Open Access
ARTICLE
Yiming Tang*, Yanqiu Chen
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062412
Abstract Semantic segmentation of novel object categories with limited labeled data remains a challenging problem in computer vision. Few-shot segmentation methods aim to address this problem by recognizing objects from specific target classes with a few provided examples. Previous approaches for few-shot semantic segmentation typically represent target classes using class prototypes. These prototypes are matched with the features of the query set to get segmentation results. However, class prototypes are usually obtained by applying global average pooling on masked support images. Global pooling discards much structural information, which may reduce the accuracy of model predictions. To… More >
Open Access
ARTICLE
Jim Wong1,2, Zhaoxiang Zang3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061006
（This article belongs to the Special Issue: Data and Image Processing in Intelligent Information Systems)
Abstract In the field of image forensics, image tampering detection is a critical and challenging task. Traditional methods based on manually designed feature extraction typically focus on a specific type of tampering operation, which limits their effectiveness in complex scenarios involving multiple forms of tampering. Although deep learning-based methods offer the advantage of automatic feature learning, current approaches still require further improvements in terms of detection accuracy and computational efficiency. To address these challenges, this study applies the U-Net 3+ model to image tampering detection and proposes a hybrid framework, referred to as DDT-Net (Deep Detail… More >
Open Access
ARTICLE
Haitao Xie, Yuliang Chen, Yunjie Zeng, Lingyu Yan, Zhizhi Wang, Zhiwei Ye*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.060783
（This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition, 2nd Edition)
Abstract With the rapid development of intelligent video surveillance technology, pedestrian re-identification has become increasingly important in multi-camera surveillance systems. This technology plays a critical role in enhancing public safety. However, traditional methods typically process images and text separately, applying upstream models directly to downstream tasks. This approach significantly increases the complexity of model training and computational costs. Furthermore, the common class imbalance in existing training datasets limits model performance improvement. To address these challenges, we propose an innovative framework named Person Re-ID Network Based on Visual Prompt Technology and Multi-Instance Negative Pooling (VPM-Net). First, we… More >
Open Access
Yaping Qi1,*, Weijie Yang2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.064061
Abstract AI-driven materials databases are transforming research by integrating experimental and computational data to enhance discovery and optimization. Platforms such as Digital Catalysis Platform (DigCat) and Dynamic Database of Solid-State Electrolyte (DDSE) demonstrate how machine learning and predictive modeling can improve catalyst and solid-state electrolyte development. These databases facilitate data standardization, high-throughput screening, and cross-disciplinary collaboration, addressing key challenges in materials informatics. As AI techniques advance, materials databases are expected to play an increasingly vital role in accelerating research and innovation. More >
Open Access
ARTICLE
Qing Xie*, Ruiyun Yu
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063345
（This article belongs to the Special Issue: Neural Architecture Search: Optimization, Efficiency and Application)
Abstract This study presents a groundbreaking method named Expo-GAN (Exposition-Generative Adversarial Network) for style transfer in exhibition hall design, using a refined version of the Cycle Generative Adversarial Network (CycleGAN). The primary goal is to enhance the transformation of image styles while maintaining visual consistency, an area where current CycleGAN models often fall short. These traditional models typically face difficulties in accurately capturing expansive features as well as the intricate stylistic details necessary for high-quality image transformation. To address these limitations, the research introduces several key modifications to the CycleGAN architecture. Enhancements to the generator involve… More >
Open Access
ARTICLE
Ali Batouche1, Souham Meshoul2,*, Hadil Shaiba3, Mohamed Batouche2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063227
Abstract The field of biometric identification has seen significant advancements over the years, with research focusing on enhancing the accuracy and security of these systems. One of the key developments is the integration of deep learning techniques in biometric systems. However, despite these advancements, certain challenges persist. One of the most significant challenges is scalability over growing complexity. Traditional methods either require maintaining and securing a growing database, introducing serious security challenges, or relying on retraining the entire model when new data is introduced—a process that can be computationally expensive and complex. This challenge underscores the… More >
Open Access
ARTICLE
Qin Wang1, Xiaofeng Wang2,*, Jianghua Li2, Ruidong Han2, Zinian Liu1, Mingtao Guo3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063141
