Research Publications Authored by SLIIT Staff
Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4195
This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
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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 A Mobile Application to Predict and Manage High Blood Pressure and Personalized Recommendations(IEEE, 2019-12-05) Rajapaksha, S. K; Abhayarathne, W. J. A; Kumari, S. G. K; De Silva, M. V. L. U; Wijesuriya, W. M. S. MThe purpose of this investigation is to present a mobile application using AI expert and how to predict and manage high blood pressure and provide personalized recommendations to lower it. Basically, the system interprets the inadequate and inappropriate intake of food is known to cause various health issues and diseases. Due to the diversity of food components and a large number of dietary sources, it is challenging to perform a real-time selection of diet patterns that must fulfill one's nutrition needs and with considering your health issues and diseases. In this research, to address this issue to present an android based system, called Smart Blood Pressure Recommendation app. The purpose of this system is to allow patients to have an easy way to monitor their health and to see how their blood pressure has changed over time. This offer advice or suggestions, without having to schedule an appointment. As the system continues to gather data from a patient, it begins to offer advice its own if it finds that the patient's current conditions fit a certain condition or pattern. To generate a recommendation, it refers to an Ontology based data model. The data model gains information about its knowledge by doctors and nutritionists that can be used by AI expert. This research helps users to identify their previous record charts of blood pressure, reliable alarms for user blood pressure medication, popup notifications, build health diary and also share log data processing through the AI expert.Publication Embargo HopOn: A Personalized Ride-Sharing System based on Socio-Economic Factors(IEEE, 2021-12-09) Thilakaratne, A; Pinnawala, N; Wijerathna, K; Senavirathna, K; Wellalage, S; Wijekoon, JRapid urbanization and increasing income levels combined with poor and insufficient road network to accommodate vehicles is causing a major traffic problem in Sri Lanka. Additionally, in urban areas, traffic congestion is leading to an increase in air and noise pollution as well. Numerous solutions were tried by authorities, yet no promising results were yielded to address these issues successfully. Contrastingly, increasing road network capacity to solve this problem is very costly and feasible only up to a certain point. Another option is to limit the number of vehicles in the city either by law or by alternative means such as ride-sharing. The best ride-sharing method available is the public transportation, however, due to the limitations of it, upper middle-class opt not to use those hence use their own vehicle to get the expected comfortability. This study is aimed at developing a ride-sharing application by profiling the users based on user reputations, vehicle type, socioeconomic variables such as education, social status, and security concerns of the users, and user ratings. Unlike existing carpooling applications that primarily depend on cost and destination to offer ride options, the proposed application further developed to consider the model of the vehicle for fare calculation.