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

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

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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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Harnessing LSTMs for Enhanced Prediction of Psychotic Episodes in Schizophrenia Spectrum 13

Harnessing LSTMs for Enhanced Prediction of Psychotic Episod...

引用

13th IEEE Congress on Evolutionary Computation, CEC 2024

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

ISBN: (纸本)9798350308365

In the quest to enhance predictive models for schizophrenia spectrum disorders, Long Short-Term Memory networks (LSTM) have been pivotal due to their adept.han-dling of temporal data sequences. This ability to process time-dependent data, enhances the monitoring of longitudinal patterns that are indicative of relapse. This study leverages LSTMs to analyze Heart Rate Variability (HRV), a key marker in neuropsychiatric evaluations, focusing on individual patient data. Emphasizing individualization, each LSTM model is carefully tai-lored to reflect the unique behavioral and physiological patterns of the patient. By customizing the analysis to accommodate the distinct fluctuations in HRV, LSTMs have shown a remarkable capacity to decode the complex, patient-specific patterns, as evidenced by a notable predictive average score of 0.972± 0.068. This figure not only validates the LSTM model's effectiveness in grappling with the diverse aspects of psychosis but also highlights its exceptional ability to adapt to the unique temporal dynamics inherent in each patient's mental health progression. © 2024 IEEE.

关键词： Diseases

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Assessing Swapping Policies as a Detailed Placement Approach 8

Assessing Swapping Policies as a Detailed Placement Approach

引用

8th South-East Europe Design Automation, computer Engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2023

作者： Arvanitakis, Ioannis Kranas, George K. Dossis, Michael Kakarountas, Athanasios Dadaliaris, Antonios N. University of Thessaly Dept. of Informatics and Telecommunications Lamia Greece University of Thessaly Computer Science & Biomedical Informatics Dept. Lamia Greece University of Western Macedonia Computer Science Dept. Kastoria Greece

ISBN: (纸本)9798350383676

Detailed placement plays a crucial role in the physical design of integrated circuits, impacting factors such as area, power consumption, and performance. Swap-based placement algorithms have gained significant attention in recent years due to their effectiveness in improving the quality of placement solutions. This paper presents a comprehensive study of swap-based detailed placement algorithms for standard cell design, highlighting their principles, advantages, challenges, and recent advancements. In this paper we present results of swap-based algorithms, using different approaches for standard cells selection and how these affect the final solution. © 2023 IEEE.

关键词： detailed placement placement standard cell design swap based policies

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Nanotechnology as a Tool for Computational Thinking Skills using Open Hardware, Embedded Systems and Repository Platform, in Industry 4.0 Era 8

Nanotechnology as a Tool for Computational Thinking Skills u...

引用

8th South-East Europe Design Automation, computer Engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2023

作者： Kalovrektis, Konstantinos Dimos, Ioannis Xanakis, Apostolos Kakarountas, Athanasios University of Thessaly Dept. of Digital Systems Larisa Greece University of Thessaly Dept. of Computer Science and Biomedical Informatics Lamia Greece

ISBN: (纸本)9798350383676

The advancement in Microelectromechanical System (MEMS) technology resulted in accurate and high-performance miniature device systems. These devices are so tiny that they are not noticeable by the human eye and exhibit excellent feasibility in miniaturization sensors due to their small dimensions, low power consumption, and superior performance. The area of sci-ence and engineering where MEMS are developed (dimensions in the manometer scale) is called Nanotechnology. Nanotechnology is one of the fastest growing scientific research related to Industry 4.0. Nanotechnology may introduce industrial skills deficits as well as opportunities for new teaching practices in several subjects and educational frameworks. In the present work, we investigate the attitude of STEM (i.e. technology/engineering) and non STEM - related instructors, regarding the integration of Nanotechnology applications in Higher education curricula. Their opinions, concerning the applied teaching method, the learning content material and expected student skills, should always be taken in to account, as they may boost any reformations proposed. Moreover, we propose a repository platform, with which instructors may interact with 3D designs and MEMs mate-rial to built their didactic plans. This work's findings is critical for the design and innovative training material and computational thinking (CT) activities, which will prepare student with skills related to Industry 4.0 demand. © 2023 IEEE.

关键词： Nanotechnology

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Navigator: A Decentralized Scheduler for Latency-Sensitive AI Workflows 8

Navigator: A Decentralized Scheduler for Latency-Sensitive A...

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8th IEEE International Conference on Edge Computing and Communications, EDGE 2024

作者： Yang, Yuting Merlina, Andrea Song, Weijia Yuan, Tiancheng Birman, Ken Vitenberg, Roman Cornell University dept. Computer Science Ithaca United States University of Oslo dept. Informatics Norway

ISBN: (纸本)9798350368499

We consider AI inference and generative query processing in distributed systems: an increasingly popular edgecomputing use case. In such systems, coscheduling of GPU memory management and task placement represents a promising opportunity. We propose Navigator, a novel framework that unifies these functions to reduce job latency while using resources efficiently, placing tasks where data dependencies will be satisfied collocating tasks from the same job (when this will not overload the host or its GPU), and efficiently managing GPU memory Comparison with other state of the art schedulers shows a significant reduction in completion times while requiring the same amount or even fewer resources. In one case, just half the servers were needed for processing the same workload. © 2024 IEEE.

