Hajer Hafaiedh^1,2, Adel Mahjoub³, Yahia Saoudi⁴, Anouar Benamor², Okba Taouali^5,*, Kamel Zidi⁶, Wad Ghaban⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 571-596, 2025, DOI:10.32604/cmes.2025.071642 - 30 October 2025

Abstract Several optimization methods, such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), are used to select the most suitable Static Synchronous Compensator (STATCOM) technology for the optimal operation of the power system, as well as to determine its optimal location and size to minimize power losses. An IEEE 14 bus system, integrating three wind turbines based on Squirrel Cage Induction Generators (SCIGs), is used to test the applicability of the proposed algorithms. The results demonstrate that these algorithms are capable of selecting the most appropriate technology while optimally sizing and locating the STATCOM to More >

Seungyeon Baek^1,#, Jueun Jeon^2,#, Byeonghui Jeong¹, Young-Sik Jeong^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1213-1236, 2025, DOI:10.32604/cmes.2025.070679 - 30 October 2025

Abstract With the advent of the sixth-generation wireless technology, the importance of using artificial intelligence of things (AIoT) devices is increasing to enhance efficiency. As massive volumes of data are collected and stored in these AIoT environments, each device becomes a potential attack target, leading to increased security vulnerabilities. Therefore, intrusion detection studies have been conducted to detect malicious network traffic. However, existing studies have been biased toward conducting in-depth analyses of individual packets to improve accuracy or applying flow-based statistical information to ensure real-time performance. Effectively responding to complex and multifaceted threats in large-scale AIoT… More >

Nimra Bari¹, Tahir Saleem², Munam Shah³, Abdulmohsen Algarni⁴, Asma Patel^5,*, Insaf Ullah^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1167-1187, 2025, DOI:10.32604/cmes.2025.070311 - 30 October 2025

Abstract Today, phishing is an online attack designed to obtain sensitive information such as credit card and bank account numbers, passwords, and usernames. We can find several anti-phishing solutions, such as heuristic detection, virtual similarity detection, black and white lists, and machine learning (ML). However, phishing attempts remain a problem, and establishing an effective anti-phishing strategy is a work in progress. Furthermore, while most anti-phishing solutions achieve the highest levels of accuracy on a given dataset, their methods suffer from an increased number of false positives. These methods are ineffective against zero-hour attacks. Phishing sites with… More >

Hui Qi¹, Jingyi Lian², Congjun Rao^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 995-1028, 2025, DOI:10.32604/cmes.2025.069722 - 30 October 2025

Abstract For the past few years, the prevalence of cardiovascular disease has been showing a year-on-year increase, with a death rate of 2/5. Coronary heart disease (CHD) rates have increased 41% since 1990, which is the number one disease endangering human health in the world today. The risk indicators of CHD are complicated, so selecting effective methods to screen the risk characteristics can make the risk prediction more efficient. In this paper, we present a comprehensive analysis of CHD risk indicators from both data and algorithmic levels, propose a method for CHD risk indicator identification based… More >

Bibhuprasad Sahu¹, Amrutanshu Panigrahi², Abhilash Pati², Ashis Kumar Pati³, Janmejaya Mishra⁴, Naim Ahmad^5,*, Salman Arafath Mohammed⁶, Saurav Mallik^7,8,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1029-1054, 2025, DOI:10.32604/cmes.2025.069618 - 30 October 2025

Abstract Metaheuristic optimization methods are iterative search processes that aim to efficiently solve complex optimization problems. These basically find the solution space very efficiently, often without utilizing the gradient information, and are inspired by the bio-inspired and socially motivated heuristics. Metaheuristic optimization algorithms are increasingly applied to complex feature selection problems in high-dimensional medical datasets. Among these, Teaching-Learning-Based optimization (TLBO) has proven effective for continuous design tasks by balancing exploration and exploitation phases. However, its binary version (BTLBO) suffers from limited exploitation ability, often converging prematurely or getting trapped in local optima, particularly when applied to… More >

