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Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

Computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad Sohail Irshad Tehreem Masood Arfan Jaffar Muhammad Rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for *** findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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A Parallel and Improved Quadrivalent Quantum-Inspired Gravitational Search Algorithm in Optimal Design of WSNs 2nd

A Parallel and Improved Quadrivalent Quantum-Inspired Gravit...

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2nd International Congress on High-Performance computing and Big data Analysis, TopHPC 2019

作者： Mirhosseini, Mina Fazlali, Mahmood Gaydadjiev, Georgi Department of Data and Computer Science Faculty of Mathematical Sciences GC Shahid Beheshti University Tehran Iran Department of Computing Imperial College London London United Kingdom Maxeler Technologies Ltd London United Kingdom

ISBN: (纸本)9783030334949

Wireless Sensor Networks (WSNs) are recently used in monitoring applications. One of the most important challenges in WSNs is determining the operational mode of sensors, decreasing the energy consumption while the connectivity requirements and the special properties are satisfied. This problem is an NP-hard one and is a time–consuming progress. In this study, an improved version of quadrivalent quantum-inspired gravitational search algorithm as a new metaheuristic, well suitable for quadrivalent problems is proposed using Not Q-Gate to optimize the performance of the WSN. Beside, to enhance the speed and the accuracy of the algorithm more, we used a parallelizing technique using Open-MP. Parallelizing this algorithm on mentioned problem is useful from four aspects;1 - accelerate the speed of the algorithm, 2 - improving the quality of solutions by letting the increasing the population size, 3 - The possibility of using the algorithm, in larger-scale WSNs and 4 - power affectivity of the base station using multicore processors. To validate the performance of our proposed approach, a comparison between this approach and the previous methods is performed. Our experiments verified that the proposed method can effectively improve the performance more than 2.25 times and the speedup faster than 4 times on an 8-core CPU. © 2019, Springer Nature Switzerland AG.

关键词： Wireless sensor networks

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Development of Low-Cost Surgical Simulator for Neuroendoscopy using Unity3D and HTC VIVE

Development of Low-Cost Surgical Simulator for Neuroendoscop...

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International Conference on Open Source Systems and Technologies (ICOSST)

作者： Aimen Fatima Faisal Bukhari Waheed Iqbal Department of Computer Science Faculty of Computing and Information Technology University of the Punjab Lahore Pakistan Department of Data Science Faculty of Computing and Information Technology University of the Punjab Lahore Pakistan

ISBN: (纸本)9781665464789

Neuroendoscopy is being utilized more frequently to treat brain tumors and hydrocephalus brought on by tumors. Neuroendoscopy is a minimally invasive operation that requires a lot of skills. Complete training is needed in hospitals to perform this procedure in the operating room. Simulators are necessary to train doctors due to the deficiency of time, resources, cost, and potential risks. With advancements in computer technology, it is becoming more common to simulate and plan surgery using a VR-based simulation. We have created and developed a surgical simulator to provide endoscopic training for the ETV procedure. A method to use multimedia capabilities for learning is to create a virtual 3D simulator using a game engine and render it using VR technology. The user-friendly collaborative environment of the Unity3D game engine offers good features. We have demonstrated how a simplex mesh depiction in a game-based simulator may effectively deform a soft body in actuality. We have successfully developed a method employing Position Based Dynamics to simulate the actual deformation of TM the cancerous brain. According to our study, the HTC Vive is a suitable VR technology for ETV training because it offers excellent precision, quick tactile feedback, a fast frame rate, and a wide field of vision. In the future, this practical tool might replace more costly training simulators.

关键词： Training Three-dimensional displays Minimally invasive surgery Tactile sensors Games Real-time systems Engines

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Part-Based Convolutional Neural Network and Dual Interactive Wasserstein Generative Adversarial Networks for Land Mark Detection and Localization of Autonomous Robots in Outdoor Environment

Part-Based Convolutional Neural Network and Dual Interactive...

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International Conference on Computational science and Technology (ICCST)

作者： S. Sindhu M. Saravanan Department of Data Science and Business Systems Faculty of School of computing SRM Institute of Science and Technology Kattankulathur Department of Networking and Communications Faculty of School of computing SRM Institute of Science and Technology Kattankulathur

