https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4581 2026-04-21T14:02:10Z https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4627 Title: Optimising Cricket Team Selection for One Day International Series Based on Match Conditions Authors: Gunawardhana, L.G.U.P. Abstract: This thesis focuses on predicting an optimal Sri Lankan cricket team for One Day International (ODI) matches by analysing player performance under different conditions, including weather conditions, opponents and venue. We try to maximise the overall team performance by predicting the best team combination from existing players. The selectors generally perform team selection considering recent performance, including batting and bowling averages of the players. These metrics provide limited insight into players’ potential performance, which leads to drop-ups of qualified. Therefore, consideration of more factors and robust machine learning is required. Our study considers overall performance, consistency, venue, opposition, and recent form of players to predict the players' performance using Random Forest Regression. Then, use the predicted performance to evaluate the player rating of each player towards the team by using Neural Networks. Previous studies have proved that Neural Networks can solve team selection problems successfully [1]. Then we select the team based on the predicted winning contributions to maximise the overall team winning probability. The study concludes by predicting the last 45 matches the Sri Lankan cricket team played during 2017-2019 with the actual playing 11 and the optimal playing 11 selected using our proposed system. We observed that the winning rate of the Sri Lankan cricket team could be improved from 37.77% to 77.77% (105% improvement) if teams were selected using our proposed system. 2022-08-09T00:00:00Z https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4626 Title: A predictive model to minimize false positive declines in Electronic Card Not Present financial transactions using feature engineering techniques Authors: Delgolla, D. M. S Abstract: In this research, we have proposed a predictive model to minimize false positive (“Legitimate transactions are being declined falsely identifying as fraudulent”) declines in electronic CNP transactions. Related to the increased popularity of digital payments FP declines are becoming a severe problem among merchants who provide digital payment solutions. It’s estimated that nearly 10% of the transactions are been declined as fraudulent transactions but only very few of them have fallen into the fraud category. To address this problem we have proposed a feature engineering technique based on behavior analysis. Our research is conducted based on a reallife CNP transactional data set from one of the largest fintech service solution providers in Sri Lanka and we have generated 130 features for each transaction and have employed an XG Boost to learn the classifier and found out that performances of the xgBoost classifier has shown nearly 6% improvement in the F-Score and obtained 0.996 for the AUC after the application of feature engineering techniques. We found out that this solution can mainly benefit the merchants who provide electronic payment solutions which involve CNP transactions to minimize false-positive declines targeting legitimate frequent customers and by the same, it minimizes the fraud losses and protects the customer’s interests. 2022-08-09T00:00:00Z https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4625 Title: A Heterogeneous Sensor Fusion Framework for Obstacle Detection in Piloted UAVs Authors: Avenash, K. Abstract: Teleoperation of Unmanned Aerial Vehicle (UAV) is a demanding task that requires skill and experience. For the most part, commercial-grade UAVs are still manually piloted. Some form of obstacle detection capability is desired in UAVs to minimize the chance of collisions and to ensure safety to human lives and properties. This thesis presents a heterogeneous sensor fusion framework for obstacle detection using complementary sensors, a monocular visual camera, and distance sensors to detect obstacles. The approach focuses on obstacles at low altitude, such as static obstacles with a large surface area and thin obstacles such as cables. The fusion of inputs is performed using fuzzy logic. The warning alerts to the pilots are sent using graphical and auditory signal methods when an obstacle is encountered. The evaluation was conducted using the simulation platform Microsoft AirSim. The approach detects thin obstacles, static large obstacles, and thin obstacles with a static obstacle in the background successfully. A case study was also conducted involving a human subject to obtain qualitative evaluation. Results obtained shows that the proposed approach has a great potential in the UAV obstacle detection. The proposed framework and the evaluation results are the contributions of this work. The thesis discusses the framework's limitations and provides an overview of aspects that should be focused on when the approach is extended and implemented for a real hardware platform. 2022-08-09T00:00:00Z