Yu Zhou¹, Jiawei Tian², Kyungtae Kang^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.077373 - 26 February 2026

Abstract Arrhythmias are a frequently occurring phenomenon in clinical practice, but how to accurately distinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conducting ECG-based studies. From a review of existing studies, two main factors appear to contribute to this problem: the uneven distribution of arrhythmia classes and the limited expressiveness of features learned by current models. To overcome these limitations, this study proposes a dual-path multimodal framework, termed DM-EHC (Dual-Path Multimodal ECG Heartbeat Classifier), for ECG-based heartbeat classification. The proposed framework links 1D ECG temporal features with 2D time–frequency More >

H. Ahmed¹, Naglaa E. Ghannam^2,*, H. Mancy³, Esraa A. Mahareek⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.075732 - 26 February 2026

Abstract Parkinson’s disease remains a major clinical issue in terms of early detection, especially during its prodromal stage when symptoms are not evident or not distinct. To address this problem, we proposed a new deep learning 2-based approach for detecting Parkinson’s disease before any of the overt symptoms develop during their prodromal stage. We used 5 publicly accessible datasets, including UCI Parkinson’s Voice, Spiral Drawings, PaHaW, NewHandPD, and PPMI, and implemented a dual stream CNN–BiLSTM architecture with Fisher-weighted feature merging and SHAP-based explanation. The findings reveal that the model’s performance was superior and achieved 98.2%, a More >

Chang Xu^1,*, Xianbo Yin², Zhiyong Zhou¹, Bomin Liu¹

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.074417 - 10 February 2026

Abstract This study presents a hybrid CNN-Transformer model for real-time recognition of affective tactile biosignals. The proposed framework combines convolutional neural networks (CNNs) to extract spatial and local temporal features with the Transformer encoder that captures long-range dependencies in time-series data through multi-head attention. Model performance was evaluated on two widely used tactile biosignal datasets, HAART and CoST, which contain diverse affective touch gestures recorded from pressure sensor arrays. The CNN-Transformer model achieved recognition rates of 93.33% on HAART and 80.89% on CoST, outperforming existing methods on both benchmarks. By incorporating temporal windowing, the model enables More >

Akbare Yaqub^1,2, Muhammad Sadiq Orakzai², Muhammad Farrukh Qureshi^3,4, Zohaib Mushtaq⁵, Imran Siddique^6,7, Taha Radwan^8,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2503-2533, 2025, DOI:10.32604/cmes.2025.071571 - 26 November 2025

Abstract Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, necessitating efficient diagnostic tools. This study develops and validates a deep learning framework for phonocardiogram (PCG) classification, focusing on model generalizability and robustness. Initially, a ResNet-18 model was trained on the PhysioNet 2016 dataset, achieving high accuracy. To assess real-world viability, we conducted extensive external validation on the HLS-CMDS dataset. We performed four key experiments: (1) Fine-tuning the PhysioNet-trained model for binary (Normal/Abnormal) classification on HLS-CMDS, achieving 88% accuracy. (2) Fine-tuning the same model for multi-class classification (Normal, Murmur, Extra Sound, Rhythm Disorder), which yielded… More >

Acácio M. R. Amaral^1,2,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3825-3859, 2025, DOI:10.32604/cmc.2025.067179 - 23 September 2025

Abstract Power converters are essential components in modern life, being widely used in industry, automation, transportation, and household appliances. In many critical applications, their failure can lead not only to financial losses due to operational downtime but also to serious risks to human safety. The capacitors forming the output filter, typically aluminum electrolytic capacitors (AECs), are among the most critical and susceptible components in power converters. The electrolyte in AECs often evaporates over time, causing the internal resistance to rise and the capacitance to drop, ultimately leading to component failure. Detecting this fault requires measuring the… More >

Batyrkhan Omarov^1,2,3,4,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2195-2253, 2025, DOI:10.32604/cmc.2025.064799 - 23 September 2025

