Scopus Index Publications

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This collection consists of all Scopus-indexed publications produced by SLIIT researchers. Scopus is recognized worldwide as a leading and reputable academic indexing database.

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    Throat AI - An Intelligent System For Detecting Foreign Objects In Lateral Neck X-Ray Images
    (Institute of Electrical and Electronics Engineers Inc., 2025) Baddewithana, P; Krishara, J; Yapa, K
    Foreign Object ingestion is a commonly encountered medical condition within the Ear, Nose, and Throat clinical domain. Timely and accurate detection of such objects is vital, as it often guides the need for surgical intervention. Among the available imaging techniques, lateral neck X-rays are the most widely used radiographs to visualize and assess the presence of FOs in the throat. However, manual interpretation of these images can be time-consuming and subject to human error, potentially leading to misdiagnosis or delayed treatment. This research presents a deep learning-based software solution, deployable via web and mobile platforms, aimed at assisting medical professionals with the automated detection of FOs in lateral neck X-rays. The system leverages state-of-the-art YOLO object detection models, specifically evaluating novel versions such as YOLO-NAS-s, YOLOv11s, and YOLOv8s-OBB to ensure high detection accuracy and deployment efficiency. The best-performing model, YOLO-NAS-s, achieved a validation accuracy of 96.3%. For deployment, the model was hosted on the Roboflow platform and accessed via a FastAPI-based middleware server. Performance evaluation showed an average inference time of approximately 2 seconds and a memory footprint of around 100 MB on standard computing hardware, demonstrating its suitability for integration into resource-constrained clinical environments. This setup highlights the system's lightweight design and real-world applicability. Training, evaluation, and testing of the deep learning models were conducted using a dataset curated from public local healthcare institutions and online medical imaging repositories.
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    Early Diagnosis and Severity Assessment of Weligama Coconut Leaf Wilt Disease and Coconut Caterpillar Infestation Using Deep Learning-Based Image Processing Techniques
    (Institute of Electrical and Electronics Engineers Inc., 2025-02-03) Vidhanaarachchi, S; Wijekoon, J. l; Abeysiriwardhana, W. A. S.P; Wijesundara, M
    Global Coconut (Cocos nucifera (L.)) cultivation faces significant challenges, including yield loss, due to pest and disease outbreaks. In particular, Weligama Coconut Leaf Wilt Disease (WCWLD) and Coconut Caterpillar Infestation (CCI) damage coconut trees, causing severe coconut production loss in Sri Lanka and nearby coconut-producing countries. Currently, both WCWLD and CCI are detected through on-field human observations, a process that is not only time-consuming but also limits the early detection of infections. This paper presents a study conducted in Sri Lanka, demonstrating the effectiveness of employing transfer learning-based Convolutional Neural Network (CNN) and Mask Region-based-CNN (Mask R-CNN) to identify WCWLD and CCI at their early stages and to assess disease progression. Further, this paper presents the use of the You Only Look Once (YOLO) object detection model to count the number of caterpillars distributed on leaves with CCI. The introduced methods were tested and validated using datasets collected from Matara, Puttalam, and Makandura, Sri Lanka. The results show that the proposed methods identify WCWLD and CCI with an accuracy of 90% and 95%, respectively. In addition, the proposed WCWLD disease severity identification method classifies the severity with an accuracy of 97%. Furthermore, the accuracies of the object detection models for calculating the number of caterpillars in the leaflets were: YOLOv5-96.87%, YOLOv8-96.1%, and YOLO11-95.9%.
