3rd International Conference on Advancements in Computing [ICAC] 2021

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Author: search.filters.author.Attanayaka, B.

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    Use of Natural Language Processing and Deep Learning towards Guiding Healthy Cholesterol Free Life
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Sasanka, D.; Malshani, H. K. N.; Wickramaratne, U.I.; Kavindi, Y.; Tissera, M.; Attanayaka, B.
    High blood cholesterol is a key risk factor for cardiovascular diseases such as coronary heart disease and stroke. This has become a severe health problem, because it causes a considerable amount of deaths annually. The major risk factors that affect a person’s cholesterol level include unawareness of cholesterol risk, unhealthy dietary habits, lack of proper exercises, and high stress conditions. In this research, novel approaches are introduced to provide an automated and personalized guidance to maintain healthy cholesterol level and raise the awareness of each risk factors mentioned above. This research associates with four novel approaches. Natural Language Processing (NLP) based Cholesterol risk analyzer, Fuzzy based Food management with Meal predictor, Machine Learning based Physical exercise planner and Stress controller. Altogether with results, this research will provide a complete and facts-proven solution to reduce and guide people towards a cholesterol-free healthy lifestyle.
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    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.