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Supervised learning applied to electrocardiogram statistical features for the detection of premature ventricular contraction

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Research on Biomedical engineering 2025年第1期41卷 1-12页

作者： Issa, Khouloud Rammal, Abbas Assaf, Rabih Ghandour, Ahmad Electrical and Communication Engineering Department College of Engineering Phoenicia University Zahrani South Lebanon Statistics and Informatics Department Faculty of Science Lebanese University Beirut Lebanon Mathematics and Computer Sciences Department Faculty of Arts and Sciences Lebanese American University Beirut Lebanon Department of Mathematics Faculty of Arts and Sciences Holy Spirit University of Kaslik Jounieh Lebanon Biomedical Engineering Department Faculty of Engineering Islamic University of Lebanon Wardenieh30014 Lebanon

Purpose: The development of an automated premature ventricular contraction (PVC) detection system has significant implications for early intervention and treatment decisions. This study aims to develop a novel approach using various supervised machine learning (ML) methods for detecting PVCs in electrocardiogram (ECG) recordings. Methodology: To achieve this, we extracted ten distinct statistical and temporal features from 33 long-term ECG recordings from the benchmark MIT-BIH arrhythmia database (MIT-BIH-AD), capturing significant characteristics from the signals. We then investigated the effectiveness of traditional ML algorithms in identifying PVCs based on these features, namely support vector machine (SVM), decision tree (DT), naïve Bayes (NB), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and artificial neural network (ANN). Results: Among these classifiers, the SVM, KNN, ANN, and DT classifiers demonstrated exceptional discriminatory power and classification performance, yielding near-perfect area under the receiver operating characteristic curve (AUC-ROC) values of 97.3%, 95.4%, 95%, and 94.1%, respectively. On the other hand, the SVM classifier emerged as the most accurate, achieving an overall accuracy of 97.39%, indicative of making correct predictions. Conclusion: These findings underscore the generalization of the proposed approach, especially since we worked on an extended dataset encompassing different types of heartbeats to discriminate PVCs from other heartbeat types, not only normal ones, and providing closer alignment with real-world scenarios. Subsequently, the robustness and accuracy of these models highlight their suitability for clinical applications and their potential for efficient implementation and deployment in the diagnosis of cardiovascular diseases (CVDs), as they demand minimal computational resources and time. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Decision trees

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XMeta-OS: A Conceptual Framework for Unified Resource Management in Distributed Edge-Cloud Environments 3rd

XMeta-OS: A Conceptual Framework for Unified Resource Manag...

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3rd International Conference on Advances in Computing Research, ACR 2025

作者： Zacharioudakis, Eleftherios Kakoulli, Elena Evripidou, Salomi Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos Pafos Cyprus National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (纸本)9783031876462

This paper introduces XMeta-OS, a meta-operating system specially designed with Linux as its foundation to unify and optimize resource management for the distributed edge-cloud when dynamic use of GPU resources is possible. This approach offer a new GPU abstraction layer and dynamic edge-cloud allocation model that offer enhanced flexibility and resource utilization over existing systems like Kubernetes and OpenStack. By using likely sensitive data, the model applied cloud-pushed deployment model. XMeta-OS manages computer resources in real-time. Therefore, fulfilling high performance various needs. It allocates CPU and GPU in real-time to workloads requiring massive parallel processing, such as machine learning, scientific simulations, real-time data analytics, and transportation tasks. Fabrication in the laboratory of an edge-cloud environment can help to assess the scalability, fault tolerance and efficiency of the system. It showcases the overall architectural design of XMeta-OS. Changes to the kernel and security solutions for safe and effective distribution are suggested. This article presents the design of the XMeta-OS. The future of XMeta-OS entails usage and testing with which we see latency, energy, computation and other such measurements in labs. XMeta-OS is a solution to cost performance, emerging application, large-scale edge-cloud deployments, and distributed GPU-enabled infrastructures. Meta-Operating System in Edge-Cloud Continuum for dynamic resource management of GPU. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cluster computing

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To Risk Analyse, or Not to Risk Analyse: That’s the Question 18th

To Risk Analyse, or Not to Risk Analyse: That’s the Ques...

