Recent Submissions
Context-Aware Behavior-Driven Pipeline Generation
(Institute of Electrical and Electronics Engineers Inc., 2025-04-24) Gunathilaka, P; Senadheera, D; Perara, S; Gunawardana, C; Thelijjagoda, S; Krishara, J
An efficient CI/CD process is crucial for modern software teams, but manual pipeline creation is error-prone and requires high DevOps expertise, slowing deployment speed and reducing productivity. This research introduces a context-aware, behavior-driven approach to fully automating CI/CD pipeline generation by analyzing GitHub user activity patterns. The proposed solution utilizes a historical analysis of repository events, developer contributions, and workload distribution to dynamically generate pipelines and assign reviewers to pull requests based on expertise. Unlike previous template-based and generative AI solutions that require manual intervention, our approach leverages pattern recognition and adaptive decision-making to continuously refine automation. This paper presents the methodology behind data collection, analysis, and pipeline generation, demonstrating its effectiveness in reducing human effort while improving software delivery efficiency. This research highlights how behavior-driven automation streamlines the complexity of CI/CD pipeline creation, enabling more adaptive and intelligent systems that effectively respond to the evolving needs of software development teams.
A Virtual Reality Immersive Experience of Sri Lanka's Sacred Heritage: VR Meditation Experience at the Ruwanweli Maha Seya using Heart Rate and HRV Tracking
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Herath, S; Hapangama, C; Gamage, U; Soyza, N; Gamage, I; Nismi, N
Meditation is a widely recognized practice that reduces stress, improves focus, and promotes mental well-being. For various reasons, it has become difficult for religious devotees to travel to more intimate places of worship, such as their sacred sites, and to worship and meditate in harmonious environments. This system involves creating a Unity-based VR application that simulates several sacred places in Anuradhapura city, including four of the eight sacred places among the “Atamasthana"” and a short five-minute meditation session. A user evaluation was conducted with 25 participants. The results showed a decrease of 5.4 BPM in average heart rate and a 10 increase in heart rate variability after the meditation session. In this research, the sacred city of Anuradhapura has been used as a sample, but any religious sacred place can be used for this purpose. Through this harmonious environment recreated through Virtual Reality (VR), users can experience mental relaxation regardless of physical location. This research is more important because it combines cultural and religious preservation with modern technological innovations. By incorporating physiological sensations into a VR meditation experience, it is possible to enhance user engagement and monitor the results of the meditation. It can be understood that the physiological effects of this VR-based meditation are a good basis to advance both the study of digital wellness and cultural scarcity.
Evaluating the Effectiveness of Mind Heaven: A Virtual Reality Meditation Solution based on Theravāda Buddhist Practices
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Dabare, C; Arachchi, D.C.T
In modern urban life, stress and anxiety are prevalent due to fast-paced environments and limited access to tranquil spaces. Buddhist meditation, known for its therapeutic effects, often requires calm settings that are hard to access in urban areas and some of these kinds of environments might experience unexpected accidents as well. This research presents a Virtual Reality (VR)-based personalized Buddhist meditation application call Mind Heaven, aimed at stress reduction and mental relaxation. The application evaluates users' moods before and after meditation, offering individualized meditative environments based on users' emotional states. Through collaboration with Theravāda Buddhist monks in Sri Lanka, the system integrates authentic meditative guidance. Additionally, smartwatch-based physiological stress tracking and post-session survey are used to verify user feedback. The pilot evaluation with twenty participants indicates mood improvement and stress reduction in indicators. This study demonstrates the potential of immersive VR in mental wellness, paving the way for future integration with brain-computer interfaces and diversified meditation techniques.
TALENT TREK: Enhancing Interview Decisions with Conversational AI
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Fernando T.P.R; Rathnakumara A.R.S.A; Wijerathna G.D.K; Abeysingha A.D.L; Rajapakse K; Haddela P.S
In the evolving landscape of recruitment, traditional hiring methods are increasingly inadequate for identifying top talent, particularly given the demands of modern, data-driven industries. This research introduces TALENT TREK, a modular, AI-powered Automated Human Resources (HR) Interview System designed to deliver fair, scalable, and multimodal candidate evaluations. The system integrates real-time job data scraping, skill forecasting, semantic Natural Language Processing (NLP) based response analysis, and facial emotion recognition within a microservices-based architecture optimized for concurrent processing. Leveraging a multi-engine speech recognition ensemble and the all-mpnet-base-v2 transformer for semantic evaluation, it achieves high correlation with human assessments while minimizing transcription and comprehension errors. A custom Convolutional Neural Network (CNN) trained on FER2013 with domain-specific augmentations supports emotion classification, from which a novel Positive Confidence Score is derived. Multimodal data fusion enables adaptive weighting based on input quality, ensuring accurate composite scoring. Extensive testing demonstrates the system's potential to enhance transparency, consistency, and efficiency in enterprise-level hiring processes.
AI Driven Smart Tourism Platform for Personalized Safe and Sustainable Travel Planning
(Institute of Electrical and Electronics Engineers Inc., 2025-12-08) Srikanthan, S; Senevirathne, C; Rasarathnam, T; Jayalath, T; Rajendran, K
Tourism planning remains challenging due to the need for group preference alignment, personalized itinerary generation, and real-time budget control, challenges that are not adequately supported by existing platforms. This paper presents an AI-driven modular framework that integrates three components: a semantic-aware group recommender that uses Sentence-BERT embeddings with a learning-to-rank model to match travelers; a hybrid itinerary planner that fuses content-based filtering, collaborative filtering, and machine-learning-based rating prediction to generate preference-aligned and geographically coherent travel plans and a predictive budgeting system that applies regression-based forecasting with live API data to provide dynamic cost estimation. The platform is developed specifically for the Sri Lankan tourism context, incorporating regional travel behavior patterns and destination characteristics into its models. Experiments indicate strong performance across all modules, including high-quality group matching, accurate itinerary prediction, and a substantial improvement in budget estimation accuracy compared with static baselines. Early user testing further highlights increased satisfaction with itinerary relevance and budget transparency. Overall, the framework demonstrates a scalable and adaptive approach to smart tourism planning, advancing personalization, collaboration, and sustainable travel support.
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The collection comprises the research output of SLIIT staff and postgraduate research students, including research publications, conference and symposium papers, books, book chapters, theses, and other scholarly materials. Access to full texts may be restricted depending on the access and licensing terms.