Sophort Siet¹, Sony Peng², Ilkhomjon Sadriddinov³, Kyuwon Park^4,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071192 - 12 January 2026

Abstract Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories. The collaborative filtering (CF) model, which depends exclusively on user-item interactions, commonly encounters challenges, including the cold-start problem and an inability to effectively capture the sequential and temporal characteristics of user behavior. This paper introduces a personalized recommendation system that combines deep learning techniques with Bayesian Personalized Ranking (BPR) optimization to address these limitations. With the strong support of Long Short-Term Memory (LSTM) networks, we apply it to identify sequential dependencies of user behavior and then incorporate… More >

Jun Xiong^*, Peng Yang, Bohan Chen, Zeming Chen

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069438 - 27 December 2025

Abstract The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system. However, various defects could be produced in the secondary equipment during long-term operation. The complex relationship between the defect phenomenon and multi-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods, which limits the real-time and accuracy of defect identification. Therefore, a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed. The defect data of secondary equipment is… 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 >

Sarfaraz Abdul Sattar Natha^1,*, Mohammad Siraj^2,*, Majid Altamimi², Adamali Shah², Maqsood Mahmud³

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4179-4201, 2025, DOI:10.32604/cmes.2025.072529 - 23 December 2025

Abstract A brain tumor is a disease in which abnormal cells form a tumor in the brain. They are rare and can take many forms, making them difficult to treat, and the survival rate of affected patients is low. Magnetic resonance imaging (MRI) is a crucial tool for diagnosing and localizing brain tumors. However, the manual interpretation of MRI images is tedious and prone to error. As artificial intelligence advances rapidly, DL techniques are increasingly used in medical imaging to accurately detect and diagnose brain tumors. In this study, we introduce a deep convolutional neural network… More >

Binu Sudhakaran Pillai¹, Raghavendra Kulkarni², Venkata Satya Suresh kumar Kondeti², Surendran Rajendran^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1141-1166, 2025, DOI:10.32604/cmes.2025.070348 - 30 October 2025

Abstract Future 6G communications will open up opportunities for innovative applications, including Cyber-Physical Systems, edge computing, supporting Industry 5.0, and digital agriculture. While automation is creating efficiencies, it can also create new cyber threats, such as vulnerabilities in trust and malicious node injection. Denial-of-Service (DoS) attacks can stop many forms of operations by overwhelming networks and systems with data noise. Current anomaly detection methods require extensive software changes and only detect static threats. Data collection is important for being accurate, but it is often a slow, tedious, and sometimes inefficient process. This paper proposes a new… More >

Shuoshuo Shen^1,2, Jin Cheng^1,2,*, Zhenyu Liu², Jianrong Tan^1,2

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.2, pp. 1-2, 2025, DOI:10.32604/icces.2025.011752

Abstract Motion stability assessment of industrial robots subject to complex dynamic properties and multi-source uncertainties in open environments registers an important yet challenging task [1–5]. To tackle this task, this study proposes a new reliability-based motion stability analysis method for industrial robots, which incorporates the moment-based method and Bayesian inference-guided probabilistic model updating strategy. To start with, the comprehensive motion system model of industrial robots is established by integrating the control, drive, and multi-body motion models. The reliability-based stability model of industrial robots is presented considering the uncertainty of parameters. Subsequently, the fractional exponential moments are… More >

Jingfu Yan, Huachun Zhou^*, Weilin Wang

Computer Systems Science and Engineering, Vol.49, pp. 499-531, 2025, DOI:10.32604/csse.2025.068151 - 10 October 2025

Abstract The 6G network architecture introduces the paradigm of Trust + Security, representing a shift in network protection strategies from external defense mechanisms to endogenous security enforcement. While ZTNs (zero-trust networks) have demonstrated significant advancements in constructing trust-centric frameworks, most existing ZTN implementations lack comprehensive integration of security deployment and traffic monitoring capabilities. Furthermore, current ZTN designs generally do not facilitate dynamic assessment of user reputation. To address these limitations, this study proposes a DPZTN (Data-plane-based Zero Trust Network). DPZTN framework extends traditional ZTN models by incorporating security mechanisms directly into the data plane. Additionally, blockchain infrastructure… More > Graphic Abstract

Abdullah G. M. Almansour^1,*, Faisal Alshomrani², Abdulaziz T. M. Almutairi³, Easa Alalwany⁴, Mohammed S. Alshuhri¹, Hussein Alshaari⁵, Abdullah Alfahaid⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2395-2418, 2025, DOI:10.32604/cmes.2025.069661 - 31 August 2025

Abstract The early and precise identification of Alzheimer’s Disease (AD) continues to pose considerable clinical difficulty due to subtle structural alterations and overlapping symptoms across the disease phases. This study presents a novel Deformable Attention Vision Transformer (DA-ViT) architecture that integrates deformable Multi-Head Self-Attention (MHSA) with a Multi-Layer Perceptron (MLP) block for efficient classification of Alzheimer’s disease (AD) using Magnetic resonance imaging (MRI) scans. In contrast to traditional vision transformers, our deformable MHSA module preferentially concentrates on spatially pertinent patches through learned offset predictions, markedly diminishing processing demands while improving localized feature representation. DA-ViT contains only More >

Mengke Ding¹, Jun Li^1,2,*, Dongyue Gao^1,*, Guotai Zhou², Borui Wang¹, Zhanjun Wu¹

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 597-612, 2025, DOI:10.32604/cmc.2025.067907 - 29 August 2025

Abstract Throughout the composite structure’s lifespan, it is subject to a range of environmental factors, including loads, vibrations, and conditions involving heat and humidity. These factors have the potential to compromise the integrity of the structure. The estimation of the fatigue life of composite materials is imperative for ensuring the structural integrity of these materials. In this study, a methodology is proposed for predicting the fatigue life of composites that integrates ultrasonic guided waves and machine learning modeling. The method first screens the ultrasonic guided wave signal features that are significantly affected by fatigue damage. Subsequently,… More >

Muhammad Salman Qamar^1,*, Muhammad Fahad Munir²

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5463-5485, 2025, DOI:10.32604/cmc.2025.064442 - 30 July 2025

Abstract Wireless Sensor Networks (WSNs) have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes (SNs). However, the operational lifespan of WSNs is significantly constrained by the limited energy resources of SNs. Current energy efficiency strategies, such as clustering, multi-hop routing, and data aggregation, face challenges, including uneven energy depletion, high computational demands, and suboptimal cluster head (CH) selection. To address these limitations, this paper proposes a hybrid methodology that optimizes energy consumption (EC) while maintaining network performance. The proposed approach integrates the Low Energy Adaptive Clustering Hierarchy with Deterministic (LEACH-D) protocol using More >