Article: Anomaly detection architecture for smart hospitals based on machine learning, time series, and image recognition analysis: survey Journal: International Journal of Medical Engineering and Informatics (IJMEI) 2025 Vol.17 No.7 pp.1 - 14 Abstract: Smart hospital networks are considered the most sensitive networks for anomalies; any tiny existence might produce very different dangerous scales. The usual anomaly detections dedicated to this kind of network are not able to analyse all the different categories and proprieties of the generated data, because the majority of them rely only on time series analysis which is not able to cover all the circulated pieces of information. For that, in this paper, we will survey a proposed anomaly detection architecture that can dominate all the data categories that exist inside the e-health network using image recognition as well as time-series analysis. Inderscience Publishers - linking academia, business and industry through research

Title: Anomaly detection architecture for smart hospitals based on machine learning, time series, and image recognition analysis: survey

Authors: Somaya Haiba; Tomader Mazri

Addresses: Networks and Telecommunication Systems, Advanced Systems Engineering Laboratory, Department of Electrical Engineering, National School of Applied Sciences, Kenitra, Morocco ' Networks and Telecommunication Systems, Advanced Systems Engineering Laboratory, Department of Electrical Engineering, National School of Applied Sciences, Kenitra, Morocco

Abstract: Smart hospital networks are considered the most sensitive networks for anomalies; any tiny existence might produce very different dangerous scales. The usual anomaly detections dedicated to this kind of network are not able to analyse all the different categories and proprieties of the generated data, because the majority of them rely only on time series analysis which is not able to cover all the circulated pieces of information. For that, in this paper, we will survey a proposed anomaly detection architecture that can dominate all the data categories that exist inside the e-health network using image recognition as well as time-series analysis.

Keywords: E-healthcare monitoring network; IoMT; smart hospitals; E-health anomaly; anomaly detection; machine learning; time-series analysis; IoT security; ImageGray analysis; medical data; Cybersecurity.

DOI: 10.1504/IJMEI.2025.149913

International Journal of Medical Engineering and Informatics, 2025 Vol.17 No.7, pp.1 - 14

Received: 11 Oct 2022
Accepted: 07 Jul 2023
Published online: 18 Nov 2025 *

Title: Anomaly detection architecture for smart hospitals based on machine learning, time series, and image recognition analysis: survey

Keep up-to-date