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 >

Cheng Cheng^*, Xuan-Ping Gong, Xiao-Yu Cheng, Lu Xiao, Xing-Ying Ma

Frontiers in Heat and Mass Transfer, Vol.23, No.3, pp. 1037-1051, 2025, DOI:10.32604/fhmt.2025.065719 - 30 June 2025

Abstract To overcome the challenges associated with predicting gas extraction performance and mitigating the gradual decline in extraction volume, which adversely impacts gas utilization efficiency in mines, a gas extraction pure volume prediction model was developed using Support Vector Regression (SVR) and Random Forest (RF), with hyperparameters fine-tuned via the Genetic Algorithm (GA). Building upon this, an adaptive control model for gas extraction negative pressure was formulated to maximize the extracted gas volume within the pipeline network, followed by field validation experiments. Experimental results indicate that the GA-SVR model surpasses comparable models in terms of mean… More >

Kun Li¹, Tianfu Li^2,*, Xiuwei Wang¹, Qingchun Meng¹, Zhenjie Wang¹, Jinyang Luo^1,2, Zhaohui Wang¹, Yuedong Yao²

Energy Engineering, Vol.122, No.6, pp. 2215-2238, 2025, DOI:10.32604/ee.2025.062876 - 29 May 2025

Abstract CO₂ flooding for enhanced oil recovery (EOR) not only enables underground carbon storage but also plays a critical role in tertiary oil recovery. However, its displacement efficiency is constrained by whether CO₂ and crude oil achieve miscibility, necessitating precise prediction of the minimum miscibility pressure (MMP) for CO₂-oil systems. Traditional methods, such as experimental measurements and empirical correlations, face challenges including time-consuming procedures and limited applicability. In contrast, artificial intelligence (AI) algorithms have emerged as superior alternatives due to their efficiency, broad applicability, and high prediction accuracy. This study employs four AI algorithms—Random Forest Regression (RFR), Genetic… More >

Muhammad Qasim^1,2, Syed M. Adnan Shah¹, Qamas Gul Khan Safi¹, Danish Mahmood², Adeel Iqbal^3,*, Ali Nauman³, Sung Won Kim^3,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4429-4445, 2025, DOI:10.32604/cmc.2025.065060 - 19 May 2025

Abstract Grains are the most important food consumed globally, yet their yield can be severely impacted by pest infestations. Addressing this issue, scientists and researchers strive to enhance the yield-to-seed ratio through effective pest detection methods. Traditional approaches often rely on preprocessed datasets, but there is a growing need for solutions that utilize real-time images of pests in their natural habitat. Our study introduces a novel two-step approach to tackle this challenge. Initially, raw images with complex backgrounds are captured. In the subsequent step, feature extraction is performed using both hand-crafted algorithms (Haralick, LBP, and Color… More >

Taher Alzahrani^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4575-4606, 2025, DOI:10.32604/cmc.2025.063448 - 19 May 2025

Abstract The rapid evolution of malware presents a critical cybersecurity challenge, rendering traditional signature-based detection methods ineffective against novel variants. This growing threat affects individuals, organizations, and governments, highlighting the urgent need for robust malware detection mechanisms. Conventional machine learning-based approaches rely on static and dynamic malware analysis and often struggle to detect previously unseen threats due to their dependency on predefined signatures. Although machine learning algorithms (MLAs) offer promising detection capabilities, their reliance on extensive feature engineering limits real-time applicability. Deep learning techniques mitigate this issue by automating feature extraction but may introduce computational overhead,… More >

Faisal Ghazi Beshaw¹, Thamir Hassan Atyia², Mohd Fadzli Mohd Salleh¹, Mohamad Khairi Ishak³, Abdul Sattar Din^1,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3553-3583, 2025, DOI:10.32604/cmc.2025.061400 - 16 April 2025

Abstract The significance of precise energy usage forecasts has been highlighted by the increasing need for sustainability and energy efficiency across a range of industries. In order to improve the precision and openness of energy consumption projections, this study investigates the combination of machine learning (ML) methods with Shapley additive explanations (SHAP) values. The study evaluates three distinct models: the first is a Linear Regressor, the second is a Support Vector Regressor, and the third is a Decision Tree Regressor, which was scaled up to a Random Forest Regressor/Additions made were the third one which was… More >

Yuan Ping^1,2,*, Huina Li¹, Chun Guo³, Bin Hao⁴

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4949-4976, 2025, DOI:10.32604/cmc.2025.057693 - 06 March 2025

Abstract Towards optimal k-prototype discovery, k-means-like algorithms give us inspirations of central samples collection, yet the unstable seed samples selection, the hypothesis of a circle-like pattern, and the unknown K are still challenges, particularly for non-predetermined data patterns. We propose an adaptive k-prototype clustering method (kProtoClust) which launches cluster exploration with a sketchy division of K clusters and finds evidence for splitting and merging. On behalf of a group of data samples, support vectors and outliers from the perspective of support vector data description are not the appropriate candidates for prototypes, while inner samples become the first candidates for… More >

Ahlem Walha¹, Amel Ksibi^2,*, Mohammed Zakariah^3,*, Manel Ayadi², Tagrid Alshalali², Oumaima Saidani², Leila Jamel², Nouf Abdullah Almujally²

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2959-3001, 2025, DOI:10.32604/cmes.2025.060545 - 03 March 2025

Abstract Dementia is a neurological disorder that affects the brain and its functioning, and women experience its effects more than men do. Preventive care often requires non-invasive and rapid tests, yet conventional diagnostic techniques are time-consuming and invasive. One of the most effective ways to diagnose dementia is by analyzing a patient’s speech, which is cheap and does not require surgery. This research aims to determine the effectiveness of deep learning (DL) and machine learning (ML) structures in diagnosing dementia based on women’s speech patterns. The study analyzes data drawn from the Pitt Corpus, which contains… More >

Jianing Hao¹, Dan Yang², Guanxiong Ren¹, Ying Zhao³, Rangling Cao^4,*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.1, pp. 141-159, 2025, DOI:10.32604/fdmp.2024.054469 - 24 January 2025

Abstract This study presents an innovative approach to calculating the failure probability of slopes by incorporating fuzzy limit-state functions, a method that significantly enhances the accuracy and efficiency of slope stability analysis. Unlike traditional probabilistic techniques, this approach utilizes a least squares support vector machine (LSSVM) optimized with a grey wolf optimizer (GWO) and K-fold cross-validation (CV) to approximate the limit-state function, thus reducing computational complexity. The novelty of this work lies in its application to one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) slope models, demonstrating its versatility and high precision. The proposed method consistently achieves… More > Graphic Abstract

Ali Alqarni^*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 389-406, 2025, DOI:10.32604/cmc.2024.059238 - 03 January 2025

Abstract Open networks and heterogeneous services in the Internet of Vehicles (IoV) can lead to security and privacy challenges. One key requirement for such systems is the preservation of user privacy, ensuring a seamless experience in driving, navigation, and communication. These privacy needs are influenced by various factors, such as data collected at different intervals, trip durations, and user interactions. To address this, the paper proposes a Support Vector Machine (SVM) model designed to process large amounts of aggregated data and recommend privacy-preserving measures. The model analyzes data based on user demands and interactions with service More >