Browsing by Author "Nawinna, D."

Now showing 1 - 3 of 3

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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.
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Impact of Non-Functional Requirements on the Success of Ubiquitous Systems
(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Sandeepani, S.; Nawinna, D.
With the recent advancements of technology, Ubiquitous Systems have rapidly become popular all over the world. It is a new paradigm that focuses on smooth integration of technology in human environments enabling users to access information and functionality anytime and anywhere. Software development companies nowadays increasingly invest in the ubiquitous system development projects in order to stay competitive and survive in the IT Industry. Success of ubiquitous system development projects heavily depends on Nonfunctional user requirements. Identification of the nonfunctional requirements is challenging since it represents the quality attributes of the system and are not directly measurable. This quantitative research aims to evaluate the different types of non-functional requirements that significantly contribute to the success of ubiquitous system development projects. This study was based on the data collected from the software industry in Sri Lanka. The results of this study indicate that both the product-related and organizational-related nonfunctional requirements strongly affect the ubiquitous systems success. The findings provide insights to the vendors of ubiquitous system development companies in the software industry.
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IoT-based Monitoring System for Oyster Mushroom Farming
(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Surige, Y.D.; Perera, W.S.M.; Gunarathna, P.K.N.; Ariyarathna, K.P.W.; Gamage, N.; Nawinna, D.
Agriculture plays a major segment in the economy of Sri Lanka, a developing country. Mushrooms, farming is a popular option among the farmers as it consumes less space and less time for growing while offering a high nutritional value, but most farmers fail to obtain the best yield from their cultivations due to the defects and inefficiencies in the manual methods that are being presently used. This paper presents an ICT solution to avoid inefficiencies in the mushroom farming process. The system is developed focusing one of the popular mushroom type ‘Oyster Mushrooms’. The system offers four functionalities to perform mushroom farming precisely The system offers four functionalities to perform mushroom farming precisely. The Environmental Monitoring function is built with the support of a Long Short Term Memory (LSTM), Harvest time detection function is developed with the support of Convolutional Neural Networks (CNN) with Mobile Net V2 model, The Disease detection and control recommendation function is based on the support of CNN with mobile Net V2 model and the Yield prediction function is developed using the support of Long Short Term Memory (LSTM), The farmer is connected to the system through a mobile application. The system can monitor the environmental factors with an accuracy of 89% and the harvest time can be detected with an accuracy of 92%. Also, the system detects the mushroom diseases with an accuracy of 99% and predicts the monthly yield of a mushroom cultivation with an accuracy of 97%. The intense use of precise farming will eventually lead to high mushroom yields.