Open Access
ARTICLE
Kanyang Jiang1, Yingkai Kang2, Ming Li2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.083294
（This article belongs to the Special Issue: AI-Driven Optimization for Secure and Sustainable Edge IoT Services)
Abstract The Internet of Vehicular Agents (IoVA) interconnects distributed AI agents across vehicular networks to deliver real-time intelligent services for vehicular users. Due to the limited computing capacity of vehicles, AI agents are deployed on nearby RoadSide Units (RSUs) to perform computation-intensive inference. As vehicles traverse RSU coverage boundaries, AI agents must migrate to target RSUs to maintain service continuity. However, the communication and computing resources at each RSU are shared among multiple co-served vehicles, creating coupled allocation decisions that jointly determine system latency and energy consumption. To address this challenge, we propose a low-latency and… More >
Open Access
ARTICLE
Yu Shi1, Yunfeng Dong1,*, Lu Tian2,3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.080577
Abstract Failure prognosis provides critical decision-making support for Integrated System Health Management (ISHM), ensuring the operational safety of satellites in orbit. Temporal Convolutional Networks (TCNs), known for their capability in processing time-series data, have become an important approach for failure prognosis. The gradual performance degradation of satellites, combined with multi-physics coupling effects, gives rise to multi-scale features. However, existing TCN based failure prognosis methods remain limited in their ability to simultaneously capture both local and global features, posing challenges when processing such multi-scale features. To address this issue, a Cascaded Temporal Convolution and Transformer Network (CTCTN)… More >
Open Access
REVIEW
Langyue Zhao1,2, Yubin Yuan3,*, Yiquan Wu2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.080232
Abstract This paper presents a systematic survey of machine vision-based surface defect detection technologies, focusing on five core challenges in the field: interference from complex backgrounds, small object detection, class imbalance, dynamic scene modeling, and cross-scenario generalization. It reviews key technical approaches corresponding to these challenges over the past five years. Furthermore, a dataset characterization analysis framework is established around these challenges, summarizing and comparing the characteristics of over 40 publicly available datasets across more than ten scenarios, including PCB, photovoltaic, metal, and pavement surfaces. Quantitative selection metrics (such as the small target coefficient and texture More >
Open Access
ARTICLE
Ambreen Memon1, Aaron Bere1, Muhammad Nadeem Ali2, Byung-Seo Kim2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.078988
Abstract The modern internet infrastructure has enabled numerous applications by providing a seamless connectivity experience across each mode of connectivity. Infrastructure-based connectivity and device-to-device (D2D) are well-known connectivity modes for internet-based applications. The selection of the underlying communication medium significantly affects energy consumption during data transfer. This study proposes an Energy-Efficient Data Dissemination Approach (EEDDA) that integrates encounter prediction with a multi-criteria decision-making (MCDM) framework to reduce infrastructure-based energy consumption in IoT mobility environments. Unlike traditional optimization approaches that focus on single-objective routing or static network models, the proposed framework dynamically selects between Device-to-Device (D2D) and More >
Open Access
ARTICLE
Aihua Wu, Chenlu Huang*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.078692
Abstract Three-dimensional (3D) point cloud semantic segmentation is a core task in indoor scene understanding, providing detailed semantic information about spatial structures and object categories in indoor environments. Although methods based on deep learning have made steady progress in recent years, accurately segmenting complex indoor scenes remains challenging due to the unordered nature of point clouds and variations across large scales. Most existing networks have limited capability for multi-scale feature aggregation and struggle to balance local geometric details with global semantic context. These issues are further exacerbated by hierarchical downsampling, which often leads to the loss… More >
Open Access
ARTICLE
Anping Wan1,2,3, Yingchang Gao1,3, Weikang Liu1, Rui Yin1, Khalil Al-Bukhaiti1,3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081719
Abstract Vertical roller mills are essential for energy-intensive grinding in cement, minerals, and metallurgy industries, consuming up to 50% of plant electricity and frequently experiencing operational instabilities (including excessive vibration and main motor current fluctuations) that drive unplanned downtime, increased wear, and reduced throughput. Despite their importance, real-time autonomous optimization remains challenging due to the nonlinear interactions among grinding pressure, feed rate, separator speed, and aerodynamic factors, which limit traditional control strategies under varying loads. This paper presents a real-time operational optimization system for large-scale vertical roller mills using big industrial data and artificial intelligence (AI).… More >
Open Access
ARTICLE
Haotian Cao1,2, Qingsheng Zhu1,2,3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081695
Abstract Point Cloud Registration (PCR) is a basic task in computer vision, mobile robotics, and autonomous driving. PCR primarily faces challenges, including insufficient registration performance in low-overlap scenarios and high computational resource consumption in large-scale point cloud scenarios. Most recent PCR methods are transformer-based. Methods like transformers have quadratic computational complexity
