Scopus Index Publications
Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/2162
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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Item Embargo Deep Learning Based Sinhala Sign Language Recognition(Institute of Electrical and Electronics Engineers Inc., 2025) Samarakoon, S.C; Weerasinghe, MDeaf individuals in Sri Lanka rely primarily on Sinhala Sign Language (SSL) for communication due to hearing impairments. However, effective communication between the Deaf and hearing populations remains challenging due to the limited knowledge of SSL among hearing individuals. This research aims to address this gap by developing an SSL gesture recognition system using computer vision and deep learning techniques. Specifically, the study compares the performance of 3D Convolutional Neural Networks (3D-CNNs) and a hybrid 2D Convolutional Neural Network with Long Short-Term Memory (2D-CNN+LSTM) for classifying short-duration spatiotemporal SSL gestures. Additionally, the research emphasizes reducing computational complexity to ensure efficient operation of the system on low-end devices. These contributions advance the accessibility and practical usability of gesture recognition systems for the Sinhala Sign Language.Publication Open Access Real-time Multi-spectral Iris Extraction in Diversified Eye Images Utilizing Convolutional Neural Networks(IEEE, 2024-07-03) Rathnayake, R; Madhushan, N; Jeeva, A; Darshani, D; Pathirana, I; Ghosh, S; Subasinghe, A; Silva, B N; Wijenayake, UIris extraction has gained prominence due to its application versatility across many domains. However, achieving real-time iris extraction poses challenges due to several factors. Learning-based algorithms outperform non-learning-based iris extraction methods, delivering superior accuracy and performance. In response, this article proposes a Convolutional Neural Networks (CNN)-based, accurate direct iris extraction mechanism for a broad spectrum of eye images. The innovation of our approach lies in its proficiency with varied image types, including those where the iris is partially obscured by the eyelid. We enhance the method’s reliability by introducing a modified Circular Hough Transform (CHT). Extensive testing demonstrates our method’s excellent real-time performance across diverse image types, even under challenging conditions. These findings underscore the proposed method’s potential as a cost-effective and computationally efficient solution for real-time iris extraction in varied application domains.