SLIIT Conference and Symposium Proceedings

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All SLIIT faculties annually conduct international conferences and symposiums. Publications from these events are included in this collection.

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Subject: search.filters.subject.Convolutional Neural Network

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    PublicationOpen Access
    FocusBoost – A Study Aid with Adaptive Learning Techniques
    (SLIIT City UNI, 2025-07-08) Prabaharan, N; Dampalessa, D.R.C.G.K.
    FocusBoost is an AI-powered adaptive learning platform designed to support children with Attention Deficit Hyperactivity Disorder (ADHD) through personalized learning experiences. By integrating video-based learning with voice input analysis, the system uses speech processing techniques to assess a child's engagement and comprehension in real-time. Based on real-time analysis, the platform dynamically adjusts content difficulty and pace to the needs of the individual learner. In practical testing, the system demonstrated high accuracy in classifying learner engagement and comprehension, with more ADHD learners reporting improved focus and content retention. Additionally, parents have noticed positive changes in their child’s study habits and attention span through its use. The site has a performance tracking accuracy page for children, which shows their level of comprehension. This research highlights the effectiveness of AI-enhanced learning for students with brain and neurological issues and its potential to improve inclusive, sustainable education practices. The system is designed with scalability in mind, allowing for multilingual support, culturally adaptive content, and future integration with medical professionals, expanding its impact across a variety of educational and therapeutic settings.
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    PublicationOpen Access
    Implementation of Smart Parking System Using Image Processing
    (Sri Lanka Institute of Information Technology, 2023-03-25) Amarasooriya, P.M.D.S.; Peiris, M.P.P.L.; Herath, H.M.D.S.
    In recent years, the number of vehicles in use has shown a steady increase, leading to a clear demand for larger parking areas. However, the traditional methods for detecting occupancy of slots in smart vehicle parking areas are no longer feasible due to the high cost of sensors and the need to monitor larger areas. In response to this challenge, the present study aims to propose a cost-effective, fast, and accurate solution for updating and indicating the real-time number of free parking slots in a parking area. Specifically, the proposed solution utilizes video footage from a camera as the input device and applies the YOLO v3 object detection algorithm for image processing to detect the coordinates of both parking lots and parked vehicles separately. To train and evaluate the model, we used the PKLot database as the dataset and tested the model's performance under different weather conditions. The proposed model achieved an average performance of 88.01%, with the highest performance demonstrated on sunny days and the lowest performance recorded on rainy days.
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    PublicationEmbargo
    An Enhanced Virtual Fitting Room using Deep Neural Networks
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Ileperuma, I.C.S.; Gunathilake, H.M.Y.V.; Dilshan, K.P.A.P.; Nishali, S.A.D.S.; Gamage, A.I.; Priyadarshana, Y.H.P.P.
    As the customer's experience in present fit-on rooms is considered as an essential part of the textile industry, these fit-on rooms play a huge role in the textile shops. It is quite an arduous method and generates problems like long queues, having to change clothes individually, privacy problems and wasting time. The proposed convolutional neural network-based Virtual Fit-on Room helps to prevent the above mentioned problems. This product contains a TV screen, two web cameras, and a PC. It captures the customer's body by using two web cameras and displays the customer's dressed body. The combination of CNN in Deep learning and AR processes the body detection and generates the customer's dressed object. The application uses the stereo vision concept to get body measurements. The system detects customer age, gender, face type, and skin tones which are used to recommend cloth styles to customers. Another requirement of this system is customizing styles according to the customer requirements and suggests different styles of clothes. The system achieved 99% accuracy when suggesting different styles using FFNN. Customers can choose clothes for another person who does not physically appear with the customer in the textile shop. The expected output delivers the most realistic dressed object to the customer which allows the efficient customizations for the textile products according to customer requirements. This product can highly influence the textile and fashion industry. Therefore, this product is suitable to compete with other applications in the industry.