Shengjia Chang, Baojiang Cui^*, Shaocong Feng

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3805-3827, 2025, DOI:10.32604/cmes.2025.067756 - 30 September 2025

Abstract With the rapid advancement of mobile communication networks, key technologies such as Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV) have enhanced the quality of service for 5G users but have also significantly increased the complexity of network threats. Traditional static defense mechanisms are inadequate for addressing the dynamic and heterogeneous nature of modern attack vectors. To overcome these challenges, this paper presents a novel algorithmic framework, SD-5G, designed for high-precision intrusion detection in 5G environments. SD-5G adopts a three-stage architecture comprising traffic feature extraction, elastic representation, and adaptive classification. Specifically, an enhanced Concrete… 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 >

Md Monir Ahammod Bin Atique^1,#, Md Ilias Bappi^1,#, Kwanghoon Choi^1,*, Kyungbaek Kim^1,*, Md Abul Ala Walid², Pranta Kumar Sarkar³

Computer Systems Science and Engineering, Vol.49, pp. 455-479, 2025, DOI:10.32604/csse.2025.069213 - 19 August 2025

Abstract Employee turnover presents considerable challenges for organizations, leading to increased recruitment costs and disruptions in ongoing operations. High voluntary attrition rates can result in substantial financial losses, making it essential for Human Resource (HR) departments to prioritize turnover reduction. In this context, Artificial Intelligence (AI) has emerged as a vital tool in strengthening business strategies and people management. This paper incorporates two new representative features, introducing three types of feature engineering to enhance the analysis of employee turnover in the IBM HR Analytics dataset. Key Machine Learning (ML) techniques were subsequently employed in this work,… More >

Farah Mohammad^1,2,*, Saad Al-Ahmadi^1,3, Jalal Al-Muhtadi^1,3

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5995-6014, 2025, DOI:10.32604/cmc.2025.063046 - 30 July 2025

Abstract Malware continues to pose a significant threat to cybersecurity, with new advanced infections that go beyond traditional detection. Limitations in existing systems include high false-positive rates, slow system response times, and inability to respond quickly to new malware forms. To overcome these challenges, this paper proposes OMD-RAS: Implementing Malware Detection in an Optimized Way through Real-Time and Adaptive Security as an extensive approach, hoping to get good results towards better malware threat detection and remediation. The significant steps in the model are data collection followed by comprehensive preprocessing consisting of feature engineering and normalization. Static… More >

Ruhul Amin¹, Md. Jamil Khan¹, Tonway Deb Nath¹, Md. Shamim Reza², Jungpil Shin^3,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3907-3919, 2025, DOI:10.32604/cmc.2025.061645 - 06 March 2025

Abstract Heart disease includes a multiplicity of medical conditions that affect the structure, blood vessels, and general operation of the heart. Numerous researchers have made progress in correcting and predicting early heart disease, but more remains to be accomplished. The diagnostic accuracy of many current studies is inadequate due to the attempt to predict patients with heart disease using traditional approaches. By using data fusion from several regions of the country, we intend to increase the accuracy of heart disease prediction. A statistical approach that promotes insights triggered by feature interactions to reveal the intricate pattern… More >

Bin Sun¹, Yinuo Wang¹, Tao Shen^1,*, Lu Zhang¹, Renkang Geng²

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4219-4236, 2025, DOI:10.32604/cmc.2025.060567 - 06 March 2025

Abstract Traffic datasets exhibit complex spatiotemporal characteristics, including significant fluctuations in traffic volume and intricate periodical patterns, which pose substantial challenges for the accurate forecasting and effective management of traffic conditions. Traditional forecasting models often struggle to adequately capture these complexities, leading to suboptimal predictive performance. While neural networks excel at modeling intricate and nonlinear data structures, they are also highly susceptible to overfitting, resulting in inefficient use of computational resources and decreased model generalization. This paper introduces a novel heuristic feature extraction method that synergistically combines the strengths of non-neural network algorithms with neural networks… More >

Mohamed A.G. Hazber^1,*, Ebrahim Mohammed Senan^2,3, Hezam Saud Alrashidi¹

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3229-3254, 2025, DOI:10.32604/cmes.2025.062302 - 03 March 2025

Abstract Hepatitis is an infection that affects the liver through contaminated foods or blood transfusions, and it has many types, from normal to serious. Hepatitis is diagnosed through many blood tests and factors; Artificial Intelligence (AI) techniques have played an important role in early diagnosis and help physicians make decisions. This study evaluated the performance of Machine Learning (ML) algorithms on the hepatitis data set. The dataset contains missing values that have been processed and outliers removed. The dataset was counterbalanced by the Synthetic Minority Over-sampling Technique (SMOTE). The features of the data set were processed… More >

Varada Rajkumar Kukkala¹, Surapaneni Phani Praveen², Naga Satya Koti Mani Kumar Tirumanadham³, Parvathaneni Naga Srinivasu^4,5,*

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1085-1112, 2024, DOI:10.32604/csse.2024.053603 - 13 September 2024

Abstract This paper investigates the application of machine learning to develop a response model to cardiovascular problems and the use of AdaBoost which incorporates an application of Outlier Detection methodologies namely; Z-Score incorporated with Grey Wolf Optimization (GWO) as well as Interquartile Range (IQR) coupled with Ant Colony Optimization (ACO). Using a performance index, it is shown that when compared with the Z-Score and GWO with AdaBoost, the IQR and ACO, with AdaBoost are not very accurate (89.0% vs. 86.0%) and less discriminative (Area Under the Curve (AUC) score of 93.0% vs. 91.0%). The Z-Score and GWO… More >

Mahmoud E. Farfoura^1,*, Ahmad Alkhatib¹, Deema Mohammed Alsekait^2,*, Mohammad Alshinwan^3,7, Sahar A. El-Rahman⁴, Didi Rosiyadi⁵, Diaa Salama AbdElminaam^6,7

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4833-4857, 2024, DOI:10.32604/cmc.2024.054849 - 12 September 2024

Abstract The article describes a new method for malware classification, based on a Machine Learning (ML) model architecture specifically designed for malware detection, enabling real-time and accurate malware identification. Using an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique (IFDRT), the authors have significantly reduced the feature space while retaining critical information necessary for malware classification. This technique optimizes the model’s performance and reduces computational requirements. The proposed method is demonstrated by applying it to the BODMAS malware dataset, which contains 57,293 malware samples and 77,142 benign samples, each with a 2381-feature… More >

Kaveh Heidary^1,*, Venkata Atluri¹, John Bland²

Journal of Cyber Security, Vol.6, pp. 69-87, 2024, DOI:10.32604/jcs.2024.051196 - 28 August 2024

Abstract This paper investigates the impact of reducing feature-vector dimensionality on the performance of machine learning (ML) models. Dimensionality reduction and feature selection techniques can improve computational efficiency, accuracy, robustness, transparency, and interpretability of ML models. In high-dimensional data, where features outnumber training instances, redundant or irrelevant features introduce noise, hindering model generalization and accuracy. This study explores the effects of dimensionality reduction methods on binary classifier performance using network traffic data for cybersecurity applications. The paper examines how dimensionality reduction techniques influence classifier operation and performance across diverse performance metrics for seven ML models. Four… More >