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Bin Gao¹, Xinyu Jiang¹, Lusheng Wang^1,*, Jun Ding¹, Yanhong Peng¹, Xin Yang², Hongzhou Yan³, Shaojie Gu^4,5,*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077091 - 09 April 2026
Abstract This paper provides a comprehensive review of recent advances in multi-scale modeling for simulating dynamic damage and fracture in metallic materials, a critical area due to the widespread application of metals and their susceptibility to complex failure in engineering practice. The paper first outlines the mechanisms of damage evolution and crack propagation across different spatial and temporal scales. It then introduces commonly used simulation approaches spanning micro- to macro-scales for studying damage and fracture in metals, analyzing the evolution of mechanical properties from defect initiation to ultimate failure, and elucidating the underlying damage mechanisms at More >

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Hongtao Guo¹, Shuai Li², Shu Li^1,*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076492 - 09 April 2026
（This article belongs to the Special Issue: Machine Learning Methods in Materials Science)
Abstract This paper reviews the research progress and application prospects of machine learning technologies in the field of polymer materials. Currently, machine learning methods are developing rapidly in polymer material research; although they have significantly accelerated material prediction and design, their complexity has also caused difficulties in understanding and application for researchers in traditional fields. In response to the above issues, this paper first analyzes the inherent challenges in the research and development of polymer materials, including structural complexity and the limitations of traditional trial-and-error methods. To address these problems, it focuses on introducing key basic… More >

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1327

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Ziang Wu¹, Xuanyu Zhang², Yinbo Yu³, Qi Zhu³, Jerry Chun-Wei Lin⁴, Chunwei Tian^5,*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.078842 - 09 April 2026
Abstract Image super-resolution is a significant area in the field of image processing, with broad applications across multiple domains. In recent years, advancements in Generative Adversarial Networks (GANs) have led to an increased adoption of GAN-based methods in image super-resolution, yielding remarkable results. However, there is still a limited amount of research that systematically and comprehensively summarizes the various GAN-based techniques for image super-resolution. This paper provides a comparative study that elucidates the application differences of GANs in this field. We begin by reviewing the development of GANs and introducing their popular variants used in image… More >

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18

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Yihao Kuang^1,2, Hong Zhang^1,2, Jiaqi Wang^1,2, Lingyu Jin^1,2, Bo Huang^1,2,*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076652 - 09 April 2026
（This article belongs to the Special Issue: Advances in Video Object Tracking: Methods, Challenges, and Applications)
Abstract 3D single object tracking (SOT) based on point clouds is a fundamental task for environmental perception in autonomous driving and dynamic scene understanding in robotics. Recent technological advancements in this field have significantly bolstered the environmental interaction capabilities of intelligent systems. This field faces persistent challenges, including feature degradation induced by point cloud sparsity, representation drift caused by non-rigid deformation, and occlusion in complex scenarios. Traditional appearance matching methods, particularly those relying on Siamese networks, are severely constrained by point cloud characteristics, often failing under rapid motions or structural ambiguities among similar objects. In response,… More >

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1060

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775

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Ali Hamidoğlu^1,2, Ali Elghirani^3,4, Ömer Melih Gül^5,6,7, Seifedine Kadry^8,*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077060 - 09 April 2026
Abstract Multi-Robot Task Allocation (MRTA) has proven its importance in the current and near-future era, wherein in every aspect of life, there will be robots to handle tasks effectively and efficiently. While there has been a growing interest in MRTA problems in the robotics industry, the question arises of how to make robots more decentralized and intelligent through rational decision-makers rather than ones that are centralized and filled with black boxes. This survey aims to address that question by examining recent MRTA literature and exploring topics including MRTA taxonomy, centralized and decentralized controls, static and dynamic… More >

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321

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52

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Nourdine Aliane^*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.078545 - 09 April 2026
（This article belongs to the Special Issue: Advanced Technologies and Intelligent Applications for Autonomous Vehicles)
Abstract The rapid advancement of unmanned aerial vehicle (UAV) technologies has increased demand for flexible autopilot platforms suitable for both research and education. Among available options, the open-source Pixhawk/PX4 autopilot has emerged as a leading solution due to its modular architecture and robust software ecosystem. This survey examines the adoption of the Pixhawk/PX4 platform in research and education. The survey covers the analysis of the Pixhawk/PX4 autopilot software development APIs, its compatibility with ROS middleware and MATLAB/Simulink environments, and environments for software/hardware-in-the-loop simulations. Additionally, it explores the integration of Cutting-Edge technologies to enhance UAVs performance. By More >

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60

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Linhui Wang^1,2, Mohd Khair Hassan^1,*, Ghulam E Mustafa Abro^3,*, Mehrullah Soomro¹, Hifza Mustafa⁴
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.074207 - 09 April 2026
Abstract Industrial intelligent systems increasingly require efficient, robust, and deployable optimization methods for resource-constrained hardware. The Sparrow Search Algorithm (SSA) has gained traction in machine learning optimization; however, existing reviews emphasize algorithmic variants and generic benchmarks while paying limited attention to industrial requirements such as real-time operation, noise tolerance, and hardware awareness. This review advances the field by developing an industrial taxonomy that aligns SSA and its hybrids with six application clusters—fault diagnosis, production scheduling, edge-intelligent control, renewable/microgrid optimization, battery prognostics, and industrial cybersecurity—characterizing task types, data regimes, latency and safety constraints, and typical failure modes;… More >

