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arXiv 2023年

作者： Jiang, Shiyi Gai, Xin Treggiari, Miriam M. Stead, William W. Zhao, Yuankang Page, C. David Zhang, Anru R. Department of Electrical & Computer Engineering Duke University DurhamNC27708 United States Department of Statistical Science Duke University DurhamNC27708 United States Department of Anesthesiology Duke University DurhamNC27708 United States Department of Biomedical Informatics Vanderbilt University NashvilleTN37235 United States Department of Biostatistics & Bioinformatics Duke University DurhamNC27708 United States Department of Computer Science Duke University DurhamNC27708 United States

Objective: Sepsis is one of the most serious hospital conditions associated with high mortality. Sepsis is the result of a dysregulated immune response to infection that can lead to multiple organ dysfunction and death. Due to the wide variability in the causes of sepsis, clinical presentation, and the recovery trajectories, identifying sepsis subphenotypes is crucial to advance our understanding of sepsis characterization, to choose targeted treatments and optimal timing of interventions, and to improve prognostication. Prior studies have described different sub-phenotypes of sepsis using organ-specific characteristics. These studies applied clustering algorithms to electronic health records (EHRs) to identify disease sub-phenotypes. However, prior approaches did not capture temporal information and made uncertain assumptions about the relationships among the sub-phenotypes for clustering procedures. Methods: We developed a time-aware soft clustering algorithm guided by clinical variables to identify sepsis subphenotypes using data available in the EHR. Results: We identified six novel sepsis hybrid sub-phenotypes and evaluated them for medical plausibility. In addition, we built an early-warning sepsis prediction model using logistic regression. Conclusion: Our results suggest that these novel sepsis hybrid sub-phenotypes are promising to provide more accurate information on sepsis-related organ dysfunction and sepsis recovery trajectories which can be important to inform management decisions and sepsis prognosis. © 2023, CC BY-NC-SA.

关键词： Clustering algorithms

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Power system performance enhancement using superconducting magnetic energy storage unit and proportional integral derivative control 2

Power system performance enhancement using superconducting m...

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2nd International Conference on Smart Power and Internet Energy Systems, SPIES 2020

作者： Handayani, Luky Abu-Siada, A. Suwarno, Suwarno Hariyanto, Nanang Djalal, Muhammad Ruswandi Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia Curtin University Electrical and Computer Engineering Department Perth Australia Politeknik Negeri Ujung Pandang Department of Energy Engineering Makassar Indonesia

ISBN: (纸本)9781728166117

Frequency oscillations in power systems may occur due to sudden load change or system disturbance. Such oscillations may result in unsynchronized and undamped signals. In a multi machine system where all generators must operate in synchronism, undamped oscillations may lead to instability. To overcome this issue, this paper proposes a damping control scheme consisting of Superconducting Magnetic Energy Storage (SMES) and Proportional Integral Differential (PID) controller to effectively damp frequency oscillations in multi-machine systems. Control parameters of the proposed SMES-PID system are tuned using Particle Swarm Optimization (PSO) algorithm. Simulation results reveal the effectiveness of the proposed controller in damping the frequency oscillations and maintain system dynamic and transient stability during various disturbance events. It is shown the design can find better quality solution in minimizing overshoot at frequency variations up to 78 % and accelerating the settling time up to 67 %. © 2020 IEEE.

关键词： Damping

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Blockchain-based Security Framework for Metaverse: A Decentralized Approach

Blockchain-based Security Framework for Metaverse: A Decentr...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Anand Singh Rajawat S.B. Goyal RamKumar Solanki Maria Simona Raboaca Traian Candin Mihaltan Zoltán Illés Chaman Verma School of Computer Science & Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya Malaysia ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania Faculty of Building Services Technical University of Cluj-Napoca Cluj-Napoca Romania Department of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest Hungary

People can act out fake social exchanges in the Metaverse, which is an online community. Security has become a problem in the Metaverse because there are so many online transactions and digital assets. In this thesis, a Blockchain-based security system for the Metaverse is proposed. This would be a decentralised way to keep the virtual world safe. Our system uses Blockchain's autonomous structure to make sure that all financial transactions in the Metaverse are safe, anonymous, and true. We suggest a new consensus process that combines Proof of Stake (PoS) and Proof of Authority (PoA) to make networks safer and allow them to grow. We also have a user-focused, decentralised identity management system that keeps their information safe. We also suggest a way to process Metaverse transactions safely that is based on smart contracts. Our method makes sure that transactional proof, recording, and validation are done in a safe and reliable way. We also show a reputation system that benefits people who follow the law and punishes people who break the law. Lastly, we test how well our suggested structure works with simulations and real-world tests. The results show that our way works to reduce possible security risks in the Metaverse and create a safe, scalable environment for online business. Overall, our Blockchain-based Security Framework for Metaverse: A Decentralised Approach is a good way to keep the security and privacy of digital activities in Metaverse.

