http://repo.lib.sab.ac.lk:8080/xmlui/handle/123456789/22 Thu, 11 Jun 2026 13:32:51 GMT 2026-06-11T13:32:51Z http://repo.lib.sab.ac.lk:8080/xmlui/handle/susl/5339 Cross-Modal Predictive Modeling of Mental Health Treatment Outcomes: A Machine Learning Framework for Comparing Psychiatric Counseling Therapy and Therapeutic AI-Chatbots DeSilva, M.T.D.; Kaushalya, P.K.D.K. Mental health problems are becoming the order of the day and burdening the traditional psychiatric guidance frameworks in terms of expenses, unreachability and waiting durations. Conversely, mental health chatbots that are based on AI have become popular because of their anonymity, 24/7, and low cost. Although both traditional counseling and chatbot approaches have feasible advantages, no standard way of operating has been established to compare the efficacy of the two with individual patients. This has been a barrier to the use of individualized mental health interventions. The paper examines the application of machine learning and in this case, the Random Forest algorithm to predict and compare the results of conventional therapy and AI chatbot assistance. Available references define the major signs of treatment success and provide an overview of the benefits and shortcomings of chatbot interventions, yet no model exists to evaluate how people can react to the alternative medium. To solve this, a random forest model was created using data on clinical therapy outcome and the results were used on the data of chatbot users to forecast possible outcomes. The reported chatbot outcomes were compared statistically and through the qualitative feedback with the expected therapy outcomes. The research will establish personal characteristics that relate to increased benefits in either form of therapy. The expected outcomes will be used in clinical decision making, enhancement of digital mental health tools and help in choosing the most appropriate treatment. Wed, 28 Jan 2026 00:00:00 GMT http://repo.lib.sab.ac.lk:8080/xmlui/handle/susl/5339 2026-01-28T00:00:00Z http://repo.lib.sab.ac.lk:8080/xmlui/handle/susl/5338 Deepfake Image and Video Detection System for Sri Lankan Facial Features Using Machine Learning Karunarathna, A.M.T.H.; Abeythunga, W.M.L.S. Deepfakes and other AI-generated manipulated images and videos have become an increasing cyber threat to Sri Lanka as AI-generated multimedia content becomes more accessible to consumers. Global AI-generated multimedia datasets used in global deepfake detection models do not include sufficient representation of Sri Lankan characteristics including: darker/mixed brown skin tone; South Asian facial structure; ethnic diversity (Sinhalese, Tamil, Muslim, Burgher); traditional clothing; and lighting found in a variety of local environments. Therefore, many international deep fake detection systems are either fail to accurately identify manipulated images of Sri Lankan faces, or fail when detecting low resolution video content captured on mobile devices that are commonly used in Sri Lanka. A system designed to detect deep faked images and videos of Sri Lankan faces is presented in this research. The system uses a CNN-based image forensic model in combination with frequency domain-based artifact analysis and landmark consistency checks to evaluate each image submitted by a user. Additionally, the system also analyzes video submissions for deep fakes by extracting frames from the input video and evaluating each frame individually using the trained image model. Finally, the results from each individual frame are aggregated into an overall decision regarding the authenticity of the video submission. A custom dataset was developed for the purposes of training the system’s models, which focuses on a variety of aspects of Sri Lankan skin tones, facial structures, cultural elements, and environmental factors. Wed, 28 Jan 2026 00:00:00 GMT http://repo.lib.sab.ac.lk:8080/xmlui/handle/susl/5338 2026-01-28T00:00:00Z http://repo.lib.sab.ac.lk:8080/xmlui/handle/susl/5337 Explainable Artificial Intelligence Approaches in NLP-Based Text Classification: A Systematic Literature Review Gunasekara, S.A.G.K.; Kaushalya, P.K.D.K. Wed, 28 Jan 2026 00:00:00 GMT http://repo.lib.sab.ac.lk:8080/xmlui/handle/susl/5337 2026-01-28T00:00:00Z http://repo.lib.sab.ac.lk:8080/xmlui/handle/susl/5336 Machine learning-based performance prediction framework for real-time 3D asset optimization in 3D modeling Praneeth, T.M.K..; Lakshan, W.D.D.; Hewaratna, A.I. This study presents a machine learning–based performance prediction framework that enables 3D artists and game developers to estimate rendering cost. such as frame rate, CPU/GPU usage, memory consumption, and draw calls during the asset creation process. With the rapid growth of the gaming and real-time graphics industry, the demand for performance optimized 3D assets has increased significantly. However, existing tools such as Unity Profiler and Unreal Insights are inherently reactive, providing feedback only after assets are imported into an engine, which leads to iterative, time-consuming optimization cycles and production delays. To address this gap, the proposed system introduces a proactive, real-time prediction approach that operates at the modeling stage. A structured dataset of 3D asset features including polygon count, vertex density, texture resolution, and shader complexity is combined with runtime performance metrics collected from Unity. Using Random Forest, XGBoost, Multi-Layer Perceptron, and Graph Convolutional Network models, the framework predicts key performance indicators with high accuracy. Preliminary experiments show best R2 values for frame-rate prediction, while maintaining millisecond-level inference latency suitable for interactive use. The trained model is integrated into Blender through a plug-in and REST based service, providing instant feedback to artists as they modify meshes, materials, and textures. A user survey indicates that 90% of participating artists perceive the tool as practically valuable for reducing optimization effort. Overall, this work introduces a proactive surrogate model for 3D asset performance prediction, with strong potential to reduce iteration cycles and streamline real-time content production pipelines. Wed, 28 Jan 2026 00:00:00 GMT http://repo.lib.sab.ac.lk:8080/xmlui/handle/susl/5336 2026-01-28T00:00:00Z