Jeongeun Ryu¹, Riyeong Kim², Soomin Lee¹, Sumin Kim¹, Hyunwoo Choi^1,2, Seongmin Kim^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.074871 - 12 January 2026

Abstract As containerized environments become increasingly prevalent in cloud-native infrastructures, the need for effective monitoring and detection of malicious behaviors has become critical. Malicious containers pose significant risks by exploiting shared host resources, enabling privilege escalation, or launching large-scale attacks such as cryptomining and botnet activities. Therefore, developing accurate and efficient detection mechanisms is essential for ensuring the security and stability of containerized systems. To this end, we propose a hybrid detection framework that leverages the extended Berkeley Packet Filter (eBPF) to monitor container activities directly within the Linux kernel. The framework simultaneously collects flow-based network… More >

Faisal Mahmud¹, Fahim Mahmud², Rashedur M. Rahman^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073519 - 12 January 2026

Abstract Federated Learning (FL) has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data, making it suitable for privacy-sensitive applications such as healthcare, finance, and smart systems. As the field continues to evolve, the research field has become more complex and scattered, covering different system designs, training methods, and privacy techniques. This survey is organized around the three core challenges: how the data is distributed, how models are synchronized, and how to defend against attacks. It provides a structured and up-to-date review of… More >

Xinli Hao¹, Qingyuan Gong², Yang Chen^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073155 - 12 January 2026

Abstract Topological information is very important for understanding different types of online web services, in particular, for online social networks (OSNs). People leverage such information for various applications, such as social relationship modeling, community detection, user profiling, and user behavior prediction. However, the leak of such information will also pose severe challenges for user privacy preserving due to its usefulness in characterizing users. Large-scale web crawling-based information probing is a representative way for obtaining topological information of online web services. In this paper, we explore how to defend against topological information probing for online web services,… More >

Anis Zarrad¹, Thomas Armstrong², Jaber Jemai^3,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072768 - 12 January 2026

Abstract Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability. While prioritization selects the most relevant test cases for optimal coverage, ranking further refines their execution order to detect critical faults earlier. This study investigates machine learning techniques to enhance both prioritization and ranking, contributing to more effective and efficient testing processes. We first employ advanced feature engineering alongside ensemble models, including Gradient Boosted, Support Vector Machines, Random Forests, and Naive Bayes classifiers to optimize test case prioritization, achieving an accuracy score of 0.98847… More >

Tao Zhang^1,*, Hailun Wang¹, Tianyue Wang¹, Tian Tian²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072696 - 12 January 2026

Abstract The critical components of gas turbines suffer from prolonged exposure to factors such as thermal oxidation, mechanical wear, and airflow disturbances during prolonged operation. These conditions can lead to a series of issues, including mechanical faults, air path malfunctions, and combustion irregularities. Traditional model-based approaches face inherent limitations due to their inability to handle nonlinear problems, natural factors, measurement uncertainties, fault coupling, and implementation challenges. The development of artificial intelligence algorithms has provided an effective solution to these issues, sparking extensive research into data-driven fault diagnosis methodologies. The review mechanism involved searching IEEE Xplore, ScienceDirect,… More >

Afnan Alhindi^*, Saad Al-Ahmadi, Mohamed Maher Ben Ismail

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072625 - 12 January 2026

Abstract Split Learning (SL) has been promoted as a promising collaborative machine learning technique designed to address data privacy and resource efficiency. Specifically, neural networks are divided into client and server sub-networks in order to mitigate the exposure of sensitive data and reduce the overhead on client devices, thereby making SL particularly suitable for resource-constrained devices. Although SL prevents the direct transmission of raw data, it does not alleviate entirely the risk of privacy breaches. In fact, the data intermediately transmitted to the server sub-model may include patterns or information that could reveal sensitive data. Moreover,… 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 >

Tahir Mahmood^1,*, Muhammad Waseem Ashraf^1,*, Shahzadi Tayyaba², Muhammad Munir³, Babiker M. A. Abdel-Banat³, Hassan Ali Dinar³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072436 - 12 January 2026

Abstract Artificial intelligence (AI) based models have been used to predict the structural, optical, mechanical, and electrochemical properties of zinc oxide/graphene oxide nanocomposites. Machine learning (ML) models such as Artificial Neural Networks (ANN), Support Vector Regression (SVR), Multilayer Perceptron (MLP), and hybrid, along with fuzzy logic tools, were applied to predict the different properties like wavelength at maximum intensity (444 nm), crystallite size (17.50 nm), and optical bandgap (2.85 eV). While some other properties, such as energy density, power density, and charge transfer resistance, were also predicted with the help of datasets of 1000 (80:20). In… More >

Shuangqing Song¹, Yuan Chen², Xuguang Hu¹, Juwei Zhang^1,3,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072392 - 12 January 2026

Abstract In multi-domain neural machine translation tasks, the disparity in data distribution between domains poses significant challenges in distinguishing domain features and sharing parameters across domains. This paper proposes a Transformer-based multi-domain-aware mixture of experts model. To address the problem of domain feature differentiation, a mixture of experts (MoE) is introduced into attention to enhance the domain perception ability of the model, thereby improving the domain feature differentiation. To address the trade-off between domain feature distinction and cross-domain parameter sharing, we propose a domain-aware mixture of experts (DMoE). A domain-aware gating mechanism is introduced within the… More >

Mohammed Alnusayri¹, Ghulam Mujtaba², Nouf Abdullah Almujally³, Shuoa S. Aitarbi⁴, Asaad Algarni⁵, Ahmad Jalal^2,6, Jeongmin Park^7,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071804 - 12 January 2026

Abstract This paper presents a unified Unmanned Aerial Vehicle-based (UAV-based) traffic monitoring framework that integrates vehicle detection, tracking, counting, motion prediction, and classification in a modular and co-optimized pipeline. Unlike prior works that address these tasks in isolation, our approach combines You Only Look Once (YOLO) v10 detection, ByteTrack tracking, optical-flow density estimation, Long Short-Term Memory-based (LSTM-based) trajectory forecasting, and hybrid Speeded-Up Robust Feature (SURF) + Gray-Level Co-occurrence Matrix (GLCM) feature engineering with VGG16 classification. Upon the validation across datasets (UAVDT and UAVID) our framework achieved a detection accuracy of 94.2%, and 92.3% detection accuracy when More >