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Titlebook: IoT and ICT for Healthcare Applications; Nishu Gupta,Sara Paiva Book 2020 Springer Nature Switzerland AG 2020 IoT and smart healthcare.ICT

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Internet of Things Applied to Mental Health: Concepts, Applications, and Perspectivese patient assistance methods, which are made available through mobile solutions for gathering data about the patient behaviour in real-world environments, identifying risk situations and providing momentary interventions and assessments. These approaches have the potential to revolutionise tradition
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A Decision Support System for Smart Health Care can be modeled as a dynamic assignment problem that handles a set of patients to be assigned to a set of beds over a given time horizon while taking into account availability constraints expressed in terms of beds, medical necessity, and patients demands, which are subject to modification over time
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Health Data Analytics: Current Perspectives, Challenges, and Future Directionso support health data analytics, each dealing with different application areas and diverse data types. These tools are analyzed. The challenges and future directions of health data analytics are also discussed.
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Implementation and Computational Level Challenges for IoT in Healthcare Applications safety net providers is required to such an extent that a worthy system exists through which care is conveyed. This is additionally called a business biological system, which compares to the requirement for an IoT environment. Accordingly, the accomplice system building square will be a significant
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Mimicking Biometrics on Smart Devices and Its Application in IoT Security for Health Systemsted to fit the cyber world, referring to a device’s state; specifically the paradigm of side channel attack is exploited. This introduces the notion that to secure devices for healthcare, the designer has to consider the “health status” of the device itself. Taking into consideration that these devi
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Intelligent Wearable IOT Continuous Monitoring System for Elderly Based on Deep Learning Algorithmuture works with a more suitable data set. The decision of deep learning analysis used either notifies or does not notify the relative according to the elderly condition. It will be compared with historical data of the patient to see abnormalities and then provide a decision either the status emerge
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