• TikTok Video Classification Based on Emotions Using Convolutional Neural Networks 

  Nagodavithana, S.D.; Rupasingha, R.A.H.M.; Kumara, B.T.G.S. (Faculty of Computing. Sabaragamuwa University of Sri Lanka., 2026-01-28)

  Emotion recognition is a product of AI and computer vision, which enables systems to decipher human emotions during a range of uses. As TikTok grows, expressions made by users in the short videos offer valuable information ...
• A Study of Machine Learning Models for Text-Based Mental Health Prediction in Sri Lanka 

  Vitharana, K.S.N.; Kumara, P.G.P. (Faculty of Computing. Sabaragamuwa University of Sri Lanka., 2026-01-28)

  The widespread use of social media has revolutionized the way people share personal problems, which is a new line of identifying the occurrence of mental illness at an early stage. This approach becomes especially vital ...
• Classification of Dental Lesions Using Camera-Captured Images with Convolutional Neural Networks 

  Senanayake, S.M.N.D.; Rupasingha R.A.H.M., R.A.H.M. (Faculty of Computing. Sabaragamuwa University of Sri Lanka., 2026-01-28)

  Oral cavity is a central part of the appearance of a person and their health condition and oral care is crucial. The consideration of good habits and the early identification of lesions are paramount, and their diagnosis ...
• Deep Learning Framework for Predicting LinkedIn Follower Count Range 

  Pathiranage, W.P.T.N.; Kumara, B.T.G.S. (Faculty of Computing. Sabaragamuwa University of Sri Lanka., 2026-01-28)

  LinkedIn has become a key platform for professional networking, where follower count increasingly reflects visibility, credibility, and digital influence. Existing research offers only limited insight into how different ...
• A Machine Learning-Based Approach for Predicting Milk Quality in Sri Lanka 

  Kodithuwakku, E.K.A.J.N.; Adeeba, S. (Faculty of Computing. Sabaragamuwa University of Sri Lanka., 2026-01-28)

  Quality of raw milk is necessary to assure food safety, human health and the survival of the dairy industry in Sri Lanka, though the existing approaches to quality assessment are mostly manual, time consuming, and are ...
• Early Detection of Psychological Risk in Sri Lankan University Students through Interpretable Machine Learning Approach 

  Perera, A.B.T.; Adeeba, S. (Faculty of Computing. Sabaragamuwa University of Sri Lanka., 2026-01-28)

  Psychological distress among university students in Sri Lanka is an increasing issue, which is compounded by academic, financial, and lifestyle issues. Traditional screening tools do not effectively predict the "At-risk" ...
• Comparative analysis of machine learning models on predicting non-alcoholic fatty liver disease stages in Sri Lanka 

  Udeshika, G.A.N.P.; deeba, S. (Faculty of Computing. Sabaragamuwa University of Sri Lanka., 2026-01-28)

  Non-Alcoholic Fatty Liver Disease (NAFLD) is emerging to be a significant problem in the worldwide, including in Sri Lanka, where it has become a major contributor to chronic liver disease. The conventional diagnostic ...
• Sentiment Analysis of Self-Published vs. Traditionally Published Books using Machine Learning 

  Jayasundara, J.M.G.N.; Adeeba, S. (Faculty of Computing. Sabaragamuwa University of Sri Lanka., 2026-01-28)

  The rapid growth of the self-publishing channels, such as Amazon Kindle Direct Publishing (KDP), has greatly changed the model of distribution of books across the world today because authors are able to bypass the ...
• Automated Evaluation of Responses Using Embedding-Based Similarity with Explainable AI Support: A Comparative Study of all-MPNet-basev2 and all-MiniLM-L6-v2 

  Ravindrashankar, M.; Adeeba, S. (Faculty of Computing. Sabaragamuwa University of Sri Lanka., 2026-01-28)

  With the rapid expansion of digital and large-scale learning environments, automated grading of student responses has gained greater significance among educators. In manual grading, there are many difficulties like time ...
• Voice-Based Accessibility for Disabled Users in Websites Using Data Mining and Regression Analysis 

  Roshan, C.; randi, J.D.T.; Amath, A.A.S. (Faculty of Computing. Sabaragamuwa University of Sri Lanka., 2026-01-28)

  Web accessibility remains a major challenge for users with physical and motor disabilities, as most websites rely on mouse- and keyboard-based interactions that are unsuitable for hands- free navigation. Existing voicebased ...
• A Multimodal Emotion-Aware Product Recommendation System Integrating Real-Time Facial Expression Recognition and BERT-Based Sentiment Analysis 