Open Access
ARTICLE
Yao-Bo Long1, Yu-Ke Ouyang2, Bo Zhuang2, Ao-Qi Liu3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081203
Abstract This study addresses the real-time visual tracking task in edge environments by proposing a robust visual servoing control system based on a higher-order sliding mode observer, enabling a quadrotor UAV to autonomously track a moving soccer ball during outdoor sports broadcasts while relying solely on a monocular camera and an inertial measurement unit, thereby eliminating any dependency on external positioning or velocity sensors such as GPS. The system adopts a hierarchical control architecture in which the observer plays a central role: operating on resource-constrained edge devices, it leverages only visual information to estimate unknown external… More >
Open Access
ARTICLE
Pangwei Wang1,*, Jie Wang1, Zipeng Wang1, Hangrui Dong2, Li Wang1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.080815
（This article belongs to the Special Issue: Intelligent Transportation System (ITS) Safety and Security)
Abstract Traffic holographic perception refers to the real-time, high-fidelity, and multi-dimensional sensing of traffic states through the fusion of heterogeneous sensors, including cameras, radars, and connected vehicle data. The multi-source perception data obtained thereby can provide a complete digital representation of the road network for the Intelligent Transportation System (ITS). However, sensors are vulnerable to environmental interference, which can result in data loss at specific points or along arterial highways for certain periods, potentially undermining system safety and decision-making reliability. To address these challenges, a deep learning method based on Graph Convolutional Networks (GCN) and Gated… More >
Open Access
ARTICLE
Bin Fang1, Qi Yu1, Han Wu2, Xingxing Hou2, Jingyu Zhang2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.080210
（This article belongs to the Special Issue: AI-Driven Optimization for Secure and Sustainable Edge IoT Services)
Abstract The integration of Internet of Things (IoT) with blockchain technology introduces significant challenges in handling massive and frequent transaction data generated by distributed IoT devices. The Unspent Transaction Output (UTXO) model, widely adopted in blockchain systems like Bitcoin, faces critical scalability issues when applied to IoT environments. This is because the datasets it processes expand rapidly, which consumes a large amount of memory and increases the disk access latency of resource-constrained IoT nodes. Existing optimization approaches exhibit limitations in dynamic adaptability and protocol compatibility. To address these challenges, we propose an improved blockchain-empowered storage service More >
Open Access
ARTICLE
Ying Tang1, Chuanyi Ma2, Feng Guo1,*, Wenhao Sun1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079851
Abstract Road Abandoned Objects (RAOs) pose significant threats to traffic safety, particularly due to their small size, irregular shapes, and unpredictable distribution in complex road environments. The primary objective of this study is to develop an accurate and real-time detection framework for RAOs while maintaining low computational cost for practical deployment. To achieve this, we propose RAO-YOLO, a lightweight vision-based detection framework built upon an enhanced YOLO architecture. Specifically, a Mixed Aggregation Network (MANet) is introduced to improve multi-scale feature representation, and a Lightweight Shared Detail-Enhanced Detection (LSDD) head is designed to enhance localization accuracy for More >
Open Access
REVIEW
Xinjie Yao1, Junjie Zhu2, Tao Hong3,4, Dengyu Zhao5, Weikai Liu6, Guangsheng Xie7,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.075316
（This article belongs to the Special Issue: Attention Mechanism-based Complex System Pattern Intelligent Recognition and Accurate Prediction)
Abstract The attention mechanism, as a key technology for enhancing the performance of deep learning, is gaining increasingly widespread attention in medical image analysis due to its ability to focus on critical features and suppress redundant information. In recent years, the continuous evolution of attention methods has significantly improved their accuracy and robustness in key medical tasks such as lesion detection, tissue segmentation, and multimodal fusion, providing crucial support for building reliable clinical decision support systems. This paper systematically reviews the advances in attention-based methods for medical image analysis, comparing their performance with mainstream models like… More >
Open Access
ARTICLE
Ahmad Almufarreh1, Rogaia Hassan Osman Hassan2,3, Ashfaq Ahmad4, Muhammad Arshad2,5,*, Choo Wou Onn6
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.082264
Abstract The explosive increase in connectivity has multiplied the volume and speed of network traffic, putting the world at greater risk from sophisticated and emerging cyber-attacks. Smart learning environments, which rely on cloud-based learning management systems, virtual classrooms, and interconnected educational devices, generate large volumes of dynamic network traffic that must be continuously monitored to protect sensitive academic data and ensure uninterrupted learning services. In this study, three supervised machine learning classifiers, namely Random Forest, Logistic Regression, and k-Nearest Neighbours (kNN), are designed and evaluated for anomaly detection using the UNSW-NB15 benchmark. Models are trained and… More >
Open Access
ARTICLE
Xiong Luo*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081416
