Browsing by Author "Eheliyagoda, D.R.M.R.R.D.R.S."

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Embargo
Data-driven Business Intelligence Platform for Smart Retail Stores
(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Eheliyagoda, D.R.M.R.R.D.R.S.; Liyanage, T.K.G.; Jayasooriya, D.C.; Nilmini, D.P.Y.C.A.; Nawinna, D.; Attanayaka, B.
The following research paper presents the design and development of a data-driven decision support platform for the effective management of contemporary retail stores in Sri Lanka. This research has four core components, as a solution to the identified shortcomings. These components are Customer Relationship Management (CRM), Supplier Relationship Management (SRM), Price and Demand estimation, and Branch and Employee Performance Monitoring and Rating. The developed system has features such as product replenishment levels, decrease capital movement, reduced material wastage, better item assortment, provide supplier service efficiency, improve employee and branch-level efficiency, and elevated client delivery. This decision support system used Machine Learning (ML) technologies such as LSTM (Long short-term memory) and ARIMA (Autoregressive integrated moving average) models, Regression, Classification, and Associate Rule Mining Algorithms as key technologies. Data were obtained from websites such as Kaggle and other free platforms for the analysis of datasets. The resulting platform was able to perform with an accuracy of over 90% for all four core components with the tested data sets. The system presented would be particularly beneficial for the top management in retail stores to make effective and efficient decisions based on predictions and analyzes provided by the system.
Open Access
Data-driven intelligence dating platform
(SLIIT, 2024-12) Eheliyagoda, D.R.M.R.R.D.R.S.
The study sought to investigate the difficulties associated with finding an ideal life partner through dating apps, particularly in the context of matching horoscopes, personal interests, and preferences. Recognizing that many dating platforms prioritize individual interests, the study highlighted the challenges posed by the confidentiality of personal data, which frequently complicates the matchmaking process. The objective of this study was to create a data-driven model that prioritized the integration of horoscope details alongside user preferences and interests while protecting users' personal information. This model attempted to recommend suitable partners by combining multiple predictive analyses based on these variables. The data collection methodology included both open-access sources and a standardized questionnaire, allowing for a comprehensive approach that incorporated multiple datasets into the model's training process. By combining personal preferences with astrological data, this innovative method aimed to transform the dating landscape by providing tailored recommendations while protecting user privacy. The research project culminated in a systematic investigation of how a datacentric approach could improve partner matching efficacy, filling significant gaps in existing dating apps that frequently overlook astrological compatibility. This abstract captured the essence of a research initiative aimed at developing an advanced predictive model to improve partner selection processes by combining personal interests and astrological insights, resulting in a more personalized and secure online dating experience.