Osamah Mohammed Alyasiri^1,2, Yu-N Cheah^1,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.078927 - 09 April 2026

Abstract Term weighting scheme and feature selection are two fundamental components in text classification (TC) systems, particularly in high-dimensional, multi-class, and imbalanced settings. Term weighting schemes aim to improve document representation by emphasizing discriminative terms across classes, while feature selection (FS) seeks to reduce dimensionality, eliminate irrelevant and redundant features, and enhance classification efficiency and effectiveness. However, most existing studies focus on FS independently of the term-weighting strategy used during document representation, thereby limiting the potential benefits of their interaction. This study addresses this gap by pursuing two main objectives. First, it employs an enhanced supervised… More >

Chengang Dong^1,2, Yongkang Ding², Jianwei Hu^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.079062 - 30 March 2026

Abstract Real-time multi-person pose estimation (MPE) built upon neural network architectures aims to simultaneously detect multiple human instances and regress joint coordinates in dynamic scenes. However, due to factors such as high model complexity and limited expression of keypoint information, both the efficiency and accuracy of real-time MPE remain to be improved. To mitigate the adverse impacts caused by the aforementioned issues, this work develops FSEM-Pose, a real-time MPE model rooted in the YOLOv10 framework. In detail, first, FSEM-Pose upgrades the backbone module of the baseline network by introducing the Feature Shuffling-Convolution (FS-Conv), which effectively reduces More >

M. Mohsin Raza^1,#, Muhammad Umair^1,#, Imran Arshad Choudhry¹, Muhammad Qasim¹, Muhammad Tahir Naseem^2,*, Mamoona Naveed Asghar³, Daniel Gavilanes^4,5,6,7, Manuel Masias Vergara^4,8,9, Imran Ashraf^10,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2025.074164 - 30 March 2026

Abstract Over the past decade, the landscape of cybersecurity has been increasingly shaped by the growing sophistication and frequency of malware attacks. Traditional detection techniques, while still in use, often fall short when confronted with modern threats that use advanced evasion strategies. This systematic review critically examines recent developments in malware detection, with a particular emphasis on the role of artificial intelligence (AI) and machine learning (ML) in enhancing detection capabilities. Drawing on literature published between 2019 and 2025, this study reviews 105 peer-reviewed contributions from prominent digital libraries including IEEE Xplore, SpringerLink, ScienceDirect, and ACM… More >

Sumbul Azeem¹, Shazia Javed^1,*, Farheen Ibraheem², Uzma Bashir¹, Nazar Waheed³, Khursheed Aurangzeb⁴

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.071977 - 12 March 2026

Abstract Data serves as the foundation for training and testing machine learning and artificial intelligence models. The most fundamental part of data is its attributes or features. The feature set size changes from one dataset to another. Only the relevant features contribute meaningfully to classification accuracy. The presence of irrelevant features reduces the system’s effectiveness. Classification performance often deteriorates on high-dimensional datasets due to the large search space. Thus, one of the significant obstacles affecting the performance of the learning process in the majority of machine learning and data mining techniques is the dimensionality of the… 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 >

Ahmed Mohammed Awad Mohammed^1,*, Omayma Husain², Mosab Hamdan³, Abdalmomen Mohammed⁴, Abdullah Ansari^5,6,7, Atef Badr¹, Abubakar Elsafi⁸, Abubakr Siddig⁹

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.075720 - 26 February 2026

Abstract Unconfined Compressive Strength (UCS) is a key parameter for the assessment of the stability and performance of stabilized soils, yet traditional laboratory testing is both time and resource intensive. In this study, an interpretable machine learning approach to UCS prediction is presented, pairing five models (Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), CatBoost, and K-Nearest Neighbors (KNN)) with SHapley Additive exPlanations (SHAP) for enhanced interpretability and to guide feature removal. A complete dataset of 12 geotechnical and chemical parameters, i.e., Atterberg limits, compaction properties, stabilizer chemistry, dosage, curing time, was used to… 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 >

Hyunki Lim^*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074138 - 10 February 2026

Abstract High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements. In particular, in a multi-label environment, higher complexity is required as much as the number of labels. Moreover, an optimization problem that fully considers all dependencies between features and labels is difficult to solve. In this study, we propose a novel regression-based multi-label feature selection method that integrates mutual information to better exploit the underlying data structure. By incorporating mutual information into the regression formulation, the model captures not only linear relationships but also complex non-linear dependencies. The proposed… More >

Ahmad Junaid¹, Abid Iqbal^2,*, Abuzar Khan¹, Ghassan Husnain^1,*, Abdul-Rahim Ahmad³, Mohammed Al-Naeem⁴

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073189 - 10 February 2026

Abstract Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness. This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail. It uses the NASA CMAPSS dataset, which has over 200,000 engine cycles from 260 engines. The process begins with systematic preprocessing, which includes imputation, outlier removal, scaling, and labelling of the remaining useful life. Dimensionality is reduced using a hybrid selection method that combines variance filtering, recursive elimination, and gradient-boosted importance scores, yielding a stable set of… More >

Fei Yu^1,2,3,*, Zhenya Diao^1,2, Hongrun Wu^1,2,*, Yingpin Chen^1,3, Xuewen Xia^1,2, Yuanxiang Li^2,3,4

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072593 - 10 February 2026

Abstract Feature selection serves as a critical preprocessing step in machine learning, focusing on identifying and preserving the most relevant features to improve the efficiency and performance of classification algorithms. Particle Swarm Optimization has demonstrated significant potential in addressing feature selection challenges. However, there are inherent limitations in Particle Swarm Optimization, such as the delicate balance between exploration and exploitation, susceptibility to local optima, and suboptimal convergence rates, hinder its performance. To tackle these issues, this study introduces a novel Leveraged Opposition-Based Learning method within Fitness Landscape Particle Swarm Optimization, tailored for wrapper-based feature selection. The… More >