USING ARTIFICIAL INTELLIGENCE TO STUDY THE EFFECTIVENESS OF TAKAFUL AND KARAMA PROGRAM MODEL IN NORTH SINAI GOVERNORATE | ||
| Journal of Environmental Studies and Sustainable Development | ||
| Article 2, Volume 2, Issue 3, September 2025 | ||
| Document Type: Researches | ||
| DOI: 10.21608/jesasd.2025.404605.1013 | ||
| Authors | ||
| Ahmed E.M. Hassan* 1; Abdel-Maksoud A. Soliman2; Saeed Gamal3; Mohamed G. Mohamed4 | ||
| 1Dept. Admin., Legal, and Econ. Environ., Inst. Environ. Stud., Arish Univ., Egypt. | ||
| 2Dept. Mathematics, Fac. Sci., Arish Univ., Egypt. | ||
| 3Dept. Statistics, Fac. Commerce, Arish Univ., Egypt. | ||
| 4Fac. Computers and Information, Arish Univ., Egypt. | ||
| Abstract | ||
| This research explores the application of artificial intelligence (AI) to enhance the effectiveness of the Takaful and Karama program model in North Sinai Governorate. The goal is to develop an AI-based system, utilizing decision trees and deep learning, to identify eligible beneficiaries more accurately and transparently. The program faces challenges such as inaccuracies in beneficiary selection, delays in aid distribution, and lack of transparency, which can lead to misallocation or failure to reach those in genuine need. The study involved a field survey of 195 applicants, collecting data through content analysis, interviews, and questionnaires. Data analysis employed AI models, revealing that deep learning models yielded lower error rates (15.38%) compared to decision trees, especially when incorporating economic and social variables. The findings indicate that economic factors, such as vehicle ownership, significantly influence model accuracy, while combining multiple features does not always improve performance. Recommendations emphasize the importance of data pre-processing, feature selection—prioritizing economic variables—and data quality enhancement to optimize model performance. The research underscores the critical role of integrating modern technological solutions into social welfare programs to improve their efficiency, fairness, and responsiveness. Implementing AI-driven models can facilitate rapid, precise support targeting vulnerable groups, particularly in regions facing economic hardships like North Sinai. The study aims to contribute to a transformative approach in social program management by adopting intelligent systems that ensure equitable and effective distribution of aid, ultimately fostering social justice and community development. | ||
| Keywords | ||
| Artificial Intelligence; Social Programs; Fair Distribution; Big Data; Efficiency Improvement | ||
|
Statistics Article View: 1 |
||
