Si-Woo Kim, Yong Soo Kim^*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073492 - 12 January 2026

Abstract Predictive maintenance (PdM) is vital for ensuring the reliability, safety, and cost efficiency of heavy-duty vehicle fleets. However, real-world sensor data are often highly imbalanced, noisy, and temporally irregular, posing significant challenges to model robustness and deployment. Using multivariate time-series data from Scania trucks, this study proposes a novel PdM framework that integrates efficient feature summarization with cost-sensitive hierarchical classification. First, the proposed last_k_summary method transforms recent operational records into compact statistical and trend-based descriptors while preserving missingness, allowing LightGBM to leverage its inherent split rules without ad-hoc imputation. Then, a two-stage LightGBM framework is developed… 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 >

Ping Wu¹, Jie Zou², Wangxin Li^1,*, Yidong Xu¹

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.070873 - 08 January 2026

Abstract The existing 2D settlement monitoring systems for utility tunnels are heavily reliant on manual interpretation of deformation data and empirical prediction models. Consequently, early anomalies (e.g., minor cracks) are often misjudged, and warnings lag by about 24 h without automated spatial localization. This study establishes a technical framework for requirements analysis, architectural design, and data-integration protocols. Revit parametric modelling is used to build a 3D tunnel model with structural elements, pipelines and 18 monitoring points (for displacement and joint width). Custom Revit API code integrated real-time sensor data into the BIM platform via an automated… More >

Xiaozhe Guo¹, Suyan Long², Ziyu Yue², Yifan Wang², Guanting Yin¹, Yuyang Wang¹, Zhaoyuan Wu^1,*

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069820 - 27 December 2025

Abstract The increasing complexity of China’s electricity market creates substantial challenges for settlement automation, data consistency, and operational scalability. Existing provincial settlement systems are fragmented, lack a unified data structure, and depend heavily on manual intervention to process high-frequency and retroactive transactions. To address these limitations, a graph-based unified settlement framework is proposed to enhance automation, flexibility, and adaptability in electricity market settlements. A flexible attribute-graph model is employed to represent heterogeneous multi-market data, enabling standardized integration, rapid querying, and seamless adaptation to evolving business requirements. An extensible operator library is designed to support configurable settlement… More >

Baixuan Han¹, Yueping Peng^1,*, Zecong Ye², Hexiang Hao¹, Xuekai Zhang¹, Wei Tang¹, Wenchao Kang¹, Qilong Li¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-31, 2026, DOI:10.32604/cmc.2025.071071 - 09 December 2025

Abstract Aiming at the problem of imbalance between detection accuracy and algorithm model lightweight in UAV aerial image target detection algorithm, a lightweight multi-category abnormal behavior detection algorithm based on improved YOLOv11n is designed. By integrating multi-head grouped self-attention mechanism and Partial-Conv, a two-way feature grouping fusion module (DFPF) was designed, which carried out effective channel segmentation and fusion strategies to reduce redundant calculations and memory access. C3K2 module was improved, and then unstructured pruning and feature distillation technology were used. The algorithm model is lightweight, and the feature extraction ability for airborne visual abnormal behavior… More >

Xiaoyi Duan, Tianqi Zou, Chenyang Wang, Yu Gu, Xiuying Li^*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-24, 2026, DOI:10.32604/cmc.2025.070683 - 09 December 2025

Abstract Person recognition in photo collections is a critical yet challenging task in computer vision. Previous studies have used social relationships within photo collections to address this issue. However, these methods often fail when performing single-person-in-photos recognition in photo collections, as they cannot rely on social connections for recognition. In this work, we discard social relationships and instead measure the relationships between photos to solve this problem. We designed a new model that includes a multi-parameter attention network for adaptively fusing visual features and a unified formula for measuring photo intimacy. This model effectively recognizes individuals More >

Zhongyun Tang^1,2,3, Hanyi Xu², Haiyang Hu^1,3,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-29, 2026, DOI:10.32604/cmc.2025.070616 - 09 December 2025

Abstract With the deep integration of smart manufacturing and IoT technologies, higher demands are placed on the intelligence and real-time performance of industrial equipment fault detection. For industrial fans, base bolt loosening faults are difficult to identify through conventional spectrum analysis, and the extreme scarcity of fault data leads to limited training datasets, making traditional deep learning methods inaccurate in fault identification and incapable of detecting loosening severity. This paper employs Bayesian Learning by training on a small fault dataset collected from the actual operation of axial-flow fans in a factory to obtain posterior distribution. This More >

Hamza Murad Khan¹, Shakila Basheer², Mohammad Tabrez Quasim³, Raja`a Al-Naimi⁴, Vijaykumar Varadarajan⁵, Anwar Khan^1,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-25, 2026, DOI:10.32604/cmc.2025.069327 - 10 November 2025

Abstract With the increasing growth of online news, fake electronic news detection has become one of the most important paradigms of modern research. Traditional electronic news detection techniques are generally based on contextual understanding, sequential dependencies, and/or data imbalance. This makes distinction between genuine and fabricated news a challenging task. To address this problem, we propose a novel hybrid architecture, T5-SA-LSTM, which synergistically integrates the T5 Transformer for semantically rich contextual embedding with the Self-Attention-enhanced (SA) Long Short-Term Memory (LSTM). The LSTM is trained using the Adam optimizer, which provides faster and more stable convergence compared… More >

Ghadah Naif Alwakid¹, Samabia Tehsin^2,*, Mamoona Humayun^3,*, Asad Farooq², Ibrahim Alrashdi¹, Amjad Alsirhani¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.069162 - 10 November 2025

Abstract Skin diseases affect millions worldwide. Early detection is key to preventing disfigurement, lifelong disability, or death. Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance, and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks (CNNs). We frame skin lesion recognition as graph-based reasoning and, to ensure fair evaluation and avoid data leakage, adopt a strict lesion-level partitioning strategy. Each image is first over-segmented using SLIC (Simple Linear Iterative Clustering) to produce perceptually homogeneous superpixels. These superpixels form the nodes of a region-adjacency graph whose edges encode… More >