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18th IFIP WG 11.12 International Symposium on Human Aspects of Information Security and Assurance, HAISA 2024

作者： Bergström, Erik Andersson, Simon Lundgren, Martin School of Engineering Jönköping University Jönköping Sweden Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden School of Informatics University of Skövde Skövde Sweden

ISBN: (纸本)9783031725586

Risk analysis is a key activity for organisations that are looking to protect their valuable information assets against threats, such as malicious actors. It is one of the essential parts of risk management and is used to justify and prioritise what assets require the attention of which potential security controls. Risk management, and more specifically, risk analysis, is an activity that should be performed continuously. However, recent studies indicate that this is not always the case. As such, this paper investigates risk analysis as it is performed in practice in different Swedish public sector organisations. The results are based on semi-structured interviews with 17 senior security experts, an analysis of standards, and a national method support aiming to fill the gap between standard and practice. The results are presented in three themes: how, when and why risk analysis is performed. Of note, we identify that there is an issue of overlooking specific assets or systems when establishing an organisational-wide risk profile and a general recognition of the necessity for risk analysis, albeit not always in alignment with a classic risk analysis. © IFIP International Federation for Information Processing 2025.

关键词： Cybersecurity Information security Risk analysis

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Enhancing Multimodal Rumor Detection with Statistical Image Features and Modal Alignment via Contrastive Learning 21st

Enhancing Multimodal Rumor Detection with Statistical Image...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Zhou, Chenyu Li, Xiuhong Li, Zhe Chen, Fan Sheng, Jiabao Chen, Bin Wang, Haoyu School of Computer Science and Technology Xinjiang University Xinjiang China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong Department of Electrical Engineering Stanford University CA United States Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai China School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai China

ISBN: (纸本)9789819601219

The swift proliferation of multimodal rumors on social media, particularly those with manipulated images and complex intermodal interactions, significantly challenges current detection methods. In response, we utilize statistical image features, including mean and variance, to capture spatial attributes effectively and improve the detection of image-tampered tweets. To tackle complex intermodal correlations, we introduce a contrastive learning approach that aligns features across modalities efficiently. Additionally, we introduce a cross-attention fusion module (CAFM) that enhances the integration of image and text modalities, thereby improving multimodal rumor detection performance. In conclusion, we propose the cross-attention fusion network (ConCAFN), leveraging contrastive learning for robust multimodal rumor detection. Extensive experiments on two real-world datasets confirm the model’s enhanced capability to detect multimodal rumors accurately, demonstrating our methods’ effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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How Lasers Exploit Photoacoustic and Photoelectric Phenomena to Inject Signals into MEMS Microphones

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Journal of Hardware and Systems Security 2025年 1-17页

作者： Cyr, Benjamin Sumaria, Vedant Long, Yan Tadigadapa, Srinivas Sugawara, Takeshi Fu, Kevin Computer Science and Engineering University of Michigan Ann Arbor USA Electrical & Computer Engineering Northeastern University Boston USA Department of Informatics The University of Electro-Communications Tokyo Japan

