Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
IoT Technologies for Smart Healthcare Buildings with Distributed Deep Learning Techniques
The integration of Internet of Things (IoT) technologies into healthcare buildings has recently shown to be a considerable problem. These buildings, commonly known as “intelligent” or “automated” buildings, employ several intelligent technologies such as building management systems, energy efficiency measures, automated systems, adaptive energy systems, wireless technologies, remote monitoring, digital infrastructure, and information and communications networks. However, the term “intelligent buildings” is not specific and can refer to various structures. The goal of this paper is to highlight the advantages of using distributed deep learning and IoT technologies to improve the intelligence and responsiveness of healthcare facilities, hence improving their overall performance. The findings of this research have practical implications, especially in the development of Smart Building applications for the healthcare sector. The objective of this study is to demonstrate that distributed deep learning and IoT technology can serve as a strong foundation for constructing efficient and scalable Smart Building applications in healthcare. The experimental results confirm that distributed deep learning and IoT technologies provide a suitable infrastructure for creating powerful and intelligent healthcare buildings. The study also reveals that smart technology offers a promising framework for enhancing the robustness and performance of intelligent buildings, leveraging the capabilities of distributed deep learning and IoT technologies.
IoT Technologies for Smart Healthcare Buildings with Distributed Deep Learning Techniques
The integration of Internet of Things (IoT) technologies into healthcare buildings has recently shown to be a considerable problem. These buildings, commonly known as “intelligent” or “automated” buildings, employ several intelligent technologies such as building management systems, energy efficiency measures, automated systems, adaptive energy systems, wireless technologies, remote monitoring, digital infrastructure, and information and communications networks. However, the term “intelligent buildings” is not specific and can refer to various structures. The goal of this paper is to highlight the advantages of using distributed deep learning and IoT technologies to improve the intelligence and responsiveness of healthcare facilities, hence improving their overall performance. The findings of this research have practical implications, especially in the development of Smart Building applications for the healthcare sector. The objective of this study is to demonstrate that distributed deep learning and IoT technology can serve as a strong foundation for constructing efficient and scalable Smart Building applications in healthcare. The experimental results confirm that distributed deep learning and IoT technologies provide a suitable infrastructure for creating powerful and intelligent healthcare buildings. The study also reveals that smart technology offers a promising framework for enhancing the robustness and performance of intelligent buildings, leveraging the capabilities of distributed deep learning and IoT technologies.
IoT Technologies for Smart Healthcare Buildings with Distributed Deep Learning Techniques
Communic.Comp.Inf.Science
Mosbah, Mohamed (Herausgeber:in) / Kechadi, Tahar (Herausgeber:in) / Bellatreche, Ladjel (Herausgeber:in) / Gargouri, Faiez (Herausgeber:in) / Guegan, Chirine Ghedira (Herausgeber:in) / Badir, Hassan (Herausgeber:in) / Beheshti, Amin (Herausgeber:in) / Gammoudi, Mohamed Mohsen (Herausgeber:in) / Hamdi, Hassen (Autor:in) / Zarrouk, Rim (Autor:in)
International Conference on Model and Data Engineering ; 2023 ; Sousse, Tunisia
Advances in Model and Data Engineering in the Digitalization Era ; Kapitel: 14 ; 172-183
21.03.2024
12 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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