http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/128 2026-09-02T19:37:37Z http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/12939 Exploring Recent NLP Advances for Tamil: Word Vectors and Hybrid Deep Learning Architectures Aravinthan, A.; Charles, E.Y.A. 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 NLP applications for various day-to-day tasks such as Language translation, Voice to text, Grammar checking, Sentiment analysis, etc. These advancements enabled the well-resourced languages to adapt themselves to the digital era while the gap for the low-resource languages widened. This research work explores the suitability of the recent advancements in NLP for Tamil, a low-resource language spoken mainly in South India, Sri Lanka, and Malaysia. In particular, this research work analyses the applicability of deep learning approaches namely word embedding, Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) for Tamil text classification tasks. The pre-trained word vectors based on word2Vec and FastText were built for Tamil and their effectiveness was evaluated. In this study, four simple hybrid CNN and Bi-GRU models were proposed for Tamil text classification and their performances were evaluated. The study found that hybrid CNN and Bi-GRU models perform better compared to the classical machine learning models, individual CNN and RNN models, and the Multilingual BERT model. Moreover, the pre-trained 300- dimensional FastText word vectors showed better performance than other pre-trained word vectors. These results confirm that the jointly learned embeddings with different deep learning architectures like CNN and RNN achieve remarkable results for Tamil text classification, thus ensuring that the deep learning approaches can be successful for NLP on Tamil text. 2024-01-01T00:00:00Z http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/12938 Identifying Suspicious Blockchain Transactions using Clustering with Explainability Samantha Tharani, J.; Charles, E.Y.A.; Rathore, P.; Hóu, Z.; Palaniswami, M.; Muthukkumarasamy, V. 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 attacks, dark market trades, money laundering and Ponzi schemes. The recent confiscation by the FBI of more than $4 million USD worth of bitcoin from the ‘Silk Road’ dark marketplace indicates the scale of the problem faced by financial regulators and law enforcement authorities. Analysing and identifying harmful actors is, therefore, necessary to regulate the transactions of digital assets. Machine learning models can assist in detecting patterns and correlations between the actors in blockchain networks that may not be apparent through traditional methods. In blockchain networks, the number of actors linked to illegal activities is significantly smaller than that of regular activities. Also, only very limited labelled transaction data is available about these malicious actors. These limitations make it harder to train supervised learning models to provide real-time proactive responses. This article represents a pioneering effort in thoroughly examining the different unsupervised learning methods for clustering suspicious behaviour of actors within blockchain networks. The proposed unsupervised learning-based analysis considers metadata and interconnectivity information of blockchain transactions. The metadata contains time-based and amount-based information. Interconnectivity data represents centrality measures and embedding vectors of the blockchain network. The quality of the identified clusters is validated using internal and external cluster validation measures. The validation results were used to identify influential features using the eXplainable AI technique Shapley (ShAP) values. The results reveal that the features related to the spending and receiving transactions strongly influenced cluster identification. Overall, the centroid-based and connectivity-based approaches identified well-separated clusters for metadata and centrality-based features of blockchain transactions. 2026-01-01T00:00:00Z http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/12937 Machine Reading Comprehension for the Tamil Language With Translated SQuAD Sinthusha, A.V.A.; Charles, E.Y.A.; Weerasinghe, R. 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 queries. It also enhances question-answering systems, benefiting educational tools, search engines, and help desks. The introduction of attention-based transformer models has significantly boosted MRC performance, especially for well-resourced languages such as English. However, MRC for low-resourced languages (LRL) remains an ongoing research area. Although Large Language Models show exceptional NLP performance, they are less effective for LRL and are expensive to train and deploy. Consequently, simpler language models that are targeted at specific tasks remain viable for these languages. This research examines high-performing language models on the Tamil MRC task, detailing the preparation of a Tamil-translated and processed SQuAD dataset to establish a standard dataset for Tamil MRC. The study analyzes the performance of multilingual transformer models on the Tamil MRC task, including Multilingual DistilBERT, Multilingual BERT, XLM-RoBERTa, MuRIL, and RemBERT. Additionally, it explores improving these models’ performance by fine-tuning them with English SQuAD, Tamil SQuAD, and a newly developed Tamil Short Story (TSS) dataset for MRC. Tamil’s agglutinative nature, which expresses grammatical information through suffixation, results in a high degree of word inflexion. Given this characteristic, the BERT score was chosen as the evaluation metric for MRC performance. The analysis shows that the XLM-RoBERTa model outperformed the others for Tamil MRC, achieving a BERT score of 86.29% on the TSS MRC test set. This superior performance is attributed to the model’s cross-lingual learning capability and the increased number of data records used for fine-tuning. The research underscores the necessity of language-specific fine-tuning of multilingual models to enhance NLP task performance, for low-resourced languages. 2025-01-01T00:00:00Z http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/12904 Transformers in Single Object Tracking: An Experimental Survey Kugarajeevan, J.; Kokul, T.; Ramanan, A.; Fernando, S. Single-object tracking is a well-known and challenging research topic in computer vision. Over the last two decades, numerous researchers have proposed various algorithms to solve this problem and achieved promising results. Recently, Transformer-based tracking approaches have ushered in a new era in single-object tracking by introducing new perspectives and achieving superior tracking robustness. In this paper, we conduct an in-depth literature analysis of Transformer tracking approaches by categorizing them into CNN-Transformer based trackers, Two-stream Two-stage fully-Transformer based trackers, and One-stream One-stage fully-Transformer based trackers. In addition, we conduct experimental evaluations to assess their tracking robustness and computational efficiency using publicly available benchmark datasets. Furthermore, we measure their performances on different tracking scenarios to identify their strengths and weaknesses in particular situations. Our survey provides insights into the underlying principles of Transformer tracking approaches, the challenges they encounter, and the future directions they may take. 2023-01-01T00:00:00Z