International Conference on Advancements in Computing [ICAC]
Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/312
The International Conference on Advancements in Computing (ICAC) is organized by the Faculty of Computing of the Sri Lanka Institute of Information Technology (SLIIT) as an open forum for academics along with industry professionals to present the latest findings and research output and practical deployments in computing.
The primary objective of ICAC is to promote innovative research that addresses real-world challenges and contributes to the social well-being of communities. The conference provides a dynamic platform for researchers from around the world to present groundbreaking findings, exchange ideas, and establish meaningful collaborations.
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Item Embargo EDU-SENSE: AI-Driven Adaptive E-Learning System for Primary Education(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Shanmugalingam, N; Maheshwaran, L; Rizvee, I; Roshni R.J; Samarakoon, U; Rajendran, KThis research addresses critical gaps in primary education by integrating AI-driven e-learning tools with traditional classroom teaching, ensuring technology supports rather than replaces teachers while improving student learning outcomes. In getting ready for high-pressure exams like Sri Lanka's Grade 5 Scholarship Examination, maintaining student engagement and emotional well-being remains a significant challenge. EDU-SENSE is an AI-driven web platform designed for primary education that integrates personalized content delivery, stress detection, and an emotion-aware chatbot. The system begins with a diagnostic pre-test to assess individual skill levels,and provides syllabus-aligned questions, and delivers targeted revision based on identified knowledge gaps. A s tress-aware module and motivational chatbot provide emotional support, while peer collaboration features promote teamwork and knowledge sharing. Using Machine Learning, Natural Language Processing, and fine-tuned Large Language Models, EDU-SENSE offers a holistic solution that enhances both academic performance and emotional well-being in young learners.Item Embargo Eye Health Monitoring and Eye Care System(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Herath H.M.S.C; Swarnajith T.H.M.P; Senanayaka W.S.H.M; Hettiarachchi H.W.R.A; Walgampaya, N; De Zoysa, RThis study presents a multi-modal AI-powered system designed for real-time eye health monitoring and early diagnosis. The proposed system integrates computer vision, machine learning, and image processing techniques to deliver non-invasive and accessible diagnostic solutions. The system comprises four key modules. The first one is a real-time eye exercise module that utilizes webcam-based eye tracking and adaptive machine learning to provide personalized routines for reducing digital eye fatigue. The second module is an AI-driven cataract detection module that analyzes retinal images to enable early diagnosis, particularly in resource-limited settings. The third module is a glaucoma detection system that tracks pupil dynamics, such as size and light reactivity, to identify early symptoms; and the fourth and final module is a color blindness and eye fatigue detection module that employs multi-modal AI techniques to assess color vision deficiencies and monitor signs of visual fatigue in real-time Collectively, these components form a comprehensive array of tools focused on improving eye health monitoring and treatment. Through the combination of advanced AI technologies with user-friendly interfaces, the proposed systems aim to democratize access to eye care, reduce the global burden of preventable vision loss, and enhance the quality of life for individuals everywhere.Publication Embargo Ayurvedic Knowledge Sharing Platform with Sinhala Virtual Assistant(2019 1st International Conference on Advancements in Computing (ICAC), SLIIT, 2019-12-05) Jayalath, A.D.A.D.S.; Nadeeshan, P.V.D.; Amarawansh, T.G.A.G.D.; Jayasuriya, H.P.; Nawinna, D. P.Apart from western medicine methods Ayurveda medicinal system is a very huge and better resulting medicinal technique. In these Ayurveda methods identification of indigenous plants to predict the medicines is very important and must do very carefully. Generally main components that we use to identify a plant are leaf, flower, trunk and root etc. Among these features, we use images of leaves and flowers. To do this we are using deep learning based CNN approaches and machine learning and technologies. Those are OpenCV, and Tensorflow classification algorithm. According to the evidences that we gathered from surveys and interviews that we conducted with the responsible parties we could find out that lots of people don’t have much knowledge about indigenous medicinal plants and their Ayurveda treatment methods. To overcome this problem we implemented Ayurveda information centralized chatbot which is able to answer user’s questions relevant to the Ayurveda and indigenous medicinal plants. Chatbot will analyze the question that user asks and will provide answers according to that. Another useful feature of this system is it provides relevant information of Ayurveda doctors. So users can find doctors according to their needs and they are able to rate and give recommendations for the doctors. That will be help others to find doctors more easily and efficiently without any doubt.Publication Embargo E-Agrigo(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Kartheepan, T.; SirigajanK, B.; Subangan, K.; Mohammed Azzam, M.A.; Bandara, P.; Mahaadikara, M.M.D.J.T.H.To feed this population, food production should be increased by at least 70%. Developing nations have a vast potential to increase the amount of food produced by doubling the current production. However, the traditional methods of farming are making agriculture unviable and inefficient. The increasing food production needs to be met by double the current level of farming. The conventional of farming is making industry uncompetitive and inefficient. This paper aims to analyze the various factors that affect the implementation of autonomous machinery in agriculture. The development of autonomous machinery for agriculture has emerged as vital step towards achieving this goal. Now a day’s farmers are planning their cultivation by finding proper weather and geographical condition on their own experience, but they are failing to cultivate profitable crop and unaware of the diseases that will affect their crops, sometimes these diseases may affect their whole crops and let the farmers to sink in zero profit. Despite these issues plays a major role, there are some other problems also have an impact like, lack of irrigation plans and question of how and where to sell their cultivated crops. By considering these major threats we have planned to propose a solution to some of the selected issues. This can be achieved by applying machine learning algorithm, Image processing and IOT systems. By using our platform farmers will get a chance to plan their yield in a profitable way by using our optimized weather and geographical data.Publication Embargo Comparative analysis of the application of Deep Learning techniques for Forex Rate prediction(IEEE, 2019-12-05) Aryal, S; Nadarajah, D; Kasthurirathna, D; Rupasinghe, L; Jayawardena, CForecasting the financial time series is an extensive field of study. Even though the econometric models, traditional machine learning models, artificial neural networks and deep learning models have been used to predict the financial time series, deep learning models have been recently employed to do predictions of financial time series. In this paper, three different deep learning models called Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Temporal Convolution Network (TCN) have been used to predict the United States Dollar (USD) to Sri Lankan Rupees (LKR) exchange rate and compared the accuracy of the models. The results indicate the superiority of CNN model over other models. We conclude that CNN based models perform best in financial time series prediction.