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3DECG-Net: ECG fusion network for multi-label cardiac arrhythmia detection

Computers in Biology and Medicine 2024年 182卷 109126-109126页

作者： Sadeghi, Alireza Hajati, Farshid Rezaee, Alireza Sadeghi, Mahdieh Argha, Ahmadreza Alinejad-Rokny, Hamid Department of Mechatronics School of Intelligent Systems College of Interdisciplinary Science and Technology University of Tehran Tehran Iran School of Science and Technology Faculty of Science Agriculture Business and Law University of New England ArmidaleNSW2350 Australia Student Research Committee Mazandaran University of Medical Sciences Sari Iran School of Biomedical Engineering UNSW Sydney RandwickNSW2052 Australia BioMedical Machine Learning Lab School of Biomedical Engineering UNSW Sydney RandwickNSW2052 Australia

Cardiovascular diseases represent the leading global cause of death, typically diagnosed and addressed through electrocardiograms (ECG), which record the heart's electrical activity. In recent years, there has been a notable surge in ECG recordings, driven by the widespread use of wearable devices. However, the limited availability of medical experts to analyze these recordings underscores the necessity for automated ECG analysis using computer-aided methods. In this study, we introduced 3DECG-Net, a deep learning model designed to detect and classify seven distinct heart states through the analysis of data fusion from 12-lead ECG in a multi-label framework. Our model leverages a residual architecture with a multi-head attention mechanism, undergoing training within a five-fold cross-validation scheme. By transforming 12-lead ECG signals into 3D data with the help of Recurrent Plot technique, 3DECG-Net achieves a noteworthy micro F1-score of 80.3 %, surpassing the performance of other state-of-the-art deep learning models developed for this specific task. Also, we present an ECG preprocessing framework to generate compact, high-quality ECG signals for potential application in future studies within this domain. We conduct an explainable AI experiment using Local Interpretable Model-agnostic Explanations (LIME) to elucidate the significance of each lead in accurately diagnosing specific arrhythmias, ensuring the logical processing of ECG data by 3DECG-Net. The findings of this study suggest that the proposed model is trustworthy and has the potential to be used as an effective diagnostic toolset for identifying heart arrhythmias. Its effectiveness can improve the diagnostic process, facilitate early treatment, and enhance overall efficiency in medical settings. © 2024

关键词： data fusion

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BZKP: Blockchain-based zero-knowledge proof model for enhancing healthcare security in Bahrain IoT smart cities and COVID-19 risk mitigation

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Arab Journal of Basic and Applied sciences 2021年第1期28卷 154-171页

作者： Al-Aswad, Hasan El-Medany, Wael M. Balakrishna, Chitra Ababneh, Nedal Curran, Kevin Department of Computer Engineering University of Bahrain Sakhir Bahrain Faculty of Science Technology Engineering & Mathematics The Open University Milton Keynes United Kingdom Department of Information Security Engineering Technology Abu Dhabi Polytechnic Abu Dhabi United Arab Emirates Computing Engineering & Intelligent Systems Ulster University Coleraine United Kingdom

Smart cities achieved digital transformation of patients’ health records through the use of new technology in IoT healthcare industry. Such technologies of using IoT and remote patient monitoring systems have become dramatically fundamental to reduce the movement of patients, and hence reducing the risk of spreading Covid-19 infection. The Ministry of Health in the Kingdom of Bahrain strives to achieve digital transformation in the healthcare industry, where the National Health Information System (I-SEHA) was launched to provide higher-quality health services. The system interconnects the public healthcare institutes, allowing access to patient’s data from any location without the hassle of moving the files physically. Digitization of medical data of patients and sharing some of the data with other institutions outside the protected networks may lead to major privacy and integrity concerns. This paper introduces Blockchain-based Zero-Knowledge Proof (BZKP) model, which is an IoT-based patient-centric model that fuses a zero-knowledge proof solution to be developed for protecting patient’s privacy, and ensures patients prior consent on any access to their data including their health status and account balance. The proposed model is developed to provide a robust and scalable architecture for data sharing, which protects the privacy of sensitive data while maintaining high availability. It also provides strong trust and integrity of data by using the immutability features of the blockchain. BZKP is based on pre-approved blockchain access tokens to address challenges of accountability and privacy in Bahrain smart cities. As a result, the model provides a secure and trusted access model between different stakeholders to share patient data while maintaining privacy, trust, and high availability. The zero-knowledge proof can be used with the smart contracts, which provides programmable actions that can be used for automating the prescriptions dispensation process for priva

