• Transformer based Semi-Supervised Learning for Skin Lesion Classification 

  Jathursika, V.; Manivannan, S. (University of Jaffna, 2025)

  Skin cancer is one of the most prevalent cancers worldwide, and early detection is crucial for successful treatment. Deep learning has shown promise in automating skin lesion classification; however, most methods require ...
• Segmentation of Fruit Surface Defects using Semi Supervised Learning 

  Saarukesan, P.; Manivannan, S. (University of Jaffna, 2025)

  Automated detection and segmentation of fruit-surface defects are essential for improving product quality, reducing dependence on manual inspection, and enabling scalable agricultural automation. Fruit defects vary widely ...
• Ordinal-Aware Contrastive Learning for Enhanced Diabetic Retinopathy Grading 

  Suthakaran, A.; Manivannan, S. (University of Jaffna, 2025)

  Diabetic retinopathy (DR) is a leading cause of preventable blindness worldwide. Automated DR grading is inherently challenging due to the ordinal nature of disease stages, where misclassifications across distant stages ...
• Few-Shot Learning for Early Detection of Coconut Whitefly Infestations using Low-Label Leaf Image Data in Sri Lanka 

  Vigneswaran, V.; Balalojanan, V.; Sukirthan, T.; Thayalan, N.; Thiruthanigesan, K.; Sumathipala, P.; Manivannan, S. (IEEE, 2025)

  Coconut whitefly (Aleurodicus dispersus) has emerged as a devastating pest threatening Sri Lanka’s vital coconut industry, causing severe yield losses and economic hardship for over 1.5 million smallholder farmers. Early ...
• Automated Foundation Structure Identification from Floorplans using Deep Learning 

  Deshitha, R.; Suthakar, S.; Manivannan, S. (University of Jaffna, 2025)

  Floorplan understanding is critical for architectural design, reconstruction, and urban planning. Accurate detection of foundation structures help to determine the stability and the functionality of buildings. We present ...
• Validation of a novel artificial intelligence-based tool for Ki-67 quantification in breast cancer: enhanced accuracy, reproducibility, and efficiency in resource-limited setting 

  Nellihela, A.P.; Rathnayake, R.M.N.D.; Asanthi, N.J.; Bandaranayake, V.C.; Ramanan, A.; Kokul, T.; Senarathna, M. (Springer, 2025)

  Background & Objectives: Ki-67 is an important proliferation marker in guiding breast cancer treatment. International Ki-67 Working Group (IKWG) recommends hot-spot, and the Global Scoring (GS) methods to quantify Ki-67 ...
• Underwater Image Enhancement Using Depth Estimation and a Transformer-GAN Model 

  Pushpanayagam, S.; Kokul, T.; Ramanan, A.; Pinidiyaarachchi, U.A.J. (IEEE, 2025)

  Underwater imaging is important for diverse applications. However, the quality of underwater images is often diminished due to light absorption, scattering, noise, and uneven illumination. These issues result in colour ...
• Transformer Tracking with Controlled Attention for Background Suppression 

  Kugarajeevan, J.; Kokul, T.; Ramanan, A.; Subha, F. (Department of Computer Science, University of Jaffna, Sri Lanka, 2025)

  Visual object tracking aims to determine the state of a target object across video frames, starting from its initial position in the first frame. Recently, one-stream Transformer-based trackers have demonstrated impressive ...
• Self-Supervised Ultrasound Image Denoising Using Noise2Void and Noisier2Noise 

  Viththagan, R.; Manivannan, S.; Kokul, T. (Department of Computer Science, University of Jaffna, Sri Lanka, 2025)

  Speckle noise in ultrasound imaging degrades image quality, leading to reduced diagnostic accuracy and challenges to automated image analysis. Conventional deep-learning methods are hindered by the scarcity of paired ...
• Functional Biomarkers of Alzheimer’s Disease: Explainable AI using Resting-State fMRI 

  Pavithira, S.; Logiraj, K.; Jegatheeswaran, T.; Subaramya, S.; Kokul, T.; Nagulan, R. (IEEE, 2025)

