Saira Ali Bhatti¹, Maqbool Khan^2,*, Arshad Ahmad³, Muhammad Shahid Anwar⁴, Leila Jamel⁵, Aisha M. Mashraqi⁶, Wadee Alhalabi^7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.074688 - 30 March 2026

Abstract Medical imaging is essential in modern health care, allowing accurate diagnosis and effective treatment planning. These images, however, often demonstrate low contrast, noise, and brightness distortion that reduce their diagnostic reliability. This review presents a structured and comprehensive analysis of advanced histogram equalization (HE)-based techniques for medical image enhancement. Our review methodology encompasses: (1) classical HE approaches and related limitations in medical domains; (2) adaptive schemes like Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogrma Equalization (CLAHE) and their advance variants; (3) brightness-preserving schemes like BBHE and MMBEBHE and related algorithms; (4) dynamic and More > Graphic Abstract

Yiming Yu¹, Yunfei Guo², Junchen Liu³, Yiping Sun⁴, Junliang Du^5,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074959 - 12 March 2026

Abstract Graph neural networks (GNNs) have shown notable success in identifying security vulnerabilities within Ethereum smart contracts by capturing structural relationships encoded in control- and data-flow graphs. Despite their effectiveness, most GNN-based vulnerability detectors operate as black boxes, making their decisions difficult to interpret and thus less suitable for critical security auditing. The information bottleneck (IB) principle provides a theoretical framework for isolating task-relevant graph components. However, existing IB-based implementations often encounter unstable optimization and limited understanding of code semantics. To address these issues, we introduce ContractGIB, an interpretable graph information bottleneck framework for function-level vulnerability More >

Yu Zhou¹, Jiawei Tian², Kyungtae Kang^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.077373 - 26 February 2026

Abstract Arrhythmias are a frequently occurring phenomenon in clinical practice, but how to accurately distinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conducting ECG-based studies. From a review of existing studies, two main factors appear to contribute to this problem: the uneven distribution of arrhythmia classes and the limited expressiveness of features learned by current models. To overcome these limitations, this study proposes a dual-path multimodal framework, termed DM-EHC (Dual-Path Multimodal ECG Heartbeat Classifier), for ECG-based heartbeat classification. The proposed framework links 1D ECG temporal features with 2D time–frequency More >

Shengkang Zhang¹, Yong Jin^2,*, Soon Poh Yap^1,*, Haoyun Fan¹, Shiyuan Li³, Ahmed El-Shafie⁴, Zainah Ibrahim¹, Amr El-Dieb⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075351 - 29 January 2026

Abstract Concrete-filled steel tubes (CFST) are widely utilized in civil engineering due to their superior load-bearing capacity, ductility, and seismic resistance. However, existing design codes, such as AISC and Eurocode 4, tend to be excessively conservative as they fail to account for the composite action between the steel tube and the concrete core. To address this limitation, this study proposes a hybrid model that integrates XGBoost with the Pied Kingfisher Optimizer (PKO), a nature-inspired algorithm, to enhance the accuracy of shear strength prediction for CFST columns. Additionally, quantile regression is employed to construct prediction intervals for… More >

Jinqian Li¹, Jianhong Wu^2,*

International Journal of Mental Health Promotion, Vol.27, No.12, pp. 1883-1896, 2025, DOI:10.32604/ijmhp.2025.071306 - 31 December 2025

Abstract Background: Platform algorithms driving content presentation are profoundly shaping the experience of younger users. While prior research has examined anxiety stemming from young adults’ social media usage, the link between upward social comparison and anxiety remains unclear. This study aims to investigate the mediating role of upward social comparison in this relationship and determine the moderating role of psychological resilience. Methods: A cross-sectional survey was conducted among 562 young Chinese adults aged 18–35 (53% female). Data were collected via an online questionnaire employing validated measurement instruments, including scales for social media usage patterns, upward comparator behaviour… More >

Zhuoxi Feng^#,1, Jinyang Liu^#,2, Zihao Wu¹, Ziran Geng¹, Zhimin Liu^1,*

Congenital Heart Disease, Vol.20, No.5, pp. 625-636, 2025, DOI:10.32604/chd.2025.069214 - 30 November 2025

