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检索条件"机构=Dept. of Computer Science and Biomedical Informatics"

共 2472 条记录，以下是1-10 订阅

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LSTM-Based Classification of Psychiatric Disorders Using Ultra-Short Raw ECG Signals

LSTM-Based Classification of Psychiatric Disorders Using Ult...

引用

2025 IEEE Symposium on Computational Intelligence in Health and Medicine, CIHM 2025

作者： Tsakmaki, Paraskevi V. Tasoulis, Sotiris Georgakopoulos, Spiros V. Plagianakos, Vassilis P. Dept. of Computer Science and Biomedical Informatics University of Thessaly Lamia Greece Dept. of Mathematics University of Thessaly Lamia Greece

ISBN: (纸本)9798331508333

Psychiatric disorders, including bipolar disorder, depression, and schizophrenia, pose substantial public health challenges due to the intricate nature of their diagnosis and management. Electrocardiogram (ECG) signals offer a noninvasive method to assess autonomic nervous system function, which is frequently dysregulated in these conditions. However, conventional ECG analysis techniques often require prolonged recordings and extensive preprocessing, limiting their applicability in real-time or large-scale settings. This study presents an approach leveraging Long Short-Term Memory (LSTM) networks for medical data classification, specifically for the identification of psychiatric disorders using ultra-short, raw ECG signals. Our methodology efficiently captures the temporal dynamics of ECG signals within brief windows, significantly reducing the need for extensive preprocessing and manual feature extraction. The proposed LSTM-based classifier achieved an impressive mean accuracy of 97.12% ± 0.006, underscoring its precision and efficacy in distinguishing between different psychiatric conditions. Our findings indicate that LSTM networks, with their capacity to effectively model complex temporal patterns in short ECG recordings, represent a promising advancement in the diagnostic landscape of psychiatry. © 2025 IEEE.

关键词： Electrocardiography

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EXAMINING CRITICAL FACTORS IN FCA-SAPO FRAMEWORK: A QUALITATIVE AND QUANTITATIVE STUDY ON THE ADOPTION OF FOG COMPUTING IN SAUDI ARABIAN PUBLIC ORGANISATIONS BY IT EMPLOYEES 18

EXAMINING CRITICAL FACTORS IN FCA-SAPO FRAMEWORK: A QUALITAT...

引用

18th International Conferences IADIS Information Systems 2025 and 23rd International Conference on e-Society 2025

作者： Alyami, Mohammed Beloff, Natalia White, Martin Dept. of Informatics University of Sussex Brighton United Kingdom Dept. of Computer Science Jazan University Jazan Saudi Arabia

ISBN: (纸本)9789898704658

Fog computing is a rapidly evolving domain within information technology and enterprise computing, providing organisations with the opportunity to greatly improve efficiency and productivity. This is particularly pertinent in developing countries, where effective resource utilisation is critical. Despite its potential, there has been limited research on the factors influencing the adoption of fog computing in Saudi Arabia, as well as the associated benefits and challenges. This study seeks to fill this gap by examining the various factors, advantages, and obstacles related to fog computing adoption within Saudi Arabian public organisations. To address the absence of a context-specific model, we employed a mixed-methods approach, gathering qualitative data through semi-structured interviews with 15 IT managers, revealing that complexity was the sole factor hindering adoption. Quantitative data was then collected from 665 IT managers and employees to validate the framework’s constructs, showing that privacy, complexity, awareness, and senior management support were insignificant among 12 factors in influencing adoption intention. The quantitative analysis complements these findings, indicating that while the majority of factors significantly influence the adoption process, a portion of the factors were found to be insignificant. Findings from both qualitative and quantitative analyses highlight the importance of technical, organisational, environmental, and financial contexts in the adoption process. These insights provide valuable guidance for IT managers in the Saudi public sector, emphasising the need to consider these factors comprehensively to achieve successful fog computing implementation. © 2025 Proceedings of the International Conferences IADIS Information Systems 2025 and e-Society 2025. All rights reserved.

关键词： Fog computing

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Integrated Word2Vec-based Speech Annotations for Enhanced EEG Decoding of Speech Intentions 13

Integrated Word2Vec-based Speech Annotations for Enhanced EE...