  Shanuga, M.; Erandi, J.D.T.; Amath, A.A.S. (Faculty of Computing. Sabaragamuwa University of Sri Lanka., 2026-01-28)

  This study presents a multimodal emotion-aware product recommendation system that integrates real-time Facial Expression Recognition (FER) and transformer-based sentiment analysis to enhance personalization in digital ...
• Enhancing Sri Lankan textile export forecasting using social media sentiment with machine learning 

  Pathiraja, Y.P.S.S.; Somaweera, W.T.S.; Kumara, B.T.G.S.; Sandaruwan, R.M.T. (Faculty of Computing. Sabaragamuwa University of Sri Lanka., 2026-01-28)

  The apparel industry has continued to be the largest export economic activity in Sri Lanka. However recent shocks such as the COVID-19 pandemic and the 2022 financial crisis revealed large vulnerabilities in conventional ...
• Enhancing Sinhala Hate Speech and Offensive Language Detection through XAI 

  Pilapitiya, H.M.H.N.; Ishanka, U.A.P. (Faculty of Computing. Sabaragamuwa University of Sri Lanka., 2026-01-28)

  The rapid growth of social media has sped up the spread of hatred, abusive and offensive content, creating an urgent need for automated detection systems especially for low-resource languages such as Sinhala. This study ...
• A Novel Sentiment Analysis-Based Hybrid Machine Learning Approach for Forecasting Financial Indices Under Economic Instability 

  Jayakody, J.A.I.N.; Rathnayaka, R.M.K.T. (Faculty of Computing. Sabaragamuwa University of Sri Lanka., 2026-01-28)

  Forecasting financial market behaviour during periods of economic instability presents a significant challenge for traditional predictive models, such as ARIMA and standard machine learning classifiers, which often rely ...
• Comparative analysis of deep learning architectures for Sri Lankan sign language recognition using MediaPipe landmarks 

  Wijesinghe, W.H.M.M.J.; Wijerathna, R.M.K.K. (Faculty of Computing. Sabaragamuwa University of Sri Lanka., 2026-01-28)

  In Sri Lanka, Sri Lankan Sign Language (SLSL) is the main way of communicating among the deaf community; however, the present recognition systems do not have the culturally adaptive methods that come with high accuracy. ...
• Tab-Net Powered Assessment for Early Recognition of Neurodevelopmental Disorders in Children Below Three Years using Symptom Analytics 

  Chamoda, B.P.; Vasanthapriyan, S.; Dampalessa, D. R. C. G. (Faculty of Computing. Sabaragamuwa University of Sri Lanka., 2026-01-28)

  Neurons are the basic building blocks of the human brain and nervous system. In the past few years, Neurodevelopmental Disorders (NDD) in toddlers has been increasing in the world year by year and this is becoming a major ...
• Detecting scam job advertisement using machine learning techniques 

  Natha, M.S.S.; Amath A.A.S., A.A.S.; Erandi, J.D.T. (Faculty of Computing. Sabaragamuwa University of Sri Lanka., 2026-01-28)

  Automated detection of scam job advertisements is critical for ensuring safer online recruitment. This study proposes a machine learning–based approach for detecting fraudulent job postings using textual and metadata ...
• Predictive modeling for injury risk and performance analysis in collegiate and female basketball players 

  Dilakshigan, A.; Erandi, J.D.T; Chandana, A.W.S. (Faculty of Computing. Sabaragamuwa University of Sri Lanka., 2026-01-28)

  This paper examines a machine-learning model of injury prediction and performance assessment on female and collegiate-level basketball players through interpretable workload-based models. The dataset was gathered among ...
• Data-driven prediction of university admission cutoff marks in Sri Lanka using machine learning 

  Theeksha, J.R.N.; Kudagamage, U.P. (Faculty of Computing. Sabaragamuwa University of Sri Lanka., 2026-01-28)

  Admissions into universities in Sri Lanka are determined through the cutoff marks published annually in the University Grants Commission (UGC), although the procedure itself is not only unclear but opaque to students as ...
• AI-powered predictive modeling and comparative machine learning analysis for improving hospital operational efficiency 

  Rathnaweera, R.C.L.U.; Somaweera, W.T.S.; Sandaruwan, R.M.T. (Faculty of Computing. Sabaragamuwa University of Sri Lanka., 2026-01-28)

  The study addresses the critical problem that Sri Lankan state-run hospitals have no data-driven predictive tools of patient movement and resource allocation that have led to longer patients waiting and poor bed utilization. ...

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