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    VAPECA - Smart Agricultural and Analysis Monitoring System
    (Institute of Electrical and Electronics Engineers, 2022-10-15) Jithmal Pitigala, P. K. D. U; Laksahan, T. M. K; Hewapathirana, S. S; Sadeepika Herath, H. M. H; Chandrasiri, S; Nadeesa Pemadasa, M. G
    Agriculture dramatically contributes to the economy by creating a monetary future for developing nations. However, in Sri Lanka, the farmers have confined resources and encounter numerous challenges to enrich their crop productivity and prevail in the competitive business world. In the directive, the farmers' knowledge about export crops and weak decision- making needs to be exposed [1]. This study has built a mobile application with budget planning, determining plant conditions, weather forecasting, analyzing harvest quality, and a price prediction system to mitigate these hardships. This application would be utilized to manage three critical plants in Sri Lanka t for extraction and export. Those are Vanilla, Pepper, and Cardamom. The key technologies used for the system are deep learning and machine learning. The overall system obtained desirable outcomes with an accuracy rate higherthan 94%-97%. The ultimate intent of this study is to achieve the optimal growth of the agriculture sector by navigating the farmers to get maximum crop yield, quality, and effective decision-making through reliable market trends and to enhance the farmers' profit
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    BlossomSnap: A Single Platform for all Anthurium Planters Based on The Sri Lankan Market
    (Institute of Electrical and Electronics Engineers, 2022-10-15) Rathnayake, R.M.S.T; Tharika Pramodi, M.L.A.D.; Gayathree, I. R; Rashmika, L.K.R; Gamage, M; Gamage, A
    The popular and extensively grown flowering plant known as the Anthurium is prized for its beauty. In Sri Lanka, anthuriums have a substantial international market. Although it is a significant field that can be further developed by expanding the market, but it has led to a lack of attention, resources, and a moderate cost of production, as well as from the absence of an appropriate market channel, all of which have led to lower productivity and quality. As a result, Anthurium growers have numerous challenges both in terms of production and marketing. This paper introduces a novel mobile application 'BlossomSnap' which involves automating and significantly enhancing the outdated manual process. Using natural language processing, machine learning, and deep learning approaches, the proposed system analyzes the diseases, pests, varieties, and the highest quality plants to create a more secure growing environment. It will provide high-quality, cost effective, and timely services. The first step of anthurium plant disease and pest diagnosis is carried out using image processing, deep learning, and machine learning technologies. In order to identify the infection stage, the following steps involve extracting, classifying, and detecting images of Anthurium flowers and leaves. The accuracy was checked by comparing actual results taken from experts with the predicted results obtained from the proposed system. 'BlossomSnap' achieves an average accuracy of more than 80% and produces a better overall result. An in-place chatbot technology is intended to assist new planters with their problems. The Anthurium plant variety and quality detection methodology is used in concert with to determine the optimum market opportunity.
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    An Automated Tool for Memory Forensics
    (2019 1st International Conference on Advancements in Computing (ICAC), SLIIT, 2019-12-05) Murthaja, M.; Sahayanathan, B.; Munasinghe, A.N.T.S.; Uthayakumar, D.; Rupasinghe, L.; Senarathne, A.
    In the present, memory forensics has captured the world’s attention. Currently, the volatility framework is used to extract artifacts from the memory dump, and the extracted artifacts are then used to investigate and to identify the malicious processes in the memory dump. The investigation process must be conducted manually, since the volatility framework provides only the artifacts that exist in the memory dump. In this paper, we investigate the four predominant domains of registry, DLL, API calls and network connections in memory forensics to implement the system ‘Malfore,’ which helps automate the entire process of memory forensics. We use the cuckoo sandbox to analyze malware samples and to obtain memory dumps and volatility frameworks to extract artifacts from the memory dump. The finalized dataset was evaluated using several machine learning algorithms, including RNN. The highest accuracy achieved was 98%, and it was reached using a recurrent neural network model, fitted to the data extracted from the DLL artifacts, and 92% accuracy was reached using a recurrent neural network model,fitted to data extracted from the network connection artifacts.
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    Mobile Based Solution to Weight Loss Planning for Children (with Obesity) in Sri Lanka
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Rajapakse, R.M.M.P.K.; Mudalige, J.M.A.I.; Perera, L.A.D.Y.S.; Warakagoda, R.N.A.M.S.C.B.; Siriwardana, S.
    Obesity is a condition where there is excess fat in the body, and it is one of the world's most extreme and dangerous dietary diseases. Genetic factors, lack of physical activity, unhealthy eating patterns, or a combination of these factors are the most common causes of obesity. This is important because it influences every part of a child's life. More, in particular, this disorder leads to poor health and negative social standing with perceptions. Nowadays, children are paying keen interest in technology and related devices. Therefore, in this research, we are planning to give a mobile-based solution with a smart band that is used to monitor the child. In this solution, we are mainly focusing on Sri Lankan children with obesity who are aged between 5-10. In our solution, there are four main sections which are, monitoring child activities, recognizing the activities, and getting relevant data, then based on those data and previous activity completion levels, this solution will suggest activities for losing weight, provide specific diet plans for each child considering the health conditions and predict the probability of having main obesity-