关键词： Blockchain COVID-19 cybersecurity IoT healthcare smart city zero-knowledge proof

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Cross View Capture for Distributed Image Compression with Decoder Side Information

Cross View Capture for Distributed Image Compression with De...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yankai Yin Zhe Sun Peiying Ruan Feng Duan Ruidong Li Chi Zhu Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai University Tianjin China Institute of Natural Sciences Kanazawa University Ishikawa Japan Faculty of Health Data Science and Graduate School of Medicine Juntendo University Chiba Japan NVIDIA AI Technology Center NVIDIA Japan Tokyo Japan Department of Systems Life Engineering Maebashi Institute of Technology Maebashi Japan

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Image compression is increasingly important in applications like intelligent driving and smart surveillance systems. This study presents a novel cross view capture distributed image compression network (CVCDIC) to improve the compression quality by using decoder side information. The CVCDIC’s decoder utilizes feature extraction networks to extract features from both the primary image and the side information. Furthermore, a multi-level cross view attention module is designed to capture interrelated details between images at multiple hierarchical levels. Finally, a spatial refinement module, constructed on the foundation of information distillation networks, is designed to further refine the quality of reconstructed images. The results show that CVCDIC can achieve an MS-SSIM of 0.978 at 0.15 bpp, surpassing DSIN (0.925), NDIC (0.956), and ATN (0.955) on the KITTI Stereo dataset.

关键词： Measurement Image coding Fuses Surveillance Rate-distortion Feature extraction Decoding

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Noise-Robust and Resource-Efficient ADMM-based Federated Learning

arXiv

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arXiv 2024年

作者： Lari, Ehsan Arablouei, Reza Gogineni, Vinay Chakravarthi Werner, Stefan The Department of Electronic Systems Norwegian University of Science and Technology Trondheim7491 Norway The Department of Information and Communications Engineering Aalto University 00076 Finland The Commonwealth Scientific and Industrial Research Organisation PullenvaleQLD4069 Australia The SDU Applied AI and Data Science The Maersk Mc-Kinney Moller Institute Faculty of Engineering University of Southern Denmark Odense5230 Denmark

Federated learning (FL) leverages client-server communications to train global models on decentralized data. However, communication noise or errors can impair model accuracy. To address this problem, we propose a novel FL algorithm that enhances robustness against communication noise while also reducing communication load. We derive the proposed algorithm through solving the weighted least-squares (WLS) regression problem as an illustrative example. We first frame WLS regression as a distributed convex optimization problem over a federated network employing random scheduling for improved communication efficiency. We then apply the alternating direction method of multipliers (ADMM) to iteratively solve this problem. To counteract the detrimental effects of cumulative communication noise, we introduce a key modification by eliminating the dual variable and implementing a new local model update at each participating client. This subtle yet effective change results in using a single noisy global model update at each client instead of two, improving robustness against additive communication noise. Furthermore, we incorporate another modification enabling clients to continue local updates even when not selected by the server, leading to substantial performance improvements. Our theoretical analysis confirms the convergence of our algorithm in both mean and the mean-square senses, even when the server communicates with a random subset of clients over noisy links at each iteration. Numerical results validate the effectiveness of our proposed algorithm and corroborate our theoretical findings. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing

arXiv

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arXiv 2024年

作者： Lari, Ehsan Arablouei, Reza Gogineni, Vinay Chakravarthi Werner, Stefan The Department of Electronic Systems Norwegian University of Science and Technology Trondheim7491 Norway Department of Information and Communications Engineering Aalto University 00076 Finland The Commonwealth Scientific and Industrial Research Organisation PullenvaleQLD4069 Australia The SDU Applied AI and Data Science The Maersk Mc-Kinney Moller Institute Faculty of Engineering University of Southern Denmark Odense5230 Denmark