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197

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40

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Hitesh Mohapatra^*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076726 - 09 April 2026
Abstract This review examines current approaches to real-time decision-making and task optimization in Internet of Things systems through the application of machine learning models deployed at the network edge. Existing literature shows that edge-based distributed intelligence reduces cloud dependency. It addresses transmission latency, device energy use, and bandwidth limits. Recent optimization strategies employ dynamic task offloading mechanisms to determine optimal workload placement across local devices and edge servers without centralized coordination. Empirical findings from the literature indicate performance improvements with latency reductions of approximately 32.8% and energy efficiency gains of 27.4% compared to conventional cloud-centric models.… More >

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2023

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REVIEW

Hamed Alqahtani¹, Gulshan Kumar^2,*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077367 - 09 April 2026
Abstract Large Language Models (LLMs) are becoming integral components of modern cybersecurity ecosystems, simultaneously strengthening defensive capabilities while giving rise to a new class of Artificial Intelligence–Generated Content (AIGC)-driven threats. This PRISMA-guided systematic review synthesises 167 peer-reviewed studies published between 2022 and 2025 and proposes a unified threat–defence–evaluation taxonomy as a central analytical framework to consolidate a previously fragmented body of research. Guided by this taxonomy, the review first examines AIGC-enabled threats, including automated and highly personalised phishing, polymorphic malware and exploit generation, jailbreak and adversarial prompting, prompt-injection attack vectors, multimodal deception, persona-steering attacks, and large-scale… More >

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281

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45

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REVIEW

Tsu-Yang Wu¹, Yehai Xue¹, Haonan Li², Saru Kumari³, Lip Yee Por^2,*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077332 - 09 April 2026
Abstract Blockchain technology, characterized by decentralization, transparency, and immutability, has been widely applied in areas such as supply chain tracking, medical data management, and the Internet of Things. However, single blockchain systems suffer from limitations in performance, scalability, and cross-chain interoperability, giving rise to the issue of “blockchain silos.” To address the challenges of data and asset circulation among heterogeneous blockchain networks, both academia and industry have proposed multi-blockchain architectures. In this paper, we categorize current multi-blockchain systems from a network topology perspective into four types: parallel architecture, hierarchical architecture, hybrid architecture, and multi-blockchain networks. We… More >

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297

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49

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REVIEW

Arifur Rahman¹, MD Azam Khan¹, Farhad Uddin Mahmud¹, Kanchon Kumar Bishnu², Ashifur Rahman³, M. F. Mridha^4,*, Md. Jakir Hossen^5,*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.071029 - 09 April 2026
Abstract Generative Artificial Intelligence (AI) is reshaping digital marketing by creating automated content, personalizing campaigns, and offering new ways to engage consumers. This systematic review examines research on generative AI, highlighting both its technological progress and the ethical, technical, and organizational hurdles that could limit its use. We used a PRISMA-based method to search major databases (ACM Digital Library, IEEE Xplore, and Scopus) for peer-reviewed studies published from 2018 to 2025. Our findings reveal major gains in text creation, image generation, and multimodal campaigns, which can lower costs and spark creative thinking. Still, data privacy, bias More >

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Mohamed Diouf¹, Elisée Toe^1,*, Manel Grichi², Haïfa Nakouri^1,3, Fehmi Jaafar¹
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077139 - 09 April 2026
Abstract Software security bugs present significant security risks to modern systems, leading to unauthorized access, data breaches, and severe operational and financial consequences. Early prediction of such vulnerabilities is therefore essential for strengthening software reliability and reducing remediation costs. This study investigates the extent to which static software quality metrics can identify vulnerable code and evaluates the effectiveness of machine learning models for large-scale security-bug prediction. We analyze a dataset of 338,442 source files, including 33,294 buggy files, collected from seven major open-source ecosystems. These ecosystems include GitHub Security Advisories (GHSA), Python Package Index (PyPI), OSS-Fuzz… More >

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Zhi Zhang^1,2, Bingyu Sun^1,*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076236 - 09 April 2026
（This article belongs to the Special Issue: Bridging the Gap: AutoML and Explainable AI for Industrial and Healthcare Innovations)
Abstract In facial landmark detection, shape deviations induced by large poses and exaggerated expressions often prevent existing algorithms from simultaneously achieving fine-grained local accuracy and holistic global shape constraints. To address this, we propose a Transformer-Conv Attention-based Method (TCAM). Built upon a hybrid coordinate-heatmap regression backbone, TCAM integrates the long-range dependency modeling of Transformers with the local feature extraction advantages of Depthwise Convolution (DWConv). Specifically, by partitioning feature maps into sub-regions and applying Transformer modeling, the module enforces sparse linear constraints on global information, effectively mitigating the issues caused by discontinuous landmark distributions. Experimental results on More >

