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All Journal Bulletin of Electrical Engineering and Informatics
Ashraf Ahmad
Princess Sumaya University for Technology
Electrical & Electronics Engineering

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Techniques of medical image encryption taxonomy Mustafa A. Al-Fayoumi; Ammar Odeh; Ismail Keshta; Ashraf Ahmad
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3850

Abstract

Medical images are one of the most significant and sensitive data types in computer systems. Sending medical images over the internet necessitates using a robust encryption scheme that is resistant to cryptographic attacks. Confidentiality is the most critical part of the three security objectives for information systems security, namely confidentiality, integrity, and availability. Confidentiality is the most critical aspect for the secure storage and transfer of medical images. In this study, we attempt to classify various encryption methods in order to assist researchers in selecting the optimal strategy for protecting sensitive patient information while transferring medical images without alteration and outline the measures that should be adopted to address challenges and concerns relevant to techniques of medical image encryption.
An innovative deep learning approach for Arabic race recognition Amal Saif; Rahmeh Ibrahim; Eman Alnagi; Ashraf Ahmad; Abdullah Aref
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10851

Abstract

In computer vision, human race detection has become a critical application across many domains, such as security and customized marketing. Deep learning approaches, such as convolutional neural network (CNN), have played an essential role in improving human race detection. Nevertheless, detecting Arabic race is still a field that has received little attention. In this paper, an Arabic human race dataset comprising the following classes: Gulf, Levant, Sudan, Egypt, and North Africa (excluding Egypt) has been collected and proposed as a starting point for Arabic race classification. This dataset has been evaluated using a simple CNN-based model and other transfer learning models: DenseNet121, VGG16, and ResNet50. The difficulty in classifying these regions lies in the similarity of border areas in people’s features and in intermarriage between different regions, which helps transfer genetic traits that distinguish one region from another. The best results in recall, F1-score, precision, and accuracy were obtained by the DenseNet121 model, which achieved an average accuracy of 0.746 across five folds.
Co-Authors Abdullah Aref Amal Saif Ammar Odeh Eman Alnagi Ismail Keshta Mustafa A. Al-Fayoumi Rahmeh Ibrahim