Shing-Tai Pan^*, Yi-Zhen Huang, Zhi-Qing Chen

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081460 - 23 July 2026

Abstract This paper proposes a multimodal fusion framework that integrates speech and visual features to enhance the accuracy of emotion recognition. The principal contribution lies in extending the visual component from single-image to multi-image emotion recognition. Specifically, the proposed framework employs an InceptionV3 Convolutional Neural Network (CNN)-based architecture to extract features from multiple facial images representing the speaker’s expressions throughout an utterance. These features are concatenated into a single vector and subsequently processed by Long Short-Term Memory (LSTM) or Hidden Markov Model (HMM) for temporal modeling. For the speech modality, Mel-Frequency Cepstral Coefficients (MFCC) or filter… More >

Menghua Li^1,2,3, Bin Ou^1,2,3,4, Jiahao Li^1,2,3, Sitong Jin^1,2,3, Yanming Zhang^1,2,3, Shuyan Fu^1,2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082514 - 30 June 2026

Abstract Accurate prediction of seepage discharge in earth-rock dams remains challenging due to the strong non-stationary and nonlinear characteristics, limited robustness of individual models, and poor interpretability of black-box approaches. To address these issues, this paper proposes an interpretable hybrid model that integrates Variational Mode Decomposition (VMD), Long Short-Term Memory (LSTM) networks, and Support Vector Machine (SVM). The model first decomposes the seepage discharge sequence and relevant lagged features using VMD. The LSTM network then captures temporal dependencies of the decomposed components, while the SVM performs regression on the original sequences and features. An adaptive fusion… More >

K. Mithra^1,*, Prem Kumar Santhanam²

Journal of Intelligent Medicine and Healthcare, Vol.4, pp. 99-108, 2026, DOI:10.32604/jimh.2026.083110 - 18 June 2026

Abstract Objective: To develop a low-cost automated cataract severity classification system operating on standard consumer-grade colour photographs of the eye, without specialised ophthalmic hardware. Methods: A hybrid framework was designed that fuses deep features from a Convolutional Neural Network (CNN) with five handcrafted Grey-Level Co-occurrence Matrix (GLCM) and intensity descriptors—mean intensity, uniformity, standard deviation, contrast, and energy—extracted from a Hough-circle-localised pupil Region of Interest (ROI). A multi-class Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel classifies each image into one of four severity grades: normal, immature, mature, or hypermature cataract. Results: The proposed fused system More >

Yue Su¹, Shukuan Zhang^1,*, Jinghao Jiao¹, Jiankang Zhong², Qianxi Zhao¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079927 - 27 April 2026

Abstract This paper introduces a novel dual-layer optimization fault diagnosis framework for inter-turn short-circuit (ITSC) faults in permanent magnet synchronous motors (PMSMs). The synergistic of a SABO-optimized VMD for enhanced feature extraction and an MFO-optimized SVM for intelligent classification is proposed. Firstly, mathematical and simulation models of ITSC faults in PMSMs are established to obtain fault phase currents and motor electromagnetic torques as characteristic fault signals. Then, the SABO algorithm is used to optimize the VMD parameters, followed by VMD decomposition of the characteristic fault signals to obtain Intrinsic Mode Functions (IMFs), and the time-domain parameters More >

Kadriye Simsek Alan^*, Busra Senel Kahyaoglu

Journal on Artificial Intelligence, Vol.8, pp. 203-230, 2026, DOI:10.32604/jai.2026.078359 - 15 April 2026

Abstract Cardiovascular diseases remain one of the leading causes of mortality worldwide, making early and reliable diagnosis a critical challenge for modern healthcare systems. In this study, a systematic comparative performance analysis of widely used machine learning algorithms is conducted for the early detection of heart disease using tabular clinical data. Rather than proposing a novel model architecture, the primary objective is to provide a fair, reproducible, and clinically meaningful evaluation of commonly adopted classifiers under consistent experimental conditions. The Kaggle Heart Failure dataset is employed, and multiple machine learning models—including tuned Random Forest, tuned XGBoost,… More >

Qian Zhang¹, Lei Fu^1,2, Renzhou Chen³, Xu Zhan^4,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074939 - 12 March 2026

Abstract Triply periodic minimal surface (TPMS) structures are widely utilized in engineering and biomedical fields owing to their superior mechanical and functional properties. However, limited by the current additive manufacturing (AM) techniques, insufficient wall thickness often leads to poor forming quality or even printing failure. Therefore, accurate prediction of wall thickness parameters during the design stage is essential. This study proposes a prediction approach for the wall thickness parameters of TPMS models by integrating a Convolutional Neural Network–Support Vector Regression (CNN-SVM) framework with Multiple Linear Regression (MLR). A total of 152 TPMS models were randomly generated,… More >

Mohammad Alshinwan¹, Radwan M. Batyha^1,2, Walaa Alayed^3,*, Saad Said Alqahtany⁴, Suhaila Abuowaida⁵, Hamza A. Mashagba⁶, Azlan B. Abd Aziz^6,*, Samir Salem Al-Bawri⁷

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.072806 - 12 March 2026

Abstract Safeguarding modern networks from cyber intrusions has become increasingly challenging as attackers continually refine their evasion tactics. Although numerous machine-learning-based intrusion detection systems (IDS) have been developed, their effectiveness is often constrained by high dimensionality and redundant features that degrade both accuracy and efficiency. This study introduces a hybrid feature-selection framework that integrates the exploration capability of Prairie Dog Optimization (PDO) with the exploitation behavior of Ant Colony Optimization (ACO). The proposed PDO–ACO algorithm identifies a concise yet discriminative subset of features from the NSL-KDD dataset and evaluates them using a Support Vector Machine (SVM) More >

Isam Bahaa Aldallal¹, Abdullahi Abdu Ibrahim^1,*, Saadaldeen Rashid Ahmed^2,3

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075212 - 10 February 2026

Abstract The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems (IDS) capable of addressing dynamic security threats under constrained resource environments. This paper proposes a hybrid IDS for IoT networks, integrating Support Vector Machine (SVM) and Genetic Algorithm (GA) for feature selection and parameter optimization. The GA reduces the feature set from 41 to 7, achieving a 30% reduction in overhead while maintaining an attack detection rate of 98.79%. Evaluated on the NSL-KDD dataset, the system demonstrates an accuracy of 97.36%, a recall of 98.42%, and an F1-score of 96.67%, with a low false More >

Moyi Zhang, Yixin Wang^*, Yu Cheng

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071021 - 12 January 2026

Abstract Modern intelligent systems, such as autonomous vehicles and face recognition, must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations. However, when neural networks learn new classes sequentially, they suffer from catastrophic forgetting—the tendency to lose knowledge of earlier classes. This challenge, which lies at the core of class-incremental learning, severely limits the deployment of continual learning systems in real-world applications with streaming data. Existing approaches, including rehearsal-based methods and knowledge distillation techniques, have attempted to address this issue but often struggle to effectively preserve decision boundaries and discriminative features… More >

Kwok Tai Chui^1,*, Varsha Arya¹, Brij B. Gupta^2,3,4,*, Miguel Torres-Ruiz⁵, Razaz Waheeb Attar⁶

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.068842 - 10 November 2025

Abstract Parkinson’s disease (PD) is a debilitating neurological disorder affecting over 10 million people worldwide. PD classification models using voice signals as input are common in the literature. It is believed that using deep learning algorithms further enhances performance; nevertheless, it is challenging due to the nature of small-scale and imbalanced PD datasets. This paper proposed a convolutional neural network-based deep support vector machine (CNN-DSVM) to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets. A customized kernel function reduces the impact… More >