Abstract Deep learning now underpins many state-of-the-art systems for biomedical image and signal processing, enabling automated lesion detection, physiological monitoring, and therapy planning with accuracy that rivals expert performance. This survey reviews the principal model families as convolutional, recurrent, generative, reinforcement, autoencoder, and transfer-learning approaches as emphasising how their architectural choices map to tasks such as segmentation, classification, reconstruction, and anomaly detection. A dedicated treatment of multimodal fusion networks shows how imaging features can be integrated with genomic profiles and clinical records to yield more robust, context-aware predictions. To support clinical adoption, we outline post-hoc explainability More >

Judy P. Yang^*, Yuan-Jun Zhang

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 669-691, 2025, DOI:10.32604/cmes.2025.068945 - 31 July 2025

Abstract Much of the research has focused on identifying bridge frequencies for health monitoring, while the bridge damping ratio also serves as an important factor in damage detection. This study presents an enhanced method for identifying bridge damping ratios using a two-axle, three-mass test vehicle, relying on wheel responses captured by only two mounted sensors. Damping ratio estimation formulas are derived using both the Hilbert Transform (HT) and Wavelet Transform (WT), with a consistent formulation that confirms accurate estimation is achievable with minimal instrumentation, particularly when addressing the support effect. A comparative analysis of the two More >

Pallabi Biswas^1,#, Samarendra Nath Sur^2,#,*, Rabindranath Bera³, Agbotiname Lucky Imoize⁴, Chun-Ta Li^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 83-146, 2025, DOI:10.32604/cmes.2025.067724 - 31 July 2025

Abstract Automotive radar has emerged as a critical component in Advanced Driver Assistance Systems (ADAS) and autonomous driving, enabling robust environmental perception through precise range-Doppler and angular measurements. It plays a pivotal role in enhancing road safety by supporting accurate detection and localization of surrounding objects. However, real-world deployment of automotive radar faces significant challenges, including mutual interference among radar units and dense clutter due to multiple dynamic targets, which demand advanced signal processing solutions beyond conventional methodologies. This paper presents a comprehensive review of traditional signal processing techniques and recent advancements specifically designed to address… More > Graphic Abstract

Yanjun Yan, Kai Chen^*, Hang Geng, Wenqian Fan, Xinrui Zhou

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1345-1379, 2023, DOI:10.32604/cmes.2023.027252 - 26 June 2023

Abstract With increasing global concerns about clean energy in smart grids, the detection of power quality disturbances (PQDs) caused by energy instability is becoming more and more prominent. It is well acknowledged that the PQD effects on power grid equipment are destructive and hazardous, which causes irreversible damage to underlying electrical/electronic equipment of the concerned intelligent grids. In order to ensure safe and reliable equipment implementation, appropriate PQD detection technologies must be adopted to avoid such adverse effects. This paper summarizes the newly proposed and traditional PQD detection techniques in order to give a quick start More > Graphic Abstract

R. Karthik¹, R. Menaka^1,*, P. Ponmathi², Daehan Won³, P. Vinitha Joshy¹, J. G. Aravindan⁴, S. Harshavardhan⁴, K. V. S. D. Aashish kumar⁴, R. Akileshkumar⁴

Computer Systems Science and Engineering, Vol.47, No.1, pp. 333-348, 2023, DOI:10.32604/csse.2023.037661 - 26 May 2023

Abstract Diastasis Recti Abdominis (DRA) is the separation of abdominal recti muscles which occurs in women during their pregnancy and postpartum time. This is because of the stretching of the linea alba, a fibrous connective tissue on the abdominal wall. The Linea Alba is elastic and retracts back after the delivery of the baby. When this tissue gets overstretched, it loses its elasticity and the gap in the abdominals may not be closed leading to DRA. The motive of this research is to analyze the postpartum rehabilitation for signals from Inertial Measurement Unit (IMU) sensors. The… More >