Research on Multi‐Scale Parallel Joint Optimization CNN for Arrhythmia Diagnosis DOI
Wenping Chen, Huibin Wang, Zhe Chen

et al.

Concurrency and Computation Practice and Experience, Journal Year: 2025, Volume and Issue: 37(4-5)

Published: Feb. 10, 2025

ABSTRACT The morphological characteristics of electrocardiograms (ECGs) serve as a fundamental basis for diagnosing arrhythmias. Convolutional neural networks (CNNs), leveraging their local receptive field properties, effectively capture the features ECG signals and have been extensively employed in automatic diagnosis However, variability duration renders single‐scale convolutional kernels inadequate fully extracting these features. To address this limitation, study proposes multi‐scale parallel joint optimization network (MPJO_CNN). proposed method utilizes varying scales to extract features, further refining via computation implementing strategy enhance classification performance. Experimental results demonstrate that on MIT‐BIH arrhythmia database, not only achieved state‐of‐the‐art performance, with an accuracy 99.41% F1 score 98.09%, but also showed high sensitivity classes fewer samples.

Language: Английский

A novel multi-head CNN design to identify plant diseases using the fusion of RGB images DOI
Yasin Kaya, Ercan Gürsoy

Ecological Informatics, Journal Year: 2023, Volume and Issue: 75, P. 101998 - 101998

Published: Jan. 21, 2023

Language: Английский

Citations

98

RETRACTED ARTICLE: A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection DOI Open Access
Yasin Kaya, Ercan Gürsoy

Soft Computing, Journal Year: 2023, Volume and Issue: 27(9), P. 5521 - 5535

Published: Jan. 4, 2023

Language: Английский

Citations

73

Tuning attention based long-short term memory neural networks for Parkinson’s disease detection using modified metaheuristics DOI Creative Commons
Aleksa Ćuk, Timea Bezdan, Luka Jovanovic

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Feb. 21, 2024

Parkinson's disease (PD) is a progressively debilitating neurodegenerative disorder that primarily affects the dopaminergic system in basal ganglia, impacting millions of individuals globally. The clinical manifestations include resting tremors, muscle rigidity, bradykinesia, and postural instability. Diagnosis relies mainly on evaluation, lacking reliable diagnostic tests being inherently imprecise subjective. Early detection PD crucial for initiating treatments that, while unable to cure chronic condition, can enhance life quality patients alleviate symptoms. This study explores potential utilizing long-short term memory neural networks (LSTM) with attention mechanisms detect based dual-task walking test data. Given performance significantly inductance by architecture training parameter choices, modified version recently introduced crayfish optimization algorithm (COA) proposed, specifically tailored requirements this investigation. proposed optimizer assessed publicly accessible real-world gait dataset, results demonstrate its promise, achieving an accuracy 87.4187 % best-constructed models.

Language: Английский

Citations

24

Feature Selection Using Selective Opposition Based Artificial Rabbits Optimization for Arrhythmia Classification on Internet of Medical Things Environment DOI Creative Commons

G S Nijaguna,

N. Dayananda Lal,

B. D. Parameshachari

et al.

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 100052 - 100069

Published: Jan. 1, 2023

An Electrocardiogram (ECG) is a non-invasive test that broadly utilized for monitoring and diagnosing the cardiac arrhythmia. irregularity of heartbeat generally defined as arrhythmia, which potentially causes fatal difficulties creates an instantaneous life risk. Therefore, arrhythmia classification challenging task because overfitting issue caused by high dimensional feature space ECG signal. In this research, incorporation Internet Medical Things (IoMT) developed with artificial intelligence to provide health people who are having work, time, time-frequency, entropy, nonlinearity features deep from Convolutional Neural Network (CNN) extracted obtain different categories signal features. The Selective Opposition (SO) strategy based Artificial Rabbits Optimization (SOARO) proposed selecting optimal subset overall avoid issue. chosen used improve done Auto Encoder (AE). Further, Shapley additive explanations (SHAP) model interpret classified output AE. MIT-BIH database evaluating SOARO-AE. performance SOARO-AE evaluated using accuracy, sensitivity, specificity, recall F1-Measure. existing researches such C-LSTM, DL-LAC-CNN, CNN-DNN, MC-ECG, FC MEAHA-CNN evaluate method. accuracy 98.89% when compared MEAHA-CNN.

Language: Английский

Citations

32

Human activity recognition from multiple sensors data using deep CNNs DOI
Yasin Kaya, E. Topuz

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(4), P. 10815 - 10838

Published: June 24, 2023

Language: Английский

Citations

30

Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems: A Medical Case Study DOI
Adel Got, Djaafar Zouache, Abdelouahab Moussaoui

et al.

Journal of Bionic Engineering, Journal Year: 2023, Volume and Issue: 21(1), P. 409 - 425

Published: Sept. 14, 2023

Language: Английский

Citations

26

LCAHA: A hybrid artificial hummingbird algorithm with multi-strategy for engineering applications DOI
Gang Hu, Jingyu Zhong,

Congyao Zhao

et al.

Computer Methods in Applied Mechanics and Engineering, Journal Year: 2023, Volume and Issue: 415, P. 116238 - 116238

Published: July 23, 2023

Language: Английский

Citations

25

Artificial hummingbird algorithm: Theory, variants, analysis, applications, and performance evaluation DOI
Buddhadev Sasmal, Arunita Das, Krishna Gopal Dhal

et al.

Computer Science Review, Journal Year: 2025, Volume and Issue: 56, P. 100727 - 100727

Published: Jan. 18, 2025

Language: Английский

Citations

2

EO-LGBM-HAR: A novel meta-heuristic hybrid model for human activity recognition DOI
E. Topuz, Yasin Kaya

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 189, P. 110004 - 110004

Published: March 17, 2025

Language: Английский

Citations

1

Time-scale image analysis for detection of fetal electrocardiogram DOI
Said Ziani,

M. Suchetha,

Achmad Rizal

et al.

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(13), P. 39755 - 39777

Published: Oct. 5, 2023

Language: Английский

Citations

18