Open Access
ARTICLE
Kuen-Suan Chen1,2,3, Tsai-Sung Lin4, Ruey-Chyn Tsaur4,*, Minh T. N. Nguyen5
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079476
Abstract As artificial intelligence, the Internet of Things, edge computing, and blockchain are increasingly integrated into long-term care (LTC) services, policymakers face complex and often non-compensatory trade-offs among affordability, workforce sustainability, service reliability, and data governance. Conventional compensatory evaluation models tend to mask critical structural weaknesses and limiting their usefulness for Smart LTC policy assessment. This study proposes and applies a Fuzzy Trade-Off-Aware Scoring with Conflicts (Fuzzy TASC) framework to evaluate Smart LTC system performance. Four digital-integration configurations—conventional cloud-based LTC, AI+IoT, AI+Edge, and AI+Blockchain—were compared across 12 OECD countries. A Monte Carlo perturbation procedure was incorporated… More >
Open Access
ARTICLE
Mohammed Shamar Yadkar1, Sefer Kurnaz1, Saadaldeen Rashid Ahmed2,3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079408
（This article belongs to the Special Issue: Development and Application of Deep Learning and Image Processing)
Abstract This advanced research describes CycleGAN-RRW, a new reversible watermarking system for secure image ownership authentication. It uses Cycle-Consistent Generative Adversarial Networks with adaptive feature encoding. In areas such as law, forensics, and telemedicine, digital images usually contain private info that may be changed or used without authorization. Existing watermarking methods may decrease image quality, may not be reversible, or need outside keys. To address these problems, our model embeds metadata into intermediate feature maps with Adaptive Instance Normalization (AdaIN), based on adversarial and perceptual loss. The dual-generator design permits two-way translation between original and watermarked… More >
Open Access
ARTICLE
Yan-Hao Huang*, Chung-Ming Kao
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079381
（This article belongs to the Special Issue: Nature-Inspired Optimization & Applications in Computer Science: From Particle Swarms to Hybrid Metaheuristics)
Abstract Photovoltaic (PV) equivalent-circuit models are widely used for performance evaluation and diagnostics, but their usefulness relies on both accurate calibration and interpretable understanding of how parameters shape current–voltage (I–V) behavior. For nonlinear and strongly coupled PV models, conventional global sensitivity analysis can be computationally demanding and offer limited insight into effect direction and operating-point dependence. This study presents an method-oriented framework that integrates nature-inspired optimization with surrogate-based explainable global sensitivity analysis under a specified operating condition. The Starfish Optimization Algorithm (SFOA) is first used for parameter identification by searching for the optimal parameter set that… More >
Open Access
REVIEW
Nourdine Aliane*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.078545
（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 >
Open Access
ARTICLE
Oskar Kapuśniak1, Adam Piórkowski2,*, Julia Lasek3, Karolina Nurzyńska4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076062
（This article belongs to the Special Issue: Artificial Intelligence in Visual and Audio Signal Processing)
Abstract The efficacy of Active Shape Models (ASM) for automated ventricular segmentation was evaluated to address the computational demands of manual segmentation and the interpretability limitations of deep learning. A statistical shape model was constructed using a limited cohort of 19 Coronary Computed Tomography Angiography (CCTA) scans derived from patients with diverse cardiac abnormalities. Principal Component Analysis (PCA) was employed to encapsulate morphological variability, and strict point correspondence was enforced to maintain topological consistency. Validation was conducted via leave-one-out cross-validation, benchmarking automated segmentations against expert-delineated ground truths using the Dice Similarity Coefficient (DSC) and Hausdorff Distance More >
Open Access
ARTICLE
Xin Yu, Xi Fang*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.075607
（This article belongs to the Special Issue: Cooperation and Autonomy in Multi-Agent Systems: Models, Algorithms, and Applications)
Abstract This paper addresses the challenging problem of multi-agent dynamic target pursuit under stringent communication constraints (including delays and range limits), where the agile targets are non-cooperative and free from such limitations. To tackle this, we propose CRS-DQN, a novel Deep Q-Network algorithm designed for this scenario. CRS-DQN enables agents to learn effective pursuit strategies through deep reinforcement learning despite partial observability and constrained information sharing. Simulation experiments systematically evaluate the impact of key parameters. The results show that pursuit performance degrades monotonically with increased communication delay. In contrast, the communication radius exhibits a non-linear effect: More >
Open Access
ARTICLE
Xingyun Hu1,2, Siqi Lu1,2,*, Liujia Cai1,2, Ye Feng1,2, Shuhao Gu1,2, Tao Hu1, Yongjuan Wang1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079484
（This article belongs to the Special Issue: Cyberspace Mapping and Anti-Mapping Techniques)
Abstract With the widespread adoption of web applications and cloud services, the OAuth 2.0-based OpenID Connect (OIDC) Single Sign-on (SSO) protocol has become the core of modern digital identity authentication. Although the OIDC protocol itself has strict security specifications, its implementation in real-world web frameworks can introduce critical vulnerabilities, particularly the improper omission of the state parameter, which leads to severe authentication forgery risks. Existing research often overlooks these implementation-level flaws, especially from a formal analysis perspective. This paper addresses this gap by formally analyzing the authentication forgery attack resulting from the missing state parameter. We construct… More >
