Faculty of Computing
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Publication Embargo A Machine Learning Approach to Predict the Personalized Next Payment Date of An Online Payment Platform(IEEE, 2022-12-09) Karunathunge, L. C. R.; Dewapura, B. N.; Perera, V. A. S.; Kavirathne, G. P. R. A.; Karunasena, A.; Pemadasa, M. G. N.Use of digital payments has risen exponentially in the recent past especially due to the COVID-19 pandemic. This is because online payment methods offer many benefits in performing their day-to-day transactions and paying utility bills such as electricity bills, water bills, telephone bills and etc. Knowing when a consumer will perform a specific online transaction, or bill payment is beneficial to an online payment platform to plan marketing campaigns since targeted marketing has become very prevalent nowadays. However, predicting this is not an easy task since thousands of transactions are happening in each and every minute of an online payment platform. This paper presents the results of a study that investigated predicting the customer personalized, utility bill payment type wise next payment date of a financial company in Sri Lanka by using machine learning techniques. This is accomplished by analyzing not only online transaction history but also customer characteristics and a holiday calendar which is specific to Sri Lanka. At the end of the study, it was identified that XGBoost Regressor is the most suitable machine learning algorithm, etc deal with this scenario which provided 91.02% accuracy. These predictions will be used for sending personalized reminders and discount offers to customers without sending general common notifications when they are planning to do an online payment. Such reminders and offers will be notified on the mobile devices of the customers and, ultimately both customers and the business owners will be benefited by this.Publication Embargo Machine Learning and Image Processing Based Approach for Improving Milk Production and Cattle Livestock Management(IEEE, 2022-12-29) Bandara, W. M. C. S.; Priyasarani, W. A. L.; Dhanarathna, Y. N.; Jaanvi, S. C. H.; Karunasena, A.; Abeywardhana, D. L.Dairy products are popularly consumed around the globe since it provides a rich source of vitamins and minerals essential for maintaining human health. Developing countries have grown their proportion of global dairy production in Sri Lanka Cattle Livestock is one of the most prospective subsectors of agriculture in Sri Lanka. Demand for quality milk products in Sri Lanka have especially increased in the recent past due to restrictions in importing dairy products.Under such circumstances cattle farmers are much encouraged to improve their milk production. However, there are many challenges in improving milk production by farmers. These include challenges in identifying breeds of cows for milk production inability of identifying diseases and conditions of farm animals hindering milk production and forecasting milk production of a farm.This research used a machine learning and image processing to identify parasite disease and heat stress of cows hindering milk production and identify breeds capable of producing quality milk. In addition, it will also use machine learning to predict heat stress level of cattle, identifying the breed types, identification of parasitic species and risk level.The machine learning model was generated with higher accuracyPublication Embargo A Machine Learning Approach to Predict Default Lease Cases in Sri Lankan Financial Institutions(IEEE, 2022-12-26) Perera, V. A. S.; Kavirathne, G. P. R. A.; Karunathunge, L. C. R.; Dewapura, B. N.; Karunasena, A.; Pemadasa, M. G. N. M.The economic growth of a country can be aided by a strong financial services industry. Therefore, financial companies play a vital role in today’s society. However, by providing credit facilities, they expose themselves to a significant amount of risks, since most of them lack a proper strategy to identify whether the customer is reliable and capable of paying back on time. Hence, it is widely acknowledged that having a proper strategy in place to manage and lessen the credit risks that these companies face is more beneficial, rather than relying on traditional manual techniques. This study is intended to propose a machine learning-based solution to predict possible financial lease defaults beforehand. The dataset used in this work was obtained from a leading finance company in Sri Lanka, where the data were related to leasing contracts and their equipment. According to the final results of this study, a deep learning model implemented using an Artificial Neural Network, which was compared against several other machine learning models, is the best to predict default lease cases in Sri Lankan financial institutions. The finalized model provides 93.93% of classification accuracy, 85.49% of F-measure, 87.69% of AUROC score, and 80.41% of Kappa score.Publication Embargo English Language Trainer for Non-Native Speakers using Audio Signal Processing, Reinforcement Learning, and Deep Learning(IEEE, 2021-12-02) Jeewantha, H. C. R.; Gajasinghe, A. N; Rajapaksha, T. N; Naidabadu, N. I; Kasthurirathna, D.; Karunasena, A.Lack of basic proficiency and confidence in writing and speaking in English is one of the major social problems faced by most non-native English speakers. Although the general adult literacy rate in Sri Lanka is above average by world standards, the English literacy rate is just 22% among the Sri Lankan adult population. Many individuals face setbacks in achieving their career and academic goals due to these language barriers. In a world where English has become a compulsory requirement to pursue higher education, career development, and performing day-to-day activities, "English Buddy" is a software solution developed to enhance the English learning experience of individuals in a more personalized and innovative way. The system provides an all-in-one solution while filling major research and market gaps in existing solutions in the e-learning domain. The system consists of a personalized learning environment, automated pronunciation error detection system, automated essay evaluation process, automated descriptive answer evaluation component based on semantic similarity, and real-time course content rating system. English Buddy is implemented using state-of-the-art technologies such as Audio Signal Processing, Reinforcement Learning, Deep Learning, and NLP. The LSTM, Sentiment Analysis, and Siamese network models have gained accuracy scores of 0.93, 0.92, and 0.81 respectively. Further, the UAT results proved that the personalized recommendations and pronunciation error detection processes are accurate and reliable. This research has been able to overcome the limitations of most existing solutions that follow traditional approaches and provide better results compared to the recent studies in the e-learning research domain.Publication Embargo CEYLAGRO: INFORMATION TECHNOLOGICAL APPROACH FOR AN OPTIMIZED AND CENTRALIZED AGRICULITURE PLATFORM(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Kaushalya, T.V.H.; Wijewardana, B.Y.S.; Karunasena, A.; Kavishika, M.G.G.; Gamage, S.T.A; Weerasinghe, L.Sri Lankan Agriculture sector can be considered as a crucial component as it contributes 18% of country GDP. As native farmers still cling to inapplicable traditional theorems and practices to track customer’s vegetable consumption trends, they failed to assure a “good price” for their harvest. Also, the plants are prone to many diseases and pests’ attacks which causes loss of the harvest. Unreliable problem identification, poor knowledge on application of fertilizers and pesticides have caused the farmers to lose their profits. As a solution to mitigate these problems, this study has built a computerized system with a vegetable price prediction system and a plant disease, pest identification system. Taking Potato as an example, the parameters of the time series model were analyzed through experiment and has built the price predictor using ARIMA model. Also, with advanced Image processing and CNN techniques Plant disease, pest identifier has built. Desirable results of the entire system have been achieved with more than 94%-97% rate of accuracy. The ultimate goal of this study is to achieve the optimal growth of the sector by navigating the users for a quality and effective decision making by reliable market trends and problem identification.