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Effective Two-Stage Double Auction for Dynamic Resource Trading in Edge Networks via Overbooking

arXiv

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arXiv 2025年

作者： Wu, Sicheng Liwang, Minghui Wang, Deqing Wang, Xianbin Wu, Chao Tang, Junyi Li, Li Jiao, Zhenzhen The School of Informatics Xiamen University Fujian China The Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University Shanghai China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai China The Department of Electrical and Computer Engineering Western University ON Canada The School of Public Affairs Zhejiang University Hangzhou310027 China School of Information Science and Engineering Lanzhou University Lanzhou China The iF-Labs Beijing Teleinfo Technology Co. Ltd. CAICT China

To facilitate responsive and cost-effective computing resource scheduling and service delivery over edge-assisted mobile networks, this paper investigates a novel two-stage double auction methodology via utilizing an interesting idea of resource overbooking to overcome dynamic and uncertain nature from edge servers (sellers) and demand from mobile devices (as buyers). The proposed auction integrates multiple essential factors such as social welfare maximization and decision-making latency (e.g., the time for determining winning seller-buyer pairs) reduction, by introducing a stagewise strategy: an overbooking-driven pre-double auction (OPDAuction) for determining long-term cooperations between sellers and buyers before practical resource transactions as Stage I, and a real-time backup double auction (RBDAuction) for handling residual resource demands during actual transactions. In particular, by applying a proper overbooking rate, OPDAuction helps with facilitating trading contracts between appropriate sellers and buyers as guidance for future transactions, by allowing the booked resources to exceed supply. Then, since pre-auctions may cause risks, our RBDAuction adjusts to real-time market changes, further enhancing the overall social welfare. More importantly, we offer an interesting view to show that our proposed two-stage auction can support significant design properties such as truthfulness, individual rationality, and budget balance. Through extensive experiments, we demonstrate good performance in social welfare, time efficiency, and computational scalability, outstripping conventional methods in dynamic edge computing settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Budget control

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From Knowledge Generation to Knowledge Verification: Examining the BioMedical Generative Capabilities of ChatGPT

arXiv

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arXiv 2025年

作者： Hamed, Ahmed Abdeen Crimi, Alessandro Misiak, Magdalena M. Lee, Byung Suk MGEN College of Engineering Northeastern University Miami MiamiFL33127 United States Department of Computer Science University of Vermont BurlingtonVT05405 United States CASCI Laboratory Binghamtom University BinghamtonNY13902 United States AGH University of Krakow Faculty of Informatics Krakow30-059 Poland Department of Physiology and Biophysics Howard University WashingtonDC20059 United States

The generative capabilities of LLM models offer opportunities for accelerating tasks but raise concerns about the authenticity of the knowledge they produce. To address these concerns, we present a computational approach that evaluates the factual accuracy of biomedical knowledge generated by an LLM. Our approach consists of two processes: generating disease-centric associations and verifying these associations using the semantic framework of biomedical ontologies. Using ChatGPT, as the selected LLM, we designed prompt-engineering processes to establish linkages between diseases and related drugs, symptoms, and genes, and assessed consistency across multiple ChatGPT models (e.g., GPT-turbo, GPT-4, etc.) Experimental results demonstrate high accuracy in identifying disease terms (88%–97%), drug names (90%–91%), and genetic information (88%–98%). However, symptom term identification was notably lower (49%–61%), due to the informal and verbose nature of symptom descriptions, which hindered effective semantic matching with the formal language of specialized ontologies. Verification of associations reveals literature coverage rates of 89%–91% for disease-drug and disease-gene pairs, while symptom-related associations exhibit lower coverage (49%–62%). Despite the high accuracy in identifying term names, our observations revealed that the generated term IDs were invalid and, in some cases, redundant. This issue may impede knowledge integration across multiple ontologies, as term IDs are essential for establishing cross-references. Overall, these findings suggest that GenAI tools (e.g., ChatGPT) can generate more reliable biomedical knowledge if extra care is taken. Employing strategies such as Retrieval Augmented Generation (RAG) and incorporating knowledge from literature or ontology terms, as supervised in-context prompting, may further enhance the accuracy and reliability of GenAI-generated biomedical knowledge. Further work is needed to investigate these directions. ©

关键词： Gene Ontology

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Implementation of ensemble machine learning classifiers to predict diarrhoea with SMOTEENN, SMOTE, and SMOTETomek class imbalance approaches

Implementation of ensemble machine learning classifiers to p...

