Open Access
ARTICLE
Nikita Golovkin1,2, Olesya Nikulenkova3, Vsevolod Pobezhimov1, Alexander Nesmelov1, Sergei Chvalun1, Fedor Sorokin3, Arthur Krupnin1,3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073161
（This article belongs to the Special Issue: Perspective Materials for Science and Industrial: Modeling and Simulation)
Abstract This study presents and verifies a hybrid methodology for reliable determination of parameters in structural rheological models (Zener, Burgers, and Maxwell) describing the viscoelastic behavior of polyurethane specimens manufactured using extrusion-based 3D printing. Through comprehensive testing, including cyclic compression at strain rates ranging from 0.12 to 120 mm/min (0%–15% strain) and creep/relaxation experiments (10%–30% strain), the lumped parameters were independently determined using both analytical and numerical solutions of the models’ differential equations, followed by cross-verification in additional experiments. Numerical solutions for creep and relaxation problems were obtained using finite element analysis, with the three-parameter Mooney-Rivlin… More >
Graphic Abstract
Open Access
ARTICLE
Yongmei Zhang*, Tianxin Zhang, Linghua Tian
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073159
（This article belongs to the Special Issue: Advances in Time Series Analysis, Modelling and Forecasting)
Abstract Marine forecasting is critical for navigation safety and disaster prevention. However, traditional ocean numerical forecasting models are often limited by substantial errors and inadequate capture of temporal-spatial features. To address the limitations, the paper proposes a TimeXer-based numerical forecast correction model optimized by an exogenous-variable attention mechanism. The model treats target forecast values as internal variables, and incorporates historical temporal-spatial data and seven-day numerical forecast results from traditional models as external variables based on the embedding strategy of TimeXer. Using a self-attention structure, the model captures correlations between exogenous variables and target sequences, explores intrinsic More >
Open Access
ARTICLE
Weishan Gao1,2, Ye Wang1,2, Xiaoyin Wang1,2, Xiaochuan Jing1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073850
Abstract In the field of intelligent surveillance, weakly supervised video anomaly detection (WSVAD) has garnered widespread attention as a key technology that identifies anomalous events using only video-level labels. Although multiple instance learning (MIL) has dominated the WSVAD for a long time, its reliance solely on video-level labels without semantic grounding hinders a fine-grained understanding of visually similar yet semantically distinct events. In addition, insufficient temporal modeling obscures causal relationships between events, making anomaly decisions reactive rather than reasoning-based. To overcome the limitations above, this paper proposes an adaptive knowledge-based guidance method that integrates external structured… More >
Open Access
ARTICLE
Danping Niu1, Yuan Ping1,*, Chun Guo2, Xiaojun Wang3, Bin Hao4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073630
Abstract With the increasing complexity of malware attack techniques, traditional detection methods face significant challenges, such as privacy preservation, data heterogeneity, and lacking category information. To address these issues, we propose Federated Dynamic Prototype Learning (FedDPL) for malware classification by integrating Federated Learning with a specifically designed K-means. Under the Federated Learning framework, model training occurs locally without data sharing, effectively protecting user data privacy and preventing the leakage of sensitive information. Furthermore, to tackle the challenges of data heterogeneity and the lack of category information, FedDPL introduces a dynamic prototype learning mechanism, which adaptively adjusts the More >
Open Access
ARTICLE
Ahmed Awad Mohamed1, Eslam Abdelhakim Seyam2,*, Ahmed R. Elsaeed3, Laith Abualigah4, Aseel Smerat5,6, Ahmed M. AbdelMouty7, Hosam E. Refaat8
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073171
Abstract In recent years, fog computing has become an important environment for dealing with the Internet of Things. Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing. Task scheduling is crucial for efficiently handling IoT user requests, thereby improving system performance, cost, and energy consumption across nodes in cloud computing. With the large amount of data and user requests, achieving the optimal solution to the task scheduling problem is challenging, particularly in terms of cost and energy efficiency. In this paper, we develop novel strategies to save energy consumption across… More >
Open Access
ARTICLE
Moyi Zhang, Yixin Wang*, Yu Cheng
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071021
Abstract Modern intelligent systems, such as autonomous vehicles and face recognition, must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations. However, when neural networks learn new classes sequentially, they suffer from catastrophic forgetting—the tendency to lose knowledge of earlier classes. This challenge, which lies at the core of class-incremental learning, severely limits the deployment of continual learning systems in real-world applications with streaming data. Existing approaches, including rehearsal-based methods and knowledge distillation techniques, have attempted to address this issue but often struggle to effectively preserve decision boundaries and discriminative features… More >
Open Access
ARTICLE
Xinli Hao1, Qingyuan Gong2, Yang Chen1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073155
（This article belongs to the Special Issue: Cyberspace Mapping and Anti-Mapping Techniques)
Abstract Topological information is very important for understanding different types of online web services, in particular, for online social networks (OSNs). People leverage such information for various applications, such as social relationship modeling, community detection, user profiling, and user behavior prediction. However, the leak of such information will also pose severe challenges for user privacy preserving due to its usefulness in characterizing users. Large-scale web crawling-based information probing is a representative way for obtaining topological information of online web services. In this paper, we explore how to defend against topological information probing for online web services,… More >
Open Access
ARTICLE
Wang Zhang1,#, Haozhuo Cao2,#, Qiangqiang Yao1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072948
（This article belongs to the Special Issue: Advances in Deep Learning and Neural Networks: Architectures, Applications, and Challenges)
Abstract Recent advances in deep learning have significantly improved image deblurring; however, existing approaches still suffer from limited global context modeling, inadequate detail restoration, and poor texture or edge perception, especially under complex dynamic blur. To address these challenges, we propose the Multi-Resolution Fusion Network (MRFNet), a blind multi-scale deblurring framework that integrates progressive residual connectivity for hierarchical feature fusion. The network employs a three-stage design: (1) TransformerBlocks capture long-range dependencies and reconstruct coarse global structures; (2) Nonlinear Activation Free Blocks (NAFBlocks) enhance local detail representation and mid-level feature fusion; and (3) an optimized residual subnetwork… More >
Open Access
ARTICLE
Tahira Khalil1, Sadeeq Jan2,*, Rania M. Ghoniem3, Muhammad Imran Khan Khalil1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072655
Abstract The contemporary era is characterized by rapid technological advancements, particularly in the fields of communication and multimedia. Digital media has significantly influenced the daily lives of individuals of all ages. One of the emerging domains in digital media is the creation of cartoons and animated videos. The accessibility of the internet has led to a surge in the consumption of cartoons among young children, presenting challenges in monitoring and controlling the content they view. The prevalence of cartoon videos containing potentially violent scenes has raised concerns regarding their impact, especially on young and impressionable minds.… More >
Open Access
ARTICLE
Jinfeng Ji1, Geunseok Yang2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071733
（This article belongs to the Special Issue: Attention Mechanism-based Complex System Pattern Intelligent Recognition and Accurate Prediction)
Abstract Automated Program Repair (APR) techniques have shown significant potential in mitigating the cost and complexity associated with debugging by automatically generating corrective patches for software defects. Despite considerable progress in APR methodologies, existing approaches frequently lack contextual awareness of runtime behaviors and structural intricacies inherent in buggy source code. In this paper, we propose a novel APR approach that integrates attention mechanisms within an autoencoder-based framework, explicitly utilizing structural code affinity and execution context correlation derived from stack trace analysis. Our approach begins with an innovative preprocessing pipeline, where code segments and stack traces are… More >
Open Access
ARTICLE
Georgia Garani1,*, George Pramantiotis2, Francisco Javier Moreno Arboleda3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071509
（This article belongs to the Special Issue: Big Data-Driven Intelligent Decision Systems)
Abstract Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation. Modern seismological research produces vast volumes of heterogeneous data from seismic networks, satellite observations, and geospatial repositories, creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making. Data warehousing technologies provide a robust foundation for this purpose; however, existing earthquake-oriented data warehouses remain limited, often relying on simplified schemas, domain-specific analytics, or cataloguing efforts. This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity. The framework integrates… More >
Open Access
ARTICLE
Nghia Dinh1, Vinh Truong Hoang1,*, Viet-Tuan Le1, Kiet Tran-Trung1, Ha Duong TTi Hong1, Bay Nguyen Van1, Hau Nguyen Trung1, Tien Ho Huong1, Kittikhun Meethongjan2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.069587
（This article belongs to the Special Issue: Emerging Machine Learning Methods and Applications)
Abstract Ensuring the reliability of power transmission networks depends heavily on the early detection of faults in key components such as insulators, which serve both mechanical and electrical functions. Even a single defective insulator can lead to equipment breakdown, costly service interruptions, and increased maintenance demands. While unmanned aerial vehicles (UAVs) enable rapid and cost-effective collection of high-resolution imagery, accurate defect identification remains challenging due to cluttered backgrounds, variable lighting, and the diverse appearance of faults. To address these issues, we introduce a real-time inspection framework that integrates an enhanced YOLOv10 detector with a Hybrid Quantum-Enhanced More >
Open Access
ARTICLE
Han-Yu Lin, Tung-Tso Tsai*, Yi-Chuan Wang
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073234
Abstract Cloud services, favored by many enterprises due to their high flexibility and easy operation, are widely used for data storage and processing. However, the high latency, together with transmission overheads of the cloud architecture, makes it difficult to quickly respond to the demands of IoT applications and local computation. To make up for these deficiencies in the cloud, fog computing has emerged as a critical role in the IoT applications. It decentralizes the computing power to various lower nodes close to data sources, so as to achieve the goal of low latency and distributed processing.… More >
Open Access
REVIEW
Shaojie Min1, Yaxiao Luo1, Kebing Liu1, Qingyuan Gong2, Yang Chen1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073175
（This article belongs to the Special Issue: Cyberspace Mapping and Anti-Mapping Techniques)
Abstract User identity linkage (UIL) across online social networks seeks to match accounts belonging to the same real-world individual. This cross-platform mapping enables accurate user modeling but also raises serious privacy risks. Over the past decade, the research community has developed a wide range of UIL methods, from structural embeddings to multimodal fusion architectures. However, corresponding adversarial and defensive approaches remain fragmented and comparatively understudied. In this survey, we provide a unified overview of both mapping and anti-mapping methods for UIL. We categorize representative mapping models by learning paradigm and data modality, and systematically compare them… More >
Open Access
ARTICLE
Hang Wen1,2, Kai Zeng1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072909
（This article belongs to the Special Issue: Omnipresent AI in the Cloud Era Reshaping Distributed Computation and Adaptive Systems for Modern Applications)
Abstract Federated learning often experiences slow and unstable convergence due to edge-side data heterogeneity. This problem becomes more severe when edge participation rate is low, as the information collected from different edge devices varies significantly. As a result, communication overhead increases, which further slows down the convergence process. To address this challenge, we propose a simple yet effective federated learning framework that improves consistency among edge devices. The core idea is clusters the lookahead gradients collected from edge devices on the cloud server to obtain personalized momentum for steering local updates. In parallel, a global momentum… More >
Graphic Abstract
Open Access
ARTICLE
Sun Park*, JongWon Kim
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072865
（This article belongs to the Special Issue: Advancing Edge-Cloud Systems with Software-Defined Networking and Intelligence-Driven Approaches)
Abstract In real-world autonomous driving tests, unexpected events such as pedestrians or wild animals suddenly entering the driving path can occur. Conducting actual test drives under various weather conditions may also lead to dangerous situations. Furthermore, autonomous vehicles may operate abnormally in bad weather due to limitations of their sensors and GPS. Driving simulators, which replicate driving conditions nearly identical to those in the real world, can drastically reduce the time and cost required for market entry validation; consequently, they have become widely used. In this paper, we design a virtual driving test environment capable of More >
Open Access
REVIEW
Meenakshi Aggarwal1, Vikas Khullar2,*, Nitin Goyal3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072673
（This article belongs to the Special Issue: Integrating Split Learning with Tiny Models for Advanced Edge Computing Applications in the Internet of Vehicles)
Abstract The Internet of Vehicles, or IoV, is expected to lessen pollution, ease traffic, and increase road safety. IoV entities’ interconnectedness, however, raises the possibility of cyberattacks, which can have detrimental effects. IoV systems typically send massive volumes of raw data to central servers, which may raise privacy issues. Additionally, model training on IoV devices with limited resources normally leads to slower training times and reduced service quality. We discuss a privacy-preserving Federated Split Learning with Tiny Machine Learning (TinyML) approach, which operates on IoV edge devices without sharing sensitive raw data. Specifically, we focus on… More >
Open Access
ARTICLE
Roshni Khedgaonkar1, Pravinkumar Sonsare2, Kavita Singh1, Ayman Altameem3, Hameed R. Farhan4, Salil Bharany5, Ateeq Ur Rehman6,*, Ahmad Almogren7,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072651
（This article belongs to the Special Issue: Artificial Intelligence and Machine Learning in Healthcare Applications)
Abstract Recent studies indicate that millions of individuals suffer from renal diseases, with renal carcinoma, a type of kidney cancer, emerging as both a chronic illness and a significant cause of mortality. Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) have become essential tools for diagnosing and assessing kidney disorders. However, accurate analysis of these medical images is critical for detecting and evaluating tumor severity. This study introduces an integrated hybrid framework that combines three complementary deep learning models for kidney tumor segmentation from MRI images. The proposed framework fuses a customized U-Net and Mask R-CNN… More >
Open Access
ARTICLE
Afnan Alhindi*, Saad Al-Ahmadi, Mohamed Maher Ben Ismail
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072625