Open Access
ARTICLE
Xu Chen, Li Yang*, Guohao Qiu
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077513
Abstract Traffic prediction plays a crucial role in the efficient operation of satellite networks. However, due to resource consumption arising from redundant training of multiple individual prediction models, the dynamic and coupled spatial-temporal relationship of traffic, and maintenance of accurate traffic proportions, this problem is non-trivial to solve. Therefore, we consider this problem and makes the following contributions. First, a multi-granularity traffic prediction framework based on a shared feature extraction is designed to jointly predict total network traffic and service-specific traffic of satellite networks. This design ensures that both global and per service predictions benefit from… More >
Open Access
REVIEW
Ali Hamidoğlu1,2, Ali Elghirani3,4, Ömer Melih Gül5,6,7, Seifedine Kadry8,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077060
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 >
Open Access
ARTICLE
Abdullah Alshammari*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.078634
（This article belongs to the Special Issue: Advances in Cybersecurity for Digital Ecosystems)
Abstract The proliferation of Internet of Things (IoT) devices in the infrastructure of smart cities has posed cybersecurity risks like never before, which have direct implications on the sustainability and energy consumption of cities. In this paper, a multi-faceted Threat-Resilient Energy-Conscious Security Framework (TRECSF) is introduced that combines intrusion detection methods powered by deep learning, blockchain-driven data integrity verification mechanism, and energy-aware security protocols in smart city ecosystems to achieve their sustainability. The new Hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model is introduced to the proposed architecture, which fulfills the purpose of the study to… More >
Open Access
ARTICLE
Yu Wang1,2, Yabin Wang1, Liang Wen1, Bingyu Li1, Mengze Qin1, Fang Li1, Zhonghua Cheng1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077026
Abstract Remaining useful life (RUL) prediction for complex equipment is a critical technology for ensuring the safe and reliable operation of industrial systems. However, existing data-driven models commonly suffer from limitations such as weak cross-operational condition generalization, insufficient physical interpretability, and unstable training on non-stationary time-series data. To address these challenges, this paper proposes a temporal degradation prediction model that integrates context adaptation and physics-consistent constraints, named the Context-Adaptive Physics-informed Time-aware meta-Network (CAPTAIN). The model incorporates four core components: a Context-Aware Meta-Learning (CAML) module that enables lightweight parameter adaptation to diverse scenarios; Physics-Informed Neural Network (PINN)… More >
Open Access
ARTICLE
Zahra Farhadpour1,*, Tan Fong Ang1,*, Chee Sun Liew2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076163
Abstract The heterogeneity and dynamic behavior of fog computing environments introduce major challenges to achieving optimal application placement. Limited fog resources and varying workloads often necessitate offloading applications beyond their local clusters, making it difficult to maintain the required level of service quality under varying conditions. In this context, placement methods must ensure a balanced trade-off between multiple objectives, such as time and cost, while maintaining reliable adherence to constraints like application deadlines and limited fog-node memory. Existing solutions, including heuristic, metaheuristic, learning-based, and hybrid optimization approaches, have been proposed to address these challenges. However, many… More >
Open Access
ARTICLE
Sarmad Dheyaa Azeez1, Muhammad Ilyas2,*, Saadaldeen Rashid Ahmed3,4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.074930
（This article belongs to the Special Issue: Advances in Machine Learning and Artificial Intelligence for Intrusion Detection Systems)
Abstract The rapid evolution of 5G-enabled Software Defined Networks (SDNs) has transformed modern communication systems by enabling ultra-low latency, massive connectivity, and high throughput. However, the increased complexity of traffic flows and the rise of sophisticated cyber-attacks such as Distributed Denial of Service (DDoS), Botnets, Fake Base Stations, and Zero-Day exploits have made intrusion detection a critical challenge. Traditional Intrusion Detection System (IDS) approaches often suffer from poor gen-eralization, high false positives, and lack of interpretability, making them unsuitable for dynamic 5G environments. This paper presents a novel Graph Neural Network (GNN) with Multi-Head Attention (MHA)… More >
Open Access
ARTICLE
Dan Wang1, Mengyi Cui1, Zhenhua Yu1,*, Yukang Liu2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.074308
Abstract Traffic flow prediction is of great importance in traffic planning, road resource management, and congestion mitigation. However, existing prediction have significant limitations in modeling multi-scale spatial-temporal features, particularly in capturing temporal periodicity and spatial dependency in dynamically evolving traffic networks. This paper proposes a novel framework of traffic flow prediction, referred to as Adaptive Graph Fusion Dual-scale Convolutional Network (AGFDCN), which integrates spatial-temporal dynamic graphs with dual-scale convolutional networks. Specifically, we introduce a Dual-Scale Temporal Network, which combines long- and short-term dilated causal convolutions with a temporal decay-aware attention mechanism to efficiently capture traffic patterns… More >