Abstract The majority of current deepfake detection methods are constrained to identifying one or two specific types of counterfeit images, which limits their ability to keep pace with the rapid advancements in deepfake technology. Therefore, in this study, we propose a novel algorithm, Stereo Mixture Density Network (SMNDNet), which can detect multiple types of deepfake face manipulations using a single network framework. SMNDNet is an end-to-end CNN-based network specially designed for detecting various manipulation types of deepfake face images. First, we design a Subtle Distinguishable Feature Enhancement Module to emphasize the differentiation between authentic and forged… More >
Open Access
ARTICLE
Yingyong Zou*, Xingkui Zhang, Tao Liu, Yu Zhang, Long Li, Wenzhuo Zhao
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062625
（This article belongs to the Special Issue: Advancements in Machine Fault Diagnosis and Prognosis: Data-Driven Approaches and Autonomous Systems)
Abstract High-speed train engine rolling bearings play a crucial role in maintaining engine health and minimizing operational losses during train operation. To solve the problems of low accuracy of the diagnostic model and unstable model due to the influence of noise during fault detection, a rolling bearing fault diagnosis model based on cross-attention fusion of WDCNN and BILSTM is proposed. The first layer of the wide convolutional kernel deep convolutional neural network (WDCNN) is used to extract the local features of the signal and suppress the high-frequency noise. A Bidirectional Long Short-Term Memory Network (BILSTM) is… More >
Open Access
ARTICLE
Poonam Sharma1, Bhisham Sharma2,*, Dhirendra Prasad Yadav3, Surbhi Bhatia Khan4,5,6,*, Ahlam Almusharraf7
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062376
（This article belongs to the Special Issue: Medical Imaging Based Disease Diagnosis Using AI)
Abstract Rapid and precise diagnostic tools for Monkeypox (Mpox) lesions are crucial for effective treatment because their symptoms are similar to those of other pox-related illnesses, like smallpox and chickenpox. The morphological similarities between smallpox, chickenpox, and monkeypox, particularly in how they appear as rashes and skin lesions, which can sometimes make diagnosis challenging. Chickenpox lesions appear in many simultaneous phases and are more diffuse, often beginning on the trunk. In contrast, monkeypox lesions emerge progressively and are typically centralized on the face, palms, and soles. To provide accessible diagnostics, this study introduces a novel method… More >
Open Access
ARTICLE
Shaya A. Alshaya*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062360
Abstract The integration of visual elements, such as emojis, into educational content represents a promising approach to enhancing student engagement and comprehension. However, existing efforts in emoji integration often lack systematic frameworks capable of addressing the contextual and pedagogical nuances required for effective implementation. This paper introduces a novel framework that combines Data-Driven Error-Correcting Output Codes (DECOC), Long Short-Term Memory (LSTM) networks, and Multi-Layer Deep Neural Networks (ML-DNN) to identify optimal emoji placements within computer science course materials. The originality of the proposed system lies in its ability to leverage sentiment analysis techniques and contextual embeddings… More >
Open Access
ARTICLE
Sony Peng1, Sophort Siet1, Ilkhomjon Sadriddinov1, Dae-Young Kim2,*, Kyuwon Park3,*, Doo-Soon Park2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061166
（This article belongs to the Special Issue: Advances in AI Techniques in Convergence ICT)
Abstract Recommendation systems (RSs) are crucial in personalizing user experiences in digital environments by suggesting relevant content or items. Collaborative filtering (CF) is a widely used personalization technique that leverages user-item interactions to generate recommendations. However, it struggles with challenges like the cold-start problem, scalability issues, and data sparsity. To address these limitations, we develop a Graph Convolutional Networks (GCNs) model that captures the complex network of interactions between users and items, identifying subtle patterns that traditional methods may overlook. We integrate this GCNs model into a federated learning (FL) framework, enabling the model to learn… More >
Open Access
ARTICLE
Yue Liu1, Qinglang Guo2, Chunyao Yang1, Yong Liao1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.060318
Abstract Processing police incident data in public security involves complex natural language processing (NLP) tasks, including information extraction. This data contains extensive entity information—such as people, locations, and events—while also involving reasoning tasks like personnel classification, relationship judgment, and implicit inference. Moreover, utilizing models for extracting information from police incident data poses a significant challenge—data scarcity, which limits the effectiveness of traditional rule-based and machine-learning methods. To address these, we propose TIPS. In collaboration with public security experts, we used de-identified police incident data to create templates that enable large language models (LLMs) to populate data More >
Open Access
ARTICLE
Chunlai Du1,#, Xin Gu1,#, Yanhui Guo2,*, Siqi Guo3, Ziwei Pang3, Yi Du3, Guoqing Du3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.060212
（This article belongs to the Special Issue: Cutting-Edge Machine Learning and AI Innovations in Medical Imaging Diagnosis)
Abstract Coronary artery disease is a highly lethal cardiovascular condition, making early diagnosis crucial for patients. Echocardiograph is employed to identify coronary heart disease (CHD). However, due to issues such as fuzzy object boundaries, complex tissue structures, and motion artifacts in ultrasound images, it is challenging to detect CHD accurately. This paper proposes an improved Transformer model based on the Feedback Self-Attention Mechanism (FSAM) for classification of ultrasound images. The model enhances attention weights, making it easier to capture complex features. Experimental results show that the proposed method achieves high levels of accuracy, recall, precision, F1 More >
