Browsing by Author "Kuruppu, T. A"

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Embargo
Automated Programming Assignment Marking Tool
(IEEE, 2022-07-18) Thenuwara, T. B. K. P; Vimalaraj, H; Wijekoon, V. U; Sathurjan, T; Reyal, S; Kuruppu, T. A; Tharmaseelan, J
Due to the enrolment of a very high number of students to programming modules, marking of programming modules is becoming a very tedious and time-consuming process. Programming assignments mainly test for the student’s ability to think logically and approach a solution to the problem. In that case, just running the script and checking the output will not be sufficient enough to award a grade to the student. Marking criteria of programming modules provide certain marks for programs which are not syntactically correct but still have a good approach. Therefore, the code has to be read line by line and the implementation should be checked carefully to provide marks. Source code analysis has become mandatory in the current scenario. This leads to immense pressure and heavy workload on the staff who mark these programs. Considering all these aspects manual marking can lead to inconsistency, biasness, waste of time and less accuracy. Therefore, the main objective of this research is to minimize these problems by implementing an automated programming module marking tool by converting source codes to parse trees, extracting features, generating feature vectors, comparing them and generating a mark along with a feedback and plagiarism report. The solution focuses on automation marking by source code analysis and plagiarism checking.
Embargo
Automated Programming Assignment Marking Tool
(IEEE, 2022-07-18) Vimalaraj, H; Thenuwara, T. B. K. P.; Wijekoon, V. U; Sathurjan, T; Reyal, S; Kuruppu, T. A; Tharmaseelan, J
Due to the enrolment of a very high number of students to programming modules, marking of programming modules is becoming a very tedious and time-consuming process. Programming assignments mainly test for the student’s ability to think logically and approach a solution to the problem. In that case, just running the script and checking the output will not be sufficient enough to award a grade to the student. Marking criteria of programming modules provide certain marks for programs which are not syntactically correct but still have a good approach. Therefore, the code has to be read line by line and the implementation should be checked carefully to provide marks. Source code analysis has become mandatory in the current scenario. This leads to immense pressure and heavy workload on the staff who mark these programs. Considering all these aspects manual marking can lead to inconsistency, biasness, waste of time and less accuracy. Therefore, the main objective of this research is to minimize these problems by implementing an automated programming module marking tool by converting source codes to parse trees, extracting features, generating feature vectors, comparing them and generating a mark along with a feedback and plagiarism report. The solution focuses on automation marking by source code analysis and plagiarism checking.
Embargo
Deep Learning-Based Smart Infotainment System for Taxi Vehicles
(IEEE, 2022-06-27) Dewalegama, M. P; de Zoysa, A.D.S; Kodikara, L. M; Dissanayake, D.M.J.C; Kuruppu, T. A; Rupasinghe, S
Nowadays, people are more intent to use IoT to ease their day-to-day work. As a result of that, the transportation industry is being adopted to more IoT-based approaches rather than traditional methods. When it comes to taxi services, companies need to keep up the competition with their rivals. Along with the high demand, there are also being reporting number of problems around the taxi industry daily. Taking some of the most common problems into consideration, such as “Smart Infotainment System” will help to resolve most of those problems. Deep learning models such as CNN (Convolutional Neural Network), YOLO and SKLearn used to develop the proposed system. As a result of that, the reputation of the taxi service will be increased, and the taxi service consumers will be able to get a comfortable and safer journey to the end of the day.
Embargo
“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.