Open Access
REVIEW
Linhui Wang1,2, Mohd Khair Hassan1,*, Ghulam E Mustafa Abro3,*, Mehrullah Soomro1, Hifza Mustafa4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.074207
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 >
Open Access
ARTICLE
Sultan Kahla1, Zuping Zhang1,*, Majed Alsafyani2, Ahmed Emara3,*, Mohammod Abdullah Bin Hossain4, Abdulwahab Osman Sheikhdon1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076402
Abstract The effective diagnosis and treatment planning require the correct classification of the cerebral neoplasia, such as glioma, meningioma, and pituitary tumors. The recent developments in the deep learning field have made a significant contribution to the field of image analysis in medicine; however, Vision Transformers (ViTs) have achieved good results but are computationally complex. This paper presents NeuroTriad-ViT, a proprietary large-scale Vision Transformer of 235 million parameters, which is represented as a high-performance teacher model to classify brain tumors. Knowledge distillation is applied in an attempt to transfer the representations that the teacher learned to… More >
Open Access
ARTICLE
Chun Wu1,2,3,4,*, Zhiqiang Ma2,3, Lina Dong2,3, Xuhui Wang2,3, Changsheng Lou1, Runqing Liu1,*, Wenli Pei4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077864
（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 >
Open Access
ARTICLE
Jie Wang, Deming Lei*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076202
（This article belongs to the Special Issue: Swarm-Based Optimization and its Cross-Disciplinary Applications in Modern Engineering)
Abstract Both flexible jobshop scheduling and parallel batch processing machine scheduling have been extensively considered; however, the flexible jobshop and parallel batch processing machine scheduling problem (FJPBPMSP) is prevalent in real-life manufacturing processes and is seldom investigated. In this study, FJPBPMSP is examined, where flexible processing and batch processing are performed sequentially. An adaptive imperialist competitive algorithm with cooperation (CAICA) is proposed to minimize makespan and total energy consumption simultaneously. In CAICA, a four-string representation is adopted, and initial empires with novel structures are formed by uniformly dividing the population. An adaptive assimilation and revolution are More >
Open Access
ARTICLE
Jiamin Xue1, Jiexi Song2,*, Diwei Shi1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.078225
（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 >
Open Access
ARTICLE
Giuseppe Marannano*, Antonino Cirello, Tommaso Ingrassia
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077884
Abstract In response to the growing need for adaptive optimization algorithms capable of handling complex, multimodal, and high-dimensional search spaces, this paper introduces the Structured Random Cycle-guided Algorithm (SRCA). SRCA is not presented as a fundamentally new optimization paradigm, but rather as an architectural synthesis and a unified adaptive framework for dynamic operator selection. Based on a cycle-structured architecture, directional and stochastic search behaviors are dynamically selected at the individual level. The algorithm orchestrates well-established structured movements with a diverse pool of stochastic exploration strategies, enabling a coherent and adaptive balance between exploration and exploitation throughout More >
Open Access
ARTICLE
Taimoor Hassan1, Ibrar Hussain1,*, Hafiz Mahfooz Ul Haque2, Hamid Turab Mirza3, Muhammad Nadeem Ali4, Byung-Seo Kim4,*, Changheun Oh4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077510
Abstract The expeditious proliferation of the smart computing paradigm has a remarkable upsurge towards Artificial Intelligence (AI) assistive reasoning with the incorporation of context-awareness. Context-awareness plays a significant role in fulfilling users’ needs whenever and wherever needed. Context-aware systems acquire contextual information from sensors/embedded sensors using smart gadgets and/or systems, perform reasoning using reinforcement learning (RL) or other reasoning techniques, and then adapt behavior. The core intention of using an RL-based reasoning strategy is to train agents to take the right actions at the right time and in the right place. Generally, agents are rewarded for… More >
Open Access
REVIEW
Hamed Alqahtani1, Gulshan Kumar2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077367
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 >
Open Access
ARTICLE
Jiao Yao, Pujie Wang, Tianyi Zhang, Chenke Zhu, Chenqiang Zhu*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077329
Abstract In accident scenarios on freeways, traffic congestion, sharp declines in capacity, and the limitations of closed systems where vehicles cannot turn around or exit freely often pose serious challenges. To address these issues, this study develops an improved Markov Decision Process (MDP) framework for dynamic emergency lane opening. Compared with traditional MDP-based traffic control models, the proposed method integrates three enhancements: Firstly, an explicit action decision space transition mechanism that couples variable speed limits with emergency lane opening decisions; Secondly, vehicle-type–differentiated actions to support fine-grained and adaptive opening strategies; and a redesigned reward function incorporating… More >
Open Access
ARTICLE
Daiki Nobayashi1,*, Meiya Tanaka2, Naoki Tanaka2, Riku Nakamura2, Kazuya Tsukamoto3, Takeshi Ikenaga1, Shu Sekigawa4, Myung Lee5
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077008
（This article belongs to the Special Issue: Advancements in Mobile Computing for the Internet of Things: Architectures, Applications, and Challenges)
