Naveed Ahmad¹, Muhammad Kaleem², Mourad Elloumi³, Muhammad Azhar Mushtaq², Ahlem Fatnassi⁴, Mohd Fazil⁵, Anas Bilal^6,*, Abdulbasit A. Darem^7,4

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074902 - 29 January 2026

Abstract Network-on-Chip (NoC) systems are progressively deployed in connecting massively parallel megacore systems in the new computing architecture. As a result, application mapping has become an important aspect of performance and scalability, as current trends require the distribution of computation across network nodes/points. In this paper, we survey a large number of mapping and scheduling techniques designed for NoC architectures. This time, we concentrated on 3D systems. We take a systematic literature review approach to analyze existing methods across static, dynamic, hybrid, and machine-learning-based approaches, alongside preliminary AI-based dynamic models in recent works. We classify them… More >

Hanifah Rahmi Fajrin^1,2, Se Dong Min^1,3,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072609 - 12 January 2026

Abstract Accurate segmentation of breast cancer in mammogram images plays a critical role in early diagnosis and treatment planning. As research in this domain continues to expand, various segmentation techniques have been proposed across classical image processing, machine learning (ML), deep learning (DL), and hybrid/ensemble models. This study conducts a systematic literature review using the PRISMA methodology, analyzing 57 selected articles to explore how these methods have evolved and been applied. The review highlights the strengths and limitations of each approach, identifies commonly used public datasets, and observes emerging trends in model integration and clinical relevance. More >

Muhammad Asim^1,*, Abdelhamied A. Ateya¹, Mudasir Ahmad Wani^1,2, Gauhar Ali¹, Mohammed ElAffendi¹, Ahmed A. Abd El-Latif¹, Reshma Siyal³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070684 - 12 January 2026

Abstract The growing developments in 5G and 6G wireless communications have revolutionized communications technologies, providing faster speeds with reduced latency and improved connectivity to users. However, it raises significant security challenges, including impersonation threats, data manipulation, distributed denial of service (DDoS) attacks, and privacy breaches. Traditional security measures are inadequate due to the decentralized and dynamic nature of next-generation networks. This survey provides a comprehensive review of how Federated Learning (FL), Blockchain, and Digital Twin (DT) technologies can collectively enhance the security of 5G and 6G systems. Blockchain offers decentralized, immutable, and transparent mechanisms for securing More >

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 >