A novel automated CNN arrhythmia classifier with memory-enhanced artificial hummingbird algorithm

Expert Systems with Applications, Journal Year: 2022, Volume and Issue: 213, P. 119162 - 119162

Published: Nov. 2, 2022

Language: Английский

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A systematic review of applying grey wolf optimizer, its variants, and its developments in different Internet of Things applications

Internet of Things, Journal Year: 2024, Volume and Issue: 26, P. 101135 - 101135

Published: Feb. 22, 2024

Language: Английский

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Deep learning for multi-grade brain tumor detection and classification: a prospective survey

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: 83(25), P. 65889 - 65911

Published: Jan. 20, 2024

Language: Английский

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Convolutional neural network combined with reinforcement learning-based dual-mode grey wolf optimizer to identify crop diseases and pests

Swarm and Evolutionary Computation, Journal Year: 2025, Volume and Issue: 94, P. 101874 - 101874

Published: Feb. 3, 2025

Language: Английский

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A novel proposed CNN–SVM architecture for ECG scalograms classification

Soft Computing, Journal Year: 2022, Volume and Issue: 27(8), P. 4639 - 4658

Published: Dec. 15, 2022

Language: Английский

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A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

Healthcare, Journal Year: 2023, Volume and Issue: 11(16), P. 2240 - 2240

Published: Aug. 9, 2023

According to the Pan American Health Organization, cardiovascular disease is leading cause of death worldwide, claiming an estimated 17.9 million lives each year. This paper presents a systematic review highlight use IoT, IoMT, and machine learning detect, predict, or monitor disease. We had final sample 164 high-impact journal papers, focusing on two categories: detection using IoT/IoMT technologies techniques. For first category, we found 82 proposals, while for second, 85 proposals. The research highlights list technologies, techniques, datasets, most discussed diseases. Neural networks have been popularly used, achieving accuracy over 90%, followed by random forest, XGBoost, k-NN, SVM. Based results, conclude that can predict diseases in real time, ensemble techniques obtained one best performances metric, hypertension arrhythmia were Finally, identified lack public data as main obstacles approaches prediction.

Language: Английский

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Swarm intelligence empowered three-stage ensemble deep learning for arm volume measurement in patients with lymphedema

Biomedical Signal Processing and Control, Journal Year: 2023, Volume and Issue: 85, P. 105027 - 105027

Published: May 17, 2023

Language: Английский

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ECG Arrhythmia Classification Using Convolutional Neural Network

International Journal of Emerging Technology and Advanced Engineering, Journal Year: 2022, Volume and Issue: 12(7), P. 186 - 195

Published: July 2, 2022

This study provides a thorough analysis of earlier DL techniques used to classify the ECG data. The large variability among individual patients and high expense labeling clinical records are main hurdles in automatically detecting arrhythmia by electrocardiogram (ECG). classification (ECG) arrhythmias using novel more effective technique is presented this research. A high-performance (ECG)-based arrhythmic beats system described research develop plan with an autonomous feature learning strategy optimization mechanism, based on heartbeat approach. We propose method efficient 12-layer, MIT-BIH Arrhythmia dataset's five micro-classes types wavelet denoising technique. Compared state-of-the-art approaches, newly enables considerable accuracy increase quicker online retraining less professional involvement.

Language: Английский

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AI-Driven Real-Time Classification of ECG Signals for Cardiac Monitoring Using i-AlexNet Architecture

Diagnostics, Journal Year: 2024, Volume and Issue: 14(13), P. 1344 - 1344

Published: June 25, 2024

The healthcare industry has evolved with the advent of artificial intelligence (AI), which uses advanced computational methods and algorithms, leading to quicker inspection, forecasting, evaluation treatment. In context healthcare, (AI) sophisticated evaluate, decipher draw conclusions from patient data. AI potential revolutionize in several ways, including better managerial effectiveness, individualized treatment regimens diagnostic improvements. this research, ECG signals are preprocessed for noise elimination heartbeat segmentation. Multi-feature extraction is employed extract features data, an optimization technique used choose most feasible features. i-AlexNet classifier, improved version AlexNet model, classify between normal anomalous signals. For experimental evaluation, proposed approach applied PTB MIT_BIH databases, it observed that suggested method achieves a higher accuracy 98.8% compared other works literature.

Language: Английский

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Metaheuristic Algorithms for 6G wireless communications: Recent advances and applications

Ad Hoc Networks, Journal Year: 2024, Volume and Issue: 158, P. 103474 - 103474

Published: March 15, 2024

Language: Английский

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