Abstract To realize local production and consumption of Spatio-temporal data (STD), it is essential to address two key challenges: (1) maintaining data locality by retaining and distributing STD close to their generation area, and (2) enabling application execution on heterogeneous and resource-constrained devices through a lightweight and portable execution platform. To address these challenges, we developed a Floating Cyber-Physical System (F-CPS) that retains both STD and the functions required to process and use the STD within a specific area. In the F-CPS, the STD Retention System directly distributes STD from the generation location and maintains the… More >
Open Access
ARTICLE
Adwaa Mohammed Abdulmajeed1, Duaa Abdul Rida Musa2, Ola Abdul Hussain2, Emad Kadum Njim3, Royal Madan4,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076622
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 >
Open Access
ARTICLE
Siqiang Zheng1,#, Yu Lu2,#, Xuetong Xu1, Kai Sun1, Lanzhu Zhang1,*, Zhiqin Qian1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076059
Abstract Pipelines play a crucial role in chemical industrial production. However, due to long operating cycles, seal failures, and internal corrosion, hazardous chemical media are prone to leak, potentially leading to serious accidents such as explosions. To address the limitations of existing pipeline leak detection methods—specifically their insufficient recognition accuracy and poor robustness in noisy environments—this paper proposes an Acoustic Emission (AE)-driven leakage state recognition method based on wavelet time-frequency maps and the Inception-V3 deep network. First, a pipeline leak experimental platform was constructed, and AE signals were collected. The signals were denoised through wavelet decomposition More >
Open Access
REVIEW
Arifur Rahman1, MD Azam Khan1, Farhad Uddin Mahmud1, Kanchon Kumar Bishnu2, Ashifur Rahman3, M. F. Mridha4,*, Md. Jakir Hossen5,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.071029
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 >
Open Access
ARTICLE
Waqar Ali1, May Altulyan2, Ghulam Farooque3, Siyuan Li4, Jie Shao4,5,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.078599
（This article belongs to the Special Issue: Next-Generation Recommender Systems: Multimodality, Generative Models, and Trustworthy Personalization)
Abstract Federated recommender systems (FedRS) enable collaborative model training while preserving user privacy, yet they remain vulnerable to adversarial attacks, unreliable client updates, and misaligned incentives in decentralized environments. Existing approaches struggle to jointly preserve personalization, robustness, and trust when user data are highly non-IID and recommendation quality is governed by ranking-oriented objectives. To address these challenges, we propose a Trustworthy Federated Recommender System (T-FedRS) that extends federated neural collaborative filtering by integrating a ranking-aware reputation mechanism and a lightweight blockchain layer for transparent incentive allocation. Personalization is preserved through locally maintained user embeddings, while item parameters… More >
Open Access
ARTICLE
Muhammad Ishtiaq, Yeonwoo Kim, Sung-Gyu Kang*, Nagireddy Gari Subba Reddy*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077416
（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 >
Open Access
ARTICLE
Kiseok Kim#, Taehoon Yoo#, Sangmin Lee, Hwangnam Kim*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077415
（This article belongs to the Special Issue: Computational Learning Methods for Unmanned Vehicles: Cross-Task Adaptation and Generalization)
Abstract Conventional Low-Rank Adaptation (LoRA) constrains weight updates to a static linear low-rank manifold, which is inherently limited when applied to Reinforcement Learning (RL) tasks for Unmanned Aerial Vehicle (UAV) applications. UAVs operate in highly dynamic and nonstationary environments where rapid variations in sensing and state transitions lead to complex, nonlinear input–output relationships. Such environmental complexity cannot be adequately modeled by a static Low-rank approximation, making conventional LoRA approaches insufficient for the high-dimensional dynamics required in UAV applications. To overcome these limitations, we propose an attention-enhanced LoRA that constructs an input-dependent and intrinsically nonlinear adaptation manifold.… More >
Open Access
ARTICLE
Mohammed A. Ahmed1, Jian Dong2,*, Ronghua Shi2, Ammar Nassr3, Hani Almaqtari3, Ala A. Alsanabani3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.075616
（This article belongs to the Special Issue: Advancements in Pattern Recognition through Machine Learning: Bridging Innovation and Application)
Abstract Facial Expression Recognition (FER) is an essential endeavor in computer vision, applicable in human-computer interaction, emotion assessment, and mental health surveillance. Although Convolutional Neural Networks (CNNs) have proven effective in Facial Emotion Recognition, they encounter difficulties in capturing long-range connections and global context. To address these constraints, we propose Spatial Quad-Similarity Network (SQSNet), an innovative hybrid framework that integrates the local feature extraction capabilities of CNNs with the global contextual modeling efficacy of Swin Transformers via a cohesive fusion technique. SQSNet introduces the Spatial Quad-Similarity (SQS) module, a feature refinement approach that amplifies discriminative characteristics… More >
Open Access
ARTICLE
A. Sivasangari1,*, V. J. K. Kishor Sonti1, J. Cruz Antony1, E. Murali1, D. Deepa1, A. Happonen2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.074429
（This article belongs to the Special Issue: Software, Algorithms and Automation for Industrial, Societal and Technological Sustainable Development)