An amplitude-modulated laser can be used to generate false, yet coherent acoustic signals on the outputs of MEMS microphones. While this vulnerability has ramifications on the security of cyber-physical systems that trust these microphones, the physical explanation of this effect remained a mystery. Without an understanding of the physical phenomena contributing to this signal injection, it is difficult to design effective and reliable defenses. In this work, we show the degree to which the mechanisms of thermoelastic bending, thermal diffusion, and photocurrent generation are used to inject signals into MEMS microphones. We provide models for each of these mechanisms, develop a procedure to empirically determine their relative contributions, and highlight the effects on eight commercial MEMS microphones. We accomplish this with a precise setup to isolate each mechanism using several laser wavelengths and a vacuum chamber. The results indicate that the injected signal on the microphone is dependent on the wavelength of the incoming light. Shorter wavelengths (such as a 450 nm blue laser) exploit photoacoustic effects, and the periodic heating and expansion of air is the dominant factor in seven of eight sample microphones. Longer wavelengths (such as a 904 nm infrared laser) exploit photoelectric effects on the sensitive ASIC, generating signals that are between 2x and 100x stronger than photoacoustic signals in six of eight sample microphones. This understanding of the physical causality of laser signal injection leads to recommendations for future laser-resistant microphone designs. These include adding light-blocking structures at the system or device level, improving to glob top application, and adding simple light or temperature sensors for injection detection. Based on the fundamental causality, we also suggest potential vulnerabilities within other sensors with similar characteristics to MEMS microphones, such as conventional microphones, ultrasonic

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Modified U-Net with attention gate for enhanced automated brain tumor segmentation

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Neural Computing and Applications 2025年第7期37卷 5521-5558页

作者： Saifullah, Shoffan Dreżewski, Rafal Yudhana, Anton Wielgosz, Maciej Caesarendra, Wahyu Faculty of Computer Science AGH University of Krakow Krakow30-059 Poland Department of Informatics Universitas Pembangunan Nasional Veteran Yogyakarta Yogyakarta55281 Indonesia Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55191 Indonesia Faculty of Computer Science Electronics and Telecommunications AGH University of Krakow Krakow30-059 Poland Faculty of Mechanical Engineering Opole University of Technology 76 Proszkowska St. Opole45-758 Poland

This study addresses the formidable challenges encountered in automated brain tumor segmentation, including the complexities of irregular shapes, ambiguous boundaries, and intensity variations across MRI modalities. Manual segmentation, plagued by subjectivity and time constraints, further exacerbates the problem. To address these issues, we propose a modified U-Net architecture with an integrated attention gate. The proposed model demonstrates high performance, with notable Dice Similarity Coefficient (DSC) and Jaccard Index (JI) values across various tumor classes, consistently exceeding 0.93 and 0.87, respectively. Incorporating Contrast-Limited Adaptive Histogram Equalization and Histogram Equalization improves segmentation accuracy, particularly in cases of Meningioma. Comparative analyses against established models reveal a DSC of 0.9521 and a JI of 0.9093, underscoring the superiority of our method. Validation in the BraTS 2021 dataset underscores the robustness of the method, achieving high DSC and JI scores in four MRI modalities, with the T2 modality demonstrating the highest performance (DSC: 0.9216, JI: 0.8556). While acknowledging these achievements, we recognize challenges related to dataset specificity and computational intensity associated with the attention gate. Future research efforts should address these issues to improve the generalizability and applicability of the method in real-world scenarios. In addition to presenting a novel automated brain tumor segmentation method, this study contributes comprehensive result values and comparative analyses with previous research, providing valuable insights into the evolving landscape of medical image analysis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Image segmentation

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Public Roads Management and Visualisation

Public Roads Management and Visualisation

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International Symposium on Applied Machine Intelligence and informatics (SAMI)

作者： Jaroslav Marko Anton Baláź Department of Computer and Informatics Faculty of Electrical Engineering and Informatics Technical University of Košice Košice Slovakia

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

The paper deals with the issue of collecting and displaying geographic information about roads in Slovakia with emphasis on information about obstacles on roads. The main objective is to design a solution for collecting and displaying the roads based on the analysis of existing systems and identifying the requirements for an optimal design. The paper starts with an analysis of available solutions and the development of requirements for the most favourable design, followed by the planning of an optimal solution for collecting and displaying road obstruction data. The focus of the paper is on the development of a method for creating and modifying geographic obstacle information, the design of a textual and graphical display of the information, and the design of a three-level model for user access to the application. The paper concludes with a discussion of the proposed solution, comparing the requirements for the optimal design with the developed design.