引用

13th International Winter Conference on Brain-computer Interface, BCI 2025

作者： Jang, Se-Na Park, Hyeong-Yeong Kim, Seong-Wook Yu, Seong-Hyun Lee, Minji Jeong, Ji-Hoon Dept. Computer Science Chungbuk National University Cheongju Korea Republic of Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Korea Republic of

ISBN: (纸本)9798331521929

Brain-computer interface (BCI) technology has promising applications as an intuitive communication tool and in fields such as language rehabilitation. This study aims to decode human speech intentions by analyzing EEG signals recorded during actual and imagined speech. EEG data were collected using a 64 channels system, with preprocessing to remove artifacts. Automatic speech recognition (ASR) was used to extract precise speech onset times, generating time-specific speech annotations corresponding with EEG data. Pretrained Word2Vec embeddings were integrated to provide semantic context, combining neural signals with high-level linguistic features. Support vector machine (SVM), linear discriminant analysis (LDA) were employed for decoding. The results demonstrate that integrating speech annotations improves decoding accuracy, even for imagined speech, highlighting the potential of BCI technology for advanced applications in communication and rehabilitation. © 2025 IEEE.

关键词： Semantics

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Advancing Healthcare Diagnostics with a Hybrid AI Model 4

Advancing Healthcare Diagnostics with a Hybrid AI Model

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Jihane, Benbrik Salma, Rattal Kamal, Ghoumid Miloud, Ar-Reyouchi El Ensao Mohammed i University Dept. Electronics Informatics and Telecommunications Oujda Morocco Abdelmalek Essaâdi University Dept. Telecommunication and Computer Science Tétouan Morocco

ISBN: (纸本)9798331523923

Artificial intelligence (AI) has significantly impacted the field of healthcare by advancing medical diagnostics and decision-making processes. This paper presents a unified framework that integrates Convolutional Multi-Layer Perceptrons (Conv MLP), Convolutional Neural Networks (ConvNet), and Vision Transformers (ViTs) to address two critical tasks: sentiment analysis of medical reports and medical image recognition. By leveraging the distinct strengths of each architecture, the proposed hybrid model improves performance, effectively balancing computational efficiency, localized feature extraction, and global dependency modeling. The findings demonstrate that the model reduces task-specific errors and enhances convergence rates, achieving superior results in sentiment and image recognition tasks. This study advances the field of medical AI by overcoming the limitations of single-architecture models and establishing the feasibility of hybrid systems for robust, multi-modal applications. © 2025 IEEE.

关键词： Multilayer neural networks

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Transforming Synthesized Netlists into a Feasible & Compliant Functional Bookshelf Format 24

Transforming Synthesized Netlists into a Feasible & Complian...

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Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and informatics

作者： George Kranas Kosmas Kariotis Antonios Dadaliaris George Stamoulis Athanasios Kakarountas Dept. of Computer Science & Biomedical Informatics University of Thessaly Lamia Greece Dept. of Informatics & Telecommunications University of Thessaly Lamia Greece Dept. of Electrical and Computer Engineering University of Thessaly Volos Greece

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LSTM-Based Classification of Psychiatric Disorders Using Ultra-Short Raw ECG Signals

LSTM-Based Classification of Psychiatric Disorders Using Ult...

引用

Computational Intelligence in Health and Medicine (CIHM), IEEE Symposium on

作者： Paraskevi V. Tsakmaki Sotiris Tasoulis Spiros V. Georgakopoulos Vassilis P. Plagianakos dept. of Computer Science and Biomedical Informatics University of Thessaly Lamia Greece dept. of Mathematics University of Thessaly Lamia Greece

ISBN: (数字)9798331508333

ISBN: (纸本)9798331508340

关键词： Noise Electrocardiography Schizophrenia Real-time systems Recording Reliability Public healthcare Psychiatry Long short term memory Medical diagnostic imaging

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Phoneme Classification in Imagined Speech Using Explainable Machine Learning 13

Phoneme Classification in Imagined Speech Using Explainable ...

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13th International Winter Conference on Brain-computer Interface, BCI 2025

作者： Kim, Sejin Kang, Hyunwook Jeong, Ji-Hoon Lee, Minji Dept. Artificial Intelligence The Catholic University of Korea Bucheon Korea Republic of Dept. Electrical and Computer Engineering Inha University Incheon Korea Republic of Dept. Computer Science Chungbuk National University Cheongju Korea Republic of Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Korea Republic of

ISBN: (纸本)9798331521929

Imagined speech is gaining attention as a next-generation paradigm for brain-computer interfaces in terms of its intuitiveness in communication. Many studies have focused on classifying imagined words as the basis of communication, but some studies on phoneme classification based on imagined speech have begun to be conducted in terms of the fact that combinations of phonemes can further expand sound images. In addition, explainability is a major issue in classification results in machine learning, but there have been few studies that analyzed the explainability of imagined speech classification. In this study, we examined explainability using SHAP for phoneme classification based on imagined speech. FEIS dataset was used, and 16 phonemes were classified using the XGBoost classifier. In 21 subjects, the average accuracy was 40.36%, the average precision was 41.7%, the average recall was 40.34%, and the average F1-score was 40.7%. In addition, when looking at Sub 04, which had the highest performance, the SHAP values were high in AF3 and F7 channels, which correspond to Wernicke’s area related to speech. In this regard, we could observe the feature importance of this area. These results prove that Wernicke’s area actually plays an important role in classifying phoneme units in imagined speech, and can provide important insights to improve the performance of imagined speech for intuitive communication based on brain-computer interface in the future. © 2025 IEEE.