Abstract In the past two decades, Precision Agriculture has received research attention since the development of robotics. Agricultural robotic equipment and drones, which can be operated by farmers, are appearing more frequently and being used to make the process of farming easier and more productive. This paper attempts to develop a modified Q-learning algorithm. A reinforcement learning algorithm called Q-learning has Q-values that are updated in order to find the best routes for the robotic devices to follow while avoiding any obstacles. Different types of terrain and other factors that influence the development of good routes… More >
Open Access
ARTICLE
Xiaoping Yang1,*, Songjie Yang2, Junqi Long1, Quanzeng Wang3, Bin Yang4, Xiaofang Cao5, Guochao Qi6
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.078105
（This article belongs to the Special Issue: Advances in Wireless Sensor Networks: Security, Efficiency, and Intelligence)
Abstract The rapid proliferation of latency-sensitive applications, coupled with the limitations of service range, has driven the integration of aerial simultaneously transmitting and reflecting reconfigurable intelligent surfaces (ASTAR-RIS) and task offloading to enhance both communication and computational efficiency in wireless sensor networks (WSNs). However, in WSNs, conventional ASTAR-RIS-assisted task offloading faces critical limitations, including restricted endurance, underutilized network caching and computing resources, and inefficient resource allocation within the optimization framework. To overcome these challenges, this paper integrates wireless power transfer (WPT) technology and proposes a novel energy-efficient ASTAR-RIS and WPT-assisted task offloading and content caching framework… More >
Open Access
ARTICLE
Quang Dong Nguyen Vo1, Gia Nhu Nguyen1, Hoang Vu Tran2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.078756
Abstract Reliable vehicle detection in urban traffic environments remains challenging, particularly for fixed-view CCTV systems deployed in Southeast Asian cities, where heterogeneous traffic composition, high traffic density, frequent occlusions, and complex visual conditions are prevalent. The absence of large-scale datasets tailored to such mixed-traffic environments poses a significant limitation to the performance and generalization capability of existing object detection models. To address this gap, this paper presents a large-scale traffic image dataset for real-time vehicle detection in Vietnamese urban environments. The proposed dataset comprises 23,364 images collected from fixed-view CCTV traffic cameras deployed across Da Nang… More >
Open Access
ARTICLE
Kai Li1, Guolei Wang2, Dunmin Lu1,*, Yanbin Yao3, Zhiyong Li4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077651
Abstract To address the need for improving the efficiency of spray painting large and complex curved surfaces, this study investigates the arm–rail coordinated spray painting operation method and proposes a robot workspace calculation method for efficient spray area partitioning. The steps for calculating the workspace under the constraints of the principal normal vector and the conical pose domain are introduced, along with an analysis of the robot’s forward and inverse kinematics. Simulation validation was conducted using a wind turbine blade as the target object. The results show that the workspace based on conical pose domain constraints More >
Open Access
ARTICLE
Sang-Hyun Lee*, Qingtao Meng
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077207
Abstract Accurate and timely detection of rice leaf diseases is critical for ensuring global food security and maximizing agricultural yields. However, existing deep learning methods often struggle to balance the high accuracy required for detecting multi-scale lesions in complex field environments with the computational efficiency necessary for edge device deployment. This paper proposes You Only Look Once for Lightweight Detection (YOLOv11-LD), a lightweight object detection model for multi-scale rice leaf disease detection in real paddy field environments. The model is built on YOLOv11n and integrates a Re-parameterized Vision Transformer (RepViT) backbone, a Bidirectional Feature Pyramid Network… More >
Open Access
ARTICLE
Jun Qi1,*, Chao Yang2, Xinliu Wang2, Junyou Yang1, Haixin Wang1, Huaqin Chen2,3, Zhenyan Li3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076511
Abstract With the diversification of electricity trading forms driven by distributed energy technologies, the continuous growth of blockchain’s chained data structure poses dual challenges to traditional B+ tree indexes in terms of query efficiency and storage costs. This paper proposes a sliding window-based learned index construction method (SW-LI). The method consists of two key components. First, block timestamp–height samples are selected using a sliding window and used to train a linear regression model that captures the timestamp-to-height mapping. Second, an adaptive window adjustment mechanism is introduced: when the prediction error within a window exceeds a threshold,… More >
Open Access
ARTICLE
Sara H. Elsayed1, Rodaina Abdelsalam1, Mahmoud A. Ismail Shoman2, Raed Alotaibi3,*, Omar Reyad4,5,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079018
（This article belongs to the Special Issue: Security and Privacy in IoT: Cross-Domain Approaches and Cryptographic Innovations)
Abstract In today’s digitally connected world, where cyber threats are becoming increasingly complex, finding modern and secure text encryption solutions that maintain maximum runtime performance while offering high-level protection is more crucial. The deployment of sophisticated security paradigms is often accompanied by a significant escalation in computational overhead. Thus, the fundamental objective resides in the mitigation of computational overhead while maintaining an uncompromising security posture. Internet of Things (IoT) devices require strong security measures for data transmission. Also, protecting communication channels against illegal access and eavesdropping has become crucial due to the exponential expansion of the… More >
