A smart model to minimize the waiting time for the cases in Egyptian Economic courts

Abstract

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