Saudi Cultural Missions Theses & Dissertations
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Item Restricted A Reinforcement Leaning Approach to Modelling and Solving Prediction Problems with Application in Bankruptcy & Financial Distress Prediction(University of Edinburgh, 2025) Abdullah, Meaad; Ouenniche, JamalIn this study, the application of Reinforcement Learning (RL) algorithms, specifically Q-Learning and SARSA, is examined in the prediction of financial distress and bankruptcy for non-financial companies in the United States and Canada. The investigation concentrates on the manner in which liquidity-related metrics and solvency indicators have impacted short-term financial health predictions over the past five years. The study aims to evaluate the performance of RL classifiers under a variety of financial distress definitions, with a focus on those that are based on liquidity metrics, using a dataset that includes non-financial information from 4,671 companies. An essential goal is to evaluate the impact of different definitions of financial distress on predictive models' precision and the extent to which RL algorithms adjust to these variations. This entails the examination of liquidity-related measures, including the Current Ratio, Quick Ratio, and Altman Z-score,..., in eight specific scenarios, each of which represents a distinct definition of financial distress. The research aims to answer several questions: What are the effects of varying definitions of financial distress on RL classifiers' performance? What is the role of liquidity-related metrics in identifying and forecasting financial distress? Based on these metrics, what is the performance of Q-Learning and SARSA algorithms in predicting financial distress in U.S. and Canadian companies? The study's objective is to provide valuable insights for businesses, investors, and regulators in corporate environments by contributing to the development of more accurate early warning systems and enhanced risk management tools. The main results show that Q-Learning is better than SARSA at predicting financial distress when using simpler, more stable measures of liquidity. It does this by achieving faster convergence and higher accuracy. However, SARSA works more effectively with more complicated and uncertain financial scenarios, especially those involving cash flow ratios.24 0Item Restricted ELEMENT AND EVENT-BASED TEST SUITE REDUCTION FOR ANDROID TEST SUITES GENERATED BY REINFORCEMENT LEARNING(University of North Texas, 2024-06-12) Alenzi, Abdullah Sawdi M; Bryce, ReneeAndroid stands as one of the most popular operating systems on a global scale. Given the popularity and the tremendous use of Android apps and the necessity of developing robust and reliable apps, it is crucial to create efficient and effective testing tools while addressing real-world time and budget constraints. Recently, automated test generation with Reinforcement Learning algorithms have shown promise, but there is room for improvement as these algorithms often produce test suites with redundant coverage. Fine tuning parameters of RL algorithms may assist, however, this comes with trade-offs and requires time consuming and careful consideration of the characteristics of the application under test and its environment. Therefore, devising cost-effective tools and techniques is imperative to mitigate this redundancy. Instead of exploring parameters of RL algorithms, we looked at minimizing test suites that have already been generated based on SARSA algorithms. In this dissertation, we hypothesize that there is room for improvement by introducing novel hybrid approaches that combine SARSA-generated test suites with greedy reduction algorithms following the principle of HGS approach. In addition, we apply an empirical study on Android test suites that reveals the value of these new hybrid methods. Our novel approaches focus on post-processing test suites by applying greedy reduction algorithms. To reduce Android test suites, we utilize different coverage criteria including Event-Based Criterion (EBC), Element-Based Criterion (ELBC), and Combinatorial-Based Sequences Criteria (CBSC) that follow the principle of combinatorial testing to generate sequences of events and elements. The proposed criteria effectively decreased the test suites generated by SARSA and revealed a high performance in maintaining code coverage. These findings suggest that test suite reduction using these criteria is particularly well suited for SARSA-generated test suites of Android apps.53 0