关键词： Memory management

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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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The Rational Construction of a Wheeler DFA 35

The Rational Construction of a Wheeler DFA

引用

35th Annual Symposium on Combinatorial Pattern Matching, CPM 2024

作者： Manzini, Giovanni Policriti, Alberto Prezza, Nicola Riccardi, Brian Dept. of Computer Science University of Pisa Italy Dept. of Mathematics Computer Science and Physics University of Udine Italy Dept. of Environmental Sciences Informatics and Statistics Ca’ Foscari University of Venice Italy Dept. of Informatics Systems and Communication University of Milano-Bicocca Italy

ISBN: (纸本)9783959773263

Deterministic Finite Wheeler Automata are a natural generalisation to regular languages of the theory of compressed data structures originated by the introduction of the Burrows-Wheeler transform. Indeed, if we can find a Wheeler automaton recognizing a given language L, such automaton can be used to design time and space efficient algorithms for representing and searching L. In this paper we introduce an alternative representation of Deterministic Wheeler Automata by showing that a natural map between strings and rational numbers in Qr0, 1q can be extended to represent the automaton’s states as intervals in Qr0, 1q. Using this representation it emerges a natural relationship between automata properties and some properties of real numbers. In addition, such representation enables us to formulate problems related to automata in a numerical setting. Although at the moment the numerical approach does not lead to time efficient algorithms, we believe this new perspective deserves further consideration. As a further demonstration of the convenience of this new representation, we use it to provide a simple proof of an unexpected result on regular languages. More precisely, we compare the size of the smallest Wheeler automaton recognizing a given language L with respect to the size of the smallest automaton, possibly non-Wheeler, recognizing the same language. We show settings in which there can be an exponential gap between the two sizes, and we discuss the implications of this result on the problem of representing regular languages. © Giovanni Manzini, Alberto Policriti, Nicola Prezza, and Brian Riccardi.

关键词： Finite automata

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Computational Thinking in Greek Educational System for K-12: towards the future teaching approach

Computational Thinking in Greek Educational System for K-12:...

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2022 Panhellenic Conference on Electronics and Telecommunications, PACET 2022

作者： Dimos, Ioannis Velaora, Chrysoula Kakarountas, Athanasios University of Thessaly Dept. of Computer Science and Biomedical Informatics Lamia Greece

ISBN: (数字)9798350399585

ISBN: (纸本)9798350399585

Computational thinking is considered an important skill set for 21st-century learners and became a subject of focus in K-12 education in recent years. It cultivates problem-solving and algorithmic thinking and can be helpful in wider aspects of everyday life, besides programming and computer science. In this paper, we investigated what is the Greek Primary and Secondary School Teachers' understanding and awareness as far as Computational Thinking is concerned. Since teachers are the agents of change, it is critical to find out how familiar and/or skilled they are with the Computational Thinking notion. Thus, we applied a qualitative questionnaire all over the whole Greek State where 406 teachers answered. The study led to a number of interesting conclusions regarding the teacher's readiness, as well as more generic aspects according to their profile and faculty. © 2022 IEEE.

关键词： Curricula

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TeachNet: A Lightweight and User-Friendly Network Visualization and Experimentation Tool 29

TeachNet: A Lightweight and User-Friendly Network Visualizat...

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29th IEEE Asia Pacific Conference on Communications, APCC 2024

作者： Islam, Safiqul Morina, Visar Welzl, Michael Feng, Boning Oslo Metropolitan University Dept. of Computer Science Oslo Norway University of Oslo Dept. of Informatics Oslo Norway

ISBN: (纸本)9798331517816

Network researchers, academics, and students often need to deal with complex scripting in order to design a network topology, configure network nodes, automate experimentation, and visualize network performance metrics. Current simulation or emulation tools lack the ability to provide a comprehensive understanding of the network. This paper introduces TeachNet, a lightweight and user-friendly network tool that provides a graphical user interface for designing, configuring, and automating network experiments. We explain TeachNet's design and present results to demonstrate its effectiveness and efficiency using our TCP experimentation-focused prototype, and Mininet in the backend. TeachNet creates a repository of log files and offers a framework for future research that is easily extensible to accommodate other tools such as OMNeT++ and ns3. © 2024 IEEE.

关键词： Graphical user interfaces

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Diving Deep into Bone Anomalies on the FracAtlas Dataset Using Deep Learning and Explainable AI

Diving Deep into Bone Anomalies on the FracAtlas Dataset Usi...

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2024 International Conference on Engineering and Computing, ICECT 2024

作者： Akhlaq, Filza Ali, Subhan Imran, Ali Shariq Daudpota, Sher Muhammad Kastrati, Zenun Dept. of Computer Science Gjovik2815 Norway Sukkur IBA University Dept. of Computer Science Sukkur Sindh Pakistan Linnaeus University Dept. of Informatics Växjö351 95 Sweden

ISBN: (纸本)9798350349719

Medical image analysis has undergone significant advancements with the integration of machine learning techniques, particularly in the realm of bone anomaly detection. The availability of recent datasets and the lack of benchmarking and explainability components provide numerous opportunities in this domain. This study proposes a benchmarking approach to a recently published FracAtlas dataset utilizing state-of-The-Art deep-learning models coupled with explainable artificial intelligence (XAI) having two distinct modules. The first module involves the binary classification of fractures in different body parts and explains the decision-making process of the best-performing model using an XAI technique known as EigenCAM. EigenCAM generates heatmaps on every layer of the YOLOv8m model to explain how the model reached a conclusion and localizes the fracture using a heatmap. To verify the heatmap, we also detected fractures using the YOLOv8m detection model, which achieved a mAP@O.5 of 59.5%, outperforming the baseline results on this dataset. The second module involves a multi-class classification task to categorize images into one of the five anatomical regions. The best-performing model for binary classification is the YOLOv8m model, with an accuracy of 83.1%, whereas the best-performing model for multi-class classification is the YOLOv8s, achieving an accuracy of 96.2%. © 2024 IEEE.

关键词： Medical imaging

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