Federated learning (FL) allows training machine learning models on distributed data without compromising privacy. However, FL is vulnerable to model-poisoning attacks where malicious clients tamper with their local models to manipulate the global model. In this work, we investigate the resilience of the partial-sharing online FL (PSO-Fed) algorithm against such attacks. PSO-Fed reduces communication overhead by allowing clients to share only a fraction of their model updates with the server. We demonstrate that this partial sharing mechanism has the added advantage of enhancing PSO-Fed’s robustness to model-poisoning attacks. Through theoretical analysis, we show that PSO-Fed maintains convergence even under Byzantine attacks, where malicious clients inject noise into their updates. Furthermore, we derive a formula for PSO-Fed’s mean square error, considering factors like stepsize, attack probability, and the number of malicious clients. Interestingly, we find a non-trivial optimal stepsize that maximizes PSO-Fed’s resistance to these attacks. Extensive numerical experiments confirm our theoretical findings and showcase PSO-Fed’s superior performance against model-poisoning attacks compared to other leading FL algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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TEVA: Training-Efficient and Verifiable Aggregation for Federated Learning for Consumer Electronics in Industry 5.0

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IEEE Transactions on Consumer Electronics 2024年

作者： Xia, Yuanjun Liu, Yining Chen, Jingxue Liang, Yangfan Khan, Fazlullah Alturki, Ryan Wang, Xiaopei Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software School of Computer and Information Security Guilin541004 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325027 China University of Technology School of Mathematical and Physical Sciences SydneyNSW2007 Australia Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems The Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing314001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China Umm AI-Qura University Makkah Department of Software Engineering College of Computing Saudi Arabia Riverside Department of Computer Science and Engineering University of California CA92521 United States

Federated learning (FL) has been widely used for privacy-preserving model updates in Industry 5.0, facilitated by 6G networks. Despite FL's privacy-preserving advantages, it remains vulnerable to attacks where adversaries can infer private data from local models or manipulate the central server (CS) to deliver falsified global models. Current privacy-preserving approaches, primarily based on the FedAvg algorithm, fail to optimize training efficiency for non-independent and identically distributed (non-IID) data. This article proposes training-efficient and verifiable aggregation (TEVA) for FL to resolve these issues. This scheme combines threshold Paillier homomorphic encryption (TPHE), verifiable aggregation, and an optimized double momentum update mechanism (OdMum). TEVA not only leverages TPHE to protect the privacy of local models but also ensures the integrity of the global model through a verifiable aggregation mechanism. Additionally, TEVA integrates the OdMum algorithm to effectively address the challenges posed by non-IID data, promoting rapid model convergence and significantly enhancing overall training efficiency. Security analysis indicates that TEVA meets the requirements for privacy protection. Extensive experimental results demonstrate that TEVA can accelerate model convergence while incurring lower computational and communication overheads. © 1975-2011 IEEE.

关键词： Differential privacy

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An Approximate Dynamic Programming Approach to Vehicle Dispatching and Relocation Using Time-Dependent Travel Times

An Approximate Dynamic Programming Approach to Vehicle Dispa...

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International Conference on intelligent Transportation

作者： Yunping Huang Nan Zheng Enming Liang Shu-Chien Hsu Renxin Zhong Department of Civil and Environmental Engineering The Hong Kong Polytechnic University Hong Kong SAR China Department of Civil Engineering Institute of Transport Studies Monash University Australia School of Data Science City University of Hong Kong Hong Kong SAR China School of Intelligent Systems Engineering Sun Yat-Sen University China

The imbalance between vehicle supply and on-demand customers has been a long-standing challenge for central ride-sourcing platforms. Current literature usually bases the design of dispatching and relocation strategies on the time-independent traffic condition (speed) assumption to reduce the problem dimension while uncertain demand and travel time subject to traffic congestion can significantly affect the optimal solutions. Therefore, we first propose a network-level traffic state estimation algorithm using functional data analysis. Then a multi-stage decision model is proposed to address the matching and repositioning of a centralized platform controlling a fleet of vehicles. Further, the customer spatial-temporal uncertainty is considered under the formulation of a stochastic programming problem. Then, an Approximate Dynamic Programming (ADP) based approach is developed for solving the multi-stage decisions efficiently. Our algorithm is evaluated in a designed simulator based on NYC yellow taxi data and the Manhattan road network. Simulation results show that the total profit can be enhanced compared with traditional time-independent traffic assumption strategies and several decision strategies.

关键词：

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Blister Package Classification Using ResNet-101 for Identification of Medication 25

Blister Package Classification Using ResNet-101 for Identifi...

