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An Examination of Harmonizing Multi-Vendor Cardiac MRI Data for Vendor-Neutral Image Segmentation 5

An Examination of Harmonizing Multi-Vendor Cardiac MRI Data ...

引用

5th International Conference on Advanced Research in computing, ICARC 2025

作者： Mohideen, Hamdaan Ayoob, Mohamed Nettasinghe, Oshan Sylvester, Vithushan Bowala, Helmini Informatics Institute of Technology School of Computing Colombo Sri Lanka

ISBN: (纸本)9798331530983

Vendor-specific scanner hardware, software, and image acquisition protocols can introduce systematic biases into MRI scans. This introduces non-biological artifacts which introduce variations across MRI images from different vendors. Further, we ratified such non-biological variances by conducting a discrepancy study on the MnMs-2 Cardiac MRI dataset. Data harmonization is the process of combining datasets from different sources while removing unwanted variations that are not caused by biological differences. This is important because it allows researchers to increase the understanding of biological differences in data, while mitigating vendor-specific biases. We used a domain adaptation methodology (which is a data harmonization method) on the dataset to unlearn domain (or vendor) specific biases by introducing a confusion loss to confuse the encoder. Furthermore, we introduced a 3D segmentation model to this method to capture spatial information. This method demonstrates the performance on the main task (segmentation) and compares the domain predictor, before and after unlearning. This shows the magnitude of mitigating vendor-specific biases of the model. In addition to the model architecture performance, we documented the discrepancy study between inter-vendor model performance to demonstrate the magnitude of impact in which vendor-specific, and non-biological variations of MRI scans can affect the model. © 2025 IEEE.

关键词： Image segmentation

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FlexiSecure - AI-Powered Fraud Detection with Real-Time Alerts and Customizable Security 5

FlexiSecure - AI-Powered Fraud Detection with Real-Time Aler...

引用

5th International Conference on Advanced Research in computing, ICARC 2025

作者： Mumthas, Nooha Informatics Institute of Technology School of Computing Colombo Sri Lanka

ISBN: (纸本)9798331530983

The rapid expansion of online financial transactions has escalated the risk of fraud, posing significant challenges for banks and financial institutions, particularly in Sri Lanka. Traditional rule-based fraud detection systems often find it challenging to balance speed and accuracy, rendering them less effective against advanced and evolving fraud schemes. This study proposes a hybrid machine learning model combining decision trees and neural networks to enhance fraud detection performance. The model incorporates advanced preprocessing techniques, including feature scaling and outlier detection, to improve data quality. It employs supervised learning to achieve high accuracy and adapt to emerging fraud patterns. Additionally, explainable AI (XAI) is incorporated to ensure transparency in detection outcomes, where the user would be entrusted to make the final decision on the transaction, thereby fostering user trust and compliance with regulations in the financial sector. The model is designed to process large volumes of transactions efficiently, which demonstrates its scalability and suitability for high-volume banking environments. © 2025 IEEE.

关键词： Semi-supervised learning

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Transforming Music: A Review of Artificial Intelligence in Jazz Reharmonization 5

Transforming Music: A Review of Artificial Intelligence in J...

引用

5th International Conference on Advanced Research in computing, ICARC 2025

作者： Jayasinghe, Sachindu Bhareti, Kushan Institute of Technology School of Computing Informatics Colombo Sri Lanka

ISBN: (纸本)9798331530983

Jazz reharmonization is a complex musical technique that improves harmonic frameworks by inventive chord progressions, augmenting the depth and emotional resonance of pieces. This paper examines the use of artificial intelligence, namely transformer networks, in the automation of jazz reharmonization, ensuring melodic coherence and artistic fidelity. This review compiles current research to highlight essential approaches, including early rule-based systems, neural networks, and advanced transformer models while emphasizing their benefits and limits. Particular emphasis is placed on the difficulties associated with dataset preparation, assessment measures, and the adaptation of AI systems to the intricacies of jazz harmony. The evaluation, via a critical examination of previous studies, identifies areas for enhancement, including real-time applications, multi-modal data integration, and cross-genre adaptation. The insights provided seek to direct future research and position transformers as a revolutionary instrument in computational musicology. This document is an extensive reference for scholars and professionals investigating the convergence of artificial intelligence and jazz music. © 2025 IEEE.

