Muhammad Sohail Irshad^1,2,*, Tehreem Masood^1,2, Arfan Jaffar^1,2, Muhammad Rashid³, Sheeraz Akram^1,2,4,*, Abeer Aljohani⁵

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4805-4824, 2024, DOI:10.32604/cmc.2024.050931

Abstract The application of deep learning techniques in the medical field, specifically for Atrial Fibrillation (AFib) detection through Electrocardiogram (ECG) signals, has witnessed significant interest. Accurate and timely diagnosis increases the patient’s chances of recovery. However, issues like overfitting and inconsistent accuracy across datasets remain challenges. In a quest to address these challenges, a study presents two prominent deep learning architectures, ResNet-50 and DenseNet-121, to evaluate their effectiveness in AFib detection. The aim was to create a robust detection mechanism that consistently performs well. Metrics such as loss, accuracy, precision, sensitivity, and Area Under the Curve… More >

R. S. Aashmi^1,*, T. Jaya²

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1295-1308, 2023, DOI:10.32604/csse.2023.027216

Abstract The integration of clusters, grids, clouds, edges and other computing platforms result in contemporary technology of jungle computing. This novel technique has the aptitude to tackle high performance computation systems and it manages the usage of all computing platforms at a time. Federated learning is a collaborative machine learning approach without centralized training data. The proposed system effectively detects the intrusion attack without human intervention and subsequently detects anomalous deviations in device communication behavior, potentially caused by malicious adversaries and it can emerge with new and unknown attacks. The main objective is to learn overall… More >