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 >

Nikita Golovkin^1,2, Olesya Nikulenkova³, Vsevolod Pobezhimov¹, Alexander Nesmelov¹, Sergei Chvalun¹, Fedor Sorokin³, Arthur Krupnin^1,3,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073161 - 12 January 2026

Abstract This study presents and verifies a hybrid methodology for reliable determination of parameters in structural rheological models (Zener, Burgers, and Maxwell) describing the viscoelastic behavior of polyurethane specimens manufactured using extrusion-based 3D printing. Through comprehensive testing, including cyclic compression at strain rates ranging from 0.12 to 120 mm/min (0%–15% strain) and creep/relaxation experiments (10%–30% strain), the lumped parameters were independently determined using both analytical and numerical solutions of the models’ differential equations, followed by cross-verification in additional experiments. Numerical solutions for creep and relaxation problems were obtained using finite element analysis, with the three-parameter Mooney-Rivlin… More > Graphic Abstract

Md Minhazul Islam^1,2, Yunfei Yin^1,2,*, Md Tanvir Islam^1,2, Zheng Yuan^1,2, Argho Dey^1,2

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072550 - 12 January 2026

Abstract Automatic segmentation of landslides from remote sensing imagery is challenging because traditional machine learning and early CNN-based models often fail to generalize across heterogeneous landscapes, where segmentation maps contain sparse and fragmented landslide regions under diverse geographical conditions. To address these issues, we propose a lightweight dual-stream siamese deep learning framework that integrates optical and topographical data fusion with an adaptive decoder, guided multimodal fusion, and deep supervision. The framework is built upon the synergistic combination of cross-attention, gated fusion, and sub-pixel upsampling within a unified dual-stream architecture specifically optimized for landslide segmentation, enabling efficient… More >

Haotian Zhang, Jing Li^*, Ming Zhu, Zhiyong Zhao, Hongli Su, Liming Sun

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072381 - 12 January 2026

Abstract In the wireless energy transmission service composition optimization problem, a key challenge is accurately capturing users’ preferences for service criteria under complex influencing factors, and optimally selecting a composition solution under their budget constraints. Existing studies typically evaluate satisfaction solely based on energy transmission capacity, while overlooking critical factors such as price and trustworthiness of the provider, leading to a mismatch between optimization outcomes and user needs. To address this gap, we construct a user satisfaction evaluation model for multi-user and multi-provider scenarios, systematically incorporating service price, transmission capacity, and trustworthiness into the satisfaction assessment… More >

Md. Ehsanul Haque¹, Md. Nurul Absur², Fahmid Al Farid³, Md Kamrul Siam⁴, Jia Uddin^5,*, Hezerul Abdul Karim^3,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071771 - 12 January 2026

Abstract Manual inspection of onba earing casting defects is not realistic and unreliable, particularly in the case of some micro-level anomalies which lead to major defects on a large scale. To address these challenges, we propose BearFusionNet, an attention-based deep learning architecture with multi-stream, which merges both DenseNet201 and MobileNetV2 for feature extraction with a classification head inspired by VGG19. This hybrid design, figuratively beaming from one layer to another, extracts the enormity of representations on different scales, backed by a pre-preprocessing pipeline that brings defect saliency to the fore through contrast adjustment, denoising, and edge… More >

Zonglin Li^1,#, Xu Zhao^2,#, Yan Cao^2,*, Yazhe Li³, Yihong Zhang¹

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071511 - 12 January 2026

Abstract With the rapid development of Internet technology, REST APIs (Representational State Transfer Application Programming Interfaces) have become the primary communication standard in modern microservice architectures, raising increasing concerns about their security. Existing fuzz testing methods include random or dictionary-based input generation, which often fail to ensure both syntactic and semantic correctness, and OpenAPI-based approaches, which offer better accuracy but typically lack detailed descriptions of endpoints, parameters, or data formats. To address these issues, this paper proposes the APIDocX fuzz testing framework. It introduces a crawler tailored for dynamic web pages that automatically simulates user interactions More >

Atheer Aleran¹, Hanan Almukhalfi¹, Ayman Noor¹, Reyadh Alluhaibi², Abdulrahman Hafez³, Talal H. Noor^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070741 - 12 January 2026

Abstract Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs. Conventional maintenance methods, such as reactive maintenance (i.e., run to failure) or time-based preventive maintenance (i.e., scheduled servicing), prove ineffective for complex systems with many Internet of Things (IoT) devices and sensors because they fall short in detecting faults at early stages when it is most crucial. This paper presents a predictive maintenance framework based on a hybrid deep learning model that integrates the capabilities of Long Short-Term Memory (LSTM) Networks and Convolutional Neural Networks (CNNs). The framework… More >

Woo Hyun Park^*, Dong Ryeol Shin

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.069949 - 12 January 2026

Abstract With the recent increase in data volume and diversity, traditional text representation techniques are struggling to capture context, particularly in environments with sparse data. To address these challenges, this study proposes a new model, the Masked Joint Representation Model (MJRM). MJRM approximates the original hypothesis by leveraging multiple elements in a limited context. It dynamically adapts to changes in characteristics based on data distribution through three main components. First, masking-based representation learning, termed selective dynamic masking, integrates topic modeling and sentiment clustering to generate and train multiple instances across different data subsets, whose predictions are… More >

Ahsan Habib Siam¹, Md. Ehsanul Haque¹, Fahmid Al Farid², Anindita Sutradhar³, Jia Uddin^4,*, Sarina Mansor^2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.069718 - 12 January 2026

Abstract As healthcare systems increasingly embrace digitalization, effective management of electronic health records (EHRs) has emerged as a critical priority, particularly in inpatient settings where data sensitivity and real-time access are paramount. Traditional EHR systems face significant challenges, including unauthorized access, data breaches, and inefficiencies in tracking follow-up appointments, which heighten the risk of misdiagnosis and medication errors. To address these issues, this research proposes a hybrid blockchain-based solution for securely managing EHRs, specifically designed as a framework for tracking inpatient follow-ups. By integrating QR code-enabled data access with a blockchain architecture, this innovative approach enhances… More >

Nghia Dinh¹, Vinh Truong Hoang^1,*, Viet-Tuan Le¹, Kiet Tran-Trung¹, Ha Duong Thi Hong¹, Bay Nguyen Van¹, Hau Nguyen Trung¹, Thien Ho Huong¹, Kittikhun Meethongjan^2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.069587 - 12 January 2026

Abstract Ensuring the reliability of power transmission networks depends heavily on the early detection of faults in key components such as insulators, which serve both mechanical and electrical functions. Even a single defective insulator can lead to equipment breakdown, costly service interruptions, and increased maintenance demands. While unmanned aerial vehicles (UAVs) enable rapid and cost-effective collection of high-resolution imagery, accurate defect identification remains challenging due to cluttered backgrounds, variable lighting, and the diverse appearance of faults. To address these issues, we introduce a real-time inspection framework that integrates an enhanced YOLOv10 detector with a Hybrid Quantum-Enhanced More >