（This article belongs to the Special Issue: Integrating Split Learning with Tiny Models for Advanced Edge Computing Applications in the Internet of Vehicles)
Abstract Split Learning (SL) has been promoted as a promising collaborative machine learning technique designed to address data privacy and resource efficiency. Specifically, neural networks are divided into client and server sub-networks in order to mitigate the exposure of sensitive data and reduce the overhead on client devices, thereby making SL particularly suitable for resource-constrained devices. Although SL prevents the direct transmission of raw data, it does not alleviate entirely the risk of privacy breaches. In fact, the data intermediately transmitted to the server sub-model may include patterns or information that could reveal sensitive data. Moreover,… More >
Open Access
ARTICLE
Hanaa Nafea1, Awais Qasim2, Sana Abdul Sattar2, Adeel Munawar3, Muhammad Nadeem Ali4, Byung-Seo Kim4,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072281
Abstract The increased connectivity and reliance on digital technologies have exposed smart transportation systems to various cyber threats, making intrusion detection a critical aspect of ensuring their secure operation. Traditional intrusion detection systems have limitations in terms of centralized architecture, lack of transparency, and vulnerability to single points of failure. This is where the integration of blockchain technology with signature-based intrusion detection can provide a robust and decentralized solution for securing smart transportation systems. This study tackles the issue of database manipulation attacks in smart transportation networks by proposing a signature-based intrusion detection system. The introduced More >
Open Access
ARTICLE
Qian Yu1,2, Gui Zhang2,*, Ying Wang1, Xin Wu2, Jiangshu Xiao2, Wenbing Kuang1, Juan Zhang2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072172
Abstract Detecting small forest fire targets in unmanned aerial vehicle (UAV) images is difficult, as flames typically cover only a very limited portion of the visual scene. This study proposes Context-guided Compact Lightweight Network (CCLNet), an end-to-end lightweight model designed to detect small forest fire targets while ensuring efficient inference on devices with constrained computational resources. CCLNet employs a three-stage network architecture. Its key components include three modules. C3F-Convolutional Gated Linear Unit (C3F-CGLU) performs selective local feature extraction while preserving fine-grained high-frequency flame details. Context-Guided Feature Fusion Module (CGFM) replaces plain concatenation with triplet-attention interactions to… More >
Open Access
ARTICLE
Yewei Xiao, Xin Du*, Wei Zeng
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072145
Abstract Audio-visual speech recognition (AVSR), which integrates audio and visual modalities to improve recognition performance and robustness in noisy or adverse acoustic conditions, has attracted significant research interest. However, Conformer-based architectures remain computational expensive due to the quadratic increase in the spatial and temporal complexity of their softmax-based attention mechanisms with sequence length. In addition, Conformer-based architectures may not provide sufficient flexibility for modeling local dependencies at different granularities. To mitigate these limitations, this study introduces a novel AVSR framework based on a ReLU-based Sparse and Grouped Conformer (RSG-Conformer) architecture. Specifically, we propose a Global-enhanced Sparse… More >
Open Access
ARTICLE
Qing Long1, Bing Yi2, Haiqiao Liu3,*, Zhiling Peng1, Xiang Liu1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072058
（This article belongs to the Special Issue: Attention Mechanism-based Complex System Pattern Intelligent Recognition and Accurate Prediction)
Abstract Accurate detection of smoke and fire sources is critical for early fire warning and environmental monitoring. However, conventional detection approaches are highly susceptible to noise, illumination variations, and complex environmental conditions, which often reduce detection accuracy and real-time performance. To address these limitations, we propose Lightweight and Precise YOLO (LP-YOLO), a high-precision detection framework that integrates a self-attention mechanism with a feature pyramid, built upon YOLOv8. First, to overcome the restricted receptive field and parameter redundancy of conventional Convolutional Neural Networks (CNNs), we design an enhanced backbone based on Wavelet Convolutions (WTConv), which expands the… More >
Open Access
ARTICLE
Sophort Siet1, Sony Peng2, Ilkhomjon Sadriddinov3, Kyuwon Park4,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071192
Abstract Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories. The collaborative filtering (CF) model, which depends exclusively on user-item interactions, commonly encounters challenges, including the cold-start problem and an inability to effectively capture the sequential and temporal characteristics of user behavior. This paper introduces a personalized recommendation system that combines deep learning techniques with Bayesian Personalized Ranking (BPR) optimization to address these limitations. With the strong support of Long Short-Term Memory (LSTM) networks, we apply it to identify sequential dependencies of user behavior and then incorporate… More >
Open Access
ARTICLE
Alexander V. Savin1,2, Elena A. Korznikova3,4, Sergey V. Dmitriev5,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072786
Abstract Due to their chiral structure, carbon nanosprings possess unique properties that are promising for nanotechnology applications. The structural transformations of carbon nanosprings in the form of spiral macromolecules derived from planar coronene and kekulene molecules (graphene helicoids and spiral nanoribbons) are analyzed using molecular dynamics simulations. The interatomic interactions are described by a force field including valence bonds, bond angles, torsional and dihedral angles, as well as van der Waals interactions. While the tension/compression of such nanosprings has been analyzed in the literature, this study investigates other modes of deformation, including bending and twisting. Depending… More >
Open Access
ARTICLE
Chen-Chiung Hsieh1,*, Chun-An Chen1, Wei-Hsin Huang2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072514
（This article belongs to the Special Issue: Development and Application of Deep Learning based Object Detection)
Abstract Container transportation is pivotal in global trade due to its efficiency, safety, and cost-effectiveness. However, structural defects—particularly in grapple slots—can result in cargo damage, financial loss, and elevated safety risks, including container drops during lifting operations. Timely and accurate inspection before and after transit is therefore essential. Traditional inspection methods rely heavily on manual observation of internal and external surfaces, which are time-consuming, resource-intensive, and prone to subjective errors. Container roofs pose additional challenges due to limited visibility, while grapple slots are especially vulnerable to wear from frequent use. This study proposes a two-stage automated… More >
Open Access
ARTICLE
Shuangqing Song1, Yuan Chen2, Xuguang Hu1, Juwei Zhang1,3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072392
（This article belongs to the Special Issue: Enhancing AI Applications through NLP and LLM Integration)
Abstract In multi-domain neural machine translation tasks, the disparity in data distribution between domains poses significant challenges in distinguishing domain features and sharing parameters across domains. This paper proposes a Transformer-based multi-domain-aware mixture of experts model. To address the problem of domain feature differentiation, a mixture of experts (MoE) is introduced into attention to enhance the domain perception ability of the model, thereby improving the domain feature differentiation. To address the trade-off between domain feature distinction and cross-domain parameter sharing, we propose a domain-aware mixture of experts (DMoE). A domain-aware gating mechanism is introduced within the… More >
Open Access
ARTICLE
Leyu Zheng1, Mingming Xiao1,*, Yi Ren2, Ke Li1, Chang Sun1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072036
（This article belongs to the Special Issue: Advancements in Evolutionary Optimization Approaches: Theory and Applications)
Abstract In a wide range of engineering applications, complex constrained multi-objective optimization problems (CMOPs) present significant challenges, as the complexity of constraints often hampers algorithmic convergence and reduces population diversity. To address these challenges, we propose a novel algorithm named Constraint Intensity-Driven Evolutionary Multitasking (CIDEMT), which employs a two-stage, tri-task framework to dynamically integrates problem structure and knowledge transfer. In the first stage, three cooperative tasks are designed to explore the Constrained Pareto Front (CPF), the Unconstrained Pareto Front (UPF), and the -relaxed constraint boundary, respectively. A CPF-UPF relationship classifier is employed to construct a problem-type-aware… More >
Open Access
ARTICLE
Sai Xu1,*, Jun Liu1,*, Shengyu Huang1, Zhi Li2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071865
（This article belongs to the Special Issue: Intelligent Computation and Large Machine Learning Models for Edge Intelligence in industrial Internet of Things)
Abstract In scenarios where ground-based cloud computing infrastructure is unavailable, unmanned aerial vehicles (UAVs) act as mobile edge computing (MEC) servers to provide on-demand computation services for ground terminals. To address the challenge of jointly optimizing task scheduling and UAV trajectory under limited resources and high mobility of UAVs, this paper presents PER-MATD3, a multi-agent deep reinforcement learning algorithm with prioritized experience replay (PER) into the Centralized Training with Decentralized Execution (CTDE) framework. Specifically, PER-MATD3 enables each agent to learn a decentralized policy using only local observations during execution, while leveraging a shared replay buffer with More >
Open Access
ARTICLE
Pham Huy Thong1, Hoang Thi Canh2,3,*, Nguyen Tuan Huy4, Nguyen Long Giang1,*, Luong Thi Hong Lan4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071776
（This article belongs to the Special Issue: Advances in Image Recognition: Innovations, Applications, and Future Directions)
Abstract Satellite image segmentation plays a crucial role in remote sensing, supporting applications such as environmental monitoring, land use analysis, and disaster management. However, traditional segmentation methods often rely on large amounts of labeled data, which are costly and time-consuming to obtain, especially in large-scale or dynamic environments. To address this challenge, we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering (SS-MPFC) algorithm, which improves segmentation accuracy and robustness, particularly in complex and uncertain remote sensing scenarios. SS-MPFC unifies three paradigms: semi-supervised learning, multi-view clustering, and picture fuzzy set theory. This integration allows the model to effectively… More >
Open Access
ARTICLE
Tao Geng1, Shuaibing Li1,*, Yunyun Yun1, Yongqiang Kang1, Hongwei Li2, Junmin Zhu2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071644
（This article belongs to the Special Issue: Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems-2nd Edition)
Abstract In order to address the challenges posed by complex background interference, high miss-detection rates of micro-scale defects, and limited model deployment efficiency in photovoltaic (PV) module defect detection, this paper proposes an efficient detection framework based on an improved YOLOv11 architecture. First, a Re-parameterized Convolution (RepConv) module is integrated into the backbone to enhance the model’s sensitivity to fine-grained defects—such as micro-cracks and hot spots—while maintaining high inference efficiency. Second, a Multi-Scale Feature Fusion Convolutional Block Attention Mechanism (MSFF-CBAM) is designed to guide the network toward critical defect regions by jointly modeling channel-wise and spatial… More >
Open Access
ARTICLE
Yang Liu, Qi Lu, Junjie Wu, Huaichang Yin, Shiwei Cheng*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070273
Abstract The development of brain-computer interfaces (BCI) based on motor imagery (MI) has greatly improved patients’ quality of life with movement disorders. The classification of upper limb MI has been widely studied and applied in many fields, including rehabilitation. However, the physiological representations of left and right lower limb movements are too close and activated deep in the cerebral cortex, making it difficult to distinguish their features. Therefore, classifying lower limbs motor imagery is more challenging. In this study, we propose a feature extraction method based on functional connectivity, which utilizes phase-locked values to construct a… More >
Open Access
ARTICLE
Illia Khurtin*, Mukesh Prasad
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.067481
Abstract The field of artificial intelligence has advanced significantly in recent years, but achieving a human-like or Artificial General Intelligence (AGI) remains a theoretical challenge. One hypothesis suggests that a key issue is the formalisation of extracting meaning from information. Meaning emerges through a three-stage interpretative process, where the spectrum of possible interpretations is collapsed into a singular outcome by a particular context. However, this approach currently lacks practical grounding. In this research, we developed a model based on contexts, which applies interpretation principles to the visual information to address this gap. The field of computer… More >
Open Access
ARTICLE
Hailong Wang1, Minglei Duan2, Lu Yao3, Hao Li1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072942
Abstract In image analysis, high-precision semantic segmentation predominantly relies on supervised learning. Despite significant advancements driven by deep learning techniques, challenges such as class imbalance and dynamic performance evaluation persist. Traditional weighting methods, often based on pre-statistical class counting, tend to overemphasize certain classes while neglecting others, particularly rare sample categories. Approaches like focal loss and other rare-sample segmentation techniques introduce multiple hyperparameters that require manual tuning, leading to increased experimental costs due to their instability. This paper proposes a novel CAWASeg framework to address these limitations. Our approach leverages Grad-CAM technology to generate class activation… More >
Open Access
ARTICLE
Mohammed Alnusayri1, Ghulam Mujtaba2, Nouf Abdullah Almujally3, Shuoa S. Aitarbi4, Asaad Algarni5, Ahmad Jalal2,6, Jeongmin Park7,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071804
（This article belongs to the Special Issue: Advances in Object Detection and Recognition)
Abstract This paper presents a unified Unmanned Aerial Vehicle-based (UAV-based) traffic monitoring framework that integrates vehicle detection, tracking, counting, motion prediction, and classification in a modular and co-optimized pipeline. Unlike prior works that address these tasks in isolation, our approach combines You Only Look Once (YOLO) v10 detection, ByteTrack tracking, optical-flow density estimation, Long Short-Term Memory-based (LSTM-based) trajectory forecasting, and hybrid Speeded-Up Robust Feature (SURF) + Gray-Level Co-occurrence Matrix (GLCM) feature engineering with VGG16 classification. Upon the validation across datasets (UAVDT and UAVID) our framework achieved a detection accuracy of 94.2%, and 92.3% detection accuracy when More >
Open Access
ARTICLE
Jia Yan Lim1,2, Siti Madiha Muhammad Amir3, Roslan Yahya3, Marta Peña Fernández2, Tze Chuen Yap1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070707
（This article belongs to the Special Issue: Design, Optimisation and Applications of Additive Manufacturing Technologies)
Abstract Additive Manufacturing, also known as 3D printing, has transformed conventional manufacturing by building objects layer by layer, with material extrusion or fused deposition modeling standing out as particularly popular. However, due to its manufacturing process and thermal nature, internal voids and pores are formed within the thermoplastic materials being fabricated, potentially leading to a decrease in mechanical properties. This paper discussed the effect of printing parameters on the porosity and the mechanical properties of the 3D printed polylactic acid (PLA) through micro-computed tomography (microCT), computational image analysis, and Charpy impact testing. The results for both… More >