关键词： Modal analysis

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Deep learning-powered mobile app for early brahmi script decipherment in sri lanka

Deep learning-powered mobile app for early brahmi script dec...

引用

2024 International Research Conference on Smart computing and Systems Engineering, SCSE 2024

作者： Gunasekara, Sakith Lafir, Muhammed Haleef Dulaj, Chavindu Haputhanthri, Lakidu Alwis, Dileeka Informatics Institute of Technology School of Computing Colombo Sri Lanka

ISBN: (纸本)9798350375688

This ongoing research delves into ancient Brahmi writings carved on stone surfaces, particularly focusing on early Brahmi characters which serves as critical artifacts to illuminate the island's historical ties with India, religious and cultural practices of the era. The study aims to develop a mobile application utilizing deep learning techniques to recognize and translate ancient early Brahmi characters, ensuring enhanced efficiency and accuracy in the decipherment process. A comprehensive literature review highlighted the absence of a mobile application, challenges of contextual translation, and difficulties in accurate predicting translation for damaged inscriptions and translation to foreign languages were identified as notable research gaps. Drawing upon interdisciplinary expertise and stakeholder analysis, the research tackles these complex challenges of script recognition, linguistic translation, accurate prediction by employing a data-driven approach, cutting-edge algorithms, and user-centric design principles. After the creation of a refined, precise digitized dataset of early Brahmi characters with found variations, the research aims to utilize semantic segmentation techniques to recognize characters using TensorFlow and Keras. OpenCV for image preprocessing, Flutter framework for development of mobile application. Expected outcomes include improved recognition accuracy, linguistically faithful translations, and user-friendly interfaces, contributing to advancements in digital humanities, cultural preservation, and computational linguistics. © 2024 IEEE.

关键词： Semantics

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Systematic Review of Fine-tuning Approaches for Large Language Models in the Context of Sinhala 5

Systematic Review of Fine-tuning Approaches for Large Langua...

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5th International Conference on Advanced Research in computing, ICARC 2025

作者： Hansaka, Sachin Sumanathilaka, Deshan Informatics Institute of Technology School of Computing Colombo006 Sri Lanka

ISBN: (纸本)9798331530983

The rapid development of large language models (LLMs) such as GPT-3, GPT-4, LlaMA, and mBERT has significantly advanced the natural language processing (NLP) field across many widely spoken languages. However, the effectiveness of these LLMs on low-resource languages such as Sinhala remained unexplored. With the scarcity of high-quality data and linguistic resources, fine-tuning LLMs in Sinhala has gained significant challenges. With a focus on Sinhala specifically, this systematic review addresses the methodologies and outcomes of adapting large language models for underrepresented languages. The purpose of this review is to synthesize existing research, identify effective strategies, contributions, and improvements, and highlight the challenges and opportunities in this domain. By analyzing various approaches, including data augmentation, transfer learning, cross-lingual transfer, parameter-efficient fine-tuning, adapter tuning, and model adaptation likewise, this review provides a comprehensive and precise idea of the current state of LLM fine-tuning on low-resource languages like Sinhala. The results highlight the importance of using creative methods and collaboration to improve LLM performance and usefulness in linguistic environments with limited resources. © 2025 IEEE.