Abstract Large multimodal models (LMMs) can produce fluent radiology reports, yet two clinically important error modes remain common: unsupported assertions and missed findings. Optimizing both under open supervision remains difficult because many pipelines still rely on overlapping parser families during training and evaluation. This paper introduces Truth-Anchored Dual-Extractor Counterfactual-Constrained Training (TA-DECT), which combines an ontology-derived atomic finding interface with four coupled objectives: structured prediction, dual-extractor minimax consistency on generated reports, deterministic counterfactual selectivity under evidence removal, and label-anchored completeness. In matched-path internal comparisons across chest radiographs (CheXpert, MIMIC-CXR, MIMIC-CXR-JPG) and chest computed tomography (CT; CT-RATE), TA-DECT More >
Open Access
ARTICLE
Zheng Xu1,2, Zihao Pan1, Ning Yang1, Daoxing Guo1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.080559
（This article belongs to the Special Issue: Aerial Innovation Spectrum: All-Domain Research in UAV Communication, Navigation, and Autonomy)
Abstract Unmanned Aerial Vehicle (UAV) communications in complex electromagnetic environments face challenges such as strong interference, high dynamic Doppler shifts, and limited onboard computing power. In these scenarios, traditional blind beamforming algorithms suffer from slow convergence and difficulty in handling Gaussian-like signals (e.g., Orthogonal Frequency Division Multiplexing (OFDM)). To address these issues, this paper proposes a Lightweight Robust Transfer learning-based Blind Beam Forming method (LRT-BF). This method constructs a self-supervised optimization framework centered on a pre-trained signal classifier and innovatively introduces a joint loss function combining classification confidence guidance with output power minimization, achieving fully blind… More >
Open Access
ARTICLE
Asma Sattar1, Maryam Bukhari2, M. Saud Khan3, Anam Mustaqeem4, Mi Young Lee5, Seungmin Rho5,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.078413
Abstract Video surveillance systems play an important role in maintaining security in smart city environments. In this context, person identification (Re-ID) systems based on deep learning are currently drawing substantial academic interest. However, these systems remain vulnerable to adversarial attacks. In existing methods, several attacks against Re-ID systems have been designed; nevertheless, they operate in the spatial domain. Existing attacks often suffer from perturbation visibility and low imperceptibility, making them easily detectable by human observers or automated detection systems. From this line of research, this study proposed a novel and potent alternative by designing frequency domain… More >
Open Access
ARTICLE
Shuaiyu Zhu1, Sergey Ablameyko1,2, Ji Li3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.082486
（This article belongs to the Special Issue: Advances in Image Generation: Theories, Architectures, and Applications)
Abstract Satellite remote sensing images pose significant challenges for object detection due to their high resolution, complex scenes, and large variations in target scales. To address the insufficient detection accuracy of the YOLOv11n model in remote sensing imagery, this paper proposes two improvement strategies. Method 1: (a) a Large Separable Kernel Attention (LSKA) mechanism is introduced into the backbone network to enhance feature extraction for small objects; (b) a Gold-YOLO structure is incorporated into the neck network to achieve multi-scale feature fusion, thereby improving the detection performance of objects at different scales. Method 2: (a) the More >
Open Access
ARTICLE
Zhichao Pei, Ou Ye*, Panyu Yang, Kaiwen He
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081490
Abstract To address the issue of insufficient transferability of existing adversarial example generation methods for vision-language pre-training (VLP) models, this paper proposes an adversarial example transfer method for VLP models based on negative sample feature perturbation. First, a novel cross-modal collaborative perturbation strategy is constructed. By introducing negative samples into the cross-modal perturbation mechanism, the strategy explores more perturbation directions, breaks the original modal alignment constraints and avoids the local focus of adversarial perturbations. Then, to reduce the computational cost, a dynamic threshold attack strategy is built to measure the modal similarity of the generated adversarial… More >
Open Access
ARTICLE
Peeyush Kumar Kamlesh1,*, Himanshi Sharma2, Shrikant Verma1, Ajay Singh Verma3,4, Reena Saxena5, Dinesh C. Sharma6
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081382
Abstract In the present work, Ba3PX3 (X = F, Cl, Br, I) all-inorganic and lead-free halide compositions have been studied as possible replacements for hybrid perovskites using first-principles calculations. All the considered materials were found to exhibit direct band gaps at the Γ-point, decreasing from 2.37 eV (Ba3PF3) to 1.48 eV (Ba3PI3). The optical calculations reveal strong absorption in the visible and near-UV regions, with the static dielectric constants ranging from 2.75 to 4.35 in the halide series. All the compounds are mechanically stable and have tuneable ductility and stiffness properties. Lattice stability is confirmed by thermodynamic analysis More >
Open Access
ARTICLE
Hongbin Wang1,2, Liusong Li1,2, Di Jiang1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079330
