{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T18:45:37Z","timestamp":1771613137734,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T00:00:00Z","timestamp":1758758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"AlZaytoonah University of Jordan\u2014Deanship of Scientific Research, Amman, Jordan","award":["2024\u20132025\/12\/27"],"award-info":[{"award-number":["2024\u20132025\/12\/27"]}]},{"name":"Gulf University for Science and Technology","award":["2024\u20132025\/12\/27"],"award-info":[{"award-number":["2024\u20132025\/12\/27"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>In the digital transformation of public services, reliable and secure data handling has become central to effective E-government operations. This study introduces a symmetry-driven neural network architecture tailored for secure, scalable, and energy-efficient data processing. The model integrates weight-sharing and symmetrical configurations to enhance efficiency and resilience. Experimental validation on three E-government datasets (95,000\u2013230,000 records) demonstrates that the proposed model improves processing speed by up to 40% and enhances adversarial robustness by maintaining accuracy reductions below 2.5% under attack scenarios. Compared with baseline neural networks, the architecture achieves higher accuracy (up to 95.1%), security (up to 98% attack prevention), and efficiency (processing up to 1600 records\/sec). These results confirm the model\u2019s applicability for large-scale, real-time E-government systems, providing a practical path for sustainable and secure digital public administration.<\/jats:p>","DOI":"10.3390\/a18100601","type":"journal-article","created":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T12:05:13Z","timestamp":1758801913000},"page":"601","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Neural Network Architectures for Secure and Sustainable Data Processing in E-Government Systems"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4173-2323","authenticated-orcid":false,"given":"Shadi","family":"AlZu\u2019bi","sequence":"first","affiliation":[{"name":"Faculty of Science and IT, Al-Zaytoonah University of Jordan, Amman 11733, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fatima","family":"Quiam","sequence":"additional","affiliation":[{"name":"Faculty of Science and IT, Al-Zaytoonah University of Jordan, Amman 11733, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ala\u2019 M.","family":"Al-Zoubi","sequence":"additional","affiliation":[{"name":"Faculty of Science and IT, Al-Zaytoonah University of Jordan, Amman 11733, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0021-2364","authenticated-orcid":false,"given":"Muder","family":"Almiani","sequence":"additional","affiliation":[{"name":"Department of MIS, GUST Engineering and Applied Innovation Research Center (GEAR), Gulf University of Science & Technology, Hawally 32093, Kuwait"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Veledar, E., Zhou, L., Veledar, O., Gardener, H., Gutierrez, C.M., Romano, J.G., and Rundek, T. 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