Research Publications

Now showing 1 - 3 of 3

Open Access
Identifying the Factors Affecting University Students’ E-Businesses
(Faculty of Humanities and Sciences, SLIIT, 2023-11-01) Abeysinghe, M. A. S. D. L.; Alibuhtto, M. C.
University students encounter economic challenges in the complex technological world, and finding part-time work can provide benefits such as earning money, gaining experience, and developing skills, although it is difficult to avoid the potential negative consequences on academic performance. This paper aims to identify influencing factors on e-business in selected universities in Sri Lanka. This study was conducted from September to October 2022, specifically in the north and eastern provinces, with 232 participants focusing on the faculties of Science, Engineering, Technology, Management and Commerce. The study uses univariate and bivariate analysis techniques, including binary logistic regression to identify the factors influencing e-business behavior among university students and explore the relationships between variables, ensuring accurate and reliable results. The study revealed that the majority were male, with a high proportion of Sinhala students in the sample. Most students own laptops /desktops and smartphones, have weekly expenses between LKR 3,000 to LKR 5,000, and prefer to work in their field of study. Also, language issues are a major issue in the university environment and are usually reported as a challenge, while many students have roommates engaged in e-business. Overall, students exhibited average competencies in computer and English literacy skills. Factors such as device usage, weekly expenses, preferred field of work, faculty, level of study, e-businesses among roommates, computer skills before entering the university, and IT courses followed were found to be the most significant factors affecting e-business among university students.
Open Access
Evaluating expressway traffic crash severity by using logistic regression and explainable & supervised machine learning classifiers
(Elsevier, 2023-07-09) Shashiprabha, M.J.P.S; Kelum, S.R.M; Meddage, D.P.P; Pasindu, H.R; Gomes, P.I.A
The number of expressway road accidents in Sri Lanka has significantly increased (by 20%) due to the expansion of the transport network and high traffic volume. It is crucial to identify the causes of these crashes for effective road safety management. However, traditional statistical methods may be insufficient due to their inherent assumptions. This study utilized explainable machine learning to investigate the factors that affect the severity of traffic crashes on expressways. The study evaluated two groups of traffic crashes: fatal or severe crashes, and other crashes that included non-severe injuries or only property damage. Five factors that contribute to crashes were analyzed: road surface condition, road alignment, location, weather condition, and lighting effect. Four machine learning models (Random Forest (RF), Decision Tree (DT), extreme gradient boosting (XGB), K-Nearest Neighbor (KNN)) were developed and compared with Logistic Regression (LR) using 223 training and 56 testing data instances. The study revealed that the machine learning algorithms provided more accurate predictions than the LR model. To explain the machine learning models, Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were used. These methods revealed that all five features decreased the possibility of occurrence of fatal accidents. SHAP and LIME explanations confirmed the known interactions between factors influencing crash severity in expressway operational conditions. These explanations increase the trust of end-users and domain experts on machine learning models. Furthermore, the study concluded that using explainable machine learning methods is more effective than traditional regression analysis in evaluating safety performance. Additionally, the results of the study can be utilized to improve road safety by providing accurate explanations for decision-making processes for black-box models. © 2023
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AI Solution to Assist Online Education Productivity via Personalizing Learning Strategies and Analyzing the Student Performance
(Institute of Electrical and Electronics Engineers, 2022-10-29) Liyanage, M.L.A.P.; Hirimuthugoda, U.J; Liyanage, N.L.T.N.; Thammita, D.H.M.M.P; Koliya Harshanath Webadu Wedanage, D; Kugathasan, A; Thelijjagoda, S
Higher productivity in online education can be attained by consistent student engagement and appropriate use of learning resources and methodologies in the form of audio, video, and text. Lower literacy rates, decreased popularity, and unsatisfactory end-user goals can result from unbalanced or inappropriate use of the aforementioned. Prior studies mainly focused on identifying and separating the elements affecting the quality of online education and pinpointing the students' preferred learning styles outside of in-person and online instruction. This has not been able to clearly show how to enhance and customize the online learning environment in order to benefit the aforementioned criteria. This case study will primarily concentrate on elements that can be personalized and optimized to improve the quality of online education. With the aid of various algorithms like logistic regression,Support Vector Machines (SVM), time series forecasting (ARIMA), deep neural networks, and Recurrent Neural Networks (RNN), which make use of machine learning and deep learning techniques, the ultimate result has been attained. To increase application and accuracy, the newly presented technique will then be presented as a web-based software application. Contrary to what is commonly believed, this applied research proposes a new all-in-one Learning Management System (LMS) for students and tutors that acts as a central hub of all the learning resources.

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