Open Access
ARTICLE
Zhongrui Jing1, Hongzhang Yang1,*, Jiangpu Guo2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.067759
（This article belongs to the Special Issue: Signal Processing for Fault Diagnosis)
Abstract Hard disk drives (HDDs) serve as the primary storage devices in modern data centers. Once a failure occurs, it often leads to severe data loss, significantly degrading the reliability of storage systems. Numerous studies have proposed machine learning-based HDD failure prediction models. However, the Self-Monitoring, Analysis, and Reporting Technology (SMART) attributes differ across HDD manufacturers. We define hard drives of the same brand and model as homogeneous HDD groups, and those from different brands or models as heterogeneous HDD groups. In practical engineering scenarios, a data center is often composed of a heterogeneous population of… More >
Open Access
ARTICLE
Yuewei Tian1, Yang Su2, Yujia Wang1, Lisa Guo1, Xuyang Wu3,*, Lei Cao4, Fang Ren3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072081
（This article belongs to the Special Issue: Advances in Object Detection and Recognition)
Abstract This study addresses the risk of privacy leakage during the transmission and sharing of multimodal data in smart grid substations by proposing a three-tier privacy-preserving architecture based on asynchronous federated learning. The framework integrates blockchain technology, the InterPlanetary File System (IPFS) for distributed storage, and a dynamic differential privacy mechanism to achieve collaborative security across the storage, service, and federated coordination layers. It accommodates both multimodal data classification and object detection tasks, enabling the identification and localization of key targets and abnormal behaviors in substation scenarios while ensuring privacy protection. This effectively mitigates the single-point… More >
Open Access
REVIEW
Zhijie Lin1, Chao Yang1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070735
（This article belongs to the Special Issue: The Applications of Artificial Intelligence in Computational Materials Science)
Abstract Sustainable aluminum alloys, renowned for their lower energy consumption and carbon emissions, present a critical path towards a circular materials economy. However, their design is fraught with challenges, including complex performance variability due to impurity elements and the time-consuming, cost-prohibitive nature of traditional trial-and-error methods. The high-dimensional parameter space in processing optimization and the reliance on human expertise for quality control further complicate their development. This paper provides a comprehensive review of Artificial Intelligence (AI) techniques applied to sustainable aluminum alloy design, analyzing their methodologies and identifying key challenges and optimization strategies. We review how… More >
Open Access
REVIEW
Himadri Nath Saha1, Dipanwita Chakraborty Bhattacharya2,*, Sancharita Dutta3, Arnab Bera3, Srutorshi Basuray4, Satyasaran Changdar5, Saptarshi Banerjee6, Jon Turdiev7
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070507
Abstract The emergence of Medical Large Language Models has significantly transformed healthcare. Medical Large Language Models (Med-LLMs) serve as transformative tools that enhance clinical practice through applications in decision support, documentation, and diagnostics. This evaluation examines the performance of leading Med-LLMs, including GPT-4Med, Med-PaLM, MEDITRON, PubMedGPT, and MedAlpaca, across diverse medical datasets. It provides graphical comparisons of their effectiveness in distinct healthcare domains. The study introduces a domain-specific categorization system that aligns these models with optimal applications in clinical decision-making, documentation, drug discovery, research, patient interaction, and public health. The paper addresses deployment challenges of Medical-LLMs, More >
Open Access
ARTICLE
Muhammad Waqar Khan1,*, Adnan Ahmed Siddiqui1, Syed Sajjad Hussain Rizvi2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070858
（This article belongs to the Special Issue: Advancements in Evolutionary Optimization Approaches: Theory and Applications)
Abstract The multi-objective optimization problems, especially in constrained environments such as power distribution planning, demand robust strategies for discovering effective solutions. This work presents the improved variant of the Multi-population Cooperative Constrained Multi-Objective Optimization (MCCMO) Algorithm, termed Adaptive Diversity Preservation (ADP). This enhancement is primarily focused on the improvement of constraint handling strategies, local search integration, hybrid selection approaches, and adaptive parameter control. The improved variant was experimented on with the RWMOP50 power distribution system planning benchmark. As per the findings, the improved variant outperformed the original MCCMO across the eleven performance metrics, particularly in terms… More >
Open Access
REVIEW
Shaoping Xiao1,*, Zhaoan Wang1, Junchao Li2, Caden Noeller1, Jiefeng Jiang3, Jun Wang4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072146
（This article belongs to the Special Issue: Advances in Object Detection: Methods and Applications)
Abstract The integration of human factors into artificial intelligence (AI) systems has emerged as a critical research frontier, particularly in reinforcement learning (RL), where human-AI interaction (HAII) presents both opportunities and challenges. As RL continues to demonstrate remarkable success in model-free and partially observable environments, its real-world deployment increasingly requires effective collaboration with human operators and stakeholders. This article systematically examines HAII techniques in RL through both theoretical analysis and practical case studies. We establish a conceptual framework built upon three fundamental pillars of effective human-AI collaboration: computational trust modeling, system usability, and decision understandability. Our… More >
Open Access
ARTICLE
Lifu He1, Zhongchu Huang1, Haidong Shao2,*, Zhangbo Hu1, Yuting Wang1, Jie Mei1, Xiaofei Zhang3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073227
（This article belongs to the Special Issue: Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems-2nd Edition)
Abstract Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction. However, several challenges remain. First, signal noise collected during blade operation masks fault features, severely impairing the fault diagnosis performance of deep learning models. Second, current blade fault diagnosis often relies on single-sensor data, resulting in limited monitoring dimensions and ability to comprehensively capture complex fault states. To address these issues, a multi-sensor fusion-based wind turbine blade fault diagnosis method is proposed. Specifically, a CNN-Transformer Coupled Feature Learning Architecture is constructed to enhance the ability to More >
Open Access
ARTICLE
Chenxi Xiu1,2,*, Xihua Chu2, Ao Mei1, Liangfei Gong1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073193
Abstract Granular materials exhibit complex macroscopic mechanical behaviors closely related to their micro-scale microstructural features. Traditional macroscopic phenomenological elasto-plastic models, however, usually have complex formulations and lack explicit relations to these microstructural features. To avoid these limitations, this study proposes a micromechanics-based softening hyperelastic model for granular materials, integrating softening hyperelasticity with microstructural insights to capture strain softening, critical state, and strain localization behaviors. The model has two key advantages: (1) a clear conceptualization, straightforward formulation, and ease of numerical implementation (via Abaqus UMAT subroutine in this study); (2) explicit incorporation of micro-scale features (e.g., contact… More >
Open Access
ARTICLE
Feng Lv*, Huili Chu, Cheng Yang, Jiajie Zhang
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072915
（This article belongs to the Special Issue: Advances in Nature-Inspired and Metaheuristic Optimization Algorithms: Theory, Applications, and Emerging Trends)
Abstract To address the issue that hybrid flow shop production struggles to handle order disturbance events, a dynamic scheduling model was constructed. The model takes minimizing the maximum makespan, delivery time deviation, and scheme deviation degree as the optimization objectives. An adaptive dynamic scheduling strategy based on the degree of order disturbance is proposed. An improved multi-objective Grey Wolf (IMOGWO) optimization algorithm is designed by combining the “job-machine” two-layer encoding strategy, the timing-driven two-stage decoding strategy, the opposition-based learning initialization population strategy, the POX crossover strategy, the dual-operation dynamic mutation strategy, and the variable neighborhood search… More >
Open Access
ARTICLE
Yanan Liu1,*, Suhao Wang1,*, Lei Cao1, Pengfei Wang1, Zheng Zhang2, Shuo Qiu1, Ruchan Dong1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072710
Abstract Unmanned Aerial Vehicles (UAVs) in Flying Ad-Hoc Networks (FANETs) are widely used in both civilian and military fields, but they face severe security, trust, and privacy vulnerabilities due to their high mobility, dynamic topology, and open wireless channels. Existing security protocols for Mobile Ad-Hoc Networks (MANETs) cannot be directly applied to FANETs, as FANETs require lightweight, high real-time performance, and strong anonymity. The current FANETs security protocol cannot simultaneously meet the requirements of strong anonymity, high security, and low overhead in high dynamic and resource-constrained scenarios. To address these challenges, this paper proposes an Anonymous Authentication… More >
Open Access
ARTICLE
Minghui Li1, Hongbo Li1,*, Jiaqi Zhu2, Xupeng Zhang1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072406
（This article belongs to the Special Issue: Advances in Object Detection: Methods and Applications)
Abstract To address the challenge of real-time detection of unauthorized drone intrusions in complex low-altitude urban environments such as parks and airports, this paper proposes an enhanced MBS-YOLO (Multi-Branch Small Target Detection YOLO) model for anti-drone object detection, based on the YOLOv8 architecture. To overcome the limitations of existing methods in detecting small objects within complex backgrounds, we designed a C2f-Pu module with excellent feature extraction capability and a more compact parameter set, aiming to reduce the model’s computational complexity. To improve multi-scale feature fusion, we construct a Multi-Branch Feature Pyramid Network (MB-FPN) that employs a… More >
Open Access
ARTICLE
Junhui Xu1, Qi Wang1,*, Chichen Lin2, Weijian Fan3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071661
Abstract Social bots are automated programs designed to spread rumors and misinformation, posing significant threats to online security. Existing research shows that the structure of a social network significantly affects the behavioral patterns of social bots: a higher number of connected components weakens their collaborative capabilities, thereby reducing their proportion within the overall network. However, current social bot detection methods still make limited use of topological features. Furthermore, both graph neural network (GNN)-based methods that rely on local features and those that leverage global features suffer from their own limitations, and existing studies lack an effective More >
Open Access
ARTICLE
Rashid Jahangir1,*, Nazik Alturki2, Muhammad Asif Nauman3, Faiqa Hanif1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071574
Abstract Human activity recognition (HAR) is a method to predict human activities from sensor signals using machine learning (ML) techniques. HAR systems have several applications in various domains, including medicine, surveillance, behavioral monitoring, and posture analysis. Extraction of suitable information from sensor data is an important part of the HAR process to recognize activities accurately. Several research studies on HAR have utilized Mel frequency cepstral coefficients (MFCCs) because of their effectiveness in capturing the periodic pattern of sensor signals. However, existing MFCC-based approaches often fail to capture sufficient temporal variability, which limits their ability to distinguish… More >
Open Access
ARTICLE
Ans D. Alghamdi*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071210
（This article belongs to the Special Issue: Artificial Intelligence in Visual and Audio Signal Processing)
Abstract Multimodal dialogue systems often fail to maintain coherent reasoning over extended conversations and suffer from hallucination due to limited context modeling capabilities. Current approaches struggle with cross-modal alignment, temporal consistency, and robust handling of noisy or incomplete inputs across multiple modalities. We propose MultiAgent-Chain of Thought (CoT), a novel multi-agent chain-of-thought reasoning framework where specialized agents for text, vision, and speech modalities collaboratively construct shared reasoning traces through inter-agent message passing and consensus voting mechanisms. Our architecture incorporates self-reflection modules, conflict resolution protocols, and dynamic rationale alignment to enhance consistency, factual accuracy, and user engagement. More >
Open Access
ARTICLE
Xiaoyi Duan, Tianqi Zou, Chenyang Wang, Yu Gu, Xiuying Li*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070683
Abstract Person recognition in photo collections is a critical yet challenging task in computer vision. Previous studies have used social relationships within photo collections to address this issue. However, these methods often fail when performing single-person-in-photos recognition in photo collections, as they cannot rely on social connections for recognition. In this work, we discard social relationships and instead measure the relationships between photos to solve this problem. We designed a new model that includes a multi-parameter attention network for adaptively fusing visual features and a unified formula for measuring photo intimacy. This model effectively recognizes individuals More >
Open Access
ARTICLE
Song Xu1,2,*, Liang Xuan1,2, Yifeng Li1,2, Qiang Zhang1,2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070472
Abstract The 6D pose estimation of objects is of great significance for the intelligent assembly and sorting of industrial parts. In the industrial robot production scenarios, the 6D pose estimation of industrial parts mainly faces two challenges: one is the loss of information and interference caused by occlusion and stacking in the sorting scenario, the other is the difficulty of feature extraction due to the weak texture of industrial parts. To address the above problems, this paper proposes an attention-based pixel-level voting network for 6D pose estimation of weakly textured industrial parts, namely CB-PVNet. On the… More >
Open Access
ARTICLE
Kirubavathi Ganapathiyappan1, Heba G. Mohamed2, Abhishek Yadav1, Guru Akshya Chinnaswamy1, Ateeq Ur Rehman3,*, Habib Hamam4,5,6,7
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.069951
（This article belongs to the Special Issue: Advances in Machine Learning and Artificial Intelligence for Intrusion Detection Systems)
Abstract The escalating complexity of modern malware continues to undermine the effectiveness of traditional signature-based detection techniques, which are often unable to adapt to rapidly evolving attack patterns. To address these challenges, this study proposes X-MalNet, a lightweight Convolutional Neural Network (CNN) framework designed for static malware classification through image-based representations of binary executables. By converting malware binaries into grayscale images, the model extracts distinctive structural and texture-level features that signify malicious intent, thereby eliminating the dependence on manual feature engineering or dynamic behavioral analysis. Built upon a modified AlexNet architecture, X-MalNet employs transfer learning to… More >
Open Access
ARTICLE
Bindi Saurabh Thakkar1, Pradeep Kumar Karsh2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072839
Abstract This study investigates the uncertain dynamic characterization of hybrid composite plates by employing advanced machine-assisted finite element methodologies. Hybrid composites, widely used in aerospace, automotive, and structural applications, often face variability in material properties, geometric configurations, and manufacturing processes, leading to uncertainty in their dynamic response. To address this, three surrogate-based machine learning approaches like radial basis function (RBF), multivariate adaptive regression splines (MARS), and polynomial neural networks (PNN) are integrated with a finite element framework to efficiently capture the stochastic behavior of these plates. The research focuses on predicting the first three natural frequencies… More >
