Research Publications Authored by SLIIT Staff

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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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    Machine Learning to Aid in the Process of Disease Detection and Management in Soilless Farming
    (IEEE, 2022-07-18) Fernando, S. D; Gamage, A; De Silva, D. H
    This research aims at enhancing the methods and techniques that are being used in disease detection when it comes to soilless farming. Soilless farming is quite famous among the Sri Lankan farmers farming in urban areas. A mobile application is launched by us and this application is capable of identifying diseases in plants, therefore, farmers do not have to rely on their years of experience to identify the diseases. A novice farmer may struggle to say what is wrong with their plants, while another farmer with many years of experience may say what the disease is with no hesitation. Both those types of farmers benefit from our mobile application equally. The said mobile application consists of four components and each of them focuses on a different service. One of those components is to detect and manage diseases in plant leaves and that component is what this research paper showcases. This particular component allows the user to capture live-images of plant leaves. Then the application processes the captured image to identify if the plant is suffering from a disease. After that, it generates a report with a set of treatments. It further analyses and alerts the user if this disease detected is going to affect the harvest.
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    Task and Process Capturing Toolkit using GUI Automation
    (IEEE, 2022-06-27) Perera, R. L; Bellanthudawa, H. P; Hevavitharana, N. D; Ariyasinghe, K. M; Wickramarathne, J; Perera, J
    Automation is one of the best ways to make easier everyone's day-to-day life. When considering the industry, if someone could be able to automate the mechanism of solving computer software issues, that will be helpful to every company. Because most companies are facing difficulties when it comes to giving IT support. Some companies are suffering from a lack of IT supporters because they have to solve the same problem on different computers at the same time. This most likely reduces the efficiency of the company and affects its performance as well. Without the proper knowledge of technology, employees tend to get a lot of technical issues while working with the technology. Here comes the opportunity to automate the IT-related problem-solving mechanism. That will save a lot of time and increase the company's efficiency as well. While carrying out the background research we thought, by automating the IT-related problem it will reduce the allocation of human power and that will directly affect company efficiency. The research team came up with the idea of creating a software product that can export an executable file by capturing selected tasks of the user's screen and its' relevant processes. Task and process capturing toolkit is a software product called the “ClickMe toolkit”. It can capture relevant processes that are happening in any computer which runs on Windows 10 platform, then make a script called “ClickMe script”, and convert it into an executable file. It can be run on any Windows 10 Platform.
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    “ServPort”: Process Reengineering in Optimization of The Process in Vehicle Service Station
    (IEEE, 2022-06-27) Withana, R. D. K; Fernando, W. S. C. S; Nethsara, V. R; Jayasinghe, N. B. A. C.T; Lokuliyana, S. L; Kuruppu, T. A
    The usage of vehicles is increasing across the world. Thus, vehicle maintenance has become a key factor when considering vehicles' continuous performance, which leads to the increasing need for vehicle service providers. However, there are some challenges faced by vehicle service providers when providing a high-quality service within a reasonable price range for their customers. A process optimization solution for vehicle service centers named as ‘ServPort’ is proposed through this study to provide a solution to the challenges experienced by vehicle service providers and to support them in providing a quality service at a fair price to their clients. According to the findings, a process optimization solution was not yet introduced for the vehicle service sector in Sri Lanka. Therefore, this paper addresses the selected machine learning models and the approaches taken to optimize the process in a vehicle service station by predicting key fields in a vehicle service station. Under this, customer retention and its impact on the vehicle service center's profitability were predicted using linear regression algorithm, which achieved 99.29% accuracy rate. In comparison, other selected machine learning models achieved lower accuracy rates. When predicting employee efficiency, decision tree model achieved 90% accuracy rate, whereas linear regression algorithm achieved only 50% accuracy rate. To predict the next vehicle service date, logistic regression algorithm, which performed with an accuracy rate of 98% was used.
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    Smart Assistant to Ease the Process of COVID-19 and Pneumonia Detection
    (IEEE, 2021-12-06) Akalanka, B. A; Senevirathne, K. D. A; Dias, M. H. V; Nimantha, W. A. R; Chathurika, K.B.A. B; Silva, C. M
    COVID -19 is one of the most contagious diseases in the 21 st century. Therefore, there's an emerging need to contrive an accurate, gradual new method to identify this deadly virus. Apropos, we present “Smart assistance to ease the process of COVID -19/pneumonia detection” mobile application that can use to identify covid-19 contemplating patient's symptoms, health history, breathing information, chest CT scan and chest X-ray images. Stage 1 of the proposed application will prognosticate the danger level of the patient utilizing symptoms, breathing information, health history using machine learning techniques. Recognition and drawing out of patient's health background information by engaging the user to maximize the accuracy of the outcome is the main objective of this stage. Stage 2 of the application will identify COVID-19 by a chest X-ray/CT scan image, and it predicts the danger level using deep learning techniques. Classify the image to predict the danger level for COVID-19 is the main objective of this phase. Subsequently, all the predictions are sent to a physician and validate the outcome. Finally, patient will be notified about the results. This automatized application is built with the intention of reducing the cost of covid-19 identification tests like PCR tests and to give precise results as soon as possible. Our motive is to show that the proposed application could be a finer alternative for already existing COVID -19 identification tests. As a result, we achieved the best accuracy of 92%, 96% for CT scan, X-ray images classification and 94.08%, 74.19% accuracy for health history information analysis and breathing information analysis. We also achieved 94%, 71% accuracies for the COVID-19 prediction model and severity level prediction model based on symptoms.