Abstract Multimodal sentiment analysis aims to fuse emotional information from data across different modalities to predict human emotional states. Although existing multimodal sentiment analysis methods have made significant progress, the heterogeneity between modalities still leads to an imbalance in feature space distribution, thereby hindering the effective learning and fusion of multimodal representations. In addition, the presence of emotion-irrelevant information in auxiliary modalities is another major factor contributing to differences in feature space distributions. To address this issue, we propose a Hierarchical Contrastive Representation Learning framework with Multimodal Feature Decoupling (HCRL-MFD). To reduce emotion-irrelevant information and optimize… More >
Open Access
ARTICLE
Yejin Kwon1, Jeongcheol Lee1, Youngbom Park2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.080249
Abstract The web-based High-Performance Computing (HPC) platform provides a simulation environment that enables users to perform computational science and engineering tasks through web services, thereby eliminating the need for complex terminal-based environments. Notwithstanding the aforementioned advantages, extant platforms frequently necessitate a considerable degree of user expertise, whilst the intricacy of simulation configuration and execution engenders limitations in terms of accessibility and usability. Furthermore, while Retrieval-Augmented Generation (RAG)-based systems are effective for information retrieval, they are insufficient for accurately constructing and invoking executable service tools. In order to address these limitations, this study proposes a user agent… More >
Open Access
ARTICLE
Kemeng Zhu1, Dingju Zhu1,2,*, Shihua Mao1, Jinchen Wu3, Depeng Kong4, Kaileung Yung5, Andrew W. H. Ip6
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.082431
Abstract Unmanned aerial vehicles (UAVs) have become an increasingly important platform for agricultural remote sensing, yet the accurate recognition of pests and diseases is frequently compromised by drastic scale variability and complex environmental backgrounds. To address these challenges, this study introduces a novel attention-driven approach centered on a Multi-Scale Grouped Channel–Spatial Dual Attention (MS-GCDA) mechanism. The MS-GCDA module achieves robust feature calibration by decoupling and jointly modeling multi-scale spatial contexts and grouped channel dependencies, which significantly enhances the model’s sensitivity to fine-grained disease symptoms while suppressing background clutter. This core mechanism is integrated into Augmented EfficientNet… More >
Open Access
ARTICLE
Vinh Truong Hoang*, Nghia Dinh, Luu Quang Phuong, Kiet Tran-Trung, Ha Duong Thi Hong, Bay Nguyen Van, Hau Nguyen Trung, Thien Ho Huong
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.078743
（This article belongs to the Special Issue: Attention Mechanism-based Complex System Pattern Intelligent Recognition and Accurate Prediction)
Abstract Visual speech recognition is a central problem in computer vision, encompassing both lip reading (visual speech recognition) and sign language recognition. Although substantial progress has been achieved independently on each task, their complementary characteristics have rarely been explored jointly. In this work we propose UniModal-LSR (Unified Multimodal Lip and Sign Recognition), a novel deep learning framework that jointly addresses lip reading and sign language recognition within a single multimodal architecture. By exploiting shared properties of visual communication channels, namely temporal dynamics, spatial articulation structure, and contextual dependencies, the proposed model enables bidirectional transfer of knowledge… More >
Open Access
ARTICLE
Li Chen1, Fan Zhang2,*, Guangwei Xie3, Yanzhao Gao1, Xiaofeng Qi1, Mingqian Sun2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.078314
Abstract Remote sensing object detection aims to identify and localize specific targets in satellite or aerial imagery. Spiking Neural Networks (SNNs), benefiting from their implicit feedback-based and event-driven brain-inspired dynamics, offer a promising solution to alleviate the high energy consumption of conventional ANN-based detection models. However, existing SNN-based approaches for remote sensing object detection—particularly for small, arbitrarily rotated objects—are still in their infancy and suffer from a substantial performance gap compared with ANN counterparts. In this work, we draw inspiration from the hierarchical sparse perception mechanisms of biological vision and integrate dynamic receptive field modulation into… More >
Open Access
ARTICLE
Kang-Woo Lee, Dong-Hee Lee*, Dae-Il Kwon*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081667
（This article belongs to the Special Issue: AI-Enabled Prognostics and Health Management: Advanced Methodologies, Intelligent Systems, and Field Applications)
Abstract Early-life cycle-life prediction for lithium-ion batteries—estimating end-of-life from initial cycles—is valuable for rapid cell screening and battery health management. We investigate whether an explicit correlation-structure descriptor can complement physics-informed ΔQ-based indicators and generic early-cycle statistical features on the Severson 124-cell benchmark. We develop a lightweight hybrid framework that combines ΔQ-based health indicators, data-driven statistical features, and Laplacian Eigenmaps embeddings derived from a Pearson-correlation feature graph, with XGBoost used as the predictor. Across five feature configurations (ΔQ Only, ΔQ + Statistics, Hybrid Append, VIF + Laplacian, and Integrated Laplacian), we evaluate pointwise regression accuracy using RMSE and R2 together… More >
Graphic Abstract
Open Access
ARTICLE
DongHwan Ku, Sun Park*, JongWon Kim
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079582
（This article belongs to the Special Issue: Advancing Edge-Cloud Systems with Software-Defined Networking and Intelligence-Driven Approaches)
