Yazeed Alkhrijah¹, Marwa Fahim², Syed Muhammad Usman³, Qasim Mehmood³, Shehzad Khalid^4,5,*, Mohamad A. Alawad¹, Haya Aldossary⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2339-2355, 2025, DOI:10.32604/cmes.2025.071512 - 26 November 2025

Abstract Atrial Fibrillation (AF) is a cardiac disorder characterized by irregular heart rhythms, typically diagnosed using Electrocardiogram (ECG) signals. In remote regions with limited healthcare personnel, automated AF detection is extremely important. Although recent studies have explored various machine learning and deep learning approaches, challenges such as signal noise and subtle variations between AF and other cardiac rhythms continue to hinder accurate classification. In this study, we propose a novel framework that integrates robust preprocessing, comprehensive feature extraction, and an ensemble classification strategy. In the first step, ECG signals are divided into equal-sized segments using a… More >

Mingxing Wu¹, Chengzhen Li¹, Xinyan Feng¹, Fei Chen², Yingchun Feng¹, Huihui Song¹, Wenyu Wang³, Faye Zhang^3,*

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1473-1487, 2025, DOI:10.32604/sdhm.2025.069811 - 17 November 2025

Abstract As a core component of power systems, the operational status of transformers directly affects grid stability. To address the problem of “domain shift” in cross-domain fault diagnosis, this paper proposes a memory-enhanced dual-stream network (MemFuse-DSN). The method reconstructs the feature space by selecting and enhancing multi-source domain samples based on similarity metrics. An adaptive weighted dual-stream architecture is designed, integrating gradient reversal and orthogonality constraints to achieve efficient feature alignment. In addition, a novel dual dynamic memory module is introduced: the task memory bank is used to store high-confidence class prototype information, and adopts an More >

Divya Arivalagan, Vignesh Ochathevan^*, Rubankumar Dhanasekaran

Congenital Heart Disease, Vol.20, No.4, pp. 477-501, 2025, DOI:10.32604/chd.2025.070372 - 18 September 2025

Abstract Background: The accurate identification of cardiac abnormalities is essential for proper diagnosis and effective treatment of cardiovascular diseases. Method: This work introduces an advanced methodology for detecting cardiac abnormalities and estimating electrocardiographic age (ECG Age) using sophisticated signal processing and deep learning techniques. This study looks at six main heart conditions found in 12-lead electrocardiogram (ECG) data. It addresses important issues like class imbalances, missing lead scenarios, and model generalizations. A modified residual neural network (ResNet) architecture was developed to enhance the detection of cardiac abnormalities. Results: The proposed ResNet demonst rated superior performance when compared with… More > Graphic Abstract

Anis Ben Ghorbal^1,*, Azedine Grine¹, Marwa M. Eid^2,3,*, El-Sayed M. El-Kenawy^4,5

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2001-2028, 2025, DOI:10.32604/cmes.2025.068212 - 31 August 2025

Abstract Preterm birth remains a leading cause of neonatal complications and highlights the need for early and accurate prediction techniques to improve both fetal and maternal health outcomes. This study introduces a hybrid approach integrating Long Short-Term Memory (LSTM) networks with the Hybrid Greylag Goose and Particle Swarm Optimization (GGPSO) algorithm to optimize preterm birth classification using Electrohysterogram signals. The dataset consists of 58 samples of 1000-second-long Electrohysterogram recordings, capturing key physiological features such as contraction patterns, entropy, and statistical variations. Statistical analysis and feature selection methods are applied to identify the most relevant predictors and More > Graphic Abstract

Deepak Mahto¹, Sudhakar Kumar¹, Sunil K. Singh¹, Amit Chhabra¹, Irfan Ahmad Khan², Varsha Arya^3,4, Wadee Alhalabi⁵, Brij B. Gupta^6,7,8,9,*, Bassma Saleh Alsulami¹⁰

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3743-3767, 2025, DOI:10.32604/cmes.2025.063427 - 30 June 2025

Abstract Cardiovascular diseases (CVDs) remain one of the foremost causes of death globally; hence, the need for several must-have, advanced automated diagnostic solutions towards early detection and intervention. Traditional auscultation of cardiovascular sounds is heavily reliant on clinical expertise and subject to high variability. To counter this limitation, this study proposes an AI-driven classification system for cardiovascular sounds whereby deep learning techniques are engaged to automate the detection of an abnormal heartbeat. We employ FastAI vision-learner-based convolutional neural networks (CNNs) that include ResNet, DenseNet, VGG, ConvNeXt, SqueezeNet, and AlexNet to classify heart sound recordings. Instead of… More >

