The abnormal fluctuations in network traffic may indicate potential security threats or system failures. Therefore, efficient network traffic prediction and anomaly detection methods are crucial for network security and traffic management. This paper proposes a novel network traffic prediction and anomaly detection model, MamNet, which integrates time-domain modeling and frequency-domain feature extraction. The model first captures the long-term dependencies of network traffic through the Mamba module (time-domain modeling), and then identifies periodic fluctuations in the traffic using Fourier Transform (frequency-domain feature extraction). In the feature fusion layer, multi-scale infor... More >

The COVID-19 pandemic exerted immense pressure on healthcare systems globally, including in Morocco, where the demand for intensive care unit (ICU) beds frequently surpassed available capacity—at times doubling it. This crisis underscored the critical need for accurate prediction of ICU length of stay (LOS) to optimize resource allocation, enhance patient care, and reduce healthcare costs. This study aims to leverage artificial intelligence (AI) to predict and optimize ICU resource allocation during the COVID-19 crisis, ensuring efficient patient triage and resource management. By integrating Random Forest (RF) and Deep Neural Networks (DNN), the research demonstrates improved accuracy in... More >