Open Access
ARTICLE
Taehoon Kim, Sehun Lee, Junho Ahn*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071597
Abstract As large, room-scale environments become increasingly common, their spatial complexity increases due to variable, unstructured elements. Consequently, demand for room-scale service robots is surging, yet most technologies remain corridor-centric, and autonomous navigation in expansive rooms becomes unstable even around static obstacles. Existing approaches face several structural limitations. These include the labor-intensive requirement for large-scale object annotation and continual retraining, as well as the vulnerability of vanishing point or line-based methods when geometric cues are insufficient. In addition, the high cost of LiDAR and 3D perception errors caused by limited wall cues and dense interior clutter… More >
Open Access
ARTICLE
Siwen Xu1, Hanning Chen2, Rui Ni1, Maowei He2, Zhaodi Ge3, Xiaodan Liang2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071398
Abstract Selective Laser Melting (SLM), an advanced metal additive manufacturing technology, offers high precision and personalized customization advantages. However, selecting reasonable SLM parameters is challenging due to complex relationships. This study proposes a method for identifying the optimal process window by combining the simulation model with an optimization algorithm. JAYA is guided by the principle of preferential behavior towards best solutions and avoidance of worst ones, but it is prone to premature convergence thus leading to insufficient global search. To overcome limitations, this research proposes a Differential Evolution-framed JAYA algorithm (DEJAYA). DEJAYA incorporates four key enhancements More >
Open Access
REVIEW
Ahmed Abdel-Wahab1, Mohammad Alkhatib2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070010
Abstract Deepfake is a sort of fake media made by advanced AI methods like Generative Adversarial Networks (GANs). Deepfake technology has many useful uses in education and entertainment, but it also raises a lot of ethical, social, and security issues, such as identity theft, the dissemination of false information, and privacy violations. This study seeks to provide a comprehensive analysis of several methods for identifying and circumventing Deepfakes, with a particular focus on image-based Deepfakes. There are three main types of detection methods: classical, machine learning (ML) and deep learning (DL)-based, and hybrid methods. There are… More >
Open Access
ARTICLE
Fatima Khan1, Amna Khan1, Tariq Ali2, Tariq Shahzad3, Tehseen Mazhar4,*, Sunawar Khan5, Muhammad Adnan Khan6,*, Habib Hamam7,8,9,10
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.068228
Abstract The rise in noise and air pollution poses severe risks to human health and the environment. Industrial and vehicular emissions release harmful pollutants such as CO2, SO2, CO, CH4, and noise, leading to significant environmental degradation. Monitoring and analyzing pollutant concentrations in real-time is crucial for mitigating these risks. However, existing systems often lack the capacity to monitor both indoor and outdoor environments effectively.This study presents a low-cost, IoT-based pollution detection system that integrates gas sensors (MQ-135 and MQ-4), a noise sensor (LM393), and a humidity sensor (DHT-22), all connected to a Node MCU (ESP8266) microcontroller. The… More >
Open Access
ARTICLE
Nur Syaiful Afrizal1, Khairul Adib Yusof1,2,*, Lokman Hakim Muhamad1, Nurul Shazana Abdul Hamid2,3, Mardina Abdullah2,4, Mohd Amiruddin Abd Rahman1, Syamsiah Mashohor5, Masashi Hayakawa6,7
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.066421
（This article belongs to the Special Issue: Advances in Pattern Recognition Applications)
Abstract Detecting geomagnetic anomalies preceding earthquakes is a challenging yet promising area of research that has gained increasing attention in recent years. This study introduces a novel reconstruction-based modeling approach enhanced by negative learning, employing a Bidirectional Long Short-Term Memory (BiLSTM) network explicitly trained to accurately reconstruct non-seismic geomagnetic signals while intentionally amplifying reconstruction errors for seismic signals. By penalizing the model for accurately reconstructing seismic anomalies, the negative learning approach effectively magnifies the differences between normal and anomalous data. This strategic differentiation enhances the sensitivity of the BiLSTM network, enabling improved detection of subtle geomagnetic More >
Open Access
ARTICLE
Junwen Huo, Haicheng Liang, Weiye Dong, Xiaoming Du*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073285
Abstract With the rapid advancement of electromagnetic launch technology, enhancing the structural stability and thermal resistance of armatures has become essential for improving the overall efficiency and reliability of railgun systems. Traditional aluminum alloy armatures often suffer from severe ablation, deformation, and uneven current distribution under high pulsed currents, which limit their performance and service life. To address these challenges, this study employs the Johnson–Cook constitutive model and the finite element method to develop armature models of aluminum matrix composites with varying heterogeneous graphene volume fractions. The temperature, stress, and strain of the armatures during operation… More >
Open Access
ARTICLE
Chia-Hui Liu*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072887
（This article belongs to the Special Issue: Advances in Secure Computing: Post-Quantum Security, Multimedia Encryption, and Intelligent Threat Defence)
Abstract The Industrial Internet of Things (IIoT) has emerged as a cornerstone of Industry 4.0, enabling large-scale automation and data-driven decision-making across factories, supply chains, and critical infrastructures. However, the massive interconnection of resource-constrained devices also amplifies the risks of eavesdropping, data tampering, and device impersonation. While digital signatures are indispensable for ensuring authenticity and non-repudiation, conventional schemes such as RSA and ECC are vulnerable to quantum algorithms, jeopardizing long-term trust in IIoT deployments. This study proposes a lightweight, stateless, hash-based signature scheme that achieves post-quantum security while addressing the stringent efficiency demands of IIoT. The… More >
Open Access
ARTICLE
Sushruta Mishra1, Sunil Kumar Mohapatra2, Kshira Sagar Sahoo3, Anand Nayyar4, Tae-Kyung Kim5,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072493
（This article belongs to the Special Issue: Advances in Machine Learning and Artificial Intelligence for Intrusion Detection Systems)
Abstract With an increase in internet-connected devices and a dependency on online services, the threat of Distributed Denial of Service (DDoS) attacks has become a significant concern in cybersecurity. The proposed system follows a multi-step process, beginning with the collection of datasets from different edge devices and network nodes. To verify its effectiveness, experiments were conducted using the CICDoS2017, NSL-KDD, and CICIDS benchmark datasets alongside other existing models. Recursive feature elimination (RFE) with random forest is used to select features from the CICDDoS2019 dataset, on which a BiLSTM model is trained on local nodes. Local models… More >
Open Access
ARTICLE
Mingming Huang1, Yunfan Ye1,*, Zhiping Cai2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072399
Abstract As a fundamental component in computer vision, edges can be categorized into four types based on discontinuities in reflectance, illumination, surface normal, or depth. While deep CNNs have significantly advanced generic edge detection, real-time multi-class semantic edge detection under resource constraints remains challenging. To address this, we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection. Our model simultaneously predicts background and four edge categories from full-resolution inputs, balancing accuracy and efficiency. Key contributions include: a multi-channel output structure expanding binary edge prediction to five classes, supported by a deep supervision More >
Open Access
ARTICLE
Yuyao Huang, Hui Shu, Fei Kang*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071552
Abstract Message structure reconstruction is a critical task in protocol reverse engineering, aiming to recover protocol field structures without access to source code. It enables important applications in network security, including malware analysis and protocol fuzzing. However, existing methods suffer from inaccurate field boundary delineation and lack hierarchical relationship recovery, resulting in imprecise and incomplete reconstructions. In this paper, we propose , a novel method for reconstructing protocol field structures based on program execution slice embedding. extracts code slices from protocol parsing at runtime, converts them into embedding vectors using a data flow-sensitive assembly language model, More >
Open Access
ARTICLE
Ran Liu, Yawen Chen, Dong Yang*, Jingjing Yang*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070734
（This article belongs to the Special Issue: Big Data and Artificial Intelligence in Control and Information System)
Abstract In the field of smart agriculture, accurate and efficient object detection technology is crucial for automated crop management. A particularly challenging task in this domain is small object detection, such as the identification of immature fruits or early stage disease spots. These objects pose significant difficulties due to their small pixel coverage, limited feature information, substantial scale variations, and high susceptibility to complex background interference. These challenges frequently result in inadequate accuracy and robustness in current detection models. This study addresses two critical needs in the cashew cultivation industry—fruit maturity and anthracnose detection—by proposing an… More >
Open Access
ARTICLE
Jiajun Sun, Shunshun Ji, Chao Zhang*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.068668
Abstract Asparagus stem blight is a devastating crop disease, and the early detection of its pathogenic spores is essential for effective disease control and prevention. However, spore detection is still hindered by complex backgrounds, small target sizes, and high annotation costs, which limit its practical application and widespread adoption. To address these issues, a semi-supervised spore detection framework is proposed for use under complex background conditions. Firstly, a difficulty perception scoring function is designed to quantify the detection difficulty of each image region. For regions with higher difficulty scores, a masking strategy is applied, while the… More >
Open Access
ARTICLE
Hongji Chen, Jianxun Zhang*, Tianze Yu, Yingzhu Zeng, Huan Zeng
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.067041
（This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
Abstract Low-light image enhancement aims to improve the visibility of severely degraded images captured under insufficient illumination, alleviating the adverse effects of illumination degradation on image quality. Traditional Retinex-based approaches, inspired by human visual perception of brightness and color, decompose an image into illumination and reflectance components to restore fine details. However, their limited capacity for handling noise and complex lighting conditions often leads to distortions and artifacts in the enhanced results, particularly under extreme low-light scenarios. Although deep learning methods built upon Retinex theory have recently advanced the field, most still suffer from insufficient interpretability… More >
Open Access
ARTICLE
Hong Zhao, Hongbin Chen*, Zhihui Guo, Ling Zhan, Shichao Li
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072776
（This article belongs to the Special Issue: Advancements in Mobile Computing for the Internet of Things: Architectures, Applications, and Challenges)
Abstract UAV-mounted intelligent reflecting surface (IRS) helps address the line-of-sight (LoS) blockage between sensor nodes (SNs) and the fusion center (FC) in Internet of Things (IoT). This paper considers an IoT assisted by multiple UAVs-mounted IRS (U-IRS), where the data from ground SNs are transmitted to the FC. In practice, energy efficiency (EE) and mission completion time are crucial metrics for evaluating system performance and operational costs. Recognizing their importance during data collection, we formulate a multi-objective optimization problem to maximize EE and minimize total mission completion time simultaneously. To characterize this tradeoff while considering optimization… More >
Open Access
ARTICLE
Sabina-Cristiana Necula*, Napoleon-Alexandru Sireteanu
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070861
Abstract Modern business information systems face significant challenges in managing heterogeneous data sources, integrating disparate systems, and providing real-time decision support in complex enterprise environments. Contemporary enterprises typically operate 200+ interconnected systems, with research indicating that 52% of organizations manage three or more enterprise content management systems, creating information silos that reduce operational efficiency by up to 35%. While attention mechanisms have demonstrated remarkable success in natural language processing and computer vision, their systematic application to business information systems remains largely unexplored. This paper presents the theoretical foundation for a Hierarchical Attention-Based Business Information System (HABIS)… More >
Open Access
ARTICLE
Haibo Lei1,2, Xu An Wang1,*, Wenhao Liu1, Lingling Wu1, Chao Zhang1, Weiwei Jiang3, Xiao Zou4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070030
（This article belongs to the Special Issue: Challenges and Innovations in Multimedia Encryption and Information Security)
Abstract With the rapid expansion of the Internet of Things (IoT), user data has experienced exponential growth, leading to increasing concerns about the security and integrity of data stored in the cloud. Traditional schemes relying on untrusted third-party auditors suffer from both security and efficiency issues, while existing decentralized blockchain-based auditing solutions still face shortcomings in correctness and security. This paper proposes an improved blockchain-based cloud auditing scheme, with the following core contributions: Identifying critical logical contradictions in the original scheme, thereby establishing the foundation for the correctness of cloud auditing; Designing an enhanced mechanism that… More >
Open Access
ARTICLE
Dong Hyun Lee1, Na Kyung Lee2, Young Seo Lee1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.069976
Abstract There has been an increasing emphasis on performing deep neural network (DNN) inference locally on edge devices due to challenges such as network congestion and security concerns. However, as DRAM process technology continues to scale down, the bit-flip errors in the memory of edge devices become more frequent, thereby leading to substantial DNN inference accuracy loss. Though several techniques have been proposed to alleviate the accuracy loss in edge environments, they require complex computations and additional parity bits for error correction, thus resulting in significant performance and storage overheads. In this paper, we propose FeatherGuard,… More >
Open Access
ARTICLE
Mohammed Debakla1,*, Ali Mezaghrani1, Khalifa Djemal2, Imane Zouaneb1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071927
（This article belongs to the Special Issue: Advanced Bio-Inspired Optimization Algorithms and Applications)
Abstract Magnetic Resonance Imaging (MRI) has a pivotal role in medical image analysis, for its ability in supporting disease detection and diagnosis. Fuzzy C-Means (FCM) clustering is widely used for MRI segmentation due to its ability to handle image uncertainty. However, the latter still has countless limitations, including sensitivity to initialization, susceptibility to local optima, and high computational cost. To address these limitations, this study integrates Grey Wolf Optimization (GWO) with FCM to enhance cluster center selection, improving segmentation accuracy and robustness. Moreover, to further refine optimization, Fuzzy Entropy Clustering was utilized for its distinctive features… More >
Open Access
ARTICLE
Jay Barach*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071705
