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Anti-Phishing Approach For Iot System In Fog Networks Based On Machine Learning Algorithms, Mahmoud Gad Awwad, Mohamed M. Ashour, El Said A. Marzouk, Eman Abdelhalim
Anti-Phishing Approach For Iot System In Fog Networks Based On Machine Learning Algorithms, Mahmoud Gad Awwad, Mohamed M. Ashour, El Said A. Marzouk, Eman Abdelhalim
Mansoura Engineering Journal
As the Internet of Things (IoT) continues to expand, ensuring the security and privacyِ of IoT systems becomes increasingly critical. Phishing attacks pose a significant threat to IoT devices and can lead to unauthorized access, data breaches, and compromised functionality. In this paper, we propose an anti-phishing approach for IoT systems in fog networks that leverages machine learning algorithms, including a .fusion with deep learning techniques We explore the effectiveness of eleven traditional machine learning algorithms combined with deep learning in detecting and preventing phishing attacks in IoT systems. By utilizing a diverse range of algorithms, we aim to enhance …
Deep Learning Based Classification Of Focal Liver Lesions With 3 And 4 Phase Contrast-Enhanced Ct Protocols, Ahmed El-Emam, Hossam El-Din Moustafa, Mohamed Moawad, Mohamed Aouf
Deep Learning Based Classification Of Focal Liver Lesions With 3 And 4 Phase Contrast-Enhanced Ct Protocols, Ahmed El-Emam, Hossam El-Din Moustafa, Mohamed Moawad, Mohamed Aouf
Mansoura Engineering Journal
It had been noticed that 3-phase and 4-phase computed tomography protocols with contrast serve as standard examinations for diagnosing liver tumors. Additionally, many patients require periodic follow-up, which entails significant radiation exposure for them. Advancements in image processing facilitate automated liver lesion segmentation. However, the challenge remains in classifying these small lesions by doctors, especially when the liver has different types of lesions with very little intensity difference. Therefore, deep learning can be utilized for the classification of liver lesions. The present work introduces a CNN-based module for the classification of liver lesions. The module consists of four stages: data …