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Conference on Information Communication Technology and Society (ICTAS)

作者： Elliot Mbunge Maureen Nokuthula Sibiya Sam Takavarasha Richard C Millham Garikayi Chemhaka Benhildah Muchemwa Tafadzwa Dzinamarira Department of Computer Science Faculty of Science and Engineering University of Eswatini (formerly Swaziland) Research Innovation and Engagement Mangosuthu University of Technology Umlazi South Africa Faculty of Management and Entrepreneurial Sciences Women's University of Africa Harare Zimbabwe Department of Information Faculty of Accounting and Informatics Durban University of Technology South Africa Department of Statistics and Demography Faculty of Social Sciences University of Eswatini Kwaluseni Eswatini Department of Computer Science Faculty of Science and Engineering University of Eswatini (formerly Swaziland) Manzini Eswatini ICAP Harare Zimbabwe

Diarrhoea continues to be a major public health burden and cause of death among children under 5 years in many developing countries. Rotavirus vaccination, hygiene practices, clean water, and health promotion are among the preventive measures implemented to improve child health. Nevertheless, tackling diarrhoea also requires the integration of ensemble machine learning (ML) into health systems to improve child health. However, the integration of ensemble classifiers into health systems in many developing countries is still nascent. Therefore, this study applied SMOTE, SMOTEEN and SMOTETomek class imbalance approaches and ensemble ML classifiers to predict diarrhoea. Ensemble methods significantly improve the performance of conventional ML classifiers. The study revealed that the ExtraTrees classifier achieved a high recall of 96.3%, accuracy of 94.3%, precision of 93.8%, and F1-score of 95% when predicting diarrhoea with SMOTEENN as compared to SMOTE and SMOTETomek. The performance of the HistGradientBoosting classifier also improved and achieved a high recall of 95.2%, accuracy of 91.5%, precision of 90.4%, and F1-score of 92.7 % . The paper also shows that ensemble methods are increasingly becoming state-of-the-art solutions for multiple challenges encountered with ML algorithms such as overfitting, computationally intensive, underfitting and representation. The paper also demonstrates how ensemble methods are becoming state-of-the-art solutions to multiple problems that arise with ML algorithms. There is a need to develop data-driven applications that incorporate ensemble methods to model and predict diarrhoea to assist policymakers to craft interventions aimed to improve child health.

关键词： Pediatrics Machine learning algorithms Computational modeling Developing countries Predictive models Prediction algorithms Classification algorithms

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MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images

arXiv

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arXiv 2023年

作者： Xu, Yanwu Sun, Li Peng, Wei Jia, Shuyue Morrison, Katelyn Perer, Adam Zandifar, Afrooz Visweswaran, Shyam Eslami, Motahhare Batmanghelich, Kayhan Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Department of Psychiatry and Behavioral Sciences Stanford University StanfordCA94305 United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA15213 United States The University of Pittsburgh Medical Center PittsburghPA15213 United States The Department of Biomedical Informatics University of Pittsburgh PittsburghPA15206 United States

This paper introduces an innovative methodology for producing high-quality 3D lung CT images guided by textual information. While diffusion-based generative models are increasingly used in medical imaging, current state-of-the-art approaches are limited to low-resolution outputs and underutilize radiology reports' abundant information. The radiology reports can enhance the generation process by providing additional guidance and offering fine-grained control over the synthesis of images. Nevertheless, expanding text-guided generation to high-resolution 3D images poses significant memory and anatomical detail-preserving challenges. Addressing the memory issue, we introduce a hierarchical scheme that uses a modified UNet architecture. We start by synthesizing low-resolution images conditioned on the text, serving as a foundation for subsequent generators for complete volumetric data. To ensure the anatomical plausibility of the generated samples, we provide further guidance by generating vascular, airway, and lobular segmentation masks in conjunction with the CT images. The model demonstrates the capability to use textual input and segmentation tasks to generate synthesized images. Algorithmic comparative assessments and blind evaluations conducted by 10 board-certified radiologists indicate that our approach exhibits superior performance compared to the most advanced models based on GAN and diffusion techniques, especially in accurately retaining crucial anatomical features such as fissure lines and airways. This innovation introduces novel possibilities. This study focuses on two main objectives: (1) the development of a method for creating images based on textual prompts and anatomical components, and (2) the capability to generate new images conditioning on anatomical elements. The advancements in image generation can be applied to enhance numerous downstream tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： computerized tomography

