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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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Launching a data Analytics Major: How Collaboration and Agility Built a Successful Program

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American Mathematical Monthly 2025年第1期132卷 77-88页

作者： Forrester, Richard Marshall, Emily Forrester, Jeffrey Richeson, David Schaefer, Jennifer Department of Data Analytics and Department of Mathematics and Computer Science Dickinson College Carlisle 17013 PA United States Department of Economics Denison University Granville 43023 OH United States Department of Data Analytics and Department of Mathematics and Computer Science Dickinson College Carlisle 17013 PA United States Department of Mathematics and Computer Science Dickinson College Carlisle 17013 PA United States

In this article, we share the details of Dickinson college’s journey to establish a data analytics major. Designed to provide students with the technical proficiency required to become a data scientist, Dickinson’s data analytics major also incorporates values of a liberal arts education, including interdisciplinary learning, critical thinking, thoughtful analysis, effective communication, and ethical considerations. We describe how sustained collaboration, a detailed plan to engage the campus community, partnerships with alumni, and support from senior leaders were essential to this achievement. We also discuss the challenges and successes encountered before and after the approval of the major. © 2024 Mathematical Association of America.

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A continuous control model of satellite attitude on DSAC-T 24

A continuous control model of satellite attitude on DSAC-T

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2024 7th International Conference on computer Information science and Artificial Intelligence, CISAI 2024

作者： Wu, Jiawei Liang, Shuaishuai Feng, Huibin College of Computer and Data Science Fuzhou University Fujian Fuzhou China School of Computer and Big Data Minjiang University Fujian Fuzhou China

ISBN: (纸本)9798400707254

The development of the aerospace technology enables countries around the world to launch a large number of satellites to carry out extraterrestrial missions. This also poses greater challenges to the control of satellites. The extremely long communication time between satellites and the ground has led to the necessity of autonomous onboard control capability for satellites. The rise of reinforcement learning (RL) technology has driven research on applying RL technology to satellite control problems. This paper attempts to apply the Distributed Soft Actor Critic with Three Improvements (DSAC-T) algorithm to the adaptive continuous control problem of satellite attitude. The problem is described as a reinforcement learning model that controls the satellite to perform a large-angle slew maneuvers and maintain a desired orientation. By setting up rotation experiments, the results show that the DSAC-T algorithm can control satellite to perform rotation tasks more quickly and smoothly than existing continuous control algorithms. © 2024 Copyright held by the owner/author(s).

关键词： Reinforcement learning

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Optimizing Coverage in Wireless Sensor Networks Using the Tumbleweed Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 250-261页

作者： Zhang, Mengfei Chu, Shu-Chuan Liu, Nengxian Huang, Kuan-Chun Pan, Jeng-Shyang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Industrial and Systems Engineering Chung Yuan Christian University Taoyuan320314 Taiwan School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

Wireless sensor networks (WSNs) have found extensive applications across various fields, significantly enhancing the convenience in our daily lives. Hence, an in-creasing number of researchers are directing their attention toward the performance of sensor nodes, with a particular emphasis on addressing the coverage challenges in WSNs. Node coverage plays a crucial role in determining the detection performance of WSNs, making it imperative to attain maximum coverage with a given number of sensor nodes. This paper applies the Tumbleweed Algorithm (TA) to the coverage problem in WSNs and validates its effectiveness through simulation experiments. The results demonstrate its unique advantages in global search and avoiding local optima, providing a new solution for the WSN coverage problem and offering valuable references for future research and practical applications. The TA algorithm simulates the lifecycle of tumbleweed from seedling to mature plant and includes the concept of growth cycles. This paper compares the TA algorithm with PSO, GWO, DE, BOA, SCA, and AOA, setting different numbers of wireless sensor nodes and varying sensing radii for the experiments. The experimental results show that the TA algorithm significantly improves coverage rates under various configurations and consistently outperforms other commonly used algorithms. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Sensor nodes

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Optimized Extreme Learning Machine with Bacterial Colony Optimization Algorithm for Disease Diagnosis in Clinical datasets

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SN computer science 2024年第5期5卷 584页