Muhammad Sajid Farooq¹, Muhammad Saleem², M.A. Khan^3,4, Muhammad Farrukh Khan⁵, Shahan Yamin Siddiqui⁶, Muhammad Shoukat Aslam⁷, Khan M. Adnan^8,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5183-5206, 2025, DOI:10.32604/cmc.2025.069641 - 23 October 2025

Abstract The rapid evolution of smart cities through IoT, cloud computing, and connected infrastructures has significantly enhanced sectors such as transportation, healthcare, energy, and public safety, but also increased exposure to sophisticated cyber threats. The diversity of devices, high data volumes, and real-time operational demands complicate security, requiring not just robust intrusion detection but also effective feature selection for relevance and scalability. Traditional Machine Learning (ML) based Intrusion Detection System (IDS) improves detection but often lacks interpretability, limiting stakeholder trust and timely responses. Moreover, centralized feature selection in conventional IDS compromises data privacy and fails to… More >

Thomas Hanne^1,*, Mohammad Jahani Moghaddam²

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4203-4236, 2025, DOI:10.32604/cmc.2025.068087 - 23 October 2025

Abstract Multi-Objective Evolutionary Algorithms (MOEAs) have significantly advanced the domain of Multi-Objective Optimization (MOO), facilitating solutions for complex problems with multiple conflicting objectives. This review explores the historical development of MOEAs, beginning with foundational concepts in multi-objective optimization, basic types of MOEAs, and the evolution of Pareto-based selection and niching methods. Further advancements, including decom-position-based approaches and hybrid algorithms, are discussed. Applications are analyzed in established domains such as engineering and economics, as well as in emerging fields like advanced analytics and machine learning. The significance of MOEAs in addressing real-world problems is emphasized, highlighting their More >

Sagvan Y. Musa^1,2, Zanyar A. Ameen^3,*, Wafa Alagal⁴, Baravan A. Asaad^5,6

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3493-3529, 2025, DOI:10.32604/cmes.2025.070019 - 30 September 2025

Abstract Urban transportation planning involves evaluating multiple conflicting criteria such as accessibility, cost-effectiveness, and environmental impact, often under uncertainty and incomplete information. These complex decisions require input from various stakeholders, including planners, policymakers, engineers, and community representatives, whose opinions may differ or contradict. Traditional decision-making approaches struggle to effectively handle such bipolar and multivalued expert evaluations. To address these challenges, we propose a novel decision-making framework based on Pythagorean fuzzy N-bipolar soft expert sets. This model allows experts to express both positive and negative opinions on a multinary scale, capturing nuanced judgments with higher accuracy. It… More >

Naif Almusallam^1,*, Junaid Qayyum^2,3

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3653-3687, 2025, DOI:10.32604/cmc.2025.067220 - 23 September 2025

Abstract This paper proposes a novel hybrid fraud detection framework that integrates multi-stage feature selection, unsupervised clustering, and ensemble learning to improve classification performance in financial transaction monitoring systems. The framework is structured into three core layers: (1) feature selection using Recursive Feature Elimination (RFE), Principal Component Analysis (PCA), and Mutual Information (MI) to reduce dimensionality and enhance input relevance; (2) anomaly detection through unsupervised clustering using K-Means, Density-Based Spatial Clustering (DBSCAN), and Hierarchical Clustering to flag suspicious patterns in unlabeled data; and (3) final classification using a voting-based hybrid ensemble of Support Vector Machine (SVM),… More >

Zehui Qi, Sixing Wu^*, Jianbin Li

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3739-3766, 2025, DOI:10.32604/cmc.2025.067180 - 23 September 2025

Abstract False Data Injection Attacks (FDIAs) pose a critical security threat to modern power grids, corrupting state estimation and enabling malicious control actions that can lead to severe consequences, including cascading failures, large-scale blackouts, and significant economic losses. While detecting attacks is important, accurately localizing compromised nodes or measurements is even more critical, as it enables timely mitigation, targeted response, and enhanced system resilience beyond what detection alone can offer. Existing research typically models topological features using fixed structures, which can introduce irrelevant information and affect the effectiveness of feature extraction. To address this limitation, this… More >