关键词： Natural language processing systems

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LawKey-Law Constitution Chatbot 8

LawKey-Law Constitution Chatbot

引用

8th SLAAI - International Conference on Artificial Intelligence, SLAAI-ICAI 2024

作者： Fernando, W Shamini Handapangoda, Mandinu Samindi, Nividula Gamage, Maneth Alwis, Dileeka Informatics Institute of Technology School of Computing Colombo Sri Lanka

ISBN: (纸本)9798331509170

In response to a pervasive lack of legal awareness among Sri Lanka, this research introduces a law constitution chatbot utilizing Natural Language Processing (NLP). The chatbot is designed to offer precise legal guidance by extracting pertinent laws from the Sri Lankan constitution based on user input. Building on existing studies at the intersection of NLP and the legal domain, this chatbot addresses the lack of relevant legal context for Sri Lanka. Reinforcement learning is integrated to refine the chatbot's behavior through user feedback, ensuring optimal selection of the most relevant laws. An exploration of applications in countries like Canada and India, where NLP and machine learning have successfully addressed legal information gaps, focuses on various legal domains in Sri Lanka with the aim of empowering the public. The research methodology involves leveraging the Sri Lankan constitution as the dataset, extracting keywords from legal documents, and employing various NLP models, and limitations of the application. A comprehensive literature review reveals the potential energy of NLP techniques in legal chatbots. The global analysis of legal chatbots significantly contributes to the theoretical foundation of this project. In summary, this project seeks to advance legal chatbot technology in Sri Lanka, utilizing NLP and reinforcement learning techniques to empower citizens with legal knowledge and enhance overall legal awareness, thereby addressing the critical issue of legal unawareness and fostering a more informed society. © 2024 IEEE.

关键词： Reinforcement learning

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Knee Osteoarthritis Detection Using Machine Learning on Pre-Processed X-ray Images and Treatment Recommendation 8

Knee Osteoarthritis Detection Using Machine Learning on Pre-...

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8th SLAAI - International Conference on Artificial Intelligence, SLAAI-ICAI 2024

作者： Rameez, Nuha Dewmi Dissanayake, G.G.D. Chanula Senindu, G.A.C. Liyanage, P.M.H. Embuldeniya, Vishmi Informatics Institute of Technology School of Computing Colombo Sri Lanka

ISBN: (纸本)9798331509170

Osteoarthritis (OA) is a degenerative joint disease marked by the gradual breakdown of cartilage and changes in the underlying bone. The condition is most prevalent in older adults and is often exacerbated by joint injuries and mechanical stress. Knee osteoarthritis (KOA) is one of the common disability adjusted life years. There is no cure for the disease, but a combination of lifestyle changes, medications, physical therapies, and sometimes knee transplants can help manage the condition. Despite being time-consuming and highly prone to user variation, manual segmentation, diagnosis, annotation of knee joints, and personalized treatment recommendations remain common procedures for diagnosing osteoarthritis in clinical practice. This research addresses these limitations and reduces diagnostic errors and time and enhance the efficiency and accuracy of knee osteoarthritis diagnosis. The proposed KOA detection model utilizes a deep learning-based approach with Convolutional Neural Networks (CNNs) to identified knee joints in x-rays, Binary classification to determine the presence of disease, a pre trained network(VGG16) and custom CNN used to classify the Kellgren-Lawrence (KL) grades, and random forest model recommending and validating treatment options based on severity. The proposed model has shown promising results by obtaining 98% overall accuracy. © 2024 IEEE.

关键词： Joints (anatomy)

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SSL400 - A Comprehensive Word Level Dataset for Sinhala Sign Language Recognition 5

SSL400 - A Comprehensive Word Level Dataset for Sinhala Sign...

