Research Publications

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    UrbanGreen - E-Waste Detection and Analysis using YOLOv5
    (Institute of Electrical and Electronics Engineers Inc., 2025) Madusanka A.R.M.S; Nawaratne D.M.R.S.; Gamage, N; Attanayaka, B
    E-waste has become a global concern that challenges environmental sustain ability. The disposal of electronic devices is often poorly managed, especially in urban areas. This research aims to develop an innovative e-waste management system suitable for urban areas, focusing on accurately identifying electronic devices and their harmful components through advanced image processing techniques. (Y olov5) The system identifies various electronic devices, harmful components and materials and assesses their recyclability, improper disposal's environmental and health impacts, empowering users to make informed decisions about disposal and recycling. The system will integrate tools to identify E-waste, promote the reuse of electronic devices, educate the public through interactive educational platforms, and locate nearby e-waste collection centers. By addressing these critical aspects of e-waste management, the project aims to provide a useful platform to manage e-waste effectively in urban areas. This paper was developed to discuss E-waste detection and analysis using YOLOv5 object detection model.
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    PublicationOpen Access
    Disease Identification and Mapping using CNN in Paddy Fields
    (Faculty of Humanities and Sciences, SLIIT, 2023-11-01) Sandeepanie, W.D.N; Rathnayake, S; Gunasinghe, A
    Rice, a globally vital staple crop, sustains over half of the world’s caloric needs while supporting the livelihoods of small-scale farmers and landless laborers. The escalating global population has led to an increased demand for rice production. Sri Lanka, renowned for its premium rice quality, has a rich history of paddy cultivation. However, a substantial portion of the country’s 708,000 hectares of paddy land remains underutilized due to water scarcity and unstable terrain. The objective of this project is to enhance paddy crop quality during the critical vegetative phase by employing machine learning and web development for early disease identification. The vegetative phase significantly influences overall yield, resistance to pests and diseases, nutrient assimilation, and environmental sustainability in agriculture. This project primarily focuses on early disease identification during this phase and presents the findings through a user-friendly map interface. Early identification of paddy diseases is vital for effective crop management and high yields. These diseases, caused by various pathogens, can severely impede plant growth and productivity if not promptly detected and treated. Identifying them early enables farmers and experts to take timely, targeted actions such as applying suitable fungicides or implementing cultural practices to control their spread and minimize crop damage. A logical map, displaying disease spread percentages, will gauge the impact of infections on paddy plants. The reliability of this mapping process hinges on model accuracy, which was rigorously validated using multiple metrics to ensure its effectiveness.
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    PublicationOpen Access
    BIOMETRIC SMART SECURITY SYSTEM WITH CHILD CARE FOR A SMART SOCIETY
    (IET- Sri Lanka Network, 2019) Lokuliyana, S; Mundigala, I. U; Sanjeewa, G. H. A
    This research is mainly focused on Infant movement detection and alerting, in order to enhance their security within the home premises. As the first move, the research focuses on the identification of the human and classifying whether an adult or a baby. Then a model was built up in three classifications to identify static and dynamic positions of the infant, through Image Processing and analysis. In order to enhance the accuracy of the custom classifiers an already trained model using 1 million image set was retrained by customized image sets. To present this research as a smart home solution modern technology were used in implementing the close connection between the infant and the parent.
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    Computer Vision Enabled Drowning Detection System
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Handalage, U.; Nikapotha, N.; Subasinghe, C.; Prasanga, T.; Thilakarthna, T.; Kasthurirathna, D.
    Safety is paramount in all swimming pools. The current systems expected to address the problem of ensuring safety at swimming pools have significant problems due to their technical aspects, such as underwater cameras and methodological aspects such as the need for human intervention in the rescue mission. The use of an automated visual-based monitoring system can help to reduce drownings and assure pool safety effectively. This study introduces a revolutionary technology that identifies drowning victims in a minimum amount of time and dispatches an automated drone to save them. Using convolutional neural network (CNN) models, it can detect a drowning person in three stages. Whenever such a situation like this is detected, the inflatable tube-mounted selfdriven drone will go on a rescue mission, sounding an alarm to inform the nearby lifeguards. The system also keeps an eye out for potentially dangerous actions that could result in drowning. This system's ability to save a drowning victim in under a minute has been demonstrated in prototype experiments' performance evaluations.
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    Ontology based Optimized Algorithms to Communicate with a Service Robot using a User Command with Unknown Terms
    (IEEE, 2020-12-10) Rajapaksha, U. U. S; Jayawardena, C
    In real world applications, seamless integration of heterogeneous robots is very important to complete a task given by high level user instruction with unknown terms to all robotic devices simultaneously. In this research, we have used the technologies in Semantic Web mainly with the use of the ontology to represent the meaning of the unknown terms in the given high level instruction. If a user has given an instruction in domestic environment as “clean My Room 01 while finding my key for the car” to clean different locations with different capabilities and there can be robot who does not the meaning of the “key”. The robot can get the meaning of the unknown term by communicating with the semantic analyzer which is working with the ontology. According to our analysis we have proved that the object represented by the unknown term can be detected more accurately with compared to existing object detection algorithms since our ontology can represents more concepts related to the given object. The results indicate that if number of unknown terms in the command are increased then the time taken to process the command also be increased.