Qun Song¹, Chao Gao¹, Han Wu¹, Zhiheng Rao¹, Huafeng Qin^1,*, Simon Fong^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-49, 2026, DOI:10.32604/cmc.2025.070918 - 09 December 2025

Abstract Metaheuristic algorithms, renowned for strong global search capabilities, are effective tools for solving complex optimization problems and show substantial potential in e-Health applications. This review provides a systematic overview of recent advancements in metaheuristic algorithms and highlights their applications in e-Health. We selected representative algorithms published between 2019 and 2024, and quantified their influence using an entropy-weighted method based on journal impact factors and citation counts. CThe Harris Hawks Optimizer (HHO) demonstrated the highest early citation impact. The study also examined applications in disease prediction models, clinical decision support, and intelligent health monitoring. Notably, the More >

Charlotte Olivia Namagembe, Mohamad Ibrahim, Md Arafatur Rahman^*, Prashant Pillai

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.070316 - 09 December 2025

Abstract The rapid proliferation of commercial unmanned aerial vehicles (UAVs) has revolutionized fields such as precision agriculture and disaster response. However, their heavy reliance on GPS navigation leaves them highly vulnerable to spoofing attacks, with potentially severe consequences. To mitigate this threat, we present a machine learning-driven framework for real-time GPS spoofing detection, designed with a balance of detection accuracy and computational efficiency. Our work is distinguished by the creation of a comprehensive dataset of 10,000 instances that integrates both simulated and real-world data, enabling robust and generalizable model development. A comprehensive evaluation of multiple classification More >

Ahmed Abdel-Wahab¹, Mohammad Alkhatib^2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-34, 2026, DOI:10.32604/cmc.2025.070010 - 09 December 2025

Abstract Deepfake is a sort of fake media made by advanced AI methods like Generative Adversarial Networks (GANs). Deepfake technology has many useful uses in education and entertainment, but it also raises a lot of ethical, social, and security issues, such as identity theft, the dissemination of false information, and privacy violations. This study seeks to provide a comprehensive analysis of several methods for identifying and circumventing Deepfakes, with a particular focus on image-based Deepfakes. There are three main types of detection methods: classical, machine learning (ML) and deep learning (DL)-based, and hybrid methods. There are… More >

Kirubavathi Ganapathiyappan¹, Heba G. Mohamed², Abhishek Yadav¹, Guru Akshya Chinnaswamy¹, Ateeq Ur Rehman^3,*, Habib Hamam^4,5,6,7

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-18, 2026, DOI:10.32604/cmc.2025.069951 - 09 December 2025

Abstract The escalating complexity of modern malware continues to undermine the effectiveness of traditional signature-based detection techniques, which are often unable to adapt to rapidly evolving attack patterns. To address these challenges, this study proposes X-MalNet, a lightweight Convolutional Neural Network (CNN) framework designed for static malware classification through image-based representations of binary executables. By converting malware binaries into grayscale images, the model extracts distinctive structural and texture-level features that signify malicious intent, thereby eliminating the dependence on manual feature engineering or dynamic behavioral analysis. Built upon a modified AlexNet architecture, X-MalNet employs transfer learning to… More >

Yeasul Kim¹, Chaeeun Won¹, Hwankuk Kim^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-28, 2026, DOI:10.32604/cmc.2025.069608 - 10 November 2025

Abstract With the increasing emphasis on personal information protection, encryption through security protocols has emerged as a critical requirement in data transmission and reception processes. Nevertheless, IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices, spanning a range of devices from non-encrypted ones to fully encrypted ones. Given the limited visibility into payloads in this context, this study investigates AI-based attack detection methods that leverage encrypted traffic metadata, eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices. Using the UNSW-NB15 and CICIoT-2023 dataset, encrypted and… More >

Puneetha B. H.^1,*, Manoj Kumar M. V.^2,*, Prashanth B. S.², Piyush Kumar Pareek³

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-33, 2026, DOI:10.32604/cmc.2025.067706 - 10 November 2025

Abstract Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational, organizational, or regulatory factors. These changes, referred to as incremental concept drift, gradually alter the behavior or structure of processes, making their detection and localization a challenging task. Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift, particularly from a control-flow perspective. The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs, with a… More >

Xingkang Jiang^*, Jing Tian, Yong Xu

Canadian Journal of Urology, Vol.32, No.6, pp. 539-540, 2025, DOI:10.32604/cju.2025.073363 - 30 December 2025

Abstract This article has no abstract. More >

Asgar Ali^1,*, Nayan Sardar², Poly Karmakar³, Sanatan Das⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3563-3626, 2025, DOI:10.32604/cmes.2025.074082 - 23 December 2025

Abstract Hybrid nanofluids have gained significant attention for their superior thermal and rheological characteristics, offering immense potential in energy conversion, biomedical transport, and electromagnetic flow control systems. Understanding their dynamic behavior under coupled magnetic, rotational, and reactive effects is crucial for the development of efficient thermal management technologies. This study develops a neuro-fuzzy computational framework to examine the dynamics of a reactive Cu–TiO₂–H₂O hybrid nanofluid flowing through a squarely elevated Riga tunnel. The governing model incorporates Hall and ion-slip effects, thermal radiation, and first-order chemical reactions under ramped thermo-solutal boundary conditions and rotational electromagnetic forces. Closed-form analytical… More >

Khulud Salem Alshudukhi^1,*, Sijjad Ali², Mamoona Humayun^3,*, Omar Alruwaili⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3029-3085, 2025, DOI:10.32604/cmes.2025.073705 - 23 December 2025

Abstract Problem: The integration of Artificial Intelligence (AI) into cybersecurity, while enhancing threat detection, is hampered by the “black box” nature of complex models, eroding trust, accountability, and regulatory compliance. Explainable AI (XAI) aims to resolve this opacity but introduces a critical new vulnerability: the adversarial exploitation of model explanations themselves. Gap: Current research lacks a comprehensive synthesis of this dual role of XAI in cybersecurity—as both a tool for transparency and a potential attack vector. There is a pressing need to systematically analyze the trade-offs between interpretability and security, evaluate defense mechanisms, and outline a… More >

Damodharan Palaniappan¹, Tan Kuan Tak², K. Vijayan³, Balajee Maram⁴, Pravin R Kshirsagar⁵, Naim Ahmad^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4107-4127, 2025, DOI:10.32604/cmes.2025.073387 - 23 December 2025

Abstract A phase-aware cross-modal framework is presented that synthesizes UWF_FA from non-invasive UWF_RI for diabetic retinopathy (DR) stratification. A curated cohort of 1198 patients (2915 UWF_RI and 17,854 UWF_FA images) with strict registration quality supports training across three angiographic phases (initial, mid, final). The generator is based on a modified pix2pixHD with an added Gradient Variance Loss to better preserve microvasculature, and is evaluated using MAE, PSNR, SSIM, and MS-SSIM on held-out pairs. Quantitatively, the mid phase achieves the lowest MAE (98.76 ± 42.67), while SSIM remains high across phases. Expert review shows substantial agreement (Cohen’s More >