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773

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Wenxuan Yu¹, Wenjing Gao¹, Jiuru Wang², Rong Hao^1,*, Jia Yu^1,*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076731 - 09 April 2026
Abstract Non-negative Matrix Factorization (NMF) is a computationally intensive matrix operation that resource-constrained clients struggle to complete locally. Privacy-preserving outsourcing allows clients to offload heavy computing tasks to powerful servers, effectively solving the problem of local computing difficulties. However, the existing privacy-preserving NMF outsourcing schemes only allow one server to perform outsourcing computation, resulting in low efficiency on the server side. In order to improve the efficiency of outsourcing computation, we propose a privacy-preserving parallel NMF outsourcing scheme with multiple edge servers. We adopt the matrix blocking technique to divide the computation task into multiple subtasks, More >

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398

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137

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Saleh Alharbi^*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.073584 - 09 April 2026
（This article belongs to the Special Issue: Towards Privacy-preserving, Secure and Trustworthy AI-enabled Systems)
Abstract Real-time threat detection in Internet of Things (IoT) networks requires scalable, privacy-preserving, and interpretable models capable of operating under strict latency constraints. This paper presents EdgeTrustX, a privacy-aware federated transformer framework that addresses these challenges by combining transformer-based representation learning with federated optimisation, differential privacy, and homomorphic encryption. The framework enables collaborative model training across heterogeneous IoT devices without exposing sensitive local data while maintaining computational feasibility for edge deployment. A multi-head attention mechanism integrated with a secure aggregation protocol supports adaptive feature weighting and privacy-protected parameter exchange. To enhance transparency, an explainability module that… More >

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96

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17

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ARTICLE

Awais Ahmad¹, Fakhri Alam Khan^2,3, Awais Ahmad⁴, Gautam Srivastava^5,6,7, Syed Atif Moqurrab^8,*, Abdul Razaque⁹, Dina S. M. Hassan^10,*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076104 - 09 April 2026
（This article belongs to the Special Issue: Complex Network Approaches for Resilient and Efficient Urban Transportation Systems)
Abstract Collision avoidance is recognized as a critical challenge in Vehicular Ad-Hoc Networks (VANETs), which demand real-time decision-making. It plays a vital role in ensuring road safety and traffic efficiency. Traditional approaches like rule-based systems and heuristic methods fail to provide optimal solutions in dynamic and unpredictable traffic scenarios. They cannot balance multiple objectives like minimizing collision risk, ensuring passenger comfort, and optimizing fuel efficiency, leading to suboptimal performance in real-world conditions. To tackle collision avoidance, this paper introduces a novel approach by defining the issue as an optimal control problem and solving it using the… More >

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481

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132

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Malik Al-Essa^1,*, Mohammad Qatawneh^2,1, Ahmad Sami Al-Shamayleh³, Orieb Abualghanam¹, Wesam Almobaideen^4,1
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076608 - 09 April 2026
（This article belongs to the Special Issue: Bridging the Gap: AutoML and Explainable AI for Industrial and Healthcare Innovations)
Abstract Machine Learning (ML) intrusion detection systems (IDS) are vulnerable to manipulations: small, protocol-valid manipulations can push samples across brittle decision boundaries. We study two complementary remedies that reshape the learner in distinct ways. Adversarial Training (AT) exposes the model to worst-case, in-threat perturbations during learning to thicken local margins; Counterfactual Augmentation (CF-Aug) adds near-boundary exemplars that are explicitly constrained to be feasible, causally consistent, and operationally meaningful for defenders. The main goal of this work is to investigate and compare how AT and CF-Aug can reshape the decision surface of the IDS. eXplainable Artificial Intelligence More >

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648

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230

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Lan Xiong, Liang Wan^*, Jingxia Ren
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076986 - 09 April 2026
（This article belongs to the Special Issue: Advances in Machine Learning and Artificial Intelligence for Intrusion Detection Systems)
Abstract The semantic complexity of large-scale malicious payloads in modern network traffic severely limits the robustness and generalization of existing Intrusion Detection Systems (IDS). This limitation presents a major challenge to network security. This paper proposes a dual-branch intrusion detection method called CPS-IDS. This method fuses deep semantic features with statistical features. The first branch uses the DeBERTav2 module. It performs deep semantic modeling on the session payload. This branch also incorporates a Time Encoder. The Time Encoder models the temporal behavior of the packet arrival interval time series. A Cross-Attention mechanism achieves the joint modeling… More >

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486

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155

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ARTICLE

Trong-Thua Huynh^1,*, De-Thu Huynh², Du-Thang Phu¹, Hong-Son Nguyen¹, Quoc H. Nguyen³
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076623 - 09 April 2026
（This article belongs to the Special Issue: Deep Learning: Emerging Trends, Applications and Research Challenges for Image Recognition)
Abstract This paper introduces MobiIris, a lightweight deep network for mobile iris recognition that enhances attention and specifically addresses the balance between accuracy and efficiency on devices with limited resources. The proposed model is based on the large version of MobileNetV3 and adds more spatial attention blocks and an embedding-based head that was trained using margin-based triplet learning, enabling fine-grained modeling of iris textures in a compact representation. To further improve discriminability, we design a training pipeline that combines dynamic-margin triplet loss, a staged hard/semi-hard negative mining strategy, and feature-level knowledge distillation from a ResNet-50 teacher.… More >