Open Access
ARTICLE
Yi Li1, Wenying Zheng2, Yu-Sheng Su3,4,5,*, Meiqin Tang6
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.059949
Abstract AI applications have become ubiquitous, bringing significant convenience to various industries. In e-commerce, AI can enhance product recommendations for individuals and provide businesses with more accurate predictions for market strategy development. However, if the data used for AI applications is damaged or lost, it will inevitably affect the effectiveness of these AI applications. Therefore, it is essential to verify the integrity of e-commerce data. Although existing Provable Data Possession (PDP) protocols can verify the integrity of cloud data, they are not suitable for e-commerce scenarios due to the limited computational capabilities of edge servers, which More >
Open Access
ARTICLE
Jian Ge1, Qin Qin1,*, Jinhua Jiang1, Zhiwei Shen2, Zimei Tu1, Yahui Zhang1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.059389
Abstract Manufacturers must identify and classify various defects in automotive sealing rings to ensure product quality. Deep learning algorithms show promise in this field, but challenges remain, especially in detecting small-scale defects under harsh industrial conditions with multimodal data. This paper proposes an enhanced version of You Only Look Once (YOLO)v8 for improved defect detection in automotive sealing rings. We introduce the Multi-scale Adaptive Feature Extraction (MAFE) module, which integrates Deformable Convolutional Network (DCN) and Space-to-Depth (SPD) operations. This module effectively captures long-range dependencies, enhances spatial aggregation, and minimizes information loss of small objects during feature More >
Open Access
ARTICLE
Federica Uccello1,2, Marek Pawlicki3,4, Salvatore D'Antonio1, Rafał Kozik3,4, Michał Choraś3,4,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062801
（This article belongs to the Special Issue: Artificial Intelligence Current Perspectives and Alternative Paths: From eXplainable AI to Generative AI and Data Visualization Technologies)
Abstract The growing sophistication of cyberthreats, among others the Distributed Denial of Service attacks, has exposed limitations in traditional rule-based Security Information and Event Management systems. While machine learning–based intrusion detection systems can capture complex network behaviours, their “black-box” nature often limits trust and actionable insight for security operators. This study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules, thereby enhancing the detection of Distributed Denial of Service (DDoS) attacks. The proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules More >
Open Access
ARTICLE
Qingqing Song1,*, Shaoliang Xia1, Zhen Wu2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062526
Abstract Load time series analysis is critical for resource management and optimization decisions, especially automated analysis techniques. Existing research has insufficiently interpreted the overall characteristics of samples, leading to significant differences in load level detection conclusions for samples with different characteristics (trend, seasonality, cyclicality). Achieving automated, feature-adaptive, and quantifiable analysis methods remains a challenge. This paper proposes a Threshold Recognition-based Load Level Detection Algorithm (TRLLD), which effectively identifies different load level regions in samples of arbitrary size and distribution type based on sample characteristics. By utilizing distribution density uniformity, the algorithm classifies data points and ultimately… More >
Open Access
ARTICLE
Amjad A. Alsuwaylimi1,*, Zaid S. Alenezi2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061674
Abstract Cyberbullying is a remarkable issue in the Arabic-speaking world, affecting children, organizations, and businesses. Various efforts have been made to combat this problem through proposed models using machine learning (ML) and deep learning (DL) approaches utilizing natural language processing (NLP) methods and by proposing relevant datasets. However, most of these endeavors focused predominantly on the English language, leaving a substantial gap in addressing Arabic cyberbullying. Given the complexities of the Arabic language, transfer learning techniques and transformers present a promising approach to enhance the detection and classification of abusive content by leveraging large and pretrained… More >
Open Access
ARTICLE
Haocheng Sun, Ping Li, Ying Li*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063029
（This article belongs to the Special Issue: Deep Neural Networks-based Convergence Technology and Applications)
Abstract Traffic flow prediction is a key component of intelligent transportation systems, particularly in data-scarce regions where traditional models relying on complete datasets often fail to provide accurate forecasts. These regions are characterized by limited sensor coverage and sparse data collection, pose significant challenges for existing prediction methods. To address this, we propose a novel transfer learning framework called transfer learning with deep knowledge distillation (TL-DKD), which combines graph neural network (GNN) with deep knowledge distillation to enable effective knowledge transfer from data-rich to data-scarce domains. Our contributions are three-fold: (1) We introduce, for the first… More >
Open Access
ARTICLE
Duc-Nghia Tran1, Viet-Manh Do1