Abstract In future smart cities, ensuring urban safety requires data-driven decision-making through real-time monitoring tailored to dynamic, complex environments. Such surveillance relies on diverse mobile sensor devices, including drones, robots, patrol vehicles, and portable sensors. However, scaling and validating these systems directly in the real world is constrained by high costs, safety risks, and limited reproducibility across operating conditions. A scalable Digital Twin (DT) model can overcome these constraints by reproducing real-world mobile surveillance in a virtual environment, enabling large-scale simulations of sensor deployment, communication scenarios, and high-density visual data processing. Nevertheless, digital twins still face… More >
Open Access
ARTICLE
Nikita Sakovich1, Dmitry Aksenov1, Ekaterina Pleshakova1,*, Sergey Gataullin1,2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079522
Abstract We introduce the DARE-Q (Distribution-Aware Residual Entropy Quantization) method—a post-training quantization method for neural network weights designed to reduce bit-width with minimal degradation of model quality. Unlike traditional approaches that solely optimize the mean squared error of weight approximation, DARE-Q additionally considers the entropy of the quantization residual, allowing for control over the statistical properties of the resulting error. The method is based on channel-wise symmetric uniform quantization with scaling based on a combined loss function that includes L2 distortion and entropy regularization. The DARE-Q method is implemented as a compact DAREQuantLinear module which can… More >
Open Access
ARTICLE
Akmalbek Abdusalomov1, Kudratjon Zohirov2, Sojida Ochilova2, Jakhongir Oramov3, Zafar Ruziyev3, Malika Rustamova4, Gulrukh Sherboboyeva5, Komil Tashev6,7, Young Im Cho1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081292
（This article belongs to the Special Issue: Integrating Generative AI with UAVs for Autonomous Navigation and Decision Making)
Abstract Unmanned aerial vehicles (UAVs) are also increasingly becoming more often in the transportation infrastructure of smart cities, so that they can successfully achieve real-time observation of traffic, emergency coordination, and two-way communication relaying. However, the security and privacy risks arising in open, highly mobile intelligent transportation systems (ITS) enabled by UAVs are critical, as they pose threats of impersonation, replay, Sybil, and tracking attacks. Secondly, standard static authentication mechanisms are unable to support dynamic risk environments and excessive resource consumption on UAV platforms with limited capacity. To address these challenges, this study introduces a Generative-AI-assisted… More >
Open Access
ARTICLE
Xiangqin Chen*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079663
Abstract Simultaneous localization and mapping (SLAM) must remain reliable when sensing suites and operating conditions vary across platforms and deployments. Beyond correspondence degradation, a dominant deployment failure mode is misweighted constraints: under distribution shift, uncertainty estimates can become miscalibrated, allowing a small set of overconfident factors to dominate iterative optimization and destabilize inference. This article presents conformal-calibrated foundation-factor graph SLAM (
Open Access
ARTICLE
Lei Wang1, Hongji Luo2, Yong Heng2, Jingnan Tang2, Xiaochuan Ju2, Jianwei An1,*, Haitao Xu1, Xianwei Zhou1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.082418
（This article belongs to the Special Issue: Advanced Networking Technologies for Intelligent Transportation and Connected Vehicles)
Abstract In intelligent connected vehicles (ICVs) system, driving users connect to service providers (SPs) to obtain location-based services (LBS). Users transmit large volumes of encrypted sensitive information related to their itineraries to SPs to access value-added services. Attackers may launch chosen-ciphertext attacks (CCA) against SPs by exploiting the malleability of homomorphic encryption. This enables adversaries to infer or steal private key information, thereby threatening the long-term privacy of user data. Furthermore, existing key management technologies in ICVs system predominantly rely on passive defense strategies and suffer from limitations such as single protection mechanisms, delayed updates, and More >
Open Access
ARTICLE
Zhihao Zhang1,#, Zhuodong Liu1,#, Xiangyu Li2, Lei Zhang1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081922
（This article belongs to the Special Issue: Recent Advances in Malware Detection)
Abstract Financial fraud detection across institutions faces a fundamental tension between the need for diverse training data and regulatory prohibitions on sharing sensitive records. Existing federated learning approaches suffer from performance degradation under non-IID distributions and substantial utility losses when uniform differential privacy is applied to inherently sparse fraud signals. To this end, this paper proposes HiFraud, a hierarchical federated framework featuring three key components: fraud-aware dynamic clustering with complementarity regularization to group institutions by fraud pattern similarity while preserving rare-type representation; star-chain knowledge transfer augmented by not-true-class distillation to propagate novel fraud patterns rapidly within… More >
Open Access
ARTICLE
Zhe Wang1, Yu Yan2, Junqi Tong1, Yijun Lin1, Dechun Yin1,*, Xiaoliang Zhao1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081155