  Alzheimer’s disease (AD) is a progressive neurodegenerative disorder affecting millions worldwide, highlighting the urgent need for early and reliable diagnostic tools. Biomarkers, measurable indicators of disease presence ...
• Enhancing Paddy Leaf Disease Classification: A Dual Stream CNN and Handcrafted Feature Approach 

  Tharmini, K.; Ramanan, A.; Kokul, T.; Subha, F. (Department of Computer Science, University of Jaffna, Sri Lanka, 2025)

  Rice is a vital global food crop, yet its production faces significant threats from leaf diseases that reduce both yield and quality. While automated disease classification models exist, current approaches face fundamental ...
• Single Image Defogging Using a Depth- And Edge-Aware Modified Cyclegan 

  Lathagini, S.; Kokul, T.; Pinidiyaarachchi, U.A.J. (Postgraduate Institute of Science (PGIS), University of Peradeniya, Sri Lanka, 2025)

  Visibility in outdoor imagery is frequently compromised by adverse atmospheric conditions, with fog representing one of the most common and challenging phenomena. Effective single-image defogging is critical for a wide ...
• Classification of Alzheimer’s Disease Stages using Weighted Brain Connectivity based Graph Convolutional Neural Network 

  Janany, S.; Logiraj, K.; Jegatheeswaran, T.; Subaramya, S.; Kokul, T.; Nagulan, R. (IEEE, 2025)

  Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that manifests in distinct stages, including cognitively normal (CN), mild cognitive impairment (MCI), and severe AD. An early and precise classification ...
• An Evaluation Study of Transformer-Based Dehazing Models on Real World Datasets 

  Vishnuga, S.; Ramanan, A.; Kokul, T. (Department of Computer Science, University of Jaffna, Sri Lanka, 2025)

  The presence of haze significantly reduces the visibility of outdoor scenes, which not only degrades the performance of many computer vision tasks but also compromises safety and reliable decision-making in practical ...
• Age and Gender Classification from Facial Images Using VGG16 and Canny Edge Detection 

  Kirushanth, U.; Kokul, T. (Department of Computer Science, University of Jaffna, Sri Lanka, 2025)

  Estimating age and gender simultaneously from a single facial image is important for many applications. Although previous deep learning approaches have achieved strong performance in gender classification, their performance ...
• Aedes Mosquito Larvae Classification Using Swin Transformers 

  Upasena, G.; Suthakar, S.; Kokul, T.; Surendran, S.N. (Department of Computer Science, University of Jaffna, Sri Lanka, 2025)

  Dengue fever is a major public health concern in Sri Lanka, where early identification of Aedes aegypti and Aedes albopictus larvae is essential for effective vector control. Manual larval identification is time-consuming, ...
• A comprehensive review and ensemble CNN approach for Bharatanatyam single-hand gesture classification 

  Kokul, T.; Ramanan, A.; Kanmanirajah, P. (Elsevier, 2025)

  Bharatanatyam, the oldest Indian classical dance form, relies on hand gestures to convey meanings and narratives. Recognizing these gestures is important for performance analysis and for supporting novice learners. This ...
• Exploring Recent NLP Advances for Tamil: Word Vectors and Hybrid Deep Learning Architectures 

  Aravinthan, A.; Charles, E.Y.A. (University of Colombo School of Computing, 2024)

  The advancements of deep learning methods and the availability of large corpora and data sets have led to an exponential increase in the performance of Natural Language Processing (NLP) methods resulting in successful ...
• Identifying Suspicious Blockchain Transactions using Clustering with Explainability 

  Samantha Tharani, J.; Charles, E.Y.A.; Rathore, P.; Hóu, Z.; Palaniswami, M.; Muthukkumarasamy, V. (Association for Computing Machinery (ACM), 2026)

  Blockchain is a distributed ledger technology that provides pseudo-anonymity among participants to maintain privacy. However, malicious actors utilise this property to hide their illegal rewards received through cyber ...
• Machine Reading Comprehension for the Tamil Language With Translated SQuAD 

  Sinthusha, A.V.A.; Charles, E.Y.A.; Weerasinghe, R. (IEEE, 2025)

  Machine Reading Comprehension (MRC) is a challenging task in Natural Language Processing (NLP), crucial for automated customer support, enabling chatbots and virtual assistants to accurately understand and respond to ...

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