Open Access
ARTICLE
Qinglei Zhang, Zhenzhen Wang*, Jianguo Duan, Jiyun Qin, Ying Zhou
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076562
（This article belongs to the Special Issue: Complex Network Approaches for Resilient and Efficient Urban Transportation Systems)
Abstract In recent years, increasing urban mobility and complex traffic dynamics have intensified the need for accurate traffic flow forecasting in intelligent transportation systems. However, existing models often struggle to jointly capture short-term fluctuations and long-term temporal dependencies under noisy and heterogeneous traffic conditions. To address this challenge, this paper proposes a hybrid traffic flow forecasting framework that integrates Density-Based Spatial Clustering of Applications with Noise (DBSCAN), the Mamba state-space model, and the Transformer architecture. The framework first applies DBSCAN to multidimensional traffic features to enhance traffic state representation and reduce noise. The prediction module alternates… More >
Open Access
ARTICLE
Longquan Ma1, Huarong Zhao1,*, Liqin Zhou1, Linbo Xie1,*, Hongnian Yu2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.075679
（This article belongs to the Special Issue: Control Theory and Application of Multi-Agent Systems)
Abstract This paper studies a sampling-based dynamic event-triggered fixed-time bipartite formation algorithm for a class of continuous-time multi-agent systems with communication constraints. First, a periodic sampling mechanism is designed to reduce the system’s communication frequency. Then, a dynamic event-triggered control algorithm based on auxiliary variables is developed for sampled-data systems to further reduce the system’s triggering frequency. Next, to enhance the convergence speed of the dynamic event-triggered control method, a dynamic event-triggered fixed-time bipartite formation control scheme is investigated. Finally, using Lyapunov stability theory, signed graph theory, and relevant inequalities, a rigorous theoretical proof of the More >
Open Access
ARTICLE
Sathish Rao Udupi, Gururaj Bolar, Manjunath Shettar*, Ashwini Bhat
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.078574
（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 >
Open Access
ARTICLE
Xue Wang1,2, Cen Yang1, Yonghang Wang1, Mingming Wang1,3, Ying Chen4, Ping Li1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077868
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 >
Open Access
ARTICLE
Jiajia Liu1,2, Shuchen Pang3, Peng Xie3, Haitao Zhou3, Chenxi Du3, Haoran Hu3, Bo Tang3, Jianhua Liu3, Fei Jia1, Huibing Zhang1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077824
（This article belongs to the Special Issue: Advanced Networking Technologies for Intelligent Transportation and Connected Vehicles)
Abstract In the unmanned aerial vehicle (UAV) assisted edge computing system, the broadcast characteristics of the UAV signal, the high mobility of the UAV, and the limited airborne energy make the task offloading strategy face challenges such as increased risk of information disclosure, limited computing resources, and the trade-off between energy consumption and flight time. To address these issues, we propose a K-means in-depth reinforcement learning algorithm based on Soft Actor-Critic (SAC). The proposed method first leverages the K-means clustering algorithm to determine the optimal deployment of ground jammers based on the final distribution of mobile… More >
Open Access
REVIEW
Tsu-Yang Wu1, Yehai Xue1, Haonan Li2, Saru Kumari3, Lip Yee Por2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077332
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 >
Open Access
ARTICLE
Nahdi Saubari1,2,*, Kunfeng Wang1,*, Rachmat Muwardi3,*, Andri Pranolo4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077087
（This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation, 2nd Edition)
Abstract This study proposes an Adaptive Pooling method based on an alpha (α) parameter to enhance the effectiveness and stability of convolutional neural networks (CNNs) in image classification tasks. Conventional pooling techniques, such as max pooling and average pooling, often exhibit limited adaptability when applied to datasets with heterogeneous distributions and varying levels of complexity. To address this limitation, the proposed approach introduces an α parameter ranging from 0 to 1 that continuously regulates the contribution of maximum-based and average-based pooling operations in a unified and flexible framework. The proposed method is evaluated using two benchmark… More >
Open Access
ARTICLE
Hao Li1,2, Zhoujingzi Qiu1,2, Jianxiao Zou1,2, Haojie Wu1, Shicai Fan1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076784
（This article belongs to the Special Issue: Advances in Intelligent Video Object Tracking and Scene Understanding)
Abstract Self-supervised monocular depth estimation has attracted considerable attention due to its ability to learn without ground-truth depth annotations and its strong scalability. However, existing approaches still suffer from inaccurate object boundaries and limited inference efficiency. To address these issues, we present a Lightweight Conditional Diffusion Model for Monocular Depth Estimation (LCDM-Mono). The proposed framework integrates an efficient diffusion inference strategy with a knowledge distillation scheme, enabling the model to generate high-quality depth maps with only two sampling steps during inference. This design substantially reduces computational overhead and ensures real-time performance on resource-constrained platforms. In addition, More >
Open Access
ARTICLE
Udit Mamodiya1,*, Indra Kishor2, P. Satish Reddy3, K. Lakshmi Kalpana3, Radha Seelaboyina4, Harish Reddy Gantla5