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748

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297

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Seohyun Yoo, Joonseo Hyeon, Jaehyuk Cho^*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077579 - 09 April 2026
（This article belongs to the Special Issue: Bridging the Gap: AutoML and Explainable AI for Industrial and Healthcare Innovations)
Abstract Despite remarkable advances in medical large language models (LLMs), their deployment in real clinical settings remains impractical due to prohibitive computational requirements and privacy regulations that restrict cloud-based solutions. Small LLMs (sLLMs) offer a promising alternative for on-premise deployment, yet they require domain-specific fine-tuning that still exceeds the hardware capacity of most healthcare institutions. Furthermore, the impact of multilingual data composition on medical sLLM performance remains poorly understood. We present a resource-efficient fine-tuning pipeline that integrates Quantized Low-Rank Adaptation (QLoRA), Fully Sharded Data Parallelism (FSDP), and Sequence Packing, validated across two model scales: MedGemma 4B… More >

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823

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Abuzar Khan¹, Abid Iqbal^2,*, Ghassan Husnain^1,*, Fahad Masood¹, Mohammed Al-Naeem³, Sajid Iqbal⁴
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077222 - 09 April 2026
（This article belongs to the Special Issue: Cloud Computing Security and Privacy: Advanced Technologies and Practical Applications)
Abstract Cloud computing now supports large-scale maritime analytics, yet offloading rich Automatic Identification System (AIS) data to the cloud exposes sensitive operational patterns and complicates compliance with cross-border privacy regulations. This work addresses the gap between growing demand for AI-driven vessel intelligence and the limited availability of practical, privacy-preserving cloud solutions. We introduce a privacy-by-design edge-cloud framework in which ports and vessels serve as federated clients, training vessel-type classifiers on local AIS trajectories while transmitting only clipped, Gaussian-perturbed updates to a zero-trust cloud coordinator employing secure and robust aggregation. Using a public AIS corpus with realistic… More >

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537

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72

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Qian Hu, Lei Tan^*, Qingjun Yuan, Zong Zuo, Yan Li
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076137 - 09 April 2026
Abstract Multi-behavior recommendation methods leverage various types of user interaction behaviors to make personalized recommendations. Behavior paths formed by diverse user interactions reveal distinctive patterns between users and items. Modeling these behavioral paths captures multidimensional behavioral features, which enables accurate learning of user preferences and improves recommendation accuracy. However, existing methods share two critical limitations: (1) Lack of modeling for the diversity of behavior paths; (2) Ignoring the impact of item attribute information on user behavior paths. To address these issues, we propose a Directed Behavior path graph-based Multi-behavior Recommendation method (DBMR). Specifically, we first construct… More >

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564

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281

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ARTICLE

Manh-Tuan Ha¹, Nhu-Nghia Bui², Dinh-Quy Vu^1,*, Thai-Viet Dang^2,*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077237 - 09 April 2026
（This article belongs to the Special Issue: Advances in Video Object Tracking: Methods, Challenges, and Applications)
Abstract In the realm of unmanned surface vehicle (USV) operations, leveraging environmental factors to enhance situational awareness has garnered significant academic attention. Developing vision systems for USVs presents considerable challenges, mainly due to variable observational conditions and angular vibrations caused by hydrodynamic forces. The paper proposed a novel MDGAN-DIFI network for end-to-end multi-object tracking (MOT), specifically designed for camera systems mounted on USVs. Beyond enhancing traditional MOT models, the proposed MDGAN-DIFI includes preprocessing modules designed to enhance the efficiency of processing input signal quality. Initially, a Deep Iterative Frame Interpolation (DIFI) module is used to stabilize… More >

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1036

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740

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Dmytro Tymoshchuk^1,*, Oleh Yasniy¹, Iryna Didych², Pavlo Maruschak^3,*, Yuri Lapusta⁴
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077062 - 09 April 2026
（This article belongs to the Special Issue: Machine Learning in the Mechanics of Materials and Structures)
Abstract This article presents an approach to predicting the hysteresis behavior of shape memory alloys (SMAs) using a Temporal Convolutional Network (TCN) deep learning model, followed by the interpretation of the results using Explainable Artificial Intelligence (XAI) methods. The experimental dataset was prepared based on cyclic loading tests of nickel-titanium wire at loading frequencies of 0.3, 0.5, 1, 3, and 5 Hz. For training, validation, and testing, 100–250 loading-unloading cycles were used. The input features of the models were stress σ (MPa), cycle number (Cycle parameter), and loading-unloading stage indicator, while the output variable was strain… More >

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734

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323

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Zuoquan Zhu^*, Menghan Wang, Xinyu Li, Mengxin Zhao
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076418 - 09 April 2026
Abstract Investigating the deformation behavior of graphene-reinforced composite structures holds significant engineering implications, while the rapid advancement of machine learning has introduced new technical approaches to structural bending analysis. In this study, we investigate the mechanical bending behavior of graphene origami (GOri)-enabled auxetic metamaterial beams using a physics-informed neural network (PINN). GOri-enabled auxetic metamaterials represent an innovative composite system characterized by a negative Poisson’s ratio (NPR) and superior mechanical performance. Here, we propose a composite beam model incorporating the modified coupled stress theory (MCST) and employing the PINN method to solve higher-order bending governing equations. Compared More >