ISBN: (纸本)9781665476560

Robot localization is a fundamental competency required by an autonomous robot because the robot's location knowledge is an essential precursor to making decisions about future actions. Accurate localization of robots or autonomous vehicles is the most important requirement for autonomous applications. In this manuscript, a part-based convolutional neural network and dual interactive Wasserstein generative adversarial networks for landmark detection and localization of autonomous robots in an outdoor environment are proposed (P-CNN-DIWGAN-LMD-LZ). This research contains two phases landmark detection phase and the localization phase. In the landmark detection phase, the part-based convolutional neural network (P-CNN) is proposed to detect landmarks in capturing images. This landmark detection process creates 3 categories of responses for every detected landmarks instance: bounding box, label, and score. The bounding box has positioning with the sizing of detected landmarks at the input imagery. Label implicates detected landmark's class name. A score signifies an abjectness score that scales bounding box membership for landmarks or background classes. In the localization phase, a dual interactive Wasserstein generative adversarial network (DIWGAN) is proposed to determine robot location coordinates. Finally, the proposed method attains high robot localization recall at high accuracy in the real-world environment. Here, the outdoor robot localization dataset is taken from the KITTI dataset. The proposed method is implemented in Python; its performance is estimated under certain performance metrics, like mean absolute error (MAE), cosine proximity (CP), and accuracy. The performance of the proposed method shows higher accuracy compared with existing approaches, like DQP-ODA-LMD-LZ, TDL-LMD-LZ, and 3D-RISS-MLEVD- LMD-LZ.

关键词： Location awareness Measurement Scientific computing Robot kinematics Generative adversarial networks Robot localization Convolutional neural networks

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EXISTENCE OF WEAK SOLUTIONS FOR OBSTACLE PROBLEMS WITH VARIABLE GROWTH IN ORLICZ-SOBOLEV SPACES

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Journal of Mathematical sciences (United States) 2024年第2期289卷 155-167页

作者： Allalou, Mouad Ait Temghart, Said Raji, Abderahmane Applied Mathematics and Scientific Computing Laboratory Faculty of Science and Technics Sultan Moulay Slimane University BP 523 Beni Mellal 23000 Morocco National Higher School for Artificial Intelligence and Data Sciences Ibn Zohr University Taroudant 83000 Morocco

This article delves into the exploration of weak solutions’ existence within the context of obstacle problems associated with the following inequality: ∫ΩV(z,w,Dw):D(v-w)dz≥g,v-w, where v lies in a convex set FO,B... 详细信息

This article delves into the exploration of weak solutions’ existence within the context of obstacle problems associated with the following inequality: ∫_ΩV(z,w,Dw):D(v-w)dz≥g,v-w, where v lies in a convex set F_O,B. The primary methodology employed in this investigation involves the utilization of You ng’s measure theory, complemented by a theorem originating from Kinderlehrer and Stampacchia, specifically tailored for reflexive Orlicz-Sobolev spaces. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： 28A33 35J88 46E30 46E35 Kinderlehrer and Stampacchia Obstacle problem Orlicz-Sobolev spaces Young’s measures

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Deep Reinforcement Learning for Resource Management in Blockchain-Enabled Federated Learning Network

IEEE Networking Letters

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IEEE Networking Letters 2022年第3期4卷 137-141页

作者： Hieu, Nguyen Quang Tran, The Anh Nguyen, Cong Luong Niyato, Dusit Kim, Dong In Elmroth, Erik University of Technology Sydney School of Electrical and Data Engineering SydneyNSW2007 Australia Nanyang Technological University School of Computer Science and Engineering Jurong West Singapore Phenikaa University Faculty of Computer Science Hanoi10000 Viet Nam Sungkyunkwan University Department of Electrical and Computer Engineering Seoul16419 Korea Republic of Umeå University Department of Computing Science Umeå901 87 Sweden

Blockchain-enabled Federated Learning (BFL) enables model updates to be stored in blockchain in a reliable manner. However, one problem is the increase of the training latency due to the mining process. Moreover, mobile devices have energy and CPU constraints. Therefore, the machine learning model owner (MLMO) needs to decide the data and energy that the mobile devices use for the training and determine the block generation rate to minimize the system latency and mining cost while achieving the target accuracy. Under the uncertainty of BFL, we propose to use deep reinforcement learning to find the optimal decisions for the MLMO. © 2019 IEEE.