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076464
（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 >
Open Access
ARTICLE
Raed Alotaibi1,*, Muhammad Atta Othman Ahmed2, Omar Reyad3,4,*, Nahla Fathy Omran5
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076156
（This article belongs to the Special Issue: Fake News Detection in the Era of Social Media and Generative AI)
Abstract The widespread use of social media has made assessing users’ tastes and preferences increasingly complex and important. At the same time, the rapid dissemination of misinformation on these platforms poses a critical challenge, driving significant efforts to develop effective detection methods. This study offers a comprehensive analysis leveraging advanced Machine Learning (ML) techniques to classify news articles as fake or true, contributing to discourse on media integrity and combating misinformation. The suggested method employed a diverse dataset encompassing a wide range of topics. The method evaluates the performance of five ML models: Artificial Neural Networks… More >
Open Access
ARTICLE
Cheng Yang, Xianghong Tang*, Jianguang Lu, Chaobin Wang
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076126
（This article belongs to the Special Issue: Artificial Intelligence Methods and Techniques to Cybersecurity)
Abstract Graph neural networks (GNNs) have demonstrated impressive capabilities in processing graph-structured data, yet their vulnerability to adversarial perturbations poses serious challenges to real-world applications. Existing defense methods often fail to handle diverse types of attacks and adapt to dynamic adversarial strategies because they typically rely on static defense mechanisms or focus narrowly on a single robustness dimension. To address these limitations, we propose an adversarial attention-based robustness strategy (AARS), which is a unified framework designed to enhance the robustness of GNNs against structural and feature perturbations. AARS operates in two stages: the first stage employs More >
Open Access
ARTICLE
Bofan Yang, Bingbing Li, Chuanping Hu*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077697
（This article belongs to the Special Issue: Artificial Intelligence Methods and Techniques to Cybersecurity)
Abstract The rapid evolution of malware obfuscation and packing techniques significantly undermines the effectiveness of traditional static detection approaches. Transforming malware binaries into grayscale or RGB images enables learning-based classification, yet existing CNN- and ViT-based models depend heavily on fixed-resolution inputs and exhibit poor robustness under cross-resolution distortions. This study proposes a lightweight and sample-adaptive Multi-Scale Vision Transformer (MSA-ViT) for efficient and robust malware image classification. MSA-ViT leverages a fixed set of input scales and integrates them using a Scale-Attention Fusion (SAF) module, where the largest-scale CLS token serves as the query to dynamically aggregate cross-scale More >
Open Access
ARTICLE
Ching-Lung Fan*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077260
（This article belongs to the Special Issue: Development and Application of Deep Learning and Image Processing)
Abstract Unmanned aerial vehicle (UAV) images have high spatial resolution and are cost-effective to acquire. UAV platforms are easy to control, and the prevalence of UAVs has led to an emerging field of remote sensing technologies. However, the details of high-resolution images often lead to fragmented classification results and significant scale differences between objects. Additionally, distinguishing between objects on the basis of shape or textural characteristics can be difficult. Conventional classification methods based on pixels and objects can indeed be ineffective at detecting complex and fine-scale land use and land cover (LULC) features. Therefore, in this More >
Open Access
ARTICLE
Zuoquan Zhu*, Menghan Wang, Xinyu Li, Mengxin Zhao
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076418
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 >
Open Access
ARTICLE
Abdulaziz A. Alsulami1,*, Badraddin Alturki2, Ahmad J. Tayeb2, Rayan A. Alsemmeari2, Raed Alsini1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076413
（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 >
Open Access
ARTICLE
Hongjiang Wang1, Jian Yu2, Tian Zhang3, Na Ren4, Nan Zhang2, Zhenyu Liu1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077997
（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 >
Open Access
ARTICLE
Chung-Wei Kuo1,2,*, Cheng-Xuan Wu1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076767
（This article belongs to the Special Issue: Secure and Intelligent Intrusion Detection for IoT and Cloud-Integrated Environments)
Abstract The rapid proliferation of the Internet of Things (IoT) has not only reshaped the digital ecosystem but also significantly widened the attack surface, leading to a surge in network traffic and diverse security threats. Deploying effective defense mechanisms in such environments is challenging, as conventional Intrusion Detection Systems (IDS) often struggle to balance computational efficiency with the reliable detection of low-frequency, high-impact threats, particularly within the tight resource constraints of edge devices. To address these limitations, we propose a lightweight, high-efficiency IDS framework specifically optimized for edge-based IoT applications, incorporating Mutual Information (MI)-based feature selection… More >
Open Access
REVIEW
Yihao Kuang1,2, Hong Zhang1,2, Jiaqi Wang1,2, Lingyu Jin1,2, Bo Huang1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076652
（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 >
Open Access
ARTICLE
Anandkumar Balasubramaniam, Seung Yeob Nam*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077582
