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.

The healthcare industry has been suffering from fraud in many facets for decades, resulting in millions of dollars lost to fictitious claims at the expense of other patients who cannot afford appropriate care. As such, accurately identifying fraudulent claims is one of the most important factors in a well-functioning healthcare system. However, over time, fraud has become harder to detect because of increasingly complex and sophisticated fraud scheme development, data unpreparedness, as well as data privacy concerns. Moreover, traditional methods are proving increasingly inadequate in addressing this issue. To solve this issue a novel evolutionary dynamic weighted search space approach (DW-WOA-SVM) is presented in the current study. The approach has different levels that work simultaneously, where the optimization algorithm is responsible for tuning the Support Vector Machine (SVM) parameters, applying the weighting procedure for the features, and using a dynamic search space to adjust the range values. Tuning the parameters benefits the performance of SVM, and the weighting technique makes it updated with importance and lets the algorithm focus on data structure in addition to optimization objectives. The dynamic search space enhances the search range during the process. Furthermore, large language models have been applied to generate the dataset to improve the quality of the data and address the lack of good dimensionality, helping to enhance the richness of the data. The experiments highlighted the superior performance of this proposed approach than other algorithms.

Deep neural networks (DNNs) are currently being deployed as machine learning technology in a wide range of important real-world applications. DNNs consist of a huge number of parameters that require millions of floating-point operations (FLOPs) to be executed both in learning and prediction modes. A more effective method is to implement DNNs in a cloud computing system equipped with centralized servers and data storage sub-systems with high-speed and high-performance computing capabilities. This paper presents an up-to-date survey on current state-of-the-art deployed DNNs for cloud computing. Various DNN complexities associated with different architectures are presented and discussed alongside the necessities of using cloud computing. We also present an extensive overview of different cloud computing platforms for the deployment of DNNs and discuss them in detail. Moreover, DNN applications already deployed in cloud computing systems are reviewed to demonstrate the advantages of using cloud computing for DNNs. The paper emphasizes the challenges of deploying DNNs in cloud computing systems and provides guidance on enhancing current and new deployments.