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53

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Adwaa Mohammed Abdulmajeed¹, Duaa Abdul Rida Musa², Ola Abdul Hussain², Emad Kadum Njim³, Royal Madan^4,*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076622 - 09 April 2026
Abstract A data-driven optimization framework that integrates machine learning surrogate models, finite element analysis (FEA), and a multi-objective optimization algorithm is used in this study for developing thermoplastic elastomer (TPE) parts for aerospace applications. By using FEA simulations and experiments, a database of input design parameters (e.g., geometry and structural shape modifier) is generated. Afterwards, we train surrogate models (e.g., Gaussian Process Regression, neural networks) to approximate mappings from design space to performance space. Finally, we propose Pareto-optimal TPE designs using the surrogate embedded in a multi-objective optimization loop (such as NSGA-II or gradient-based methods). The… More >

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263

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54

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Hye-Min Kwon, Sung-Jun Lee, Chang-Lae Kim^*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077155 - 09 April 2026
（This article belongs to the Special Issue: Computational Approaches for Tribological Materials and Surface Engineering)
Abstract UV-curable acrylic polymers are promising for advanced coating applications; however, they suffer from low mechanical strength and wear resistance. This study investigated the effects of zinc oxide (ZnO) nanoparticle incorporation (0, 1, 3, and 5 wt.%) on mechanical, surface, and tribological properties of UV-curable acrylic polymer nanocomposites. The elastic modulus increased from 9.41 MPa (bare polymer) to 22.39 MPa (5 wt.% ZnO), a 138% improvement. X-ray diffraction (XRD) analysis confirmed the formation of a crystalline region at the polymer-ZnO interface, with crystallite sizes reaching 121.94 nm compared to 7.95 nm for the bare-polymer. Surface roughness More >

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469

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Chun Wu^1,2,3,4,*, Zhiqiang Ma^2,3, Lina Dong^2,3, Xuhui Wang^2,3, Changsheng Lou¹, Runqing Liu^1,*, Wenli Pei⁴
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077864 - 09 April 2026
（This article belongs to the Special Issue: Advances in Computational Materials Science: Focusing on Atomic-Scale Simulations and AI-Driven Innovations)
Abstract The intermetallic compounds with modulated electronic structure can provide more catalytically active sites and enhance electrocatalytic performance. In this study, the first-principles calculation method has been employed to investigate the potential of L1₀-NiM (M = Mn, Fe, Co, Cu, Zn, Mo) intermetallic compounds for electrocatalytic hydrogen evolution reaction (HER). Firstly, the L1₀-NiM present a homogenized charge transfer environment, where the Bader charge difference on the catalyst surface is below 0.13 e, significantly mitigating the locally strong adsorption of adsorbates in Ni. Additionally, the L1₀-NiM also fine-tunes the antibonding orbital interactions with adsorbates, facilitating both water dissociation and More >

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37

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Jiamin Xue¹, Jiexi Song^2,*, Diwei Shi^1,*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.078225 - 09 April 2026
（This article belongs to the Special Issue: Computational Modeling and Simulation of Energy and Environmental Materials)
Abstract MAB phases are a class of layered ternary transition-metal borides, characterized by hard M-B slabs interleaved with softer A-element layers, and thus hold promise for wear-resistant and high-temperature structural applications. However, their compositional space and structural diversity remain insufficiently explored, limiting guidance for synthesis and property optimization. In this work, we perform a comprehensive exploration and screening of the MAB family using high-throughput first-principles calculations. We systematically identify 855 candidate MAB compounds with orthorhombic and hexagonal structures across multiple transition-metal families, which form the starting pool for subsequent stability and property evaluation. The workflow evaluates… More >

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41

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Chan-Woo Kim^#, Sung-Jun Lee^#, Chang-Lae Kim^*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077143 - 09 April 2026
（This article belongs to the Special Issue: Computational Approaches for Tribological Materials and Surface Engineering)
Abstract Epoxy resins are widely used as protective coatings due to their excellent adhesion and chemical resistance; however, their inherent brittleness and susceptibility to shear stress-induced crack propagation limit their tribological performance. This study investigates the stress distribution mechanisms governing the wear resistance of solvent-textured epoxy coatings using finite element analysis (FEA) and experimental validation. Three solvents with distinct volatilities—acetone, methyl ethyl ketone (MEK), and ethyl acetate (EA)—generated characteristic surface morphologies through Marangoni convection, with roughness ranging from Ra = 0.17 μm (EA) to 0.66 μm (acetone). X-ray diffraction (XRD) and Fourier-transform infrared (FT-IR) spectroscopy confirmed… More >

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182

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Udit Mamodiya^1,*, Indra Kishor², P. Satish Reddy³, K. Lakshmi Kalpana³, Radha Seelaboyina⁴, Harish Reddy Gantla⁵
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076464 - 09 April 2026
（This article belongs to the Special Issue: AI and Multiscale Modeling in the Development of Optoelectronic and Thermoelectric Materials)
Abstract The direct conversion of solid-state heat to electricity using thermoelectric materials has attracted attention; however, their effective application is limited because of the challenge of ensuring a balance between the microstructural features at the quantum, mesoscale, and continuum scales. Current computational and machine-learning methods have a small design space, wherein few to no interactions between the electronic structure, phonon transport, and device-level are considered. This makes it difficult to discover stable high-figure of merit (ZT) settings that are manufacturable and strong in the actual working environment. This study presents a multiscale hybrid optimization framework that… More >