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A Critical Analysis of Deployed Use Cases for Quantum Key Distribution and Comparison with Post-Quantum Cryptography

arXiv

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arXiv 2025年

作者： Aquina, Nick Cimoli, Bruno Das, Soumya Hövelmanns, Kathrin Weber, Fiona Johanna Okonkwo, Chigo Rommel, Simon Škorić, Boris Monroy, Idelfonso Tafur Verschoor, Sebastian Department of Electrical Engineering Eindhoven University of Technology Flux Building Eindhoven5612 AZ Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology MetaForum Building Eindhoven5612 AZ Netherlands Eindhoven Quantum Hub Eindhoven University of Technology Qubit Building Eindhoven5612 AZ Netherlands Informatics Institute University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands

Quantum Key Distribution (QKD) is currently being discussed as a technology to safeguard communication in a future where quantum computers compromise traditional public-key cryptosystems. In this paper, we conduct a comprehensive security evaluation of QKD-based solutions, focusing on real-world use cases sourced from academic literature and industry reports. We analyze these use cases, assess their security and identify the possible advantages of deploying QKD-based solutions. We further compare QKD-based solutions with Post-Quantum Cryptography (PQC), the alternative approach to achieving security when quantum computers compromise traditional public-key cryptosystems, evaluating their respective suitability for each scenario. Based on this comparative analysis, we critically discuss and comment on which use cases QKD is suited for, considering factors such as implementation complexity, scalability, and long-term security. Our findings contribute to a better understanding of the role QKD could play in future cryptographic infrastructures and offer guidance to decision-makers considering the deployment of QKD. © 2025, CC BY-SA.

关键词： Qubits

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Epileptic EEG signal classification using convolutional neural networks based on optimum window length and FFT's length 20

Epileptic EEG signal classification using convolutional neur...

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8th International Conference on computer and Communications Management, ICCCM 2020

作者： Santoso, Irwan Budi Purnama, I. Ketut Eddy Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Informatics Engineering Universitas Islam Negeri Maulana Malik Ibrahim Malang Indonesia Department of Electrical Engineering Department of Computer Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9781450387668

This paper presents the method for the epilepsy classification based on electroencephalogram (EEG) signals. This method uses the spectrogram image of EEG signals and convolutional neural networks (CNN).The spectrogram image is obtained by mapping the spectrogram value of the results of the short-time Fourier Transform (STFT) to the RGB color map. The best spectrogram for CNN is determined based on the length of fast Fourier transform (FFT) and window length in the windowing technique. The proposed method is evaluated with the epileptic EEG signal datasets. The experimental results show CNN works optimally with spectrogram images from STFT on the window length of 128 and the length of FFT of 128. In these conditions, the performance of CNN in the classification of epilepsy is better than others and able to compete with existing methods. © 2020 ACM.

关键词： Convolutional neural networks

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Deep Learning-Based Leaf Image Analysis for Tomato Plant Disease Detection and Classification

Deep Learning-Based Leaf Image Analysis for Tomato Plant Dis...

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IEEE International Conference on Image Processing

作者： Ammar Chouchane Abdelmalik Ouamanea El Ouanas Belabbaci Yassine Himeur Abbes Amira University Center of Barika Amdoukal Road Barika Algeria Laboratory of LI3C University of Biskra Biskra Algeria Laboratory of Medical Informatics (LIMED) Faculty of Technology University of Bejaia Bejaia Algeria College of Engineering and Information Technology University of Dubai Dubai UAE Department of Computer Science University of Sharjah UAE

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Tomato plant disease detection and classification, utilizing leaf images through deep learning, intersects the fields of plant pathology and agriculture. Deep learning has demonstrated significant potential in accurately identifying and classifying various plant diseases from leaf images. In this study, we introduce a hybrid system that combines a potent machine learning algorithm, Exponential Discriminant Analysis (EDA), with a transfer learning process leveraging recent and advanced deep networks, including ResNet50, Darknet53, DenseNet201, and EfficientNetB0. This system was evaluated using two challenging datasets: Taiwan and PlantVillage tomato leaf datasets. The experimental results underscore the high competitiveness of the proposed method, achieving mean accuracies of 98.29% and $\mathbf{9 8. 0 9 \%}$ on these datasets, respectively.

关键词： Deep learning Plant diseases Pathology Machine learning algorithms Image analysis Accuracy Plants (biology)

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