Research Papers - Dept of Computer Systems Engineering

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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.