关键词： Roads Planning Object recognition informatics Machine intelligence

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ChainSafari: A General and Efficient Blockchain Verifiable Query Scheme with Real-Time Synchronization 24th

ChainSafari: A General and Efficient Blockchain Verifiable ...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xu, Yuwei Dai, Shengjiang Zeng, Junyu Cao, Jie He, Ran Xiang, Qiao School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Engineering Research Center of Blockchain Application Supervision and Management Nanjing China Department of Electrical and Computer Engineering Queen’s University Kingston Canada School of Informatics Xiamen University Xiamen China

ISBN: (纸本)9789819615414

With the rapid advancement of blockchain technology, its utilization has become widespread across various domains, resulting in the accumulation of significant volumes of valuable data. This trend has created an increasing demand for efficient querying of blockchain data. However, the inherent structure of blockchain leads to low querying efficiency and limited support for complex queries. Therefore, there is a trend toward migrating data to off-chain storage for querying purposes. The untrustworthy nature of the off-chain environment necessitates data validation. However, existing validation schemes incur high costs and fail to ensure the timely updating of the off-chain data. To address the challenges above, we propose ChainSafari, a general, efficient, and real-time synchronized scheme for verifiable querying on the blockchain. In our scheme, we design a general data model for storing transactions of various blockchains and develop an efficient authenticated data structure (ADS) based on this data model, enabling efficient verifiable queries for multiple data types in parallel. Furthermore, we propose a multi-party synchronization detection and recovery protocol to ensure the consistency of on-chain and off-chain data. Finally, we implement a prototype system of ChainSafari and conduct extensive experiments. The experimental results demonstrate the superior query throughput and enhanced data validation efficiency of ChainSafari compared to existing schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Structured Query Language

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Unsupervised Domain Adaptation With Synchronized Self-Training for Cross-Domain Motor Imagery Recognition

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IEEE Journal of Biomedical and Health informatics 2025年第5期29卷 3664-3677页

作者： Chen, Peiyin Liu, Xiaofeng Ma, Chao Wang, He Yang, Xiong Grebogi, Celso Gu, Xiao Gao, Zhongke Hohai University College of Artificial Intelligence and Automation Changzhou213200 China Tianjin University School of Electrical and Information Engineering Tianjin300072 China National University of Singapore Institute for Health Innovation and Technology Department of Electrical and Computer Engineering 119077 Singapore University of Aberdeen Institute for Complex Systems and Mathematical Biology King's College AberdeenAB24 3UE United Kingdom University of Oxford Computational Health Informatics Laboratory Institute of Biomedical Engineering OxfordOX3 7DQ United Kingdom

Robust decoding performance is essential for the practical deployment of brain-computer interface (BCI) systems. Existing EEG decoding models often rely on large amounts of annotated data collected through specific experimental setups, which fail to address the heterogeneity of data distributions across different domains. This limitation hinders BCI systems from effectively managing the complexity and variability of real-world data. To overcome these challenges, we propose Synchronized Self-Training Domain Adaptation (SSTDA) for cross-domain motor imagery classification. Specifically, SSTDA leverages labeled signals from a source domain and applies self-training to unlabeled signals from a target domain, enabling the simultaneous training of a more robust classifier. The raw EEG signals are mapped into a latent space by a feature extractor for discriminative representation learning. A domain-shared latent space is then learned by optimizing the feature extractor with both source and target samples, using an easy-tohard self-training process. We validate the method with extensive experiments on two public motor imagery datasets: Dataset IIa of BCI Competition IV and the High Gamma dataset. In the inter-subject task, our method achieves classification accuracies of 64.43% and 80.40%, respectively. It also outperforms existing methods in the inter-session task. Moreover, we develope a new six-class motor imagery dataset and achieve test accuracies of 77.09% and 80.18% across different datasets. All experimental results demonstrate that our SSTDA outperforms existing algorithms in inter-session, inter-subject, and inter-dataset validation protocols, highlighting its capability to learn discriminative, domain-invariant representations that enhance EEG decoding performance. © 2013 IEEE.

关键词： Brain computer interface

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