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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Sandeep, C.S. Margala, Martin School of Computing and Informatics University of Louisiana at Lafayette United States

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531935

This research presents a way to forecast Parkinson's disease in the early stages by means of spectrogram investigation and deep belief networks (DBNs). The aforesaid way concentrates on the use of spectrograms in order to get the best results and the extraction of innovative features and patterns from the signal of the speech or motor, which is indicative of Parkinson's disease. By using DBNs, which is a resilient deep learning mechanism, the system has the good feature of learning deep representations of the data, which makes the detection and classification accurate. The coupling of spectrograms with DBNs gives the model perspective to notice even the less severe differences in the input signals which are often missed out by traditional methods. The outlined aggregate demonstrates a prologue to a system that would be an accurate and automated solution to the problem, which in the end could be a help to healthcare professionals in a timely procurement of intervention and treatment. The proposed model makes clear the the ability of advanced machine learning techniques to solve hard neurological disorders and enhance patient outcomes. © 2025 IEEE.

关键词： Neurodegenerative diseases

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Incentive-Compatible Federated Learning with Stackelberg Game Modeling

arXiv

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arXiv 2025年

作者： Javaherian, Simin Turney, Bryce Chen, Li Tzeng, Nian-Feng School of Computing and Informatics University of Louisiana Lafayette United States

Federated Learning (FL) has gained prominence as a decentralized machine learning paradigm, allowing clients to collaboratively train a global model while preserving data privacy. Despite its potential, FL faces significant challenges in heterogeneous environments, where varying client resources and capabilities can undermine overall system performance. Existing approaches primarily focus on maximizing global model accuracy, often at the expense of unfairness among clients and suboptimal system efficiency, particularly in non-IID (nonIndependent and Identically Distributed) settings. In this paper, we introduce FLamma, a novel Federated Learning framework based on adaptive gamma-based Stackelberg game, designed to address the aforementioned limitations and promote fairness. Our approach allows the server to act as the leader, dynamically adjusting a decay factor while clients, acting as followers, optimally select their number of local epochs to maximize their utility. Over time, the server incrementally balances client influence, initially rewarding higher-contributing clients and gradually leveling their impact, driving the system toward a Stackelberg Equilibrium. Extensive simulations on both IID and non-IID datasets show that our method significantly improves fairness in accuracy distribution without compromising overall model performance or convergence speed, outperforming traditional FL baselines. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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FracturePro: Automated Bone Fracture Identification and Classification Using an Ensemble Deep Learning Approach

FracturePro: Automated Bone Fracture Identification and Clas...

引用

Advanced Research in computing (ICARC), International Conference on

作者： Madushani Rodrigo Banuka Athuraliya Department of Computing Informatics Institue of Technology Colombo Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

In modern medical diagnostics, machine learning (ML) has emerged as a transformative tool, reshaping healthcare practices. This study investigates the application of ML for the timely and accurate detection of bone fractures, a critical aspect of orthopedic care. Delayed diagnosis can result in complications such as malunion or non-union, underscoring the need for real-time, precise identification of fracture types and locations. While X-ray interpretation relies heavily on the expertise of medical professionals, poor image quality often poses significant challenges. To address these issues, this research introduces a novel ML-based model for fracture segmentation and classification. The model leverages advanced architectures, including ResNet, DenseNet, and U-Net, to perform fracture segmentation and classification with high accuracy. Additionally, image enhancement and preprocessing techniques are incorporated to mitigate the limitations posed by low-quality radiographic images. The findings highlight the potential of ML in improving diagnostic precision and efficiency, ultimately enhancing patient outcomes in orthopedic care.

关键词： Location awareness Image quality Image segmentation Accuracy Spirals Computational modeling Computer architecture Bones Diagnostic radiography X-ray imaging

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Job Resumes Recommendation using Integration of Fuzzy Discernibility Matrix Feature Selection and Convolutional Neural Network Multi-label Text Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第13期49卷 49-58页

作者： Hayami, Regiolina Yusoff, Nooraini Daud, Kauthar Mohd Fatma, Yulia Department of Informatics Engineering Faculty Computer Science Universitas Muhammadiyah Riau Indonesia Faculty of Data Science and Computing Universiti Malaysia Kelantan 16100 Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Malaysia

The manual analysis of job resumes poses specific challenges, including the time-intensive process and the high likelihood of human error, emphasizing the need for automation in content-based recommendations. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNN) for Multi-label Text Classification (MLTC), offer a promising solution for addressing these challenges through artificial intelligence. While CNN is renowned for its robust feature extraction capabilities, it faces specific challenges such as managing high-dimensional data and poor data interpretability. To address these limitations, this study employs the Fuzzy Discernibility Matrix (FDM) feature selection technique to determine the relevance of skills for each job vacancy. FDM assigns weights to the features of each job category and ranks their relevance based on the highest scores, which are then utilized in the MLTC CNN model. The integration of FDM and MLTC CNN serves as the foundation for generating content-based recommendations derived from key features in job resumes. This study produces a content-based job recommendation system that displays the top three job categories, accompanied by explanations of the skills supporting each selected category. These recommendations also consider cosine similarity values between analyzed items and the integrated results of FDM and MLTC CNN. The application of FDM for feature weighting has been proven to enhance multi-label text classification outcomes by providing better insights during the feature selection process. With a recall of 97.26%, precision of 94.81%, and accuracy of 98.58%, the MLTC model integrating FDM feature selection and CNN demonstrates robust performance characteristics in content-based job recommendation tasks. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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

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

引用

Advanced Research in computing (ICARC), International Conference on

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

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

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.

