Research Publications
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Publication Open Access ResQNet: A Multi-Layer Hybrid AI Framework for Priority-Aware Disaster Communication over Decentralized Mesh Networks(Sri Lanka Institute of Information Technology, 2026-05-21) Perera,W.M.REffective emergency communication is critical during disaster events, yet conventional infrastructure frequently fails through physical damage, power outages, and network congestion precisely when reliable coordination is most needed. This paper presents ResQNet, a decentralized hybrid intelligent framework designed to prioritize and route distress messages under post-disaster network conditions. The system employs a multi-layer processing pipeline integrating rule-based keyword scoring, TF-IDF with Linear Support Vector Classification (LinearSVC), a novel Distress-Aware Priority Evaluation (DAPE) heuristic, fuzzy logic-based trust evaluation, and mesh network routing using Breadth-First Search (BFS). An iterative development process upgraded the ML classifier from Multinomial Naive Bayes to LinearSVC with an expanded training corpus of 100 labeled bilingual messages, improving standalone ML accuracy from 32.5% to 70.0%. End-to-end evaluation on 80 live dashboard-tested messages demonstrates that the full hybrid pipeline achieves 71.2% classification accuracy — a 38.7 percentage-point improvement over the original ML baseline — with HIGH-priority message recall of 0.87, confirming reliable detection of safety-critical content. The system maintains stable message delivery across Dense, Sparse, and Damaged network topologies with a mean hop count of 3.4, and priority ordering effectiveness of 79.9%. Trust evaluation identified 21.2% of messages as low-credibility inputs, demonstrating resilience against noisy and misleading content. These results confirm that combining complementary AI techniques produces a significantly more robust disaster communication system than single-method approaches, with meaningful potential for real-world deployment.Publication Open Access Real-Time Embedded System for Inattentive Driver Monitoring(SLIIT, 2022-02-11) Nalmi, R; Clerence, A.; Buddhika, P; SaranyanOne of the causes of motor vehicle accidents in Sri Lanka is driver inattention or drowsiness. In the field of intelligent transportation systems, continuous research and development are conducted to address this contemporary issue. Many approaches, such as driver assistance and drowsiness detection systems, have been proposed to overcome this fatality. The purpose of this research was to implement a product that can maximise road safety while improving the transport sector's efficiency and reliability of the logistics chain to reinforce the country's economic growth. In this paper, the correlation between the preprocessed vehicular parameters and visual features are used to analyse the driver state and make predictions of the driver's perfomance. The proposed system uses computer vision and fuzzy logic inference implemented on the singleboard computer Raspberry Pi to detect facial features and to determine the driver's drowsiness state, an ELM327 is used to read the vehicle parameters from the Electronic Control Unit (ECU) and motion sensors were used to obtain the steering angle. The data acquired is stored in a cloud platform using REST API. The database also contains driver details. The system uses a fingerprint scanner to identify the driver. An actuator was installed in the vehicle to alert the driver when the system detects inattentiveness. Overall the proposed project provided satisfying experimental results. It can be used as a solution to improve road safety and a supporting tool for the logistics sector to monitor vehicles and driver performance.