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 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.Publication Embargo 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.