Abstract Human Resource (HR) operations increasingly rely on cloud-based platforms that provide hiring, payroll, employee management, and compliance services. These systems, typically built on multi-tenant microservice architectures, offer scalability and efficiency but also expand the attack surface for adversaries. Ransomware has emerged as a leading threat in this domain, capable of halting workflows and exposing sensitive employee records. Traditional defenses such as static hardening and signature-based detection often fail to address the dynamic requirements of HR Software as a Service (SaaS), where continuous availability and privacy compliance are critical. This paper presents a Moving Target Defense… More >
Open Access
ARTICLE
Meixi Chu1, Xinyu Jiang1,*, Yushu Tao2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071533
（This article belongs to the Special Issue: Intelligent Computation and Large Machine Learning Models for Edge Intelligence in industrial Internet of Things)
Abstract Industrial operators need reliable communication in high-noise, safety-critical environments where speech or touch input is often impractical. Existing gesture systems either miss real-time deadlines on resource-constrained hardware or lose accuracy under occlusion, vibration, and lighting changes. We introduce Industrial EdgeSign, a dual-path framework that combines hardware-aware neural architecture search (NAS) with large multimodal model (LMM) guided semantics to deliver robust, low-latency gesture recognition on edge devices. The searched model uses a truncated ResNet50 front end, a dimensional-reduction network that preserves spatiotemporal structure for tubelet-based attention, and localized Transformer layers tuned for on-device inference. To reduce… More >
Open Access
ARTICLE
Karim Boudjebbour1,2, Abdelkader Belkhir1, Hamza Kheddar2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071532
Abstract Identifying the community structure of complex networks is crucial to extracting insights and understanding network properties. Although several community detection methods have been proposed, many are unsuitable for social networks due to significant limitations. Specifically, most approaches depend mainly on user–user structural links while overlooking service-centric, semantic, and multi-attribute drivers of community formation, and they also lack flexible filtering mechanisms for large-scale, service-oriented settings. Our proposed approach, called community discovery-based service (CDBS), leverages user profiles and their interactions with consulted web services. The method introduces a novel similarity measure, global similarity interaction profile (GSIP), which… More >
Open Access
REVIEW
Lei Wang1,2, Menghan Wei2, Ziwei Huangfu3, Shunjie Zhu2, Xuejian Ge1,*, Zhengquan Li4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071295
（This article belongs to the Special Issue: Advanced Networking Technologies for Intelligent Transportation and Connected Vehicles)
Abstract Iterative Learning Control (ILC) provides an effective framework for optimizing repetitive tasks, making it particularly suitable for high-precision applications in both precision manufacturing and intelligent transportation systems (ITS). This paper presents a systematic review of ILC’s developmental progress, current methodologies, and practical implementations across these two critical domains. The review first analyzes the key technical challenges encountered when integrating ILC into precision manufacturing workflows. Through case studies, it evaluates demonstrated improvements in positioning accuracy, surface finish quality, and production throughput. Furthermore, the study examines ILC’s applications in ITS, with particular focus on vehicular motion control More >
Open Access
ARTICLE
Jun Myeong Kim, Jang Young Jeong, Shin Jin Kang, Beomjoo Seo*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071084
（This article belongs to the Special Issue: AI-Powered Software Engineering)
Abstract Game Quality Assurance (QA) currently relies heavily on manual testing, a process that is both costly and time-consuming. Traditional script- and log-based automation tools are limited in their ability to detect unpredictable visual bugs, especially those that are context-dependent or graphical in nature. As a result, many issues go unnoticed during manual QA, which reduces overall game quality, degrades the user experience, and creates inefficiencies throughout the development cycle. This study proposes two approaches to address these challenges. The first leverages a Large Language Model (LLM) to directly analyze gameplay videos, detect visual bugs, and… More >
Open Access
ARTICLE
Lino Gonzalez-Garcia*, Miguel-Angel Sicilia, Elena García-Barriocanal
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070813
Abstract Classifying job offers into occupational categories is a fundamental task in human resource information systems, as it improves and streamlines indexing, search, and matching between openings and job seekers. Comprehensive occupational databases such as NET or ESCO provide detailed taxonomies of interrelated positions that can be leveraged to align the textual content of postings with occupational categories, thereby facilitating standardization, cross-system interoperability, and access to metadata for each occupation (e.g., tasks, knowledge, skills, and abilities). In this work, we explore the effectiveness of fine-tuning existing language models (LMs) to classify job offers with occupational descriptors… More >
Open Access
ARTICLE
Salahudin Robo1,2, Triyanna Widiyaningtyas1,*, Wahyu Sakti Gunawan Irianto1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073011
Abstract Recommendation systems are an integral and indispensable part of every digital platform, as they can suggest content or items to users based on their respective needs. Collaborative filtering is a technique often used in various studies, which produces recommendations by analyzing similarities between users and items based on their behavior. Although often used, traditional collaborative filtering techniques still face the main challenge of sparsity. Sparsity problems occur when the data in the system is sparse, meaning that only a portion of users provide feedback on some items, resulting in inaccurate recommendations generated by the system.… More >
Open Access
ARTICLE
Haotian Zhang, Jing Li*, Ming Zhu, Zhiyong Zhao, Hongli Su, Liming Sun
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072381
Abstract In the wireless energy transmission service composition optimization problem, a key challenge is accurately capturing users’ preferences for service criteria under complex influencing factors, and optimally selecting a composition solution under their budget constraints. Existing studies typically evaluate satisfaction solely based on energy transmission capacity, while overlooking critical factors such as price and trustworthiness of the provider, leading to a mismatch between optimization outcomes and user needs. To address this gap, we construct a user satisfaction evaluation model for multi-user and multi-provider scenarios, systematically incorporating service price, transmission capacity, and trustworthiness into the satisfaction assessment… More >
Open Access
ARTICLE
Shih-Lin Lin*, Cheng-Wei Li
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071915
（This article belongs to the Special Issue: Intelligent Vehicles and Emerging Automotive Technologies: Integrating AI, IoT, and Computing in Next-Generation in Electric Vehicles)
Abstract This study focuses on developing a deep learning model capable of recognizing vehicle brands and models, integrated with a law enforcement intelligence platform to overcome the limitations of existing license plate recognition techniques—particularly in handling counterfeit, obscured, or absent plates. The research first entailed collecting, annotating, and classifying images of various vehicle models, leveraging image processing and feature extraction methodologies to train the model on Microsoft Custom Vision. Experimental results indicate that, for most brands and models, the system achieves stable and relatively high performance in Precision, Recall, and Average Precision (AP). Furthermore, simulated tests… More >
Open Access
ARTICLE
Xiaorui Zhang1,2,*, Chunlin Yuan3, Wei Sun4, Ting Wang5
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071043
（This article belongs to the Special Issue: Advances in Deep Learning and Neural Networks: Architectures, Applications, and Challenges)
Abstract Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression, but existing models have significant shortcomings. On the one hand, Transformer multi-head self-attention modeling of global temporal dependency has problems of high computational overhead and feature similarity. On the other hand, fixed-size convolution kernels are often used, which have weak perception ability for emotional regions of different scales. Therefore, this paper proposes a video emotion recognition model that combines multi-scale region-aware convolution with temporal interactive sampling. In terms of space, multi-branch large-kernel stripe convolution is used to More >
Open Access
ARTICLE
Zia Ur Rehman1, Ahmad Syed2,*, Abu Tayab3, Ghanshyam G. Tejani4,5,*, Doaa Sami Khafaga6, El-Sayed M. El-kenawy7,8
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070310
（This article belongs to the Special Issue: Advances in Image Recognition: Innovations, Applications, and Future Directions)
Abstract Object detection, a major challenge in computer vision and pattern recognition, plays a significant part in many applications, crossing artificial intelligence, face recognition, and autonomous driving. It involves focusing on identifying the detection, localization, and categorization of targets in images. A particularly important emerging task is distinguishing real animals from toy replicas in real-time, mostly for smart camera systems in both urban and natural environments. However, that difficult task is affected by factors such as showing angle, occlusion, light intensity, variations, and texture differences. To tackle these challenges, this paper recommends Group Sparse YOLOv8 (You… More >
Open Access
ARTICLE
Bello Musa Yakubu1,*, Nor Shahida Mohd Jamail 2, Rabia Latif 2, Seemab Latif 3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072426
（This article belongs to the Special Issue: Advances in IoT Security: Challenges, Solutions, and Future Applications)
Abstract Federated Learning (FL) enables joint training over distributed devices without data exchange but is highly vulnerable to attacks by adversaries in the form of model poisoning and malicious update injection. This work proposes Secured-FL, a blockchain-based defensive framework that combines smart contract–based authentication, clustering-driven outlier elimination, and dynamic threshold adjustment to defend against adversarial attacks. The framework was implemented on a private Ethereum network with a Proof-of-Authority consensus algorithm to ensure tamper-resistant and auditable model updates. Large-scale simulation on the Cyber Data dataset, under up to 50% malicious client settings, demonstrates Secured-FL achieves 6%–12% higher accuracy, More >
Open Access
ARTICLE
Yuanyuan Wang1,*, Yemeng Zhu1, Xiuchuan Chen1, Tongtong Yin1, Shiwei Su2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072404
Abstract To solve the false detection and missed detection problems caused by various types and sizes of defects in the detection of steel surface defects, similar defects and background features, and similarities between different defects, this paper proposes a lightweight detection model named multiscale edge and squeeze-and-excitation attention detection network (MSESE), which is built upon the You Only Look Once version 11 nano (YOLOv11n). To address the difficulty of locating defect edges, we first propose an edge enhancement module (EEM), apply it to the process of multiscale feature extraction, and then propose a multiscale edge enhancement… More >
Open Access
ARTICLE
Haohui Su1, Xuan Zhang1,*, Lvjun Zheng1, Xiaojie Shen2, Hua Liao1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072093
Abstract Distributed Denial-of-Service (DDoS) attacks pose severe threats to Industrial Control Networks (ICNs), where service disruption can cause significant economic losses and operational risks. Existing signature-based methods are ineffective against novel attacks, and traditional machine learning models struggle to capture the complex temporal dependencies and dynamic traffic patterns inherent in ICN environments. To address these challenges, this study proposes a deep feature-driven hybrid framework that integrates Transformer, BiLSTM, and KNN to achieve accurate and robust DDoS detection. The Transformer component extracts global temporal dependencies from network traffic flows, while BiLSTM captures fine-grained sequential dynamics. The learned… More >
Open Access
ARTICLE
Muntaham Inaam Hashmi1, Muhammad Ayaz Khan2, Khwaja Mansoor ul Hassan1, Suliman A. Alsuhibany3,*, Ainur Abduvalova4, Asfandyar Khan5
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071452
Abstract Cyber-criminals target smart connected devices for spyware distribution and security breaches, but existing Internet of Things (IoT) security standards are insufficient. Major IoT industry players prioritize market share over security, leading to insecure smart products. Traditional host-based protection solutions are less effective due to limited resources. Overcoming these challenges and enhancing the security of IoT Devices requires a security design at the network level that uses lightweight cryptographic parameters. In order to handle control, administration, and security concerns in traditional networking, the Gateway Node offers a contemporary networking architecture. By managing all network-level computations and… More >
Open Access
ARTICLE
Musheng Chen1,2, Qiang Wen1, Xiaohong Qiu1,2, Junhua Wu1,*, Wenqing Fu1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071207
Abstract In multi-modal emotion recognition, excessive reliance on historical context often impedes the detection of emotional shifts, while modality heterogeneity and unimodal noise limit recognition performance. Existing methods struggle to dynamically adjust cross-modal complementary strength to optimize fusion quality and lack effective mechanisms to model the dynamic evolution of emotions. To address these issues, we propose a multi-level dynamic gating and emotion transfer framework for multi-modal emotion recognition. A dynamic gating mechanism is applied across unimodal encoding, cross-modal alignment, and emotion transfer modeling, substantially improving noise robustness and feature alignment. First, we construct a unimodal encoder More >
Open Access
ARTICLE
Charlotte Olivia Namagembe, Mohamad Ibrahim, Md Arafatur Rahman*, Prashant Pillai
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070316
Abstract The rapid proliferation of commercial unmanned aerial vehicles (UAVs) has revolutionized fields such as precision agriculture and disaster response. However, their heavy reliance on GPS navigation leaves them highly vulnerable to spoofing attacks, with potentially severe consequences. To mitigate this threat, we present a machine learning-driven framework for real-time GPS spoofing detection, designed with a balance of detection accuracy and computational efficiency. Our work is distinguished by the creation of a comprehensive dataset of 10,000 instances that integrates both simulated and real-world data, enabling robust and generalizable model development. A comprehensive evaluation of multiple classification More >
Open Access
ARTICLE
Agit Amrullah, Ferda Ernawan*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.069229
Abstract This paper proposes a tamper detection technique for semi-fragile watermarking using Quantization-based Discrete Cosine Transform (DCT) for tamper localization. In this study, the proposed embedding strategy is investigated by experimental tests over the diagonal order of the DCT coefficients. The cover image is divided into non-overlapping blocks of size 8 × 8 pixels. The DCT is applied to each block, and the coefficients are arranged using a zig-zag pattern within the block. In this study, the low-frequency coefficients are selected to examine the impact of the imperceptibility score and tamper detection accuracy. High accuracy of… More >
Open Access
ARTICLE
Hui Chen1, Mohammed A. H. Ali1,*, Bushroa Abd Razak1, Zhenya Wang2, Yusoff Nukman1, Shikai Zhang1, Zhiwei Huang1, Ligang Yao3, Mohammad Alkhedher4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.067286
Abstract Accurate and real-time traffic-sign detection is a cornerstone of Advanced Driver-Assistance Systems (ADAS) and autonomous vehicles. However, existing one-stage detectors miss distant signs, and two-stage pipelines are impractical for embedded deployment. To address this issue, we present YOLO-SMM, a lightweight two-stage framework. This framework is designed to augment the YOLOv8 baseline with three targeted modules. (1) SlimNeck replaces PAN/FPN with a CSP-OSA/GSConv fusion block, reducing parameters and FLOPs without compromising multi-scale detail. (2) The MCA model introduces row- and column-aware weights to selectively amplify small sign regions in cluttered scenes. (3) MPDIoU augments CIoU loss… More >