（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 >
Open Access
ARTICLE
Fengqi Hao1,2,3, Yawen Hou2,3, Conghui Gao2,3, Jinqiang Bai2,3, Gang Liu4, Hoiio Kong1,*, Xiangjun Dong1,2,3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077252
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 >
Open Access
ARTICLE
Wenxuan Yu1, Wenjing Gao1, Jiuru Wang2, Rong Hao1,*, Jia Yu1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076731
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 >
Open Access
ARTICLE
Yang Lei1,2, Kangshuo Zhu3,4,*, Bo Jiang1, Yaodong Wang3,4, Feiyu Jia1, Zhaoning Wang1, Falin Qi1, Qiming Qu1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077314
（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 >
Open Access
ARTICLE
Chan-Woo Kim#, Sung-Jun Lee#, Chang-Lae Kim*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077143
（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 >
Open Access
ARTICLE
Mohamed Diouf1, Elisée Toe1,*, Manel Grichi2, Haïfa Nakouri1,3, Fehmi Jaafar1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077139
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 >
Open Access
ARTICLE
Pratik Goswami1,#, Hamid Naseem2,#, Khizar Abbas3,*, Kwonhue Choi1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077012
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 >
Open Access
ARTICLE
Seohyun Yoo, Joonseo Hyeon, Jaehyuk Cho*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077579
（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 >
Open Access
ARTICLE
Kexin Jiang1,2, Yong Fan2, Liang Wen1, Zhigang Xie1, Enzhi Dong1, Bo Zhu1, Zhonghua Cheng1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077392
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 >
Open Access
ARTICLE
Manh-Tuan Ha1, Nhu-Nghia Bui2, Dinh-Quy Vu1,*, Thai-Viet Dang2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077237
（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 >
Open Access
ARTICLE
Hye-Min Kwon, Sung-Jun Lee, Chang-Lae Kim*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077155
（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 >
Open Access
ARTICLE
Lan Xiong, Liang Wan*, Jingxia Ren
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076986
（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 >
Open Access
REVIEW
Hongtao Guo1, Shuai Li2, Shu Li1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076492
（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 >
Open Access
ARTICLE
Qiuxiao Mou, Haoyu Gui, Xianghong Tang*, Jianguang Lu
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076251
（This article belongs to the Special Issue: Advances in Time Series Analysis, Modelling and Forecasting)
Abstract Electrocardiogram (ECG) is a widely used non-invasive tool for diagnosing cardiovascular diseases. ECG zero-shot classification involves pre-training a model on a large dataset to classify unknown disease categories. However, existing ECG feature extraction networks often neglect key lead signals and spatial topology dependencies during cross-modal alignment. To address these issues, we propose a multimodal channel compression graph attention alignment network (MCCGAA). MCCGAA incorporates a channel attention module (CAM) to effectively integrate key lead features and a graph attention-based alignment network to capture spatial dependencies, enhancing cross-modal alignment. Additionally, MCCGAA employs a log-sum-exp loss function, improving More >
Open Access
ARTICLE
Zhi Zhang1,2, Bingyu Sun1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076236
（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 >
Open Access
ARTICLE
Zhu Fang1,2,*, Zhengquan Xu1,2, Weizhen He3, Bohao Xu3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076043
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 >
Open Access
ARTICLE
Abuzar Khan1, Abid Iqbal2,*, Ghassan Husnain1,*, Fahad Masood1, Mohammed Al-Naeem3, Sajid Iqbal4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077222
（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 withcross-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-designedge-cloud framework in which ports and vessels serve as federated clients, training vessel-type classifiers on local AIStrajectories while transmitting only clipped, Gaussian-perturbed updates to a zero-trust cloud coordinator employingsecure and robust aggregation. Using a public AIS corpus with realistic non-IID client partitions, our… More >
Open Access
REVIEW
Hitesh Mohapatra*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076726
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 >
Open Access
ARTICLE
Qian Hu, Lei Tan*, Qingjun Yuan, Zong Zuo, Yan Li
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076137
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 >
Open Access
ARTICLE
Dmytro Tymoshchuk1,*, Oleh Yasniy1, Iryna Didych2, Pavlo Maruschak3,*, Yuri Lapusta4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077062
（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 >
Open Access
ARTICLE
Yan-Hao Huang*, Yu-Tse Huang
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076800
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 >
Open Access
ARTICLE
Trong-Thua Huynh1,*, De-Thu Huynh2, Du-Thang Phu1, Hong-Son Nguyen1, Quoc H. Nguyen3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076623
（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 >
Open Access
ARTICLE
Malik Al-Essa1,*, Mohammad Qatawneh2,1, Ahmad Sami Al-Shamayleh3, Orieb Abualghanam1, Wesam Almobaideen4,1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076608
（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 >
Open Access
ARTICLE
Awais Ahmad1, Fakhri Alam Khan2,3, Awais Ahmad4, Gautam Srivastava5,6,7, Syed Atif Moqurrab8,*, Abdul Razaque9, Dina S. M. Hassan10,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076104
（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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