Scopus Index Publications

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This collection consists of all Scopus-indexed publications produced by SLIIT researchers. Scopus is recognized worldwide as a leading and reputable academic indexing database.

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    Smart Exam Evaluator for Object-Oriented Programming Modules
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Wickramasinghe, M.L.; Wijethunga, H.P.; Yapa, S.R.; Vishwajith, D.M.D.; Samaratunge Arachchillage, U.S.S.; Amarasena, N.
    Worldwide educators considered that, automate the evaluation of programming language-based exams is a more challenging task due to its complexity and the diversity of solutions implemented by students. This research investigates and provides insight into the applicability and development of a java based online exam evaluator as a solution to traditional onerous manual exam assessment methodology. The proposed system allows students to take online exams in Java for an implemented source code in a practical exam, automatically reporting the results to the administrator simultaneously. Accordingly, this research examines existing methods, identifies their limitations, and explores the significance of introducing a smart object-oriented program-based exam evaluator as a solution. This method minimizes all human errors and makes the system more efficient. An automated answer checker checks and marks are given as human-counterpart and generate a report with possible suggestions for improvement of the answer scripts and generate a classification report to predict the student’s final exam marks. This software application uses a Knowledge base, Abstract Syntax tree (AST), ANTLR, Image processing, and Machine Learning (ML) as key technologies. The proposed system gains a higher accuracy of 95% as performed by a separate human-counterpart. These results show a high level of accuracy and automate marking is the major emphasis to save human evaluation effort and maximize productivity.
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    Enhance the Safety Measurements in Railways with the Aid of IoT and Image Processing
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Vedasingha, K. S.; Perera, K. K. M. T.; Hathurusinghe, K. I.; Akalanka, H. W. I.; Amarasena, N.; Dissanayake, N.R.
    Railways provide the most convenient and economically beneficial mode of transportation, and it has been the most popular transportation method. According to the past analyzed data, it reveals a considerable number of accidents which occurred at railways, caused damages to not only precious lives but also to the economy. The goal of this research is to minimize the railway accidents by developing “Railway Process Automation System” while ensuring human safety with use of Internet of Things (IoT) and image processing techniques. The system can detect the current location of the train and close the railway gate automatically. As usual, if the system fails to close the rail gate due to any failure, the proposed system can identify the current location and close the rail gate through decision making system by using past data. The proposed system introduces further two features which named as Railway track crack detection and motion detection which play a significant role in reducing the risk of railway accidents. Moreover, the system is capable of detecting rule violations at a level crossing by using sensors. The proposed system is implemented through a prototype and tested with real-world scenarios to gain the above 90% of accuracy.