作者： Vigneshvaran, P. Kathiravan, A. Vijaya Department of Data Science Shanmuga Industries Arts & amp Science College Tamilnadu Tiruvannamalai India Department of Computer Applications Sri Meenakshi Government College for Women Madurai India

Currently, the computational disease diagnostic models are mainly for approximating the non-linear complex sensitive patterns in the medical data. For the most accurate diagnostic, it is to be processed by more sophisticated algorithms. The clinical data is obtained from various clinical tests conducted on both infected and healthy individuals. It is crucial to precisely identify the presence of sickness to provide treatment promptly. Disease detection models must accurately differentiate between true positive and negative outcomes. Manual interpretations may result in either false positives or false negatives. This study aims to create a universal framework for diagnosing diseases on clinical datasets. For this scenario, the Extreme Learning Machine (ELM) is combined with the Bacterial Colony Optimization (BCO) method to boost prediction accuracy and speed up global convergence. For efficient selection of the extreme learning machine model’s optimal weights and biases, it is recommended to use the BCO algorithm's neighborhood-based communication strategy. Eight different UCI medical datasets are used to analyze the performance of the developed optimized ELM method. The experimental results showed that the suggested BCO-ELM outperformed in terms of accuracy, sensitivity, specificity, precision, f-measure, ROC-AUC, convergence speed. It obtained approximately 5 to 10 percent maximal accuracy and f-measure than the existing traditional approaches and obtained an AUC of 0.9 to 1 on all datasets. The experimental results confirmed that the developed approach produced high classification accuracy when compared with other existing approaches. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Artificial neural network Bacterial colony optimization Convergence rate Disease prediction Extreme learning machine

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A Novel Approach for Heart Disease Classification using Cascaded XGBoost Algorithm 3

A Novel Approach for Heart Disease Classification using Casc...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Rajitha, M. Megala, S. Santhana School of Computer Studies RVS College of Arts and Science Sulur Coimbatore India Artificial Intelligence and Data Science SNS College of Engineering Coimbatore India Department of Computer technology Hindustan College of Arts and Science Coimbatore India

ISBN: (纸本)9798331532420

Heart disease remains a leading cause of mortality worldwide. Accurate and timely diagnosis is crucial for effective treatment and prevention. This research proposes a novel approach using a cascaded XGBoost model to improve the accuracy and efficiency of heart disease diagnosis. With gradient boosting, the cascaded XGBoost model effectively handles complex interactions within the dataset, leading to superior performance compared to traditional machine learning techniques like logistic regression, support vector machines, and decision trees. The proposed model was evaluated on a standard heart disease dataset, demonstrating significant improvements in terms of accuracy, precision, and recall. Furthermore, the model's ability to identify key factors contributing to heart disease provides valuable insights for clinical decision-making. This research highlights the potential of machine learning techniques, particularly cascaded XGBoost, in advancing the field of heart disease diagnosis and improving patient outcomes. © 2024 IEEE.

关键词： Contrastive Learning

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Contrastive Consensus Graph Learning for Multi-View Clustering

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 2027-2030页

作者： Shiping Wang Xincan Lin Zihan Fang Shide Du Guobao Xiao the College of Computer and Data Science Fuzhou UniversityFuzhou 350108China the College of Computer and Control Engineering Minjiang UniversityFuzhou 350108China

Dear Editor,This letter proposes a contrastive consensus graph learning model for multi-view *** are usually built to outline the correlation between multi-model objects in clustering task,and multiview graph clusteri... 详细信息

关键词： letter integrate property

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JobViz:Skill-driven visual exploration of job advertisements

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Visual Informatics 2024年第3期8卷 18-28页

作者： Ran Wang Qianhe Chen Yong Wang Lewei Xiong Boyang Shen School of Journalism and Information Communication Huazhong University of Science and TechnologyWuhanChina School of Computer Science and Technology Huazhong University of Science and TechnologyWuhanChina College of Computing and Data Science Nanyang Technological UniversitySingaporeSingapore Wuhan National Laboratory for Optoelectronics WuhanChina Philosophy and Social Science Laboratory of Big Data and National Communication Strategy Ministry of EducationWuhanChina

