Danping Niu¹, Yuan Ping^1,*, Chun Guo², Xiaojun Wang³, Bin Hao⁴

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073630 - 12 January 2026

Abstract With the increasing complexity of malware attack techniques, traditional detection methods face significant challenges, such as privacy preservation, data heterogeneity, and lacking category information. To address these issues, we propose Federated Dynamic Prototype Learning (FedDPL) for malware classification by integrating Federated Learning with a specifically designed K-means. Under the Federated Learning framework, model training occurs locally without data sharing, effectively protecting user data privacy and preventing the leakage of sensitive information. Furthermore, to tackle the challenges of data heterogeneity and the lack of category information, FedDPL introduces a dynamic prototype learning mechanism, which adaptively adjusts the More >

Hang Wen^1,2, Kai Zeng^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072909 - 12 January 2026

Abstract Federated learning often experiences slow and unstable convergence due to edge-side data heterogeneity. This problem becomes more severe when edge participation rate is low, as the information collected from different edge devices varies significantly. As a result, communication overhead increases, which further slows down the convergence process. To address this challenge, we propose a simple yet effective federated learning framework that improves consistency among edge devices. The core idea is clusters the lookahead gradients collected from edge devices on the cloud server to obtain personalized momentum for steering local updates. In parallel, a global momentum… More > Graphic Abstract

Pham Huy Thong¹, Hoang Thi Canh^2,3,*, Nguyen Tuan Huy⁴, Nguyen Long Giang^1,*, Luong Thi Hong Lan⁴

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071776 - 12 January 2026

Abstract Satellite image segmentation plays a crucial role in remote sensing, supporting applications such as environmental monitoring, land use analysis, and disaster management. However, traditional segmentation methods often rely on large amounts of labeled data, which are costly and time-consuming to obtain, especially in large-scale or dynamic environments. To address this challenge, we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering (SS-MPFC) algorithm, which improves segmentation accuracy and robustness, particularly in complex and uncertain remote sensing scenarios. SS-MPFC unifies three paradigms: semi-supervised learning, multi-view clustering, and picture fuzzy set theory. This integration allows the model to effectively… More >

Yuyao Huang, Hui Shu, Fei Kang^*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071552 - 12 January 2026

Abstract Message structure reconstruction is a critical task in protocol reverse engineering, aiming to recover protocol field structures without access to source code. It enables important applications in network security, including malware analysis and protocol fuzzing. However, existing methods suffer from inaccurate field boundary delineation and lack hierarchical relationship recovery, resulting in imprecise and incomplete reconstructions. In this paper, we propose ProRE, a novel method for reconstructing protocol field structures based on program execution slice embedding. ProRE extracts code slices from protocol parsing at runtime, converts them into embedding vectors using a data flow-sensitive assembly language model, More >

Karim Boudjebbour^1,2, Abdelkader Belkhir¹, Hamza Kheddar^2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071532 - 12 January 2026

Abstract Identifying the community structure of complex networks is crucial to extracting insights and understanding network properties. Although several community detection methods have been proposed, many are unsuitable for social networks due to significant limitations. Specifically, most approaches depend mainly on user–user structural links while overlooking service-centric, semantic, and multi-attribute drivers of community formation, and they also lack flexible filtering mechanisms for large-scale, service-oriented settings. Our proposed approach, called community discovery-based service (CDBS), leverages user profiles and their interactions with consulted web services. The method introduces a novel similarity measure, global similarity interaction profile (GSIP), which… More >

Binjiang Hu^1,*, Yihua Zhu², Liang Tu^1,2, Zun Ma³, Xian Meng³, Kewei Xu³

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069777 - 27 December 2025

Abstract This paper proposes an equivalent modeling method for photovoltaic (PV) power stations via a particle swarm optimization (PSO) K-means clustering (KMC) algorithm with passive filter parameter clustering to address the complexities, simulation time cost and convergence problems of detailed PV power station models. First, the amplitude–frequency curves of different filter parameters are analyzed. Based on the results, a grouping parameter set for characterizing the external filter characteristics is established. These parameters are further defined as clustering parameters. A single PV inverter model is then established as a prerequisite foundation. The proposed equivalent method combines the… More >

Salahudin Robo^1,2, Triyanna Widiyaningtyas^1,*, Wahyu Sakti Gunawan Irianto¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.073011 - 09 December 2025

Abstract Recommendation systems are an integral and indispensable part of every digital platform, as they can suggest content or items to users based on their respective needs. Collaborative filtering is a technique often used in various studies, which produces recommendations by analyzing similarities between users and items based on their behavior. Although often used, traditional collaborative filtering techniques still face the main challenge of sparsity. Sparsity problems occur when the data in the system is sparse, meaning that only a portion of users provide feedback on some items, resulting in inaccurate recommendations generated by the system.… More >

Fang Liu^*, Xianghui Meng, Jiachen Li, Sibo Guo

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-21, 2026, DOI:10.32604/cmc.2025.069243 - 09 December 2025

Abstract With the popularization of smart devices, Location-Based Services (LBS) greatly facilitates users’ life, but at the same time brings the risk of users’ location privacy leakage. Existing location privacy protection methods are deficient, failing to reasonably allocate the privacy budget for non-outlier location points and ignoring the critical location information that may be contained in the outlier points, leading to decreased data availability and privacy exposure problems. To address these problems, this paper proposes a Mix Location Privacy Preservation Method Based on Differential Privacy with Clustering (MLDP). The method first utilizes the DBSCAN clustering algorithm… More >

Noreen Fuentes¹, Janeth Ugang¹, Narcisan Galamiton¹, Suzette Bacus¹, Samantha Shane Evangelista², Fatima Maturan², Lanndon Ocampo^2,3,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-26, 2026, DOI:10.32604/cmc.2025.068104 - 10 November 2025

Abstract This study demonstrates a novel integration of large language models, machine learning, and multi-criteria decision-making to investigate self-moderation in small online communities, a topic under-explored compared to user behavior and platform-driven moderation on social media. The proposed methodological framework (1) utilizes large language models for social media post analysis and categorization, (2) employs k-means clustering for content characterization, and (3) incorporates the TODIM (Tomada de Decisão Interativa Multicritério) method to determine moderation strategies based on expert judgments. In general, the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation… 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 >