引用

5th International Conference on Advanced Research in computing, ICARC 2025

作者： Abhishek, Yohan Sumanathilaka, T.G.D.K. Informatics Institute of Technology School of Computing Colombo006 Sri Lanka

ISBN: (纸本)9798331530983

In the context of increasing digital accessibility and the need for inclusive communication technologies, the demand for automated sign language recognition has grown significantly. However, Sinhala Sign Language (SSL), used by the Sri Lankan deaf community, remains underexplored due to the lack of labelled datasets and the challenges of developing recognition models for low-resource languages. This study addresses this gap by introducing an SSL dataset, specifically curated to support real-time recognition of 384 commonly used words in daily and work-related contexts. The methodology for dataset creation involved expert consultations, a structured data collection process, and multi-step labelling. These steps ensured the accurate labeling of video data, which can be used for machine learning tasks like gesture recognition, sentence formation, and speech translation. The proposed dataset lays the foundation for SSL recognition models that are fast, lightweight, and optimized for low-resource environments. Additionally, we explore the performance of initial recognition models using this dataset, demonstrating its applicability in real-world scenarios. The dataset and research outcomes provide valuable resources for the development of inclusive AI-driven communication tools for the Sinhala-speaking deaf community. This contribution addresses the pressing need for better technological support for underrepresented languages in the field of sign language recognition and processing. © 2025 IEEE.

关键词： Multi-task learning

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Parameter Efficient Diverse Paraphrase Generation Using Sequence-Level Knowledge Distillation 5

Parameter Efficient Diverse Paraphrase Generation Using Sequ...

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5th International Conference on Advancements in Computational Sciences, ICACS 2024

作者： Jayawardena, Lasal Yapa, Prasan School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (纸本)9798350394764

Over the past year, the field of Natural Language Generation (NLG) has experienced an exponential surge, largely due to the introduction of Large Language Models (LLMs). These models have exhibited the most effective performance in a range of domains within the Natural Language Processing and Generation domains. However, their application in domain-specific tasks, such as paraphrasing, presents significant challenges. The extensive number of parameters makes them difficult to operate on commercial hardware, and they require substantial time for inference, leading to high costs in a production setting. In this study, we tackle these obstacles by employing LLMs to develop three distinct models for the paraphrasing field, applying a method referred to as sequence-level knowledge distillation. These distilled models are capable of maintaining the quality of paraphrases generated by the LLM. They demonstrate faster inference times and the ability to generate diverse paraphrases of comparable quality. A notable characteristic of these models is their ability to exhibit syntactic diversity while also preserving lexical diversity, features previously uncommon due to existing data quality issues in datasets and not typically observed in neural-based approaches. Human evaluation of our models shows that there is only a 4% drop in performance compared to the LLM teacher model used in the distillation process, despite being 1000 times smaller. This research provides a significant contribution to the NLG field, offering a more efficient and cost-effective solution for paraphrasing tasks. © 2024 IEEE.

关键词： Distillation

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Enhancing Fan Base Engagement through Explainable Self-Learning Sentiment Analysis 10

Enhancing Fan Base Engagement through Explainable Self-Learn...

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10th International Multidisciplinary Engineering Research Conference on Moratuwa Engineering Research, MERCon 2024

作者： Lye, Mikael Wijesinghe, Nethmi Informatics Institute of Technology School of Computing Colombo Sri Lanka

ISBN: (纸本)9798331529048

Individuals and brands with large fan bases face difficulty in understanding fan sentiment and its potential impact on fan engagement and performance. This is particularly pertinent within fast-paced sports such as Formula 1, where fan opinions can significantly influence driver and team morale. To address the above-mentioned problem, this study proposes the use of deep learning-based sentiment analysis techniques to enhance fan base engagement. The system would act as a tool to automate the process of marketing and public relations teams by analysing textual data provided by the fan base and providing meaningful insight/reports of the fan base's emotion of the posted content. This is achieved by a novel multi-class sentiment analysis system that utilizes a fine-tuned Distil-BERT model for classification, self-supervised techniques for self-improvement, and an explainable AI (XAI) approach for interpretability. The proposed system demonstrated strong performance during testing and evaluation, achieving an overall accuracy of 82% and an F1-score of 80%. Overall, the systems components and structure focuses on utilizing the least amount of resources while also maintaining a high prediction accuracy and speed. This ultimately results in a budget friendly and robust tool that can be integrated into bigger analytics systems. © 2024 IEEE.

关键词： Budget control

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