Research Papers - Dept of Computer Systems Engineering

Permanent URI for this collection https://rda.sliit.lk/handle/123456789/1253

Browse

Filters

2018 - 201912020 - 20253

Advanced Search

Filter by

Settings

Has files: YesSubject: search.filters.subject.machine learning

Search Results

Now showing 1 - 4 of 4
  • Thumbnail Image
    PublicationOpen Access
    Real Time Accident Detection and Emergency Response Using Drones, Machine Learning and LoRa Communication
    (Science and Information Organization, 2025) Bandara H.M.S.I.D; Maduhansa H.K.T.P; Jayasinghe S.S; Samararathna A.K.S.R; Fernando, H; Lokuliyana, S
    Road accidents and delayed emergency responses remain a major concern in urban environments, contributing to over 1.4 million fatalities globally each year. With rapid urbanization and increasing vehicle density, timely detection and efficient traffic management are critical to reducing the impact of such events. This study proposes a real time Accident Detection and Emergency Response System with integrating Machine Learning IoT enabled drones and LoRa communication. The system combines real time accident detection using CCTV, drone assisted fire detection for post accident scenarios, crime activity monitoring and automated traffic management to reduce congestion and improve public safety. LoRa ensure long range, energy-efficient communication. ML models improve detection accuracy across accidents, fires, crimes and vehicles. Figures and sensor data are analyzed in real time to trigger alerts and assist emergency responders. The system supports scalable integration with existing urban infrastructure, promoting the development of smart city safety frameworks. By minimizing emergency response time, limiting secondary incidents and improving situational awareness, the proposed solution addresses critical gaps in current urban safety systems. It offers a practical, intelligent and adaptive approach to accident mitigation and traffic control in smart cities.
  • Thumbnail Image
    PublicationEmbargo
    E-tongue -A smart tool to predict safe consumption of groundwater
    (IEEE, 2020) Alahakoon, A. M. P. B; Nibraz, M. M; Gunarathna, P. M. S. S. B; Thenuja, S; Kahandawaarchchi, K. A. D. C. P; Gamage, N. D. U
    — In Sri Lanka, reportedly 59% of the population depends on water from natural sources. The government has taken necessary action to provide a quality water, there has always been a need to educate people about the importance of maintaining water quality, the importance of using betterquality water, and necessary precautions to be taken to avoid the Chronic Kidney Disease (CKD). Prior studies of the problems that has to induce to implement an E-Tongue: a smart device to predict safe consumption of groundwater, which is identify the quality of a groundwater in real-time by designing an Internet of Things (IoT) device to read the value of water quality parameters and GPS to fetch location which will be then transferred to cloud environment for an easy access by the machine learning model to process and identify the Water Quality Index (WQI). It will then predict the water quality parameter levels that could be changed in the future and check the possibility of CKD. All the outputs will be finally displayed via the mobile application with 73% accuracy.
  • Thumbnail Image
    PublicationOpen Access
    Intrusion detection system with correlation engine and vulnerability assessment
    (SCIENCE & INFORMATION SAI ORGANIZATION LTD, 2018-09-01) Waidyarathna, D. W. Y. O; Nayantha, W. V. A. C; Wijesinghe, W. M. T. C; Abeywardena, K. Y
    —The proposed Intrusion Detection System (IDS) which is implemented with modern technologies to address certain prevailing problems in existing intrusion detection systems’ is capable of giving an advanced output to the security analyst. Even though the network of an organization has been secured internally as well as externally the intruders find ways to penetrate the network. With the system that is proposed activities of those intruders can be identified with a higher probability even if managed to bypass security controls of the network. The goal of this project is to give a reliable output to the system users where all the alerts are more accurate and correlated using HIDS alerts and NIDS alerts which is similar to the modern SIEM concept. The system will perform as a centralized IDS by getting inputs from both HIDS and NIDS which gives data regarding the activities of hosts and network traffic. With those implementations, the system is capable of monitoring host activities, monitoring network traffic with existing tools and give a correlated output which is more accurate, advanced and reliable prioritizing the possible attacks by using machine learning techniques and rule-based correlation techniques. With all these capabilities final product is a fully automated Intrusion Detection System which gives correlated alerts as outputs with a less rate of false positives compared to the existing systems.
  • Thumbnail Image
    PublicationEmbargo
    Academic Depression Detection Using Behavioral Aspects for Sri Lankan University Students
    (2021 3rd International Conference on Advancements in Computing (ICAC) -SLIIT, 2021-12-09) Gamage, M.A.; Matara Arachchi, R.; Naotunna, S.; Rubasinghe, T.; Silva, C.; Siriwardana, S.
    Academic Depression is a widespread problem among undergraduate students in Sri Lanka. It is exhausting and has a detrimental impact on students' academic life. Therefore, the development of a technique to estimate the probability of depression among undergraduates is a blessed respite. Depression is mostly caused by a failure to check students' psychological well-being on a regular basis. Identifying depression at the college level, leading the students to get proper therapy treatments. If a counselor detects depression in a student early enough, he/she can successfully assist the student in overcoming depression. However, keeping track of the substantial changes that occur in students because of depression becomes challenging for the counselor with a considerable number of undergraduates. The advancement of image processing and machine learning fields has contributed to the creation of effective algorithms capable of identifying depression probability. Depression Possibility Detection Tool (DPDT) is considered an effective automated tool that brings the depression probability of a certain student. In DPDT, the result is generated by concerning four main strategies. They are facial expressions, eye movements, behavior changes (step count and phone usage), and physical conditions (heart rate and sleep rate). Convolutional Neural Network (CNN) with Visual Geometry Group 16 (VGG16) model, Residual Neural Network (ResNet-50), Random Forest (RF) classifier is the main models and techniques used in the system. More than 93% of accuracy was generated in every trained model. The paper concludes the system overview along with four strategies, literature review, methodologies, conclusion, and future works.