emilia Balas

Manisha Sharma

Basant Agarwal

Ramesh Chandra Poonia

Deep Learning Techniques for Biomedical and Health Informatics provides readers with the state-of-the-art in deep learning-based methods for biomedical and health informatics. The book covers not only the best-performing methods, it also presents implementation methods. The book includes all the prerequisite methodologies in each chapter so that new researchers and practitioners will find it very useful. Chapters go from basic methodology to advanced methods, including detailed descriptions of proposed approaches and comprehensive critical discussions on experimental results and how they are applied to Biomedical Engineering, Electronic Health Records, and medical image processing. Examines a wide range of Deep Learning applications for Biomedical Engineering and Health Informatics, including Deep Learning for drug discovery, clinical decision support systems, disease diagnosis, prediction and monitoring Discusses Deep Learning applied to Electronic Health Records (EHR), including health data structures and management, deep patient similarity learning, natural language processing, and how to improve clinical decision-making Provides detailed coverage of Deep Learning for medical image processing, including optimizing medical big data, brain image analysis, brain tumor segmentation in MRI imaging, and the future of biomedical image analysis

Deep Learning Techniques for Biomedical and Health Informatics

Janmenjoy Nayak

Sandeep Kumar

Mohammad Khan

Goncalo Marques

Cyber-Physical Systems: AI and COVID-19 highlights original research which addresses current data challenges in terms of the development of mathematical models, cyber-physical systems-based tools and techniques, and the design and development of algorithmic solutions, etc. It reviews the technical concepts of gathering, processing and analyzing data from cyber-physical systems (CPS) and reviews tools and techniques that can be used. This book will act as a resource to guide COVID researchers as they move forward with clinical and epidemiological studies on this outbreak, including the technical concepts of gathering, processing and analyzing data from cyber-physical systems (CPS). The major problem in the identification of COVID-19 is detection and diagnosis due to non-availability of medicine. In this situation, only one method, Reverse Transcription Polymerase Chain Reaction (RT-PCR) has been widely adopted and used for diagnosis. With the evolution of COVID-19, the global research community has implemented many machine learning and deep learning-based approaches with incremental datasets. However, finding more accurate identification and prediction methods are crucial at this juncture. Offers perspectives on the design, development and commissioning of intelligent applications Provides reviews on the latest intelligent technologies and algorithms related to the state-of-the-art methodologies of monitoring and mitigation of COVID-19 Puts forth insights on how future illnesses can be supported using intelligent corona virus monitoring techniques

AI and COVID-19

Cyber-Physical Systems

Valentina E. Balas

G. R. Sinha

Tarun Kumar Sharma

Mehul Mahrishi

Pankaj Dadheech

This book constitutes the refereed proceedings of the 5th International Conference on Emerging Technologies in Computer Engineering, ICETCE 2021, held in Jaipur, India, in February 2022. The 40 revised full papers along with 20 short papers presented were carefully reviewed and selected from 235 submissions. The papers are organized according to the following topical headings: ​cognitive computing; Internet of Things (IoT); machine learning and applications; soft computing; data science and big data analytics; blockchain and cyber security.

5th International Conference, ICETCE 2022, Jaipur, India, February 4–5, 2022, Revised Selected Papers

Emerging Technologies in Computer Engineering: Cognitive Computing and Intelligent IoT

Srikanta Patnaik

Richi Nayak

Namita Mittal

This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field.

Deep Learning-Based Approaches for Sentiment Analysis

Christopher Prendergast

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