Faculty of Computing

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    Assistant Zone – Homeschooling Assistance System based on Natural Language Processing
    (IEEE, 2022-12-09) Premendran, K; Bopearachchi, S.B.D.D.; Senevirathna, S.D.M.; Giridaran, S; Archchana, K; Ganegoda, D; Thelijjagoda, S
    As a developing country, most people give their highest priority to education. When focusing on building an e-learning platform to improve the knowledge of students and teacher-student interactivity, the pandemic season can be mentioned as the main blocker which highly impacted the education field. Not only by considering the pandemic situation but also by addressing the concerns when it comes to teacher and student evaluation and psychological levels of students who are undergoing different difficulties, the “Home Schooling Assistance System” (Assistant Zone) has been introduced as a solution. The Assistant Zone has been initiated with three unique features which are valuable for both students and teachers. This system analyzes the strengths, weaknesses and evaluates the student performance, suggests study materials to improve themselves, provides solutions to the problems faced by the students, teachers, and parents and measures the performance of teachers based on their students, and recommends learning materials for the low-performing teachers. The Assistant Zone fulfills the targeted problems and introduces the above-mentioned three unique features with the use of Natural Language Processing (NLP) such as the BERT algorithm and Machine Learning models such as the Recurrent Neural Network, Forward Neural Network, and Gaussian Model.
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    HopOn: A Personalized Ride-Sharing System based on Socio-Economic Factors
    (IEEE, 2021-12-09) Thilakaratne, A; Pinnawala, N; Wijerathna, K; Senavirathna, K; Wellalage, S; Wijekoon, J
    Rapid urbanization and increasing income levels combined with poor and insufficient road network to accommodate vehicles is causing a major traffic problem in Sri Lanka. Additionally, in urban areas, traffic congestion is leading to an increase in air and noise pollution as well. Numerous solutions were tried by authorities, yet no promising results were yielded to address these issues successfully. Contrastingly, increasing road network capacity to solve this problem is very costly and feasible only up to a certain point. Another option is to limit the number of vehicles in the city either by law or by alternative means such as ride-sharing. The best ride-sharing method available is the public transportation, however, due to the limitations of it, upper middle-class opt not to use those hence use their own vehicle to get the expected comfortability. This study is aimed at developing a ride-sharing application by profiling the users based on user reputations, vehicle type, socioeconomic variables such as education, social status, and security concerns of the users, and user ratings. Unlike existing carpooling applications that primarily depend on cost and destination to offer ride options, the proposed application further developed to consider the model of the vehicle for fare calculation.