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758

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419

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Xue Wang^1,2, Cen Yang¹, Yonghang Wang¹, Mingming Wang^1,3, Ying Chen⁴, Ping Li^1,*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077868 - 09 April 2026
Abstract Tungsten-copper laminated composites are promising materials for high heat-flux applications, but their performance is often limited by interfacial instability caused by the thermal-mechanical mismatch between tungsten and copper. In this study, W/W-30Cu/CuCrZr three-layer composites are fabricated by high-pressure torsion (HPT) processing. Experimental characterization and molecular dynamics (MD) simulations are used to systematically investigate the influence of HPT process parameters and intermediate-layer composition on the evolution of microstructure and mechanical properties. HPT processing significantly refines the grains of the W-xCu composites and enhances their homogeneity. After applying 15 revolutions of HPT on W-30Cu composites, the crystallite… More >

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Sathish Rao Udupi, Gururaj Bolar, Manjunath Shettar^*, Ashwini Bhat
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.078574 - 09 April 2026
（This article belongs to the Special Issue: Advanced Computational Modeling and Simulations for Engineering Structures and Multifunctional Materials: Bridging Theory and Practice)
Abstract Glass fiber-reinforced polymer composites (GFRPCs) are extensively utilized in the aerospace, automotive, and structural sectors; nevertheless, their heterogeneous and abrasive characteristics result in rapid tool wear during drilling. Drill flank wear among various wear mechanisms notably influences hole quality and dimensional accuracy. This research investigates the impact of spindle speed, feed rate, and drill diameter on flank wear during dry drilling of GFRPC laminates with high-speed steel (HSS) twist drills. A full-factorial design with 81 experiments is used to create a comprehensive dataset. ANOVA indicates that spindle speed is the dominant factor affecting wear changes,… More >

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Muhammad Ishtiaq, Yeonwoo Kim, Sung-Gyu Kang^*, Nagireddy Gari Subba Reddy^*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077416 - 09 April 2026
（This article belongs to the Special Issue: Machine Learning Methods in Materials Science)
Abstract The long-term reliability of 1.25Cr-0.5Mo steels in high-temperature service critically depends on their creep rupture behavior, which is strongly influenced by alloy composition, microstructural characteristics, and testing conditions. In this study, an advanced Artificial Neural Network (ANN) model was developed to accurately predict the creep-rupture life of 1.25Cr-0.5Mo steels, offering a data-driven framework for alloy design and service-life assessment. The model incorporated eleven compositional variables (C, Si, Mn, P, S, Ni, Cr, Mo, Cu, Al, N), average grain size, non-metallic inclusions (NMI), steel properties including hardness measured on the Rockwell B scale (HRB) yield strength… More >

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Yasemin O. Ciftci¹, İlknur K. Durukan¹, Upasana Rani², Peeyush Kumar Kamlesh^3,*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.078722 - 09 April 2026
Abstract This study examines the phase-dependent structural, electronic, optical, and thermodynamic characteristics of the cubic, tetragonal, and orthorhombic phases of BaTiO₃ using DFT simulations. Lattice parameters and bulk moduli computed through structural optimizations within the GGA-PBE framework are in good agreement with existing experimental and theoretical studies. All phases exhibit negative formation energies, indicating thermodynamic stability, with the orthorhombic phase being the most stable. Electronic structure calculations reveal indirect band gaps of 2.86, 2.96, and 3.43 eV for the cubic, tetragonal, and orthorhombic phases, respectively. The density of states analysis indicates that O-p states dominate the valence… More >

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Yunlang Zhan^1,2, Fuhao Fan^1,2, Xilin Li^1,2, Zhenfei Zhan^1,*, Yongzhi Jiang¹, Yutong Yang^2,*, Shiyao Huang²
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077966 - 09 April 2026
Abstract The batch-to-batch variability in low-pressure die casting (LPDC), caused by inherent process parameter fluctuations, poses a significant challenge to consistent quality. However, traditional single-point optimization methods ignore parameter fluctuations. This study presents a robust design framework to overcome this limitation. First, an integrated simulation workflow was established by coupling ProCAST casting simulation with Abaqus finite element analysis to predict shrinkage pore volume and load-bearing capacity (LBC). Subsequently, a dataset was constructed from the integrated simulations, and then served to develop a surrogate model using the Extreme Gradient Boosting algorithm. Finally, robust process windows were derived… More >

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Semih Nart¹, Emre Güler², Melek Güler², Gökay Uğur^3,*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.079045 - 09 April 2026
Abstract Fluoride materials are renowned for their exceptional optical transparency, ionic conductivity, and chemical stability, making them indispensable in a wide range of technological applications. Despite the previous extensive research on simple metal fluorides, the complex metal fluoride family—particularly compounds with AB₂F₈ stoichiometry—remains largely unexplored. In this work, we present the first comprehensive density functional theory (DFT) investigation of the rare and formerly unreported MgAu₂F₈ complex metal fluoride, systematically revealing its electronic, optical, and thermoelectric properties under varying hydrostatic pressures. Our results reveal that MgAu₂F₈ undergoes a remarkable transformation from a wide-bandgap semiconductor at ambient conditions to a More >