关键词： Training Image segmentation Analytical models Adaptation models Three-dimensional displays Accuracy Magnetic resonance imaging Computational modeling Data models Biological information theory

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

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

引用

Advanced Research in computing (ICARC), International Conference on

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

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

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.

关键词： Translation Large language models Transfer learning Collaboration Linguistics Data augmentation Natural language processing Tuning Systematic literature review Synthetic data

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

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

引用

Advanced Research in computing (ICARC), International Conference on

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

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

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.

关键词： Sign language Translation Speech recognition Pressing Machine learning Streaming media Data collection Real-time systems Communications technology Labeling

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IAPS: Decreasing Software-Based Packet Steering Overhead Through Interrupt Reduction

IAPS: Decreasing Software-Based Packet Steering Overhead Thr...

引用

International Conference on Information Networking

作者： Maike Helbig Younghoon Kim College of Computing and Informatics Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

Packet Steering is often performed in hardware, rendering the software-based packet steering mechanisms of the Linux kernel obsolete. However, the ability to redistribute packets later during network processing could help achieve a more parallelized network stack. Unfortunately, existing software-based packet steering schemes yield minimal performance gain at high communication costs. This paper proposes Interrupt Avoidance Packet Steering (IAPS), a novel packet steering scheme that reduces software-based packet steering overhead. IAPS decreases communication costs by avoiding hardware interrupt triggers during packet steering. IAPS increases application throughput by up to $\mathbf{4. 3 \%}$ and the packet per hardware interrupt ratio by up to 4 x.

关键词： Costs Linux Performance gain Throughput Rendering (computer graphics) Hardware Kernel

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Advancements in Natural Language Processing for Automatic Text Summarization

arXiv

引用

arXiv 2025年

作者： Jayatilleke, Nevidu Weerasinghe, Ruvan Senanayake, Nipuna School of Computing Informatics Institute of Technology Colombo Sri Lanka

The substantial growth of textual content in diverse domains and platforms has led to a considerable need for Automatic Text Summarization (ATS) techniques that aid in the process of text analysis. The effectiveness of text summarization models has been significantly enhanced in a variety of technical domains because of advancements in Natural Language Processing (NLP) and Deep Learning (DL). Despite this, the process of summarizing textual information continues to be significantly constrained by the intricate writing styles of a variety of texts, which involve a range of technical complexities. Text summarization techniques can be broadly categorized into two main types: abstractive summarization and extractive summarization. Extractive summarization involves directly extracting sentences, phrases, or segments of text from the content without making any changes. On the other hand, abstractive summarization is achieved by reconstructing the sentences, phrases, or segments from the original text using linguistic analysis. Through this study, a linguistically diverse categorizations of text summarization approaches have been addressed in a constructive manner. In this paper, the author explored existing hybrid techniques that have employed both extractive and abstractive methodologies. In addition, the pros and cons of various approaches discussed in the literature are also investigated. Furthermore, a comparative analysis conducted to evaluate the generated summaries utilizing language generation models, employing different techniques and matrices. This survey endeavors to provide a comprehensive overview of ATS by presenting the progression of language processing regarding this task through a breakdown of diverse systems and architectures accompanied by technical and mathematical explanations of their operations. Finally, following the conclusion, a discussion is held regarding the limitations, challenges, and potential future work that can be conducted within this

关键词： Deep learning

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Aspect-Based Sentiment Analysis of Student Reviews on MOOC Platforms 5

Aspect-Based Sentiment Analysis of Student Reviews on MOOC P...

引用

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

作者： Dissanayake, A.D.M.B.B. Fernando, Pumudu A. Informatics Institute of Technology Department of Computing Colombo Sri Lanka

ISBN: (纸本)9798331530983

The rapid growth of Massive Open Online Courses (MOOCs), particularly in the post-COVID-19 era, has transformed education and led to a substantial increase in student-generated reviews. However, manual analysis of these reviews is impractical due to their sheer volume, and traditional binary sentiment analysis lacks the granularity needed to extract actionable insights. This study proposes a novel framework leveraging Aspect-Based Sentiment Analysis (ABSA) to classify MOOC reviews into seven critical aspects. The model was trained on a 100,000-review dataset from Coursera and a manually annotated 10,000-review dataset from Udemy. Various machine learning techniques were evaluated, with the final model integrating Long Short-Term Memory (LSTM) networks and Support Vector Machines (SVM). The SVM achieved notable performance, with accuracy rates of 93.7% for sentiment prediction and 88.64% for aspect classification on the Coursera dataset, and 73.91% for sentiment prediction and 66.95% for aspect classification on the Udemy dataset. These results underscore the model's efficacy in delivering a comprehensive and platform-independent approach to analyzing MOOC reviews, providing valuable insights for educators and course developers. © 2025 IEEE.

关键词： Emotion Recognition

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