As digital governance increasingly shapes the future of public administration, the demand for secure and efficient E-government services continues to rise. Traditional neural networks, though successful across various fields, often face challenges in scalability, security, and processing speed when dealing with governmental data. This study introduces a novel framework by embedding symmetrical principles within a neural network architecture, aiming to strengthen data protection and streamline operational efficiency in E-government systems. By integrating symmetry at the architectural level, the model reduces redundant computations, leading to faster and more resilient data processing. Moreover, this approach enhances the system’s defense against adversarial threats, a critical concern for public sector applications. The proposed model specifically addresses the unique requirements of E-government platforms, focusing on secure data transmission and robust resistance to security vulnerabilities. Our experimental evaluations highlight notable improvements in processing speeds and security performance, demonstrating the model’s practical potential for Realtime public sector operations. Beyond immediate applications, this work lays a strong foundation for further research into symmetry-driven network designs, offering promising solutions to the complex challenges inherent in managing sensitive public data.

Lately, the use of the Internet has led to an increase in social networking sites. The world has become an open environment, and social networking sites have been increasingly used to exchange medical experiences, and they have been adopted in many cases as basic references in obtaining medical advice, which has led to the misuse of medicines. A growing problem, abuse of prescription medications can have a negative impact on all age groups and come with adverse health consequences, as individuals in societies become susceptible to many drug interactions and serious side effects and reduced drug efficacy, which makes a simple health problem turn into a complex health problem; our study aims to classify drug use in Arabic content in social media (use, abuse) by using both Machine Learning (ML) algorithms and AraBERT model. Many studies detect the drug abuse in the English language. There are no studies on Arabic language. Arabic social media dataset was created from Facebook with nearly 7,000 posts. We used different ML classifiers; the most famous of them are Support Vector Machine (SVM), Decision Tree (J48), Naive Bayes (NB), K-Nearest Neighbor (KNN) and Random Forest (RF). We also applied AraBERT based model; CNN-AraBERT, RNN-AraBERT, and LSTM-AraBERT. The classifier's accuracy was evaluated by calculating the F1-Measure, Recall, and Precision measurements. The results indicated that CNN-AraBERT classifier is given the highest value of F1-Measure for Facebook dataset for both classification tasks with (98.3%) for binary classification and (90.99%) for multi-classification.

Summary Acceptable error rate, low quality assessment, and time complexity are the major problems in image segmentation, which needed to be discovered. A variety of acceleration techniques have been applied and achieve real time results, but still limited in 3D. HMM is one of the best statistical techniques that played a significant rule recently. The problem associated with HMM is time complexity, which has been resolved using different accelerator. In this research, we propose a methodology for transferring HMM matrices from image to another skipping the training time for the rest of the 3D volume. One HMM train is generated and generalized to the whole volume. The concepts behind multi‐orientation geometrical segmentation has been employed here to improve the quality of HMM segmentation. Axial, saggital, and coronal orientations have been considered individually and together to achieve accurate segmentation results in less processing time and superior quality in the detection accuracy.

Parallel programming has many benefits that can help developers and researchers to improve the performance of some algorithms to become more efficient in real life. This is especially true for systems involving medical images. Image segmentation for volume extraction is a famous segmentation process that takes long time to finish execution. In this paper, we consider a new version of the Fuzzy C-Means (FCM) segmentation algorithm (known as IT2FPCM) and provide a parallel implementation of it that is 12X time faster than the sequential implementation. The considered algorithm is based on Interval Type-2 FCM and combines fuzzy and possibilistic ideas in order to obtain higher accuracy. We conduct our experiments using two different machines and the results show that the improvement gains for both machines 11X and 12X, respectively.