Open Access
ARTICLE
Ateeqa Jalal1,, Umar Farooq1,4,5, Ihsan Rabbi1,4, Afzal Badshah2, Aurangzeb Khan1,4, Muhammad Mansoor Alam3,4 and Mazliham Mohd Su'ud4,
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.068289
Abstract The exponential growth of Internet of Things (IoT) devices, autonomous systems, and digital services is generating massive volumes of big data, projected to exceed 291 zettabytes by 2027. Conventional cloud computing, despite its high processing and storage capacity, suffers from increased network latency, network congestion, and high operational costs, making it unsuitable for latency-sensitive applications. Edge computing addresses these issues by processing data near the source but faces scalability challenges and elevated Total Cost of Ownership (TCO). Hybrid solutions, such as fog computing, cloudlets, and Mobile Edge Computing (MEC), attempt to balance cost and performance;… More >
Open Access
ARTICLE
Xin Ma1,2, Jin Lei3,4,*, Chenying Pei4 and Chunming Wu4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.069353
（This article belongs to the Special Issue: Advances in Object Detection: Methods and Applications)
Abstract This study proposes a lightweight apple detection method employing cascaded knowledge distillation (KD) to address the critical challenges of excessive parameters and high deployment costs in existing models. We introduce a Lightweight Feature Pyramid Network (LFPN) integrated with Lightweight Downsampling Convolutions (LDConv) to substantially reduce model complexity without compromising accuracy. A Lightweight Multi-channel Attention (LMCA) mechanism is incorporated between the backbone and neck networks to effectively suppress complex background interference in orchard environments. Furthermore, model size is compressed via Group_Slim channel pruning combined with a cascaded distillation strategy. Experimental results demonstrate that the proposed model More >
Open Access
ARTICLE
Xiaodong Zhang1, Wenhan Hou2,*, Juanjuan Wang3, Leixiao Li1, Pengfei Yue1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072384
Abstract Blockchain offers a promising solution to the security challenges faced by the Internet of Vehicles (IoV). However, due to the dynamic connectivity of IoV, blockchain based on a single-chain structure or Directed Acyclic Graph (DAG) structure often suffer from performance limitations. The DAG lattice structure is a novel blockchain model in which each node maintains its own account chain, and only the node itself is allowed to update it. This feature makes the DAG lattice structure particularly suitable for addressing the challenges in dynamically connected IoV environment. In this paper, we propose a blockchain architecture… More >
Open Access
REVIEW
Qun Song1, Chao Gao1, Han Wu1, Zhiheng Rao1, Huafeng Qin1,*, Simon Fong1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070918
Abstract Metaheuristic algorithms, renowned for strong global search capabilities, are effective tools for solving complex optimization problems and show substantial potential in e-Health applications. This review provides a systematic overview of recent advancements in metaheuristic algorithms and highlights their applications in e-Health. We selected representative algorithms published between 2019 and 2024, and quantified their influence using an entropy-weighted method based on journal impact factors and citation counts. CThe Harris Hawks Optimizer (HHO) demonstrated the highest early citation impact. The study also examined applications in disease prediction models, clinical decision support, and intelligent health monitoring. Notably, the More >
Open Access
ARTICLE
Yaxin Zhao1, Qi Han2, Hui Shu2, Yan Guang2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070511
（This article belongs to the Special Issue: AI-Powered Software Engineering)
Abstract Large Language Models (LLMs) are increasingly applied in the field of code translation. However, existing evaluation methodologies suffer from two major limitations: (1) the high overlap between test data and pretraining corpora, which introduces significant bias in performance evaluation; and (2) mainstream metrics focus primarily on surface-level accuracy, failing to uncover the underlying factors that constrain model capabilities. To address these issues, this paper presents TCode (Translation-Oriented Code Evaluation benchmark)—a complexity-controllable, contamination-free benchmark dataset for code translation—alongside a dedicated static feature sensitivity evaluation framework. The dataset is carefully designed to control complexity along multiple dimensions—including syntactic… More >
Open Access
ARTICLE
Yuchao Hou1,2, Biaobiao Bai3, Shuai Zhao3, Yue Wang3, Jie Wang3, Zijian Li4,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.069789
Abstract Recently, large-scale deep learning models have been increasingly adopted for point cloud classification. However, these methods typically require collecting extensive datasets from multiple clients, which may lead to privacy leaks. Federated learning provides an effective solution to data leakage by eliminating the need for data transmission, relying instead on the exchange of model parameters. However, the uneven distribution of client data can still affect the model’s ability to generalize effectively. To address these challenges, we propose a new framework for point cloud classification called Federated Dynamic Aggregation Selection Strategy-based Multi-Receptive Field Fusion Classification Framework (FDASS-MRFCF).… More >
Open Access
ARTICLE
Rekha Phadke1, Abdul Lateef Haroon Phulara Shaik2, Dayanidhi Mohapatra3, Doaa Sami Khafaga4,*, Eman Abdullah Aldakheel4, N. Sathyanarayana5
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.067564
Abstract Recently, the Internet of Things (IoT) technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart devices. Furthermore, the IoT plays a key role in multiple domains, including industrial automation, smart homes, and intelligent transportation systems. However, an increasing number of connected devices presents significant challenges related to efficient resource allocation and system responsiveness. To address these issue, this research proposes a Modified Walrus Optimization Algorithm (MWaOA) for effective resource management in smart IoT systems. In the proposed MWaOA, a crowding process… More >
Open Access
ARTICLE
Muhammad Ejaz1, Muhammad Asim2,*, Mudasir Ahmad Wani2,3, Kashish Ara Shakil4,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072464
Abstract The integration of High-Altitude Platform Stations (HAPS) with Reconfigurable Intelligent Surfaces (RIS) represents a critical advancement for next-generation wireless networks, offering unprecedented opportunities for ubiquitous connectivity. However, existing research reveals significant gaps in dynamic resource allocation, joint optimization, and equitable service provisioning under varying channel conditions, limiting practical deployment of these technologies. This paper addresses these challenges by proposing a novel Fairness-Aware Deep Q-Learning (FAIR-DQL) framework for joint resource management and phase configuration in HAPS-RIS systems. Our methodology employs a comprehensive three-tier algorithmic architecture integrating adaptive power control, priority-based user scheduling, and dynamic learning mechanisms. More >
Open Access
ARTICLE
Yuntian Wang1,2, Taohua Liang1,2, Yuan Zhou1,2, Weimei Shi1,2, Lijuan Huang1,2, Yuzhu Guo3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071624
Abstract This investigation utilizes non-equilibrium molecular dynamics (NEMD) simulations to explore shock-induced spallation in single-crystal tantalum across shock velocities of 0.75–4 km/s and initial temperatures from 300 to 2000 K. Two spallation modes emerge: classical spallation for shock velocity below 1.5 km/s, with solid-state reversible Body-Centered Cubic (BCC) to Face-Centered Cubic (FCC) or Hexagonal Close-Packed (HCP) phase transformations and discrete void nucleation-coalescence; micro-spallation for shock velocity above 3.0 km/s, featuring complete shock-induced melting and fragmentation, with a transitional regime (2.0–2.5 km/s) of partial melting. Spall strength decreases monotonically with temperature due to thermal softening. Elevated temperatures More >
Open Access
ARTICLE
Tai Liu1,2, Mao Ye2,*, Feng Wu3, Chao Zhu2, Bo Chen2, Guoyan Zhang1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072692
（This article belongs to the Special Issue: Smart Roads, Smarter Cars, Safety and Security: Evolution of Vehicular Ad Hoc Networks)
Abstract With the continuous advancement of unmanned technology in various application domains, the development and deployment of blind-spot-free panoramic video systems have gained increasing importance. Such systems are particularly critical in battlefield environments, where advanced panoramic video processing and wireless communication technologies are essential to enable remote control and autonomous operation of unmanned ground vehicles (UGVs). However, conventional video surveillance systems suffer from several limitations, including limited field of view, high processing latency, low reliability, excessive resource consumption, and significant transmission delays. These shortcomings impede the widespread adoption of UGVs in battlefield settings. To overcome these… More >
Open Access
ARTICLE
Bin Zhang*, Zhancheng Xu
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.069979
（This article belongs to the Special Issue: Deep Learning: Emerging Trends, Applications and Research Challenges for Image Recognition)
Abstract This study aimed to enhance the performance of semantic segmentation for autonomous driving by improving the 2DPASS model. Two novel improvements were proposed and implemented in this paper: dynamically adjusting the loss function ratio and integrating an attention mechanism (CBAM). First, the loss function weights were adjusted dynamically. The grid search method is used for deciding the best ratio of 7:3. It gives greater emphasis to the cross-entropy loss, which resulted in better segmentation performance. Second, CBAM was applied at different layers of the 2D encoder. Heatmap analysis revealed that introducing it after the second… More >
Open Access
ARTICLE
Bing Wei, Ming Zhong, Qian Chen, Yi Wu*, Yubin Li
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.069910
Abstract In erasure-coded storage systems, updating data requires parity maintenance, which often leads to significant I/O amplification due to “write-after-read” operations. Furthermore, scattered parity placement increases disk seek overhead during repair, resulting in degraded system performance. To address these challenges, this paper proposes a Cognitive Update and Repair Method (CURM) that leverages machine learning to classify files into write-only, read-only, and read-write categories, enabling tailored update and repair strategies. For write-only and read-write files, CURM employs a data-difference mechanism combined with fine-grained I/O scheduling to minimize redundant read operations and mitigate I/O amplification. For read-write files,… More >
Open Access
ARTICLE
Xinwei Zhu, Jianxun Zhang*, Huan Zeng
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.068578
Abstract Underwater images often affect the effectiveness of underwater visual tasks due to problems such as light scattering, color distortion, and detail blurring, limiting their application performance. Existing underwater image enhancement methods, although they can improve the image quality to some extent, often lead to problems such as detail loss and edge blurring. To address these problems, we propose FENet, an efficient underwater image enhancement method. FENet first obtains three different scales of images by image downsampling and then transforms them into the frequency domain to extract the low-frequency and high-frequency spectra, respectively. Then, a distance… More >
Open Access
ARTICLE
Bing Zhang, Wenqi Shi*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.067470
（This article belongs to the Special Issue: Cyberspace Mapping and Anti-Mapping Techniques)
Abstract To address the challenge of low survival rates and limited data collection efficiency in current virtual probe deployments, which results from anomaly detection mechanisms in location-based service (LBS) applications, this paper proposes a novel virtual probe deployment method based on user behavioral feature analysis. The core idea is to circumvent LBS anomaly detection by mimicking real-user behavior patterns. First, we design an automated data extraction algorithm that recognizes graphical user interface (GUI) elements to collect spatio-temporal behavior data. Then, by analyzing the automatically collected user data, we identify normal users’ spatio-temporal patterns and extract their… More >
Open Access
ARTICLE
Chuang-Chieh Lin1, Yung-Shen Huang2, Shih-Yeh Chen2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072890
（This article belongs to the Special Issue: Omnipresent AI in the Cloud Era Reshaping Distributed Computation and Adaptive Systems for Modern Applications)
Abstract With the rapid development of generative artificial intelligence (GenAI), the task of story visualization, which transforms natural language narratives into coherent and consistent image sequences, has attracted growing research attention. However, existing methods still face limitations in balancing multi-frame character consistency and generation efficiency, which restricts their feasibility for large-scale practical applications. To address this issue, this study proposes a modular cloud-based distributed system built on Stable Diffusion. By separating the character generation and story generation processes, and integrating multi-feature control techniques, a caching mechanism, and an asynchronous task queue architecture, the system enhances generation… More >
Open Access
ARTICLE
Jianchun Wen1, Minghao Zhu1,*, Bo Gao2, Zhaojian Liu1, Xuehan Li3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.069671
Abstract Urban traffic generates massive and diverse data, yet most systems remain fragmented. Current approaches to congestion management suffer from weak data consistency and poor scalability. This study addresses this gap by proposing the Urban Traffic Congestion Unified Metadata Model (UTC-UMM). The goal is to provide a standardized and extensible framework for describing, extracting, and storing multisource traffic data in smart cities. The model defines a two-tier specification that organizes nine core traffic resource classes. It employs an eXtensible Markup Language (XML) Schema that connects general elements with resource-specific elements. This design ensures both syntactic and… More >
Open Access
ARTICLE
Zeyu Chen1, Jian Sun2,*, Zhengda Huan1, Ziyi Zhang1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071182
Abstract To address the issues of poor adaptability in resource allocation and low multi-agent cooperation efficiency in Joint Radar and Communication (JRC) systems under dynamic environments, an intelligent optimization framework integrating Deep Reinforcement Learning (DRL) and Graph Neural Network (GNN) is proposed. This framework models resource allocation as a Partially Observable Markov Game (POMG), designs a weighted reward function to balance radar and communication efficiencies, adopts the Multi-Agent Proximal Policy Optimization (MAPPO) framework, and integrates Graph Convolutional Networks (GCN) and Graph Sample and Aggregate (GraphSAGE) to optimize information interaction. Simulations show that, compared with traditional methods More >
Open Access
ARTICLE
Haoxin Sun1, Xiao Yu1,*, Jiale Li1, Yitong Xu1, Jie Yu1, Huanhuan Li1, Yuanzhang Li2, Yu-An Tan2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070930
Abstract Since the advent of smart contracts, security vulnerabilities have remained a persistent challenge, compromsing both the reliability of contract execution and the overall stability of the virtual currency market. Consequently, the academic community has devoted increasing attention to these security risks. However, conventional approaches to vulnerability detection frequently exhibit limited accuracy. To address this limitation, the present study introduces a novel vulnerability detection framework called GNNSE that integrates symbolic execution with graph neural networks (GNNs). The proposed method first constructs semantic graphs to comprehensively capture the control flow and data flow dependencies within smart contracts. More >
Open Access
ARTICLE
