Titlebook: Connected Health in Smart Cities; Abdulmotaleb El Saddik,M. Shamim Hossain,Burak Kan Book 2020 Springer Nature Switzerland AG 2020 Health

龙卷风

Deep Learning in Smart Health: Methodologies, Applications, Challenges,pter presents an overview of deep learning techniques that are applied to smart healthcare. Deep learning techniques are frequently applied to smart health to enable AI-based recent technological development to healthcare. Furthermore, the chapter also introduces challenges and opportunities in deep learning particularly in the healthcare domain.

蕨类

Orchiectomy

Charade

inveigh

epidermis

https://doi.org/10.1007/978-3-531-90196-1 studies that fulfilled the predefined criteria were used. Data was extracted from 39 articles for the evaluation of the different health technologies and their uses..mHealth and phones are the most popular type used for health promotion, as it is present in 36% of the articles evaluated. Other succ

Reverie

https://doi.org/10.1007/978-3-531-90196-1its main functionalities and components. Among these, the use of a standardized method for the treatment of a massive amount of patient data is necessary in order to integrate all the collected information resulting from the development of a great number of new m-Health devices and applications. Ele

jovial

Udo Kuckartz,Anke Rheingans-HeintzeEG classification, this work focuses on developing CNN-based deep learning methods for such purpose. We propose a multiple-CNN feature fusion architecture to extract and fuse features by using subject-specific frequency bands. CNN has been designed with variable filter sizes and split convolutions f