Abstract Congenitally corrected transposition of the great arteries (CCTGA) is a rare congenital heart disease characterized by atrioventricular, ventriculoarterial, and conduction system discordance, commonly accompanied by atrioventricular block (AVB). Pacing in patients with CCTGA and AVB (both pediatric and adult) poses challenges in strategy selection, procedural complexity, and clinical decision-making due to limited evidence. Conventional morphological left ventricular pacing is widely adopted but may induce ventricular dyssynchrony, heart failure, and tricuspid valve dysfunction. While cardiac resynchronization therapy serves as an upgrade for pacing-induced cardiomyopathy and heart failure, its application may be limited by coronary sinus anatomical… More >

Abdullrahman A. Al-Shamma’a¹, Hassan M. Hussein Farh^1,*, Ridwan Taiwo², Al-Wesabi Ibrahim³, Abdulrhman Alshaabani¹, Saad Mekhilef ⁴, Mohamed A. Mohamed^5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1303-1347, 2025, DOI:10.32604/cmes.2025.071302 - 26 November 2025

Abstract Optimal sizing and allocation of distributed generators (DGs) have become essential computational challenges in improving the performance, efficiency, and reliability of electrical distribution networks. Despite extensive research, existing approaches often face algorithmic limitations such as slow convergence, premature stagnation in local minima, or suboptimal accuracy in determining optimal DG placement and capacity. This study presents a comprehensive scientometric and systematic review of global research focused on computer-based modelling and algorithmic optimization for renewable DG sizing and placement. It integrates both quantitative and qualitative analyses of the scholarly landscape, mapping influential research domains, co-authorship structures, the More >

Hamed Alqahtani¹, Gulshan Kumar^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1439-1479, 2025, DOI:10.32604/cmes.2025.067738 - 31 August 2025

Abstract Domain Generation Algorithms (DGAs) continue to pose a significant threat in modern malware infrastructures by enabling resilient and evasive communication with Command and Control (C&C) servers. Traditional detection methods—rooted in statistical heuristics, feature engineering, and shallow machine learning—struggle to adapt to the increasing sophistication, linguistic mimicry, and adversarial variability of DGA variants. The emergence of Large Language Models (LLMs) marks a transformative shift in this landscape. Leveraging deep contextual understanding, semantic generalization, and few-shot learning capabilities, LLMs such as BERT, GPT, and T5 have shown promising results in detecting both character-based and dictionary-based DGAs, including… More >

Chunhong Guo¹, Huifang Liu², Jingfu Guo^3,*

Journal of Psychology in Africa, Vol.35, No.3, pp. 411-418, 2025, DOI:10.32604/jpa.2025.065763 - 31 July 2025

Abstract We explored the effects of algorithmic opacity on employees’ playing dumb and evasive hiding rather than rationalized hiding. We examined the mediating role of job insecurity and the moderating role of employee-AI collaboration. Participants were 421 full-time employees (female = 46.32%, junior employees = 31.83%) from a variety of organizations and industries that interact with AI. Employees filled out data on algorithm opacity, job insecurity, knowledge hiding, employee-AI collaboration, and control variables. The results of the structural equation modeling indicated that algorithm opacity exacerbated employees’ job insecurity, and job insecurity mediated between algorithm opacity and More >

Hossana Maghiri Sufficient^*, Abdulazeez Murtala Mohammed, Bashir Danjuma

Journal of Cyber Security, Vol.7, pp. 239-253, 2025, DOI:10.32604/jcs.2025.066312 - 14 July 2025

Abstract The rapid integration of artificial intelligence (AI) into ethical hacking practices has transformed vulnerability discovery and threat mitigation; however, it raises pressing ethical questions regarding responsibility, justice, and privacy. This paper presents a PRISMA-guided systematic review of twelve peer-reviewed studies published between 2015 and March 2024, supplemented by Braun and Clarke’s thematic analysis, to map four core challenges: (1) autonomy and human oversight, (2) algorithmic bias and mitigation strategies, (3) data privacy preservation mechanisms, and (4) limitations of General Data Protection Regulation (GDPR) and the European Union’s AI Act in addressing AI-specific risks, alongside the… More >