Malvinder Singh Bali1, Weiwei Jiang2,*, Saurav Verma3, Kanwalpreet Kour4, Ashwini Rao3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070866
Abstract In recent years, Blockchain Technology has become a paradigm shift, providing Transparent, Secure, and Decentralized platforms for diverse applications, ranging from Cryptocurrency to supply chain management. Nevertheless, the optimization of blockchain networks remains a critical challenge due to persistent issues such as latency, scalability, and energy consumption. This study proposes an innovative approach to Blockchain network optimization, drawing inspiration from principles of biological evolution and natural selection through evolutionary algorithms. Specifically, we explore the application of genetic algorithms, particle swarm optimization, and related evolutionary techniques to enhance the performance of blockchain networks. The proposed methodologies More >
Open Access
ARTICLE
Abu Tayab1,*, Yanwen Li1, Ahmad Syed2, Ghanshyam G. Tejani3,4,*, Doaa Sami Khafaga5, El-Sayed M. El-kenawy6, Amel Ali Alhussan7, Marwa M. Eid8,9
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070583
（This article belongs to the Special Issue: Advances in Vehicular Ad-Hoc Networks (VANETs) for Intelligent Transportation Systems)
Abstract Autonomous connected vehicles (ACV) involve advanced control strategies to effectively balance safety, efficiency, energy consumption, and passenger comfort. This research introduces a deep reinforcement learning (DRL)-based car-following (CF) framework employing the Deep Deterministic Policy Gradient (DDPG) algorithm, which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning. Utilizing real-world driving data from the highD dataset, the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios. The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control (MPC-ACC) controller. Results show that the… More >
Open Access
ARTICLE
Le Wang1, Bing Xu1,*, Peng Liu2, En Yuan1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070794
Abstract Deep learning has been recognized as an effective method for indoor positioning. However, most existing real-valued neural networks (RVNNs) treat the two constituent components of complex-valued channel state information (CSI) as real-valued inputs, potentially discarding useful information embedded in the original CSI. In addition, existing positioning models generally face the contradiction between computational complexity and positioning accuracy. To address these issues, we combine graph neural network (GNN) with complex-valued neural network (CVNN) to construct a lightweight indoor positioning model named CGNet. CGNet employs complex-valued convolution operation to directly process the original CSI data, fully exploiting… More >
Open Access
ARTICLE
Yunhao Yu1, Boda Zhang1, Meiling Dizha1, Ruibin Wen1, Fuhua Luo1, Xiang Guo1, Zhenyong Zhang2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070577
Abstract Load frequency control (LFC) is a critical function to balance the power consumption and generation. The grid frequency is a crucial indicator for maintaining balance. However, the widely used information and communication infrastructure for LFC increases the risk of being attacked by malicious actors. The dynamic load altering attack (DLAA) is a typical attack that can destabilize the power system, causing the grid frequency to deviate from its nominal value. Therefore, in this paper, we mathematically analyze the impact of DLAA on the stability of the grid frequency and propose the network parameter regulation (NPR)… More >
Open Access
ARTICLE
Zhonghao Wang1,2, Xin Liu1,2,*, Changhua Yue3, Haiwen Yuan4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071813
Abstract To address critical challenges in nighttime ship detection—high small-target missed detection (over 20%), insufficient lightweighting, and limited generalization due to scarce, low-quality datasets—this study proposes a systematic solution. First, a high-quality Night-Ships dataset is constructed via CycleGAN-based day-night transfer, combined with a dual-threshold cleaning strategy (Laplacian variance sharpness filtering and brightness-color deviation screening). Second, a Cross-stage Lightweight Fusion-You Only Look Once version 8 (CLF-YOLOv8) is proposed with key improvements: the Neck network is reconstructed by replacing Cross Stage Partial (CSP) structure with the Cross Stage Partial Multi-Scale Convolutional Block (CSP-MSCB) and integrating Bidirectional Feature Pyramid More >
Open Access
ARTICLE
Dike Chen1,2,3, Zhiyong Qin2, Ji Zhang2, Hongyuan Wang1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072494
Abstract To address the challenges of small target detection and significant scale variations in unmanned aerial vehicle (UAV) aerial imagery, which often lead to missed and false detections, we propose Multi-scale Feature Fusion YOLO (MFF-YOLO), an enhanced algorithm based on YOLOv8s. Our approach introduces a Multi-scale Feature Fusion Strategy (MFFS), comprising the Multiple Features C2f (MFC) module and the Scale Sequence Feature Fusion (SSFF) module, to improve feature integration across different network levels. This enables more effective capture of fine-grained details and sequential multi-scale features. Furthermore, we incorporate Inner-CIoU, an improved loss function that uses auxiliary More >
Open Access
ARTICLE
Haoxuanye Ji*, Zhiliang Chen, Pengfei Jiang, Ziyue Wang, Ting Yu, Wei Zhang
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071880
（This article belongs to the Special Issue: Advances in Efficient Vision Transformers: Architectures, Optimization, and Applications)
Abstract Foreign body classification on coal conveyor belts is a critical component of intelligent coal mining systems. Previous approaches have primarily utilized convolutional neural networks (CNNs) to effectively integrate spatial and semantic information. However, the performance of CNN-based methods remains limited in classification accuracy, primarily due to insufficient exploration of local image characteristics. Unlike CNNs, Vision Transformer (ViT) captures discriminative features by modeling relationships between local image patches. However, such methods typically require a large number of training samples to perform effectively. In the context of foreign body classification on coal conveyor belts, the limited availability… More >
Open Access
ARTICLE
Yan Kong1, Xinpeng Guo2, Chih-Hsien Hsia3,4,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071269
Abstract With the development of technology, diffusion model-based solvers have shown significant promise in solving Combinatorial Optimization (CO) problems, particularly in tackling Non-deterministic Polynomial-time hard (NP-hard) problems such as the Traveling Salesman Problem (TSP). However, existing diffusion model-based solvers typically employ a fixed, uniform noise schedule (e.g., linear or cosine annealing) across all training instances, failing to fully account for the unique characteristics of each problem instance. To address this challenge, we present Graph-Guided Diffusion Solvers (GGDS), an enhanced method for improving graph-based diffusion models. GGDS leverages Graph Neural Networks (GNNs) to capture graph structural information… More >
Open Access
ARTICLE
Hafsa Sidaq1, Lei Wang1, Sghaier Guizani2,*, Hussain Haider3, Ateeq Ur Rehman4,*, Habib Hamam5,6,7
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071254
Abstract Numerous sectors, such as education, the IT sector, and corporate organizations, transitioned to virtual meetings after the COVID-19 crisis. Organizations now seek to assess participants’ fatigue levels in online meetings to remain competitive. Instructors cannot effectively monitor every individual in a virtual environment, which raises significant concerns about participant fatigue. Our proposed system monitors fatigue, identifying attentive and drowsy individuals throughout the online session. We leverage Dlib’s pre-trained facial landmark detector and focus on the eye landmarks only, offering a more detailed analysis for predicting eye opening and closing of the eyes, rather than focusing… More >
Open Access
ARTICLE
Eman Alsalmi, Abeer Alhuzali*, Areej Alhothali
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071012
（This article belongs to the Special Issue: Advances in Deep Learning and Neural Networks: Architectures, Applications, and Challenges)
Abstract Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems. Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted features that limit their adaptability across various systems. In this study, we propose a hybrid model, BertGCN, that integrates BERT-based contextual embedding with Graph Convolutional Networks (GCNs) to identify anomalies in raw system logs, thereby eliminating the need for log parsing. The BERT module captures semantic representations of log messages, while the GCN models the structural relationships among log entries through a text-based graph. This combination More >
Open Access
ARTICLE
Xu Tao1, Qiang Xiao2, Zhaoqi Jin2, Hao Li1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070790
Abstract Image fusion technology aims to generate a more informative single image by integrating complementary information from multi-modal images. Despite the significant progress of deep learning-based fusion methods, existing algorithms are often limited to single or dual-dimensional feature interactions, thus struggling to fully exploit the profound complementarity between multi-modal images. To address this, this paper proposes a parallel multi-dimensional complementary fusion network, termed PMCFusion, for the task of infrared and visible image fusion. The core of this method is its unique parallel three-branch fusion module, PTFM, which pioneers the parallel synergistic perception and efficient integration of… More >
Open Access
ARTICLE
Qiuru Fu1, Shumao Zhang1, Shuang Zhou1, Jie Xu1,*, Changming Zhao2, Shanchao Li3, Du Xu1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070493
Abstract Knowledge graphs often suffer from sparsity and incompleteness. Knowledge graph reasoning is an effective way to address these issues. Unlike static knowledge graph reasoning, which is invariant over time, dynamic knowledge graph reasoning is more challenging due to its temporal nature. In essence, within each time step in a dynamic knowledge graph, there exists structural dependencies among entities and relations, whereas between adjacent time steps, there exists temporal continuity. Based on these structural and temporal characteristics, we propose a model named “DKGR-DR” to learn distributed representations of entities and relations by combining recurrent neural networks More >
Open Access
ARTICLE
Cyreneo Dofitas1, Yong-Woon Kim2, Yung-Cheol Byun3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.069374
Abstract Recent advances in deep learning have significantly improved flood detection and segmentation from aerial and satellite imagery. However, conventional convolutional neural networks (CNNs) often struggle in complex flood scenarios involving reflections, occlusions, or indistinct boundaries due to limited contextual modeling. To address these challenges, we propose a hybrid flood segmentation framework that integrates a Vision Transformer (ViT) encoder with a U-Net decoder, enhanced by a novel Flood-Aware Refinement Block (FARB). The FARB module improves boundary delineation and suppresses noise by combining residual smoothing with spatial-channel attention mechanisms. We evaluate our model on a UAV-acquired flood More >
Open Access
ARTICLE
Guangyu Huo, Chang Su, Xiaoyu Zhang*, Xiaohui Cui, Lizhong Zhang
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072147
（This article belongs to the Special Issue: Advancing Network Intelligence: Communication, Sensing and Computation)
Abstract Long-term traffic flow prediction is a crucial component of intelligent transportation systems within intelligent networks, requiring predictive models that balance accuracy with low-latency and lightweight computation to optimize traffic management and enhance urban mobility and sustainability. However, traditional predictive models struggle to capture long-term temporal dependencies and are computationally intensive, limiting their practicality in real-time. Moreover, many approaches overlook the periodic characteristics inherent in traffic data, further impacting performance. To address these challenges, we introduce ST-MambaGCN, a State-Space-Based Spatio-Temporal Graph Convolution Network. Unlike conventional models, ST-MambaGCN replaces the temporal attention layer with Mamba, a state-space More >
Open Access
ARTICLE
Shaobo Kang, Mingzhi Yang*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071488
Abstract Fabric defect detection plays a vital role in ensuring textile quality. However, traditional manual inspection methods are often inefficient and inaccurate. To overcome these limitations, we propose FD-YOLO, an enhanced lightweight detection model based on the YOLOv11n framework. The proposed model introduces the Bi-level Routing Attention (BRAttention) mechanism to enhance defect feature extraction, enabling more detailed feature representation. It proposes Deep Progressive Cross-Scale Fusion Neck (DPCSFNeck) to better capture small-scale defects and incorporates a Multi-Scale Dilated Residual (MSDR) module to strengthen multi-scale feature representation. Furthermore, a Shared Detail-Enhanced Lightweight Head (SDELHead) is employed to reduce More >
Open Access
ARTICLE
Jiajia Liu1,*, Junyi Lin2, Wenxiang Dong2, Xuan Zhao2, Jianhua Liu2, Huiru Li3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071397
（This article belongs to the Special Issue: Deep Learning: Emerging Trends, Applications and Research Challenges for Image Recognition)
Abstract Single Image Super-Resolution (SISR) seeks to reconstruct high-resolution (HR) images from low-resolution (LR) inputs, thereby enhancing visual fidelity and the perception of fine details. While Transformer-based models—such as SwinIR, Restormer, and HAT—have recently achieved impressive results in super-resolution tasks by capturing global contextual information, these methods often suffer from substantial computational and memory overhead, which limits their deployment on resource-constrained edge devices. To address these challenges, we propose a novel lightweight super-resolution network, termed Binary Attention-Guided Information Distillation (BAID), which integrates frequency-aware modeling with a binary attention mechanism to significantly reduce computational complexity and parameter… More >
Open Access
ARTICLE
Yuang Chen1,2, Yong Li1,*, Fang Lin1,2, Shuhan Lv1,2, Jiaze Jiang1,2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070990
Abstract Aiming at the problem of potential information noise introduced during the generation of ghost feature maps in GhostNet, this paper proposes a novel lightweight neural network model called ResghostNet. This model constructs the Resghost Module by combining residual connections and Adaptive-SE Blocks, which enhances the quality of generated feature maps through direct propagation of original input information and selection of important channels before cheap operations. Specifically, ResghostNet introduces residual connections on the basis of the Ghost Module to optimize the information flow, and designs a weight self-attention mechanism combined with SE blocks to enhance feature More >
Open Access
ARTICLE
Kirubavathi Ganapathiyappan1,*, Kiruba Marimuthu Eswaramoorthy1, Abi Thangamuthu Shanthamani1, Aksaya Venugopal1, Asita Pon Bhavya Iyyappan1, Thilaga Manickam1, Ateeq Ur Rehman2,*, Habib Hamam3,4,5,6
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070067
（This article belongs to the Special Issue: Advances in Machine Learning and Artificial Intelligence for Intrusion Detection Systems)
Abstract The growing use of Portable Document Format (PDF) files across various sectors such as education, government, and business has inadvertently turned them into a major target for cyberattacks. Cybercriminals take advantage of the inherent flexibility and layered structure of PDFs to inject malicious content, often employing advanced obfuscation techniques to evade detection by traditional signature-based security systems. These conventional methods are no longer adequate, especially against sophisticated threats like zero-day exploits and polymorphic malware. In response to these challenges, this study introduces a machine learning-based detection framework specifically designed to combat such threats. Central to… More >
Open Access
ARTICLE
Longyue Li1, Guoqing Zhang1, Bo Cao1, Shuqi Wang2, Ye Tian1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070622
