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检索条件"机构=Computing and Informatics"

共 15978 条记录，以下是11-20 订阅

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Efficient Medical File Protection via Adaptive CRP-based Ephemeral Key Encapsulation and Robust Error Correction 15

Efficient Medical File Protection via Adaptive CRP-based Eph...

引用

15th IEEE Annual computing and Communication Workshop and Conference, CCWC 2025

作者： Alshammari, Adil Miandoab, Dina Ghanai Assiri, Sareh Cambou, Bertrand Riggs, Brit School of Informatics and Computing Northern Arizona University FlagstaffAZ United States

ISBN: (纸本)9798331507695

Ahstract- The healthcare sector faces significant challenges in balancing adaptability, security, and resource efficiency in medi-cal file management. This paper introduces a lightweight protocol that optimizes cryptographic components for using ephemeral key encapsulation, combining adaptive Challenge Response Pair with robust error correction. Designed for limited resource environments, the proposed approach minimizes computational overhead, ensures reliable key recovery, and provides privacy protection. The execution results validate its performance, making it a practical solution for a decentralized healthcare system. © 2025 IEEE.

关键词： Electronic health record

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Exploring Quantum Processors in Decentralized Edge computing for Improved Data Processing 2

Exploring Quantum Processors in Decentralized Edge Computing...

引用

2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Pitkar, Harshad Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States

ISBN: (纸本)9798331505745

The rapid growth of data generated by Internet of Things (IoT) devices necessitates the development of advanced computational frameworks that can efficiently handle real-time data processing. Traditional cloud and edge computing systems, while effective, face significant challenges, including high latency, limited computational resources, and data transfer bottlenecks. To address these issues, this study introduces a hybrid approach that integrates quantum computing into the edge-cloud continuum. The proposed method utilizes Quantum- Neural Networks (QNNs) in conjunction with local edge processing, leveraging quantum autoencoders for feature compression, and incorporating fully decentralized edge computing with quantum processors to enhance data processing capabilities. Additionally, a hybrid model combining QNNs with classical machine learning techniques is implemented at the edge for improved classification accuracy. The proposed system's performance is evaluated through extensive experiments across multiple datasets, comparing the results of quantum-based models with traditional classical and hybrid approaches. The results show a significant improvement in classification accuracy, reduced feature compression time, and enhanced processing efficiency, particularly in resource-constrained environments. The results underscore the potential of quantum machine learning in real-time, large-scale applications, marking an important step toward the practical integration of quantum computing in distributed computing systems. © 2025 IEEE.

关键词： Qubits

来源：评论

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LSTM-QGAN: Scalable NISQ Generative Adversarial Network

LSTM-QGAN: Scalable NISQ Generative Adversarial Network

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Chu, Cheng Hastak, Aishwarya Chen, Fan Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

ISBN: (纸本)9798350368741

Current quantum generative adversarial networks (QGANs) still struggle with practical-sized data. First, many QGANs use principal component analysis (PCA) for dimension reduction, which, as our studies reveal, can diminish the QGAN's effectiveness. Second, methods that segment inputs into smaller patches processed by multiple generators face scalability issues. In this work, we propose LSTM-QGAN, a QGAN architecture that eliminates PCA preprocessing and integrates quantum long short-term memory (QLSTM) to ensure scalable performance. Our experiments show that LSTM-QGAN significantly enhances both performance and scalability over state-of-the-art QGAN models, with visual data improvements, reduced Fréchet Inception Distance scores, and reductions of 5× in qubit counts, 5× in single-qubit gates, and 12× in two-qubit gates. © 2025 IEEE.

关键词： Long Short-Term Memory NISQ Quantum Generative Adversarial Network

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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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A Comprehensive Review of Bias Detection and XAI Techniques in NLP 5

A Comprehensive Review of Bias Detection and XAI Techniques ...

引用

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

作者： Bhavaneetharan, Loganathan Informatics Institute of Technology Department of Computing Colombo Sri Lanka

ISBN: (纸本)9798331530983

Bias in Natural Language Processing (NLP) models pose urgent ethical and societal challenges by perpetuating stereotypes and inequalities. This review provides a comprehensive overview of state-of-the-art Explainable AI (XAI) techniques - such as LIME, SHAP, and Integrated Gradients - for detecting and mitigating bias. We detail our methodology for selecting relevant studies and analyze key NLP datasets (e.g., StereoSet, CrowS-Pairs) to uncover specific limitations like language coverage and intersectional gaps. Moreover, we highlight emerging challenges in scalability, cultural diversity, and regulatory compliance. By integrating these findings, we propose actionable strategies for fair and transparent NLP systems, positioning this work as a foundation for ongoing improvements in equitable AI solutions. © 2025 IEEE.

关键词： Natural language processing systems

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FracturePro: Automated Bone Fracture Identification and Classification Using an Ensemble Deep Learning Approach 5

FracturePro: Automated Bone Fracture Identification and Clas...

引用

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

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

ISBN: (纸本)9798331530983

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. © 2025 IEEE.

关键词： Diagnosis

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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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Parkinson’s Disease Prediction Using Spectrograms and Deep Belief Networks 14

Parkinson’s Disease Prediction Using Spectrograms and Deep ...

引用

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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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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