International Conference on Advancements in Computing [ICAC]

Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/312

The International Conference on Advancements in Computing (ICAC) is organized by the Faculty of Computing of the Sri Lanka Institute of Information Technology (SLIIT) as an open forum for academics along with industry professionals to present the latest findings and research output and practical deployments in computing.

The primary objective of ICAC is to promote innovative research that addresses real-world challenges and contributes to the social well-being of communities. The conference provides a dynamic platform for researchers from around the world to present groundbreaking findings, exchange ideas, and establish meaningful collaborations.

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Subject: search.filters.subject.Reinforcement learning

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    Emission Aware Multi-Metric Reinforcement Learning for Adaptive Traffic Signal Control in Sri Lanka
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Shangeeth A; Perera S.A.N; Munaweera K.D.M.P.; Sumathipala, P; Nawarathne, M
    Urban intersections in Sri Lanka experience growing congestion and greenhouse gas emissions, while signal control remains fixed-time and insensitive to demand variability. An adaptive traffic signal control framework based on reinforcement learning (RL) is proposed. The control task is formulated as a Markov decision process with state features comprising queue length, waiting time, braking events, and estimated emissions. A Deep Q-Network (DQN) is trained in the SUMO-TraCI microsimulation under static and dynamic demand scenarios. Multiple reward formulations are examined, including emission-only and multi-metric designs with normalized, weighted components. Experiments show that emission-only rewards yield unstable learning, increased queuing, and no measurable emission reduction, indicating the limitations of single-metric optimization. A balanced reward combining queue length and braking achieves the most favorable trade-off, reducing average delay by up to 22% and CO2 emissions by 15% relative to fixed-time baselines. The framework is further instantiated on OpenStreetMap-derived models of Sri Lankan intersections (Borella, Kollupitiya), demonstrating applicability to local geometries and operating conditions. The results indicate that emission-aware RL controllers can outperform fixed-time operation and offer a viable pathway for deployment to support congestion mitigation and emission-reduction objectives.
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    Context-Aware Incremental Learning Pipeline Triggers
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Perara, S; Senadheera, D; Gunathilake, P; Gunawardana, C; Thelijjagoda, S; Krishara, J
    Modern Continuous Integration/Continuous Delivery pipelines require intelligent and adaptive learning mechanisms to enhance software deployment stability and efficiency. While previous studies have introduced Pull Request prioritization techniques and predictive models for build failures, they do not offer real-time recommendations based on Pull Request criticality and build stability. This research presents a GitHub app based on reinforcement learning for intelligent Pull Request prioritization and deployment advisory in Continuous Integration/Continuous Delivery workflows. T he system combines natural language processing with contextual features to dynamically determine whether a Pull Request should be prioritized for early deployment or flagged for urgent review based on its priority level and associated build failure risk. Due to limited labeled feedback data, the study employs a hybrid approach using supervised learning for initial classification, enhanced by reinforcement learning for continuous adaptation. Using a dataset of 682148 Pull Requests from 28835 repositories, experimental results demonstrate significant improvements in deployment efficiency, achieving 92.01% accuracy in Pull Request classification with strong developer alignment. This research presents a comprehensive solution for optimizing Continuous Integration/Continuous Delivery pipelines through intelligent Pull Request prioritization with deployment advisory.