最大限度缩短埃及经济法院案件等待时间的智能模型

احمد توفيق, Mahmoud Mohamed Bahloul
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引用次数: 0

摘要

埃及的经济法院在解决与经济和商业事务有关的纠纷方面发挥着至关重要的作用。这些案件涉及广泛的问题,需要及时且具有成本效益的解决方案,以确保有效管理埃及的大型建筑项目。机器学习在埃及经济法院处理的案件中发挥着至关重要的作用。它有助于分析大量数据、识别模式和趋势,并根据历史数据进行预测。本研究利用由埃及经济法院 3633 项法院裁决组成的数据集,深入研究了各种算法的应用,以识别阿拉伯语文本中的类似法院裁决。各种算法的应用促进了可比法院裁决的有效选择,从而加快了在最短时间内得出有效结论的过程。对分类器性能的评估采用了准确度、精确度、召回率和 F1 分数等指标。值得注意的是
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A smart model to minimize the waiting time for the cases in Egyptian Economic courts
The economic courts in Egypt play a crucial role in resolving disputes related to economic and commercial matters. These cases cover a wide range of issues that require timely and cost-effective resolutions to ensure the effective management of huge construction projects in Egypt. Machine learning plays a crucial role in the cases handled by Egyptian Economic Courts. It helps in the analysis of large amounts of data, identifying patterns and trends, and making predictions based on historical data. The research delves into the application of various algorithms to identify similar court rulings within Arabic texts, using a dataset comprising 3633 court rulings from the Economic Court in Egypt. The employment of various algorithms has facilitated the efficient selection of comparable court rulings, thus expediting the process of deriving effective conclusions within a minimal timeframe. The evaluation of classifier performance was conducted using metrics such as accuracy, precision, recall, and the F1 score. Notably, the
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