Abstract As software applications grow increasingly large and complex, traditional code vulnerability detection methods struggle with performance and efficiency. Although code visualization-based algorithms have demonstrated effectiveness in capturing sparse features and complex workflows in large-scale source code, their capacity to extract global semantic information and intricate long-range dependencies remains limited. Recent large language model (LLM)-based approaches have shown promising accuracy by leveraging rich contextual information, but their high computational cost often limits practical efficiency. To address these challenges, we propose VulSCP, a new framework that integrates sequential convolution with a parallel attention mechanism. Specifically, VulSCP first… More >
Open Access
ARTICLE
Junyi Wang1, Jianghai Geng1,*, Jiaqi Liu2, Haibin Zhu3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.080895
Abstract In intelligent manufacturing and remanufacturing systems, the thermal safety of the power distribution infrastructure is crucial for ensuring production continuity, equipment reliability, and operational resilience. Traditional temperature monitoring methods often have problems such as high deployment costs, strong environmental sensitivity, or limited physical interpretability in distributed workshop environments. To address these limitations, this study proposes a physically information-driven intelligent thermal color-changing fault identification framework. Based on thermochromic experiments, irreversible color-changing coatings are selected, which are combined with a visual-based computing pipeline for autonomous overheating detection. The framework proposes a thermal fault temperature identification algorithm based… More >
Open Access
REVIEW
Yuyin Ma1, Yufang Liu1, Yijun Lu2, Zhen Tian3, Fujiang Yuan4, Yanhong Peng4,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.080309
（This article belongs to the Special Issue: Computational Materials Design and Intelligent Processing for Advanced Alloys and Manufacturing Systems)
Abstract Additive manufacturing (AM) has emerged as a transformative technology in modern manufacturing, offering unprecedented capabilities for producing complex geometries and customized components. However, the widespread adoption of AM is hindered by insufficient quality control, stemming from the multi-factor coupling characteristics of the manufacturing process. Machine learning (ML) presents a promising solution by enabling data-driven approaches to process optimization, quality prediction, and defect detection. This review examines the application landscape of ML techniques in AM through comprehensive analysis of recent literature. The study categorizes ML applications into four primary domains: real-time process monitoring and control, process… More >
Open Access
ARTICLE
Rubina Castro1,2, Bruno Silva1,3, Luiz Guerreiro Lopes1,4, Fábio Mendonça1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079979
（This article belongs to the Special Issue: Advances in Nature-Inspired and Metaheuristic Optimization Algorithms: Theory, Applications, and Emerging Trends)
Abstract Path planning for autonomous underwater vehicles requires reliable and computationally efficient methods, particularly in cluttered environments. This work presents a comparative evaluation of representative approaches, including metaheuristic optimization methods (continuous genetic algorithm, particle swarm optimization, gray wolf optimizer, and Jaya), a sampling-based method (probabilistic roadmap with genetic refinement), a reactive strategy (artificial potential fields), and a control-based approach (model predictive control with control barrier functions). The algorithms are assessed in a controlled two-dimensional simulated workspace with randomly generated obstacles and systematically increasing obstacle density. Each configuration is evaluated across multiple independent trials using metrics such… More >
Open Access
REVIEW
Shan Jiang1, Wenxin You2, Haoran Zhang3, Shichang Xuan3,*, Jiaxing Shen4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079321
Abstract Large Language Models (LLMs) have been playing a transformative role in natural language understanding and generation, yet adapting LLMs to domain-specific and privacy-sensitive data remains challenging under centralized training. Federated Learning (FL) provides a promising alternative by enabling training LLMs collaboratively without sharing raw data. However, integrating FL and LLMs introduces new challenges, including model size, device heterogeneity, non-IID data, and alignment requirements. This survey offers a structured overview of the federated LLM ecosystem. We present a comprehensive taxonomy encompassing system architectures, advanced data strategies for addressing heterogeneity, and retrieval-augmented generation in federated contexts. Additionally, More >
Open Access
ARTICLE
Mohammed Saad Javeed1, MD AL Rafi2, Arifa Akter Eva3, Muhammad Firoz Mridha3, Qiangfu Zhao4,*, Jungpil Shin4,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076825
（This article belongs to the Special Issue: Advances in Machine Learning and Artificial Intelligence for Intrusion Detection Systems)
Abstract Hybrid and multi-cloud infrastructures make IP address management (IPAM) difficult, especially when IP and Domain Name System (DNS) records must stay consistent across on-premises networks and cloud platforms. Traditional IPAM tools often lack deep automation and cross-platform visibility, which leads to DNS drift, IP conflicts, and configuration errors. This paper proposes a unified, Application Programming Interface (API)-driven IPAM framework that integrates Infoblox Network Identity Operating System (NIOS) with Amazon Web Services (AWS) Route53 and Azure DNS using Infrastructure-as-Code and CI/CD pipelines. We generate an IPAM event log from Infoblox API simulations and fuse it with More >
Open Access
ARTICLE
Jiayi Tang1, Liang Cao2,*, Guanghui Xu1, Manqi Dong2, Ming Li3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081488
