Faculty of Computing

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    Driving Through a Bend: Detection of Unsafe Driving Patterns and Prevention of Heavy Good Vehicle Rollovers
    (IEEE, 2022-01-17) Siriwardana, E. M. A. K; Piyawardana, V. S; Chandrasiri, S. S; Kaushalya, S. G. L. D. H; Sampath, K.D. Amila
    Road Traffic Crashes are simply ordinary within the present world. However, heavy goods vehicles (HGV) rollover has become a significant problem worldwide. Depending on the data collected, the sources used, and several key factors contribute to HGV overturning. Accidents overturn due to longer reaction time, shriveled driving performance, lack of driving experience, and driver carelessness. In further consideration, over-steering to turning over, not steering enough to stay in lane, over speed, high located center of gravity, weather condition, road condition, and the road's curves are the most contributing reasons to the overturning of a long vehicle. Thus, this paper proposes machine learning processes to overcome these problems and reduce the HGV rollovers. The proposed system includes a vehicle-equipped system and a ground-based operational surveillance camera. The Vehicle-equipped system can determine the safe speed at which the vehicle should travel according to the type of vehicle and curvature of the road and can detect road cracks and notify the driver by sending the notification to the vehicle dashboard screen. The ground-based driver support system can detect safe speed for HGVs and determine various other traffic parameters which can affect the HGV rollover accidents.
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    Digital Tool for Prevention, Identification and Emergency Handling of Heart Attacks
    (IEEE, 2021-09-30) Mihiranga, A; Shane, D; Indeewari, B; Udana, A; Nawinna, D. P; Attanayaka, B
    Heart attack is one of the most frequent causes of death in adults. The majority of heart attacks lead to death before any treatment is given to patients. The conventional mode of healthcare is passive, whereby patients themselves call the healthcare services requesting assistance. Consequently, if they are unconscious when heart failure occurs, they normally fail to call the service. To prevent patients from further harm and save their lives, the early and on-time diagnosis important. This paper presents an innovative web and mobile solution designed using it as Internet of Things (IoT) technology and Machine learning concepts to effectively manage heart patients, the ‘CARDIIAC’ system. This system can predict potential heart attack based on a set of identified risk factors. The system also can identify an actual heart attack using the readings from a wearable IoT device and notify the patient. The system is also equipped with emergency event coordination functionalities. Therefore, ‘CARDIIAC’ provides a holistic care for heart patients by effectively monitoring and managing emergencies related to heart diseases. This would be a socially important system to reduce the number of heart patients who die due to the inability to get immediate treatment.
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    AI Based Cyber Threats and Vulnerability Detection, Prevention and Prediction System
    (IEEE, 2019-12-05) Amarasinghe, A. M. S. N; Wijesinghe, W. A. C. H; Nirmana, D. L. A; Jayakody, A; Priyankara, A. M. S
    Security of the computer systems is the most important factor for single users and businesses, because an attack on a system can cause data loss and considerable harm to the businesses. Due to the increment of the range of the cyber-attacks, anti-virus scanners cannot fulfil the need for protection. Hence, the increment of the skill level that required for the development of cyber threats and the availability of the attacking tools on the internet, the need for Artificial Intelligence-based systems, is a must to the users. The proposed approach is an automated system that consists of a mechanism to deploy vulnerabilities and a rich database with known vulnerabilities. The Convolutional Neural Networks detects the vulnerabilities and the artificial intelligence-based generative models do the prevention process and improves reliability. The prediction procedure implemented using the algorithm called “Time Series” and the model called “SARIMA”. These implementations give an output with considerable accuracy.