关键词： Neural networks

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Anomaly-Free Prior Guided Knowledge Distillation for Industrial Anomaly Detection

Anomaly-Free Prior Guided Knowledge Distillation for Industr...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Li, Gang Chen, Tianjiao Li, Min Han, Delong Zhou, MingLe Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China City University of Macau Faculty of Data Science China

ISBN: (纸本)9781665410205

In industrial manufacturing, visual anomaly detection is critical for maintaining product quality by detecting and preventing production anomalies. Anomaly detection methods based on knowledge distillation demonstrate promising performance in addressing the unpredictability and diversity of anomalies. However, they suffer from a lack of effective guidance from anomaly-free priors when handling anomalous features and underutilize multi-scale features during the segmentation scoring stage, yielding suboptimal detection results. To alleviate these issues, we propose an Anomaly-free Prior Guided knowledge distillation (APG) for industrial anomaly detection. Firstly, it filters the abnormal features by training the de-noising target network with knowledge distillation structure. Concurrently, we propose the Prior Perception Propagation Module (P3M), which extracts more efficient anomaly-free features by imposing constraints on anomalous features. Secondly, we propose the Multi-scale Prior Guided Fusion Module (MPGFM) to improve anomaly detection accuracy by utilizing anomaly-free features from the target network as priors to guide the generation and fusion of cross-scale differential features. Finally, the Global Perception Enhancement Module (GPEM) is proposed to construct an anomaly scoring network, leveraging comprehensive scene features to enhance the detection and localization performance of numerous small-target anomalies in industrial manufacturing. Extensive experiments on the MVTecAD and BTAD datasets show that the proposed method demonstrates a consistent and significant outperformance against competing methods. © 2024 IEEE.

关键词： Anomaly detection

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Beyond Normal: Learning Spatial Density Models of Node Mobility

arXiv

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arXiv 2024年

作者： Gao, Wanxin Nikolaidis, Ioanis Harms, Janelle Faculty of Data Science City University of Macau China Department of Computing Science University of Alberta Edmonton Canada

Learning models of complex spatial density functions, representing the steady-state density of mobile nodes moving on a two-dimensional terrain, can assist in network design and optimization problems, e.g., by accelerating the computation of the density function during a parameter sweep. We address the question of applicability for off-the-shelf mixture density network models for the description of mobile node density over a disk. We propose the use of Möbius distributions to retain symmetric spatial relations, yet be flexible enough to capture changes as one radially traverses the disk. The mixture models for Möbius versus Gaussian distributions are compared and the benefits of choosing Möbius distributions become evident, yet we also observe that learning mixtures of Möbius distributions is a fragile process, when using current tools, compared to learning mixtures of Gaussians. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Analyzing the Convergence of Federated Learning with Biased Client Participation 9th

Analyzing the Convergence of Federated Learning with Bias...

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19th International Conference on Advanced data Mining and Applications, ADMA 2023

作者： Tan, Lei Hu, Miao Zhou, Yipeng Wu, Di School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China School of Computing Faculty of Science and Engineering Macquarie University SydneyNSW2109 Australia

ISBN: (纸本)9783031466632

Federated Learning (FL) is a promising decentralized machine learning framework that enables a massive number of clients (e.g., smartphones) to collaboratively train a global model over the Internet without sacrificing their privacy. Though FL’s efficacy in non-convex problems is proven, its convergence amidst biased client participation lacks theoretical study. In this paper, we analyze the convergence of FedAvg on non-convex problems, which is the most renowned FL algorithm. We assume even data distribution but non-IID among clients, and elucidate the convergence rate of FedAvg in situations characterized by biased client participation. Our analysis reveals that biased client participation can significantly reduce the precision of the FL model. We validate this through trace-driven experiments, demonstrating that unbiased client participation results in 11% to 50% higher test accuracy compared to extremely biased client participation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial intelligence

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Unleashing the Potential of Decentralized Federated Learning in Healthcare: A Comprehensive Survey on Insights and Challenges 9

Unleashing the Potential of Decentralized Federated Learning...

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9th IEEE International Conference on data science in Cyberspace, DSC 2024

作者： Cheng, Haibo Liu, Wenjian Qu, Youyang Fu, Kexue Wang, Qinyuan City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

Federated learning (FL) is a distributed learning method that reduces data transmission and privacy risks. However, the existence of a central server may become a target of attackers, resulting in an increased risk of user privacy leakage. The emergence of Decentralized Federated Learning (DFL) is posited to enhance data privacy and system reliability by reducing reliance on central servers and enhancing local data control, especially in the medical field, where personal health data is protected. This research investigates the application of (DFL) in the medical domain, focusing on its limitations. This study employs a systematic literature review, adhering to PRISMA guidelines, to comprehensively understand DFL's current landscape and challenges in healthcare. The key findings highlight four principal obstacles faced by DFL: security and privacy, communication efficiency, data and model heterogeneity, and incentive mechanisms. It also explores the potential for DFL to facilitate personalized healthcare treatments by large datasets distributed across various medical institutions. This research fills a significant void by systematically reviewing DFL technologies in healthcare. It provides insights into applications, challenges, and future directions for participants seeking secure and efficient healthcare data handling. © 2024 IEEE.

关键词： Federated learning

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