An arrhythmia classification using a deep learning and optimisation-based methodology DOI
Suvita Rani Sharma, Birmohan Singh, Manpreet Kaur

и другие.

Journal of Medical Engineering & Technology, Год журнала: 2025, Номер unknown, С. 1 - 9

Опубликована: Фев. 14, 2025

The work proposes a methodology for five different classes of ECG signals. utilises moving average filter and discrete wavelet transformation the remove baseline wandering powerline interference. preprocessed signals are segmented by R peak detection process. Thereafter, greyscale scalograms images have been formed. features extracted using EfficientNet-B0 deep learning model. These normalised z-score normalisation method then optimal selected hybrid feature selection method. is constructed utilising two methods Self Adaptive Bald Eagle Search (SABES) optimisation algorithm. proposed has applied to classification types beats. acquired 99.31% accuracy.

Язык: Английский

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

Ecological Informatics, Год журнала: 2023, Номер 75, С. 101998 - 101998

Опубликована: Янв. 21, 2023

Язык: Английский

Процитировано

100

RETRACTED ARTICLE: A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection DOI Open Access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, Год журнала: 2023, Номер 27(9), С. 5521 - 5535

Опубликована: Янв. 4, 2023

Язык: Английский

Процитировано

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

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Фев. 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.

Язык: Английский

Процитировано

25

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

и другие.

IEEE Access, Год журнала: 2023, Номер 11, С. 100052 - 100069

Опубликована: Янв. 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.

Язык: Английский

Процитировано

32

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

Multimedia Tools and Applications, Год журнала: 2023, Номер 83(4), С. 10815 - 10838

Опубликована: Июнь 24, 2023

Язык: Английский

Процитировано

30

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

и другие.

Journal of Bionic Engineering, Год журнала: 2023, Номер 21(1), С. 409 - 425

Опубликована: Сен. 14, 2023

Язык: Английский

Процитировано

26

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

Congyao Zhao

и другие.

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2023, Номер 415, С. 116238 - 116238

Опубликована: Июль 23, 2023

Язык: Английский

Процитировано

25

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

и другие.

Computer Science Review, Год журнала: 2025, Номер 56, С. 100727 - 100727

Опубликована: Янв. 18, 2025

Язык: Английский

Процитировано

2

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

M. Suchetha,

Achmad Rizal

и другие.

Multimedia Tools and Applications, Год журнала: 2023, Номер 83(13), С. 39755 - 39777

Опубликована: Окт. 5, 2023

Язык: Английский

Процитировано

18

Complex-valued artificial hummingbird algorithm for global optimization and short-term wind speed prediction DOI
Liuyan Feng, Yongquan Zhou, Qifang Luo

и другие.

Expert Systems with Applications, Год журнала: 2024, Номер 246, С. 123160 - 123160

Опубликована: Янв. 8, 2024

Язык: Английский

Процитировано

9