（This article belongs to the Special Issue: M5S: Multiphysics Modelling of Multiscale and Multifunctional Materials and Structures)
Abstract The accelerated design of next-generation semiconductor interconnects faces a critical “applicability gap”. Purely data-driven models effectively navigate vast chemical spaces, but they often yield candidates that are theoretically performant yet violate practical manufacturing constraints. To bridge this disconnect, this study proposes a neuro-symbolic decision support framework that systematically integrates inductive graph learning with deductive engineering logic for Safe-by-Design material screening. The framework operates through a hierarchical dual-stream architecture. First, an inductive Graph Neural Network (GNN) engine transforms 3D crystal structures into topological graph representations to predict thermodynamic stability and metallicity with high discriminative power (AUC… More >
Open Access
ARTICLE
Peiying Zhang1,2, Yihong Yu1,2, Lizhuang Tan3,4,*, Shuqing He5, Jian Wang6, Ameer El-Sayed7
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.081224
（This article belongs to the Special Issue: Advanced Edge Computing and Artificial Intelligence in Smart Environment)
Abstract As a core information infrastructure in the 6G era, the Space-Air-Ground Integrated Network (SAGIN) integrates space-based, air-based, and ground-based network resources to achieve seamless communication across all domains. However, its characteristics such as heterogeneous node coupling and dynamic topology changes make it prone to cascading failures, severely threatening critical business continuity in Internet of Things (IoT) applications spanning smart cities, healthcare, transportation, and industrial automation. This paper conducts systematic research addressing challenges including modeling difficulties in SAGIN cascading failure propagation, insufficient coordination of defense strategies, and poor resource adaptability. First, a multi-factor coupled dynamic model… More >
Open Access
REVIEW
Hitesh Mohapatra*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.080961
（This article belongs to the Special Issue: AI-Driven Optimization for Secure and Sustainable Edge IoT Services)
Abstract This review systematically analyzes Reinforcement Learning approaches for self-healing in energy-constrained secure edge IoT networks across 82 studies from 2020 to 2026. Unlike existing surveys that focus on general RL applications, the proposed review focuses on a three-level taxonomy that uniquely addresses edge IoT deployment realities through formulation-scope-hardware mapping. The work develops a novel three-level taxonomy classifying recovery scope (node, link, service, network), RL formulations (tabular, deep, multi-agent, model-based), and constraint integration (energy, latency, security, hybrid), revealing service migration dominance at 30% coverage and node recovery achieving 38% maximum energy savings. Normalized performance baselines establish More >
Open Access
ARTICLE
Xionglve Li1, Changsheng Hou2,*, Yuzhou Huang3, Zhenyu Qiu1, Gang Hu1, Bingnan Hou1, Wei Dong1, Zhiping Cai1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.080452
Abstract The Internet inter-domain paths, i.e., the AS paths, are important for network management, traffic engineering, and security. Due to business confidentiality, security, and privacy, the AS path information is non-public. Due to limited measurement resources, obtaining AS path information by measurement-based approaches is not scalable. Therefore, path inference approaches are proposed to broaden the availability of path information. These approaches assume that AS paths remain stable over a certain period of time, yet conflicting research findings question this assumption. Furthermore, the duration of the “certain period of time” is not clearly defined. Thus, we aim… More >
Open Access
ARTICLE
Xuan-Thuc Nguyen1, Le-Minh Nguyen1, Ngoc-Quynh Nguyen1, Nhu-Nghia Bui2, Dinh-Quy Vu3,*, Thai-Viet Dang2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.080008
（This article belongs to the Special Issue: Aerial Innovation Spectrum: All-Domain Research in UAV Communication, Navigation, and Autonomy)
Abstract The development of unmanned automated vehicles (UAVs) has become a key focus in aerial robotics, fueling the need for navigation systems capable of performing complex and delicate tasks with speed and precision. However, the end-to-end path tracking process often encounters challenges in learning efficiency, generalization, and varying environmental conditions. In this paper, we propose the novel IRL-TP framework for learning-based UAVs’ trajectory planning that employs a deep inverse reinforcement learning (IRL) approach. Firstly, the RL-based path planner must develop a reward function that effectively captures flight safety, collision avoidance, trajectory smoothness, and navigation efficiency within… More >
Open Access
REVIEW
Manjodh Kaur1, Princy Randhawa2,*, Jitendra Jaiswal2, Deepak Dubal3, Ravindra N. Bulakhe4,5, Deepanraj Balakrishnan6, Nithesh Naik7,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079503
Abstract This review emphasizes the growing role of artificial intelligence (AI) in transforming the materials discovery process into a data-driven and autonomous approach. It systematically traces the evolution of scientific paradigms in materials science and examines how machine learning, generative models, and AI agents are revolutionizing the design, screening, and optimization of materials. A key contribution is a detailed, step-by-step machine learning framework that guides researchers through data collection, preprocessing, feature engineering, model development, and validation, utilizing publicly available materials databases and computational tools. Additionally, the review discusses the latest advances in generative AI and autonomous More >