Lucas Leal Lima¹, Angélica Souza Gouveia¹, Analice Martins Duarte¹, Filipe Schitini Salgado², Nathália Silva Oliveira¹, Monique da Silva Bonjour¹, Iana Pedro da Silva Quadros¹, Maria Goreti Almeida Oliveira¹, Flavia Maria Silva Carmo², Elizabeth Pacheco Batista Fontes¹, Humberto Josué de Oliveira Ramos^1,3,*

Phyton-International Journal of Experimental Botany, Vol.94, No.6, pp. 1787-1810, 2025, DOI:10.32604/phyton.2025.064068 - 27 June 2025

Abstract Sound contains mechanical signals that can promote physiological and biochemical changes in plants. Insects produce different sounds in the environment, which may be relevant to plant behavior. Thus, we evaluated whether signaling cascades are regulated differently by ecological sounds and whether they trigger molecular responses following those produced by herbivorous insects. Soybean plants were treated with two different sounds: chewing herbivore and forest ambient. The responses were markedly distinct, indicating that sound signals may also trigger specific cascades. Enzymes involved in oxidative metabolism were responsive to both sounds, while salicylic acid (SA) was responsive only… More >

Ali Mohammad Alqudah¹, Zahra Moussavi^1,2,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3753-3841, 2025, DOI:10.32604/cmc.2025.063643 - 19 May 2025

Abstract This review presents a comprehensive technical analysis of deep learning (DL) methodologies in biomedical signal processing, focusing on architectural innovations, experimental validation, and evaluation frameworks. We systematically evaluate key deep learning architectures including convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformer-based models, and hybrid systems across critical tasks such as arrhythmia classification, seizure detection, and anomaly segmentation. The study dissects preprocessing techniques (e.g., wavelet denoising, spectral normalization) and feature extraction strategies (time-frequency analysis, attention mechanisms), demonstrating their impact on model accuracy, noise robustness, and computational efficiency. Experimental results underscore the superiority of deep learning… More >

Borja Bordel Sánchez^1,*, Fernando Rodríguez-Sela¹, Ramón Alcarria², Tomás Robles¹

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1821-1853, 2025, DOI:10.32604/cmc.2025.059012 - 16 April 2025

Abstract The Industry 4.0 revolution is characterized by distributed infrastructures where data must be continuously communicated between hardware nodes and cloud servers. Specific lightweight cryptosystems are needed to protect those links, as the hardware node tends to be resource-constrained. Then Pseudo Random Number Generators are employed to produce random keys, whose final behavior depends on the initial seed. To guarantee good mathematical behavior, most key generators need an unpredictable voltage signal as input. However, physical signals evolve slowly and have a significant autocorrelation, so they do not have enough entropy to support high-randomness seeds. Then, electronic… More >

Wenxia Wang, Jinchen Xu, Xiaodong Ding, Zhihui Song, Yizhen Huang, Xin Zhou, Zheng Shan^*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1363-1390, 2024, DOI:10.32604/cmc.2024.054055 - 15 October 2024

Abstract With the wide application of drone technology, there is an increasing demand for the detection of radar return signals from drones. Existing detection methods mainly rely on time-frequency domain feature extraction and classical machine learning algorithms for image recognition. This method suffers from the problem of large dimensionality of image features, which leads to large input data size and noise affecting learning. Therefore, this paper proposes to extract signal time-domain statistical features for radar return signals from drones and reduce the feature dimension from 512 × 4 to 16 dimensions. However, the downscaled feature data… More >

Abbas Ali Hassan, Fardin Abdali-Mohammadi^*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 971-983, 2024, DOI:10.32604/cmc.2024.053817 - 15 October 2024

Abstract From a medical perspective, the 12 leads of the heart in an electrocardiogram (ECG) signal have functional dependencies with each other. Therefore, all these leads report different aspects of an arrhythmia. Their differences lie in the level of highlighting and displaying information about that arrhythmia. For example, although all leads show traces of atrial excitation, this function is more evident in lead II than in any other lead. In this article, a new model was proposed using ECG functional and structural dependencies between heart leads. In the prescreening stage, the ECG signals are segmented from… More >