关键词： Adversarial machine learning

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Animated Virtual Therapist in Virtual Reality Environment for People with Parkinson's Disease 7

Animated Virtual Therapist in Virtual Reality Environment fo...

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7th IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Li, Wenting Agada, Ruth Yan, Jie Washington University Biomedical Engineering Dept. St. Louis United States Bowie State University Department of Computer Science Bowie United States

ISBN: (纸本)9798331521578

This study investigates the effectiveness of virtual rehabilitation using a 3D animated virtual therapist delivering the LSVT-BIG program for people with Parkinson's disease (PwPD). Integrating an animated virtual therapist in a VR environment aims to overcome accessibility barriers, especially for individuals in remote areas. This VR-enhanced system provides immersive experiences, real-time feedback, and posture correction through computer vision, along with progress-tracking via personalized reports. This approach offers potential improvements in accessibility, engagement, and motivation, which may contribute to enhanced quality of life for individuals with Parkinson's disease. © 2025 IEEE.

关键词： Virtual environments

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Quality Assurance for LLM-RAG Systems: Empirical Insights from Tourism Application Testing 18

Quality Assurance for LLM-RAG Systems: Empirical Insights fr...

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18th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025

作者： Ahmed, Bestoun S. Baader, Ludwig Otto Bayram, Firas Jagstedt, Siri Magnusson, Peter Karlstad University Sweden & American University of Bahrain Dept. Mathematics and Computer Science Bahrain Ludwig Maximilian University Munich Dept. Mathematics Informatics and Statistics Munich Germany Karlstad University Dept. Mathematics and Computer Science Karlstad Sweden Karlstad University CTF Service Research Center Karlstad Sweden

ISBN: (纸本)9798331534677

This paper presents a comprehensive framework for testing and evaluating quality characteristics of Large Language Model (LLM) systems enhanced with Retrieval-Augmented Generation (RAG) in tourism applications. Through systematic empirical evaluation of three different LLM variants across multiple parameter configurations, we demonstrate the effectiveness of our testing methodology in assessing both functional correctness and extra-functional properties. Our framework implements 17 distinct metrics that encompass syntactic analysis, semantic evaluation, and behavioral evaluation through LLM judges. The study reveals significant information about how different architectural choices and parameter configurations affect system performance, particularly highlighting the impact of temperature and top-p parameters on response quality. The tests were carried out on a tourism recommendation system for the Varmland region in Sweden, utilizing standard and RAG-enhanced configurations. The results indicate that the newer LLM versions show modest improvements in performance metrics, though the differences are more pronounced in response length and complexity rather than in semantic quality. The research contributes practical insights for implementing robust testing practices in LLM-RAG systems, providing valuable guidance to organizations deploying these architectures in production environments. © 2025 IEEE.

关键词： Software quality

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Gendering Robots in Human-Robot Interaction: An Interdisciplinary Approach 20

Gendering Robots in Human-Robot Interaction: An Interdiscipl...

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20th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2025

作者： Akalin, Neziha Arnelid, Maria Harrison, Katherine School of Engineering Jönköping University Dept. of Computer Science and Informatics Jönköping Sweden Linköping University Unit of Gender Studies Dept. of Thematic Studies Linköping Sweden

ISBN: (纸本)9798350378931

This paper emphasizes the importance of interdisciplinary collaboration in exploring the concept of robot gender within Human-Robot Interaction (HRI). It draws on a case study of the authors' own collaboration, where interdisciplinary discussions on the nature of gender informed the design of a multimethod study. This approach allowed us to avoid assigning a binary gender to Pepper robot, while still encouraging participants to reflect on their own gendering practices during interactions with robots. Additionally, we invite readers to consider alternative ways of conceptualizing robot gender in HRI - specifically, as fluid and performative rather than binary, which relies on stereotypical cues. After describing how our discussions on gender influenced and reshaped the study design, we offer practical advice on fostering interdisciplinary collaborations. These suggestions focus on communication strategies, mindset, and the practical setup of collaborative studies. We hope that these recommendations will inspire other researchers to continue exploring new and interdisciplinary ways of approaching robot gender in HRI. © 2025 IEEE.

关键词： Social robots

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