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25th International Computer science and engineering Conference, ICSEC 2021

作者： Hnoohom, Narit Maitrichit, Nagorn Chotivatunyu, Pitchaya Sornlertlamvanich, Virach Mekruksavanich, Sakorn Jitpattanakul, Anuchit Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakorn Pathom Thailand Musashino University Faculty of Data Science Tokyo Japan University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9781665411974

This research aimed to fine-Tune image classification with deep learning techniques to verify the dispensing of prescriptions in hospitals. The proposed approach will be able to help pharmacies reduce the errors that lead to patients receiving the wrong medications. The image classification model uses a double-side transformed image dataset with download from Highlighted Deep Learning (HDL) paper. The dataset collected two-hundred seventy-Two images for types of medicine blister packs, including 72 images of front-side and backside merged with a horizontal cropped background, which were used for training the model. The blister package image dataset uses a deep learning model with a ResNet-101 pre-Trained model from the TensorFlow framework. The experimental results indicated that the TensorFlow framework achieved higher precision, recall, and F1-score than the Caffe framework. A ResNet-101 model with histogram equalization in the front and backside has the highest accuracy at 100 percent. © 2021 IEEE.

关键词： Image classification

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The Positive Effect of Attention Module in Few-Shot Learning for Plant Disease Recognition 5

The Positive Effect of Attention Module in Few-Shot Learning...

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5th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2022

作者： Lin, Hong Tse, Rita Tang, Su-Kit Qiang, Zhen-Ping Pau, Giovanni Macao Polytechnic University Faculty of Applied Sciences China Macao Polytechnic University Eng. Res. Ctr. of Appl. Technol. on Mach. Transl. and Artif. Intelligence of Ministry of Education China Southwest Forestry University College of Big Data and Intelligent Engineering KunMing China University of Bologna Department of Computer Science and Engineering-DISI Bologna Italy University of California Samueli Computer Science Department Los Angeles United States

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

Few-shot learning is good solution for plant disease recognition which can generalize to new categories by using few samples. However, the features extracted from few shots are limited. Attention is a technique for focusing on the significant features which can help to obtain better feature representation. In this work, we use a naive metric-based few-shot learning network as the baseline method, exploit the effect of different kinds of attention module: channel attention, spatial attention and hybrid attention. In experiments, we choose the representative modules of each attention category to show their effects in few-shot learning paradigm: SE, ASPP, CBAM and Triplet Attention. We conduct experiments with two data settings of PlantVillage, and illustrate the usage these attention modules in Residual Networks. The results indicate that the different attention modules can improve recognition accuracy to varying degrees. Attention can be used as effective improvement of feature representation under few-shot condition. © 2022 IEEE.

关键词： Learning systems

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Decision-Making Framework For AMR Fleet Size In Manufacturing Environments

Decision-Making Framework For AMR Fleet Size In Manufacturin...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Catarina Rema Romão Santos Henrique Piqueiro Diogo Miguel Matos Paulo Moura Oliveirat Pedro Costa Manuel F. Silva Fillipe L. Ribeiro CRIIS - Centre for Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal Department of Engineering University of Trás-os-Montes and Alto Douro (UTAD) Vila Real Portugal Faculty of Engineering University of Porto (FEUP) Porto Portugal ISEP Polytechnic of Porto rua Dr. António Bernardino de Almeida Porto Portugal JPM Industry Vale de Cambra Portugal

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Industry 4.0 is transforming manufacturing environments, with robotics being a key technology that enhances various capabilities. The flexibility of Autonomous Mobile Robots has led to the rise of multi-robot systems in industrial settings. Considering the high cost of these robots, it is essential to determine the best fit of number and type before making any major investments. Simulation and modeling are valuable decision-support tools, allowing the simulation of different setups to address robot fleet sizing issues. This paper introduces a decision-support framework that combines a fleet manager software stack with the FlexSim simulator, helping decision-makers determine the most suitable mobile robots fleet size tailored to their needs. Unlike previous approaches, the developed solution integrates the same real robot coordination software in both simulation and actual deployment, ensuring that tested scenarios accurately reflect real-world conditions. A case study was conducted to evaluate the framework, involving multiple tasks of loading and unloading materials within a warehouse. Five different scenarios with varying fleet sizes were simulated, and their performances assessed. The analysis concluded that, for the case study under consideration, a fleet of three robots was the most suitable, considering relevant key performance indicators. The results confirmed that the developed solution is an effective alternative for addressing the problem and represents a novel technology with no prior state-of-the-art equivalents.

关键词： Service robots Robot kinematics Decision making Dynamic scheduling Software Batteries Software reliability Resource management Mobile robots Autonomous robots

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