Abstract Modern battlefields exhibit high dynamism, where traditional static weighting methods in combat effectiveness assessment fail to capture real-time changes in indicator values, leading to limited assessment accuracy—especially critical in scenarios like sudden electronic warfare or degraded command, where static weights cannot reflect the operational value decay or surge of key indicators. To address this issue, this study proposes a dynamic adaptive weighting method for evaluation indicators based on G1-CRITIC-PIVW. First, the G1 (Sequential Relationship Analysis Method) subjective weighting method—translates expert knowledge into indicator importance rankings—leverages expert knowledge to quantify the relative importance of indicators via… More >
Open Access
ARTICLE
Sanghyuk Lee1, Eunmi Lee2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070504
Abstract Hesitation analysis plays a crucial role in decision-making processes by capturing the intermediary position between supportive and opposing information. This study introduces a refined approach to addressing uncertainty in decision-making, employing existing measures used in decision problems. Building on information theory, the Kullback–Leibler (KL) divergence is extended to incorporate additional insights, specifically by applying temporal data, as illustrated by time series data from two datasets (e.g., affirmative and dissent information). Cumulative hesitation provides quantifiable insights into the decision-making process. Accordingly, a modified KL divergence, which incorporates historical trends, is proposed, enabling dynamic updates using conditional More >
Open Access
ARTICLE
Yi Cao1, Kuo Zhang1, Chengsheng Yuan2,*, Linglong Zhu1, Wentao Ge2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070158
Abstract Generative steganography uses generative stego images to transmit secret message. It also effectively defends against statistical steganalysis. However, most existing methods focus primarily on matching the feature distribution of training data, often neglecting the sequential continuity between moves in the game. This oversight can result in unnatural patterns that deviate from real user behavior, thereby reducing the security of the hidden communication. To address this issue, we design a Gomoku agent based on the AlphaZero algorithm. The model engages in self-play to generate a sequence of plausible moves. These moves form the basis of the… More >
Open Access
ARTICLE
Bohui Li1, Bin Wang1, Linjie Wu1, Xingjuan Cai1,*, Maoqing Zhang2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070592
（This article belongs to the Special Issue: Advanced Edge Computing and Artificial Intelligence in Smart Environment)
Abstract Federated Learning (FL) provides an effective framework for efficient processing in vehicular edge computing. However, the dynamic and uncertain communication environment, along with the performance variations of vehicular devices, affect the distribution and uploading processes of model parameters. In FL-assisted Internet of Vehicles (IoV) scenarios, challenges such as data heterogeneity, limited device resources, and unstable communication environments become increasingly prominent. These issues necessitate intelligent vehicle selection schemes to enhance training efficiency. Given this context, we propose a new scenario involving FL-assisted IoV systems under dynamic and uncertain communication conditions, and develop a dynamic interval multi-objective More >
Open Access
ARTICLE
Chang Su, Liangliang Zhao*, Dongbing Xiang
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071319
Abstract To address low learning efficiency and inadequate path safety in spraying robot navigation within complex obstacle-rich environments—with dense, dynamic, unpredictable obstacles challenging conventional methods—this paper proposes a hybrid algorithm integrating Q-learning and improved A*-Artificial Potential Field (A-APF). Centered on the Q-learning framework, the algorithm leverages safety-oriented guidance generated by A-APF and employs a dynamic coordination mechanism that adaptively balances exploration and exploitation. The proposed system comprises four core modules: (1) an environment modeling module that constructs grid-based obstacle maps; (2) an A-APF module that combines heuristic search from A* algorithm with repulsive force strategies from… More >
Open Access
ARTICLE
Mo Hou1,2,3,#,*, Bin Xu4,#, Wen Shang1,2,3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071282
Abstract Image captioning, a pivotal research area at the intersection of image understanding, artificial intelligence, and linguistics, aims to generate natural language descriptions for images. This paper proposes an efficient image captioning model named Mob-IMWTC, which integrates improved wavelet convolution (IMWTC) with an enhanced MobileNet V3 architecture. The enhanced MobileNet V3 integrates a transformer encoder as its encoding module and a transformer decoder as its decoding module. This innovative neural network significantly reduces the memory space required and model training time, while maintaining a high level of accuracy in generating image descriptions. IMWTC facilitates large receptive… More >
Open Access
ARTICLE
Zilin Zhang1, Yan Liu1,*, Jia Liu2, Senbao Hou3, Yuping Zhang1, Chenyuan Wang1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072286
Abstract With the growing demand for more comprehensive and nuanced sentiment understanding, Multimodal Sentiment Analysis (MSA) has gained significant traction in recent years and continues to attract widespread attention in the academic community. Despite notable advances, existing approaches still face critical challenges in both information modeling and modality fusion. On one hand, many current methods rely heavily on encoders to extract global features from each modality, which limits their ability to capture latent fine-grained emotional cues within modalities. On the other hand, prevailing fusion strategies often lack mechanisms to model semantic discrepancies across modalities and to… More >
Open Access
ARTICLE
He Duan, Shi Zhang*, Dayu Li
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.069628
Abstract Internet of Things (IoT) interconnects devices via network protocols to enable intelligent sensing and control. Resource-constrained IoT devices rely on cloud servers for data storage and processing. However, this cloud-assisted architecture faces two critical challenges: the untrusted cloud services and the separation of data ownership from control. Although Attribute-based Searchable Encryption (ABSE) provides fine-grained access control and keyword search over encrypted data, existing schemes lack of error tolerance in exact multi-keyword matching. In this paper, we proposed an attribute-based multi-keyword fuzzy searchable encryption with forward ciphertext search (FCS-ABMSE) scheme that avoids computationally expensive bilinear pairing… More >
Open Access
ARTICLE
Yali Cao1, Weijian Hu1,2, Lingfang Li1,*, Minchao Li1, Meng Xu2, Ke Han2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.069373
Abstract Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems (ITS), playing a pivotal role in mitigating congestion, enhancing route optimization, and improving the utilization efficiency of roadway infrastructure. However, existing methods struggle in complex traffic scenarios due to static spatio-temporal embedding, restricted multi-scale temporal modeling, and weak representation of local spatial interactions. This study proposes Bi-STAT+, an enhanced bidirectional spatio-temporal attention framework to address existing limitations through three principal contributions: (1) an adaptive spatio-temporal embedding module that dynamically adjusts embeddings to capture complex traffic variations; (2) frequency-domain analysis in the temporal dimension for… More >
Open Access
ARTICLE
Wen-Tsai Sung1, Indra Griha Tofik Isa2,3, Sung-Jung Hsiao4,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070922
Abstract Mango is a plant with high economic value in the agricultural industry; thus, it is necessary to maximize the productivity performance of the mango plant, which can be done by implementing artificial intelligence. In this study, a lightweight object detection model will be developed that can detect mango plant conditions based on disease potential, so that it becomes an early detection warning system that has an impact on increasing agricultural productivity. The proposed lightweight model integrates YOLOv7-Tiny and the proposed modules, namely the C2S module. The C2S module consists of three sub-modules such as the… More >
Open Access
ARTICLE
Jinglu Chen1, Mengpan Chen2, Wenhao Zhang2,*, Huihui Ren2, Daniel Dajun Zeng1,2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.069331
Abstract Temporal knowledge graph completion (TKGC), which merges temporal information into traditional static knowledge graph completion (SKGC), has garnered increasing attention recently. Among numerous emerging approaches, translation-based embedding models constitute a prominent approach in TKGC research. However, existing translation-based methods typically incorporate timestamps into entities or relations, rather than utilizing them independently. This practice fails to fully exploit the rich semantics inherent in temporal information, thereby weakening the expressive capability of models. To address this limitation, we propose embedding timestamps, like entities and relations, in one or more dedicated semantic spaces. After projecting all embeddings into… More >
Open Access
ARTICLE
Yuqiang Wu1,2, Zhao Ji1, Guanqi You1, Zihan Zhang1, Chaoping Lu3, Huanliang Xu1, Zhaoyu Zhai1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070414
Abstract Understanding fish movement trajectories in aquaculture is essential for practical applications, such as disease warning, feeding optimization, and breeding management. These trajectories reveal key information about the fish’s behavior, health, and environmental adaptability. However, when multi-object tracking (MOT) algorithms are applied to the high-density aquaculture environment, occlusion and overlapping among fish may result in missed detections, false detections, and identity switching problems, which limit the tracking accuracy. To address these issues, this paper proposes FishTracker, a MOT algorithm, by utilizing a Tracking-by-Detection framework. First, the neck part of the YOLOv8 model is enhanced by introducing… More >
Open Access
ARTICLE
Baixuan Han1, Yueping Peng1,*, Zecong Ye2, Hexiang Hao1, Xuekai Zhang1, Wei Tang1, Wenchao Kang1, Qilong Li1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071071
（This article belongs to the Special Issue: Advances in Deep Learning and Neural Networks: Architectures, Applications, and Challenges)
Abstract Aiming at the problem of imbalance between detection accuracy and algorithm model lightweight in UAV aerial image target detection algorithm, a lightweight multi-category abnormal behavior detection algorithm based on improved YOLOv11n is designed. By integrating multi-head grouped self-attention mechanism and Partial-Conv, a two-way feature grouping fusion module (DFPF) was designed, which carried out effective channel segmentation and fusion strategies to reduce redundant calculations and memory access. C3K2 module was improved, and then unstructured pruning and feature distillation technology were used. The algorithm model is lightweight, and the feature extraction ability for airborne visual abnormal behavior… More >
Open Access
ARTICLE
Wei Liu1,*, Ruiyang Wang1, Guangwei Liu2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070328
（This article belongs to the Special Issue: Reinforcement Learning: Algorithms, Challenges, and Applications)
Abstract Q-learning is a classical reinforcement learning method with broad applicability. It can respond effectively to environmental changes and provide flexible strategies, making it suitable for solving robot path-planning problems. However, Q-learning faces challenges in search and update efficiency. To address these issues, we propose an improved Q-learning (IQL) algorithm. We use an enhanced Ant Colony Optimization (ACO) algorithm to optimize Q-table initialization. We also introduce the UCH mechanism to refine the reward function and overcome the exploration dilemma. The IQL algorithm is extensively tested in three grid environments of different scales. The results validate the… More >
Open Access
ARTICLE
Woo Hyun Park*, Dong Ryeol Shin
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.069949
Abstract With the recent increase in data volume and diversity, traditional text representation techniques are struggling to capture context, particularly in environments with sparse data. To address these challenges, this study proposes a new model, the Masked Joint Representation Model (MJRM). MJRM approximates the original hypothesis by leveraging multiple elements in a limited context. It dynamically adapts to changes in characteristics based on data distribution through three main components. First, masking-based representation learning, termed selective dynamic masking, integrates topic modeling and sentiment clustering to generate and train multiple instances across different data subsets, whose predictions are… More >
Open Access
ARTICLE
Fang Liu*, Xianghui Meng, Jiachen Li, Sibo Guo
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.069243
（This article belongs to the Special Issue: Differential Privacy: Techniques, Challenges, and Applications)
Abstract With the popularization of smart devices, Location-Based Services (LBS) greatly facilitates users’ life, but at the same time brings the risk of users’ location privacy leakage. Existing location privacy protection methods are deficient, failing to reasonably allocate the privacy budget for non-outlier location points and ignoring the critical location information that may be contained in the outlier points, leading to decreased data availability and privacy exposure problems. To address these problems, this paper proposes a Mix Location Privacy Preservation Method Based on Differential Privacy with Clustering (MLDP). The method first utilizes the DBSCAN clustering algorithm… More >
Open Access
ARTICLE
Weiping Zeng1, Xiangping Bryce Zhai1,2,3,*, Cheng Sun1, Liusha Jiang1,2, Yicong Du3, Xuefeng Yan1,3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070777
Abstract With the expanding applications of unmanned aerial vehicles (UAVs), precise flight evaluation has emerged as a critical enabler for efficient path planning, directly impacting operational performance and safety. Traditional path planning algorithms typically combine Dubins curves with local optimization to minimize trajectory length under 3D spatial constraints. However, these methods often overlook the correlation between pilot control quality and UAV flight dynamics, limiting their adaptability in complex scenarios. In this paper, we propose an intelligent flight evaluation model specifically designed to enhance multi-waypoint trajectory optimization algorithms. Our model leverages a decision tree to integrate attitude More >
Open Access
ARTICLE
Zhongyun Tang1,2,3, Hanyi Xu2, Haiyang Hu1,3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070616
Abstract With the deep integration of smart manufacturing and IoT technologies, higher demands are placed on the intelligence and real-time performance of industrial equipment fault detection. For industrial fans, base bolt loosening faults are difficult to identify through conventional spectrum analysis, and the extreme scarcity of fault data leads to limited training datasets, making traditional deep learning methods inaccurate in fault identification and incapable of detecting loosening severity. This paper employs Bayesian Learning by training on a small fault dataset collected from the actual operation of axial-flow fans in a factory to obtain posterior distribution. This More >
Open Access
ARTICLE
Yifan Zhang1, Yong Gan2,*, Mengke Tang1, Xinxin Gan3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.068880
（This article belongs to the Special Issue: Advances in Deep Learning and Neural Networks: Architectures, Applications, and Challenges)
Abstract High-resolution remote sensing imagery is essential for critical applications such as precision agriculture, urban management planning, and military reconnaissance. Although significant progress has been made in single-image super-resolution (SISR) using generative adversarial networks (GANs), existing approaches still face challenges in recovering high-frequency details, effectively utilizing features, maintaining structural integrity, and ensuring training stability—particularly when dealing with the complex textures characteristic of remote sensing imagery. To address these limitations, this paper proposes the Improved Residual Module and Attention Mechanism Network (IRMANet), a novel architecture specifically designed for remote sensing image reconstruction. IRMANet builds upon the Super-Resolution… More >
Login
Register
Submit a Paper
Propose a Special lssue