Yunrui Bi^1,*, Qiliang Yang¹, Qinglin Ding¹, Bin Ran², Kun Liu¹, Mingjie Zhang¹

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084267 - 13 August 2026

Abstract To improve regional traffic signal coordination under uncertain and dynamic traffic conditions, this paper proposes a hybrid Type-2 fuzzy reinforcement learning framework integrated with Beetle Antennae Search (BAS) and Deep Q-Network (DQN), named Type-2 fuzzy Beetle Antennae Search and Deep Q-Network (T2-BAS-DQN). In this framework, DQN remains active during online signal control, while the Type-2 fuzzy module provides uncertainty-aware correction for phase selection and green-time adjustment. BAS is used only in the offline training stage to optimize a low-dimensional parameter vector related to fuzzy correction, reward adjustment, and coordination pressure. A 3 × 3 Simulation… More >

Ramakrishna Gandi^1,*, Geetha A.¹, Ramasubbareddy B.²

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.077569 - 15 June 2026

Abstract In the fields of affective computing, human-computer interaction, and psychological evaluation, the capacity to recognize emotions is crucial. Unimodal systems in the form of visual systems or of the physiological type are usually not designed to capture the complexity that exists in emotional states. The paper proposes NeuroVision: Multimodal Emotion Recognition System, combining facial video frames information and electroencephalogram (EEG) based information to enhance the accuracy and stability of the system. The system applies ResNet50 on the spatial information of facial expressions, Vision Transformer (ViT) on the temporal movements in the video, and an EEG-MLP… More >

Ezz El-Din Hemdan¹, Haitham Elwahsh^2,3, Samah Alshathri^4,*, Amged Sayed^5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079305 - 27 May 2026

Abstract Ensuring robust methods for maintaining high levels of medical data security is crucial in the Medical Internet of Things (IoT) for the protection of sensitive patient data during real-time transmission and analysis. Electroencephalography (EEG) signals in medical IoT systems are transmitted through cloud and edge networks, which create risks of cyber threats, unauthorized access, and data breaches. Consequently, there is an urgent need for efficient encryption methods to ensure the confidentiality of EEG signals during classification and prediction processes, as several state-of-the-art models either neglect security during classification or suffer from increased computational overhead that… More >

Trinh Thi Nhu Quynh¹, Hoa-Cuc. Nguyen², Bich-Ngoc. Mach², Thanh Q. Nguyen^3,4,*

Structural Durability & Health Monitoring, Vol.20, No.2, 2026, DOI:10.32604/sdhm.2025.070202 - 31 March 2026

Abstract This study introduces a method for processing various types of random real-world signals from bridges in both experimental models and real-world scenarios using a wireless sensor system. By analyzing and processing signals collected during actual traffic on bridges, the study identifies and provides parameters that meet current quality inspection requirements to ensure the safety of bridge users. The parameters investigated in this study include deformation, natural frequency, amplitude, impact factor, and damping coefficient. Research has determined and highlighted key parameters to assess the quality of bridge spans to meet quality inspection standards. Using actual traffic More >

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 >

Fazal Ur Rehman¹, Yazeed Alkhrijah², Syed Muhammad Usman³, Muhammad Irfan^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.076332 - 26 February 2026

Abstract Automated detection of Motor Imagery (MI) tasks is extremely useful for prosthetic arms and legs of stroke patients for their rehabilitation. Prediction of MI tasks can be performed with the help of Electroencephalogram (EEG) signals recorded by placing electrodes on the scalp of subjects; however, accurate prediction of MI tasks remains a challenge due to noise that is incurred during the EEG signal recording process, the extraction of a feature vector with high interclass variance, and accurate classification. The proposed method consists of preprocessing, feature extraction, and classification. First, EEG signals are denoised using 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 >

Thi Kim Chi Duong¹, Bich-Ngoc. Mach², Hoa-Cuc. Nguyen², Thi Nhu Quynh Trinh², Thanh Q. Nguyen^3,4,*

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.070185 - 08 January 2026

Abstract This study explores theoretical insights and experimental results on monitoring load-carrying capacity degradation in bridge spans through frequency analysis. Experiments were conducted on real bridge structures, including the Binh Thuan Bridge, focusing on analyzing the power spectral density (PSD) of vibration signals under random traffic loads. Detailed digital models of various bridge spans with different structural designs and construction periods were developed to ensure diversity. The study utilized PSD to analyze the vibration signals from the bridge spans under various loading conditions, identifying the vibration frequencies and the corresponding response regions. The research correlated the… More >

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 >