Titlebook: Deep Learning Techniques for Biomedical and Health Informatics; Sujata Dash,Biswa Ranjan Acharya,Arpad Kelemen Book 2020 Springer Nature S

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Design of Observational Studiesng the quality of clinical healthcare enormously. Such kind of intelligent decision making in healthcare and clinical practice is also expected to result in holistic treatment. In this chapter, we review and accumulate various existing DL techniques and their applications for decision support in cli

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OTFT Modelling and Characteristicsm that consists of exercises and preferable sports. We try to exploit an “Actor-Critic” model for enhancing the ability of the model to continuously update information seeking strategies based on user’s real-time feedback. Health industry usually deals with long-term issues. Traditional recommender

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https://doi.org/10.1007/978-3-319-21188-6aracteristics fit right to the nature of deep learning. Therefore, we believe it is the right time to summarize the current status, to review and learn from the state-of-the-art medical-based NLP techniques. Different from the existing reviews, we examine and categorize the current deep learning-bas

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https://doi.org/10.1007/978-3-030-80139-7ery effectively so as to identify the correlation between the presence of diabetes and HRV signal variations in the most accurate and fast manner. We discuss several deep learning architectures which can be effectively used for HRV signal analysis for the purpose of detection of diabetes. It can be

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Reliability and Congestion Controlsent complex structures, self-learning and efficiently process large amounts of MRI-based image data. Initially the chapter starts with brain tumor introduction and its various types. In the next section, various preprocessing techniques are discussed. Preprocessing is a crucial step for the correct

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Deep Learning Techniques for Biomedical and Health Informatics