Open Access
ARTICLE
Zhen Yan1, Jiani Huang1, Yanlin Gu1, Qingqing Xu1, Yuyu Guo1, Kun Lin2, Juan Hou1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079204
（This article belongs to the Special Issue: Mechanical Behavior of Materials with Advanced Modeling and Characterization)
Abstract This study addresses the challenge of balancing “high deposition efficiency with large layer thickness” and “component mechanical integrity” in Laser Powder Bed Fusion (LPBF) additive manufacturing. Using 304L stainless steel as an example, a hybrid modeling strategy combining physical mechanism models and residual machine learning was proposed, achieving accurate prediction of densification at H = 60, 90, and 120 μm (test set R2 = 0.833, MAE = 0.104). Within the Doehlert matrix experimental design framework, the coupled effects of laser power, scanning speed, and scanning spacing on densification behavior, microstructure evolution, and mechanical response at different… More >
Open Access
ARTICLE
Chellaiah Ayyanar, Sumit Pramanik*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079080
（This article belongs to the Special Issue: Mechanical Behavior of Materials with Advanced Modeling and Characterization)
Abstract The potential of nontoxic elastomers like polydimethylsiloxane (PDMS) and bioceramic hydroxyapatite (HA) crystals has been demonstrated in numerous advanced applications. However, their crosslinking behavior in a composite system has not yet been modeled through simulation. Therefore, we employed a simulation-based approach to construct initial unit cell models of PDMS and HA, and for the first time, created PDMS-HA molecular structures using Materials Studio (MS) software. Molecular dynamics (MD) methods were applied to gain deeper insight into the structural framework and physical properties of PDMS, HA, and PDMS-HA composite. Equilibrium state via Forcite, physical, chemical, and thermal… More >
Open Access
ARTICLE
Chanyoung Kim, Jion Kim, Byeong-Seok Shin*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.080992
Abstract Diffusion-based methods have substantially improved the performance of full-body Text-to-Motion (T2M) generation from natural language descriptions. Despite this progress, accurately capturing the fine-grained semantics of composite prompts remains challenging. Approaches that rely solely on a single global text condition often fail to retain part-specific semantic cues, leading to deviations in the motions of certain body parts from the intended descriptions. Recent methods have attempted to address this by incorporating both global and local conditions, yet these are typically combined using fixed ratios or applied in separate stages, which restricts their adaptability to evolving semantic requirements… More >
Graphic Abstract
Open Access
ARTICLE
Ammar Odeh*, Osama Alhaj Hassan, Anas Abu Taleb
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.080940
Abstract The rapid growth of sophisticated malware and the increasing diversity of computing environments have exposed critical limitations in traditional centralized malware detection systems, particularly in data privacy, scalability, and adaptability. This study proposes a privacy-preserving, collaborative malware-detection framework that leverages federated learning to improve detection accuracy while keeping sensitive data local to participating devices. The objective is to address emerging malware threats by combining behavioral and memory-based analysis within a decentralized learning paradigm. The proposed framework employs federated learning to train a global malware detection model without transferring raw data. Each client locally extracts discriminative… More >
Open Access
ARTICLE
Zhaoxu Zhou, Yanjiang Liu, Zibin Dai*, Junwei Li
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.080072
Abstract Stream ciphers are simple to implement and fast at encrypting and decrypting data, making them very important in information security. Boolean functions are a core part of stream ciphers. However, their mainstream hardware implementations face two main problems, including wasted area resources and excessive critical path delay. These issues limit the energy efficiency and integration level of stream cipher chips. To address these problems, this paper proposes an energy-efficient design method for a 64-bit Boolean function reconfigurable operation unit (BFROU), aiming to improve the computational efficiency of Boolean functions in stream ciphers. To optimize the… More >
Open Access
ARTICLE
Bhanu Talwar1,*, Puneet Thapar1, Tahani Alsubait2, Mai Alduailij3, Ateeq Ur Rehman4,*, Salil Bharany5
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.080977
（This article belongs to the Special Issue: Advanced Localization and Multi-Sensor Fusion in WSN, IoT & VANET)
Abstract Wireless Sensor Networks (WSNs) play a vital role in smart city Internet of Things (IoT) applications, including environmental monitoring, intelligent transportation, and infrastructure management. However, limited battery capacity, uneven energy consumption, and inefficient clustering and routing mechanisms significantly reduce network lifetime, reliability, and scalability, especially in large-scale IoT deployments. Traditional routing protocols often rely on single-objective optimization or static clustering strategies, which fail to maintain long-term energy balance and stable communication performance. To address these challenges, this paper proposes iPAFAR, a Pareto-based multi-objective clustering and routing framework designed for IoT-enabled WSNs. The proposed model formulates… More >
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