Online job advertisements on various job portals or websites have become the most popular way for people to find potential career opportunities ***,the majority of these job sites are limited to offering fundamental filters such as job titles,keywords,and compensation *** often poses a challenge for job seekers in efficiently identifying relevant job advertisements that align with their unique skill sets amidst a vast sea of ***,we propose well-coordinated visualizations to provide job seekers with three levels of details of job information:a skill-job overview visualizes skill sets,employment posts as well as relationships between them with a hierarchical visualization design;a post exploration view leverages an augmented radar-chart glyph to represent job posts and further facilitates users’swift comprehension of the pertinent skills necessitated by respective positions;a post detail view lists the specifics of selected job posts for profound analysis and *** using a real-world recruitment advertisement dataset collected from 51Job,one of the largest job websites in China,we conducted two case studies and user interviews to evaluate *** results demonstrated the usefulness and effectiveness of our approach.

关键词： Visual exploration Job advertisements Skill-driven

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dTEE: A Declarative Approach to Secure IoT Applications Using TrustZone 23

dTEE: A Declarative Approach to Secure IoT Applications Usin...

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23rd ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2024

作者： Sun, Tong Li, Borui Teng, Yixiao Gao, Yi Dong, Wei Zhejiang University The State Key Laboratory of Blockchain and Data Security College of Computer Science China Southeast University School of Computer Science and Engineering China

ISBN: (纸本)9798350362015

Internet of Things (IoT) applications have recently been widely used in safety-critical scenarios. To prevent sensitive information leaks, IoT device vendors provide hardware-assisted protections, called Trusted Execution Environments (TEEs), like ARM Trust-Zone. Programming a TEE-based application requires separate code for two components, significantly slowing down the development process. Existing solutions tackle this issue by automatic code partition while not successfully applying it in two complicated scenarios: adding trusted logic and interactions with secure *** propose dTEE, a declarative approach to secure IoT applications based on TrustZone. dTEE proposes a rapid approach that enables developers to declare tiered-sensitive variables and functions of existing applications. Besides, dTEE automatically transforms device drivers into trusted ones. We evaluate dTEE on four real-world IoT applications and seven micro-benchmarks. Results show that dTEE achieves high expressiveness for supporting 50% more applications than existing approaches and reduces 90% of the lines of code against handcrafted development. © 2024 IEEE.

关键词： Internet of things

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Intra-Patient and Inter-Patient Multi-Classification of Severe Cardiovascular Diseases Based on CResFormer

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Tsinghua science and Technology 2023年第2期28卷 386-404页

作者： Dengao Li Changcheng Shi Jumin Zhao Yi Liu Chunxia Li College of Data Science Taiyuan University of TechnologyJinzhong 030600China College of Information and Computer Taiyuan University of TechnologyJinzhong 030600China Shanxi Bethune Hospital Taiyuan 030032China

Severe cardiovascular diseases can rapidly lead to *** present,most studies in the deep learning field using electrocardiogram(ECG)are performed on intra-patient experiments for the classification of coronary artery disease(CAD),myocardial infarction,and congestive heart failure(CHF).By contrast,actual conditions are inter-patient *** this study,we proposed a deep learning network,namely,CResFormer,with dual feature extraction to improve accuracy in classifying such ***,fixed segmentation of dual-lead ECG signals without preprocessing was used as input ***,one-dimensional convolutional layers performed moderate dimensionality reduction to accommodate subsequent feature ***,ResNet residual network block layers and transformer encoder layers sequentially performed feature extraction to obtain key associated abstract ***,the Softmax function was used for ***,the focal loss function is used when dealing with unbalanced *** average accuracy,sensitivity,positive predictive value,and specificity of four classifications of severe cardiovascular diseases are 99.84%,99.68%,99.71%,and 99.90%in intra-patient experiments,respectively,and 97.48%,93.54%,96.30%,and 97.89%in inter-patient experiments,*** addition,the model performs well in unbalanced datasets and shows good noise ***,the model has great application potential in diagnosing CAD,MI,and CHF in the actual clinical environment.

关键词： dual-lead ECG signals coronary artery disease myocardial infarction congestive heart failure interpatient experiments intra-patient experiments CResFormer

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