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Chinmai Bhat^1,2, Mayur Jiyalal Prajapati², Yulius Shan Romario³, Wojciech Macek⁴, Maziar Ramezani⁵, Cho-Pei Jiang^1,2,*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.075197 - 09 April 2026
（This article belongs to the Special Issue: Design, Optimisation and Applications of Additive Manufacturing Technologies)
Abstract This study aims to develop a digital twin framework for fabricating automotive components through additive manufacturing (AM) technology. The framework comprises topology optimization (TO), finite element analysis (FEA), and fabrication analysis using Simufact Additive, which ensures the first-time-right fabrication of the component. Using TO-FEA, the component is designed with reduced overall weight without compromising the structural and functional performance. After the successful design of the component, it is analyzed for fabrication feasibility before undergoing the actual fabrication process. In the present study, an automotive flange fork is designed and fabricated through AM laser powder-bed fusion… More >

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Xiaoming Du¹, Jiahui Guo¹, Gaohan Liao¹, Tianfu Li^2,*, Haicheng Liang^1,*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.078760 - 09 April 2026
Abstract Graphene has excellent mechanical, electrical and optical properties, which make it an ideal reinforcement phase for aluminum matrix composites. However, graphene is easy to agglomerate and has poor wettability with the aluminum matrix, resulting in unsatisfactory effects when added to the aluminum matrix. In this paper, the effects of transition metals (Cu, Ni, Co) on the bonding properties at the graphene/aluminum interface were systematically investigated using first-principles calculations. The computational results reveal significant differences in the effects of various metals and their crystal plane orientations on interface stability and bonding strength. Among Cu, Ni, Co… More >

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Mahmoud M. A. Kamel^1,2, Yu Fu³, S. Z. Abeer⁴, Zaman Mohamed Al-Delfi⁴, Yijiang Peng^1,*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.078775 - 09 April 2026
（This article belongs to the Special Issue: Advanced Computational Modeling and Simulations for Engineering Structures and Multifunctional Materials: Bridging Theory and Practice)
Abstract Crumb rubber concrete (CRC) has emerged as a sustainable solution to the environmental challenges posed by rubber waste. This study introduces an advanced mixed-random-aggregate mesoscale model for CRC based on the Base Force Element Method (BFEM) and the complementary energy principle. The model incorporates different rubber substitution ratios (0%–30%), rubber particle sizes (2 mm and 4 mm), and specimen dimensions (edge lengths of 100, 150, and 300 mm). These parameters are considered to investigate their effects on the mechanical properties and failure mechanisms of CRC. Accordingly, the numerical results include stress–strain responses, elastic modulus, and… More >

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Yang Lei^1,2, Kangshuo Zhu^3,4,*, Bo Jiang¹, Yaodong Wang^3,4, Feiyu Jia¹, Zhaoning Wang¹, Falin Qi¹, Qiming Qu¹
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077314 - 09 April 2026
（This article belongs to the Special Issue: Intelligent Transportation System (ITS) Safety and Security)
Abstract The safe operation of railway systems necessitates efficient and automated inspection of tunnel defects. While deep learning offers solutions, a clear pathway for selecting and optimizing the latest object detectors for distinct defects under strict speed constraints is lacking. This paper presents a two-stage, task-specific framework for high-speed tunnel defect detection. First, this study conducts a comprehensive comparative analysis of state-of-the-art YOLO models (YOLOv5s, YOLOv8s, YOLOv10s, YOLOv11s) on self-constructed datasets. This systematic comparison identifies YOLOv5s as the optimal model for crack detection, achieving an mAP@0.5 of 0.939 at 77.5 FPS, sufficient for inspection at 50… More >

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Pratik Goswami^1,#, Hamid Naseem^2,#, Khizar Abbas^3,*, Kwonhue Choi^1,*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077012 - 09 April 2026
Abstract In a distributed edge computing environment, Internet of Things (IoT) and Vehicular-IoT (V-IoT) devices communicate through Wireless Sensor Networks (WSNs) by collecting and transmitting data from different environments. Although energy efficiency is always a critical challenge in WSN due to limited battery power, along with the demand for fast communication over edge devices in 5G and beyond 5G scenarios. Therefore, to overcome the challenges, an advanced hierarchical agent-based power management scheme is proposed for WSNs that optimizes energy distribution while maintaining reliable communication. The proposed model employs Master Agents (MAs), Coordination Agents (CoAs), and Task More >

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Hongjiang Wang¹, Jian Yu², Tian Zhang³, Na Ren⁴, Nan Zhang², Zhenyu Liu^1,*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077997 - 09 April 2026
（This article belongs to the Special Issue: Intelligent Computation and Large Machine Learning Models for Edge Intelligence in industrial Internet of Things)
Abstract The rapid deployment of Industrial Internet of Things (IIoT) systems, such as large-scale photovoltaic (PV) power stations in modern power grids, has created a strong demand for edge-intelligent fault localization methods that can operate reliably under strict computational and memory constraints. In this work, we propose an edge-intelligent photovoltaic fault localization framework that integrates intelligent computation with classical sub-pixel optimization. The framework adopts a modular, edge-oriented design in which a radial basis function (RBF) network is first employed as a lightweight screening module to enable conditional execution, thereby reducing unnecessary computation for non-faulty samples. For… More >

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Kexin Jiang^1,2, Yong Fan², Liang Wen¹, Zhigang Xie¹, Enzhi Dong¹, Bo Zhu¹, Zhonghua Cheng^1,*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077392 - 09 April 2026
Abstract With the growing deployment of unmanned aerial vehicles (UAVs), reliable engine health state assessment (HSA) requires methods that are interpretable, auditable, and transferable under noisy data and varying operating conditions. This paper proposes an AHP-enhanced, data-driven HSA framework that builds a unified health vector from four indicators—remaining useful life (RUL) health, absolute state, relative degradation, and condition health. Indicator weights are derived using AHP with consistency checking, and the resulting continuous health index is mapped through nonlinear stretching and four-level thresholds to produce actionable health grades. Experiments on the NASA CMAPSS benchmark (FD001) evaluate conventional More >

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Yan-Hao Huang^*, Yu-Tse Huang
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076800 - 09 April 2026
Abstract Diabetic Retinopathy (DR) is a common microvascular complication of diabetes that progressively damages the retinal blood vessels and, without timely treatment, can lead to irreversible vision loss. In clinical practice, DR is typically diagnosed by ophthalmologists through visual inspection of fundus images, a process that is time-consuming and prone to inter- and intra-observer variability. Recent advances in artificial intelligence, particularly Convolutional Neural Networks (CNNs) and Transformer-based models, have shown strong potential for automated medical image classification and decision support. In this study, we propose a Position-Wise Attention-Enhanced Vision Transformer (PWAE-ViT), which integrates a positional attention… More >

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331

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Ruida Liu, Dan Wang^*, Jiaming Chen, Meng Xu
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.074554 - 09 April 2026
Abstract Accurate prediction of shoulder joint angles based on surface electromyography (sEMG) signals is critical in human–machine interaction and rehabilitation engineering. However, due to the shoulder joint’s complex degrees of freedom, dynamically varying muscle coordination patterns, and the susceptibility of sEMG signals to cross-talk and noise interference, achieving high-precision prediction remains challenging. In this study, LASENet (BiLSTM–Attention–SE Network) is proposed as an end-to-end deep learning framework that integrates a bidirectional long short-term memory network (BiLSTM), a multi-head self-attention (MHSA) mechanism, and a squeeze-and-excitation (SE) block to predict shoulder joint angles across three degrees of freedom directly More >

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Fengqi Hao^1,2,3, Yawen Hou^2,3, Conghui Gao^2,3, Jinqiang Bai^2,3, Gang Liu⁴, Hoiio Kong^1,*, Xiangjun Dong^1,2,3
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077252 - 09 April 2026
Abstract Field–road classification, a fine-grained form of agricultural machinery operation-mode identification, aims to use Global Navigation Satellite System (GNSS) trajectory data to assign each trajectory point a semantic label indicating whether the machine is performing field work or travelling on roads. Existing methods struggle with highly imbalanced class distributions, noisy measurements, and intricate spatiotemporal dependencies. This paper presents AgroGeoDB-Net, a unified framework that combines a residual BiLSTM backbone with two tightly coupled innovations: (i) a Density-Aware Local Interpolator (DALI), which balances the minority road class via density-aware interpolation while preserving road-segment structure; and (ii) a geometry-aware… More >

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Abdulaziz A. Alsulami^1,*, Badraddin Alturki², Ahmad J. Tayeb², Rayan A. Alsemmeari², Raed Alsini¹
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076413 - 09 April 2026
（This article belongs to the Special Issue: Advances in IoT Security: Challenges, Solutions, and Future Applications)
Abstract Intrusion Detection Systems (IDS) play a critical role in protecting networked environments from cyberattacks. They have become increasingly important in smart environments such as the Internet of Things (IoT) systems. However, IDS for IoT networks face critical challenges due to hardware constraints, including limited computational resources and storage capacity, which lead to high feature dimensionality, prediction uncertainty, and increased processing cost. These factors make many conventional detection approaches unsuitable for real-time IoT deployment. To address these challenges, this paper proposes an adaptive intrusion detection framework that intelligently balances detection accuracy and computational efficiency. The proposed… More >

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Zhu Fang^1,2,*, Zhengquan Xu^1,2, Weizhen He³, Bohao Xu³
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076043 - 09 April 2026
Abstract In view of the problem that the IP address jump law is easy to predict in the current mobile target defense, this paper proposes a network address jump active defense method based on a dynamic random graph, designed to improve the unpredictability of IP address translation. Firstly, in order to make IP address transformation unpredictable in space and time, a random graph model is designed to generate a pseudo-random sequence of IP address randomization; these pseudo-random can meet the unpredictability of IP address translation in both space and time. Then, based on these pseudo-random sequences… More >

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Anandkumar Balasubramaniam, Seung Yeob Nam^*
CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077582 - 09 April 2026
（This article belongs to the Special Issue: Advances in Machine Learning and Artificial Intelligence for Intrusion Detection Systems)
Abstract The increasing connectivity of modern vehicles exposes the in-vehicle controller area network (CAN) bus to various cyberattacks, including denial-of-service, fuzzy injection, and spoofing attacks. Existing machine learning and deep learning intrusion detection systems (IDS) often rely on labeled data, struggle with class imbalance, lack interpretability, and fail to generalize well across different datasets. This paper proposes a lightweight and interpretable IDS framework based on non-negative matrix factorization (NMF) to address these limitations. Our contributions include: (i) evaluating NMF as both a standalone unsupervised detector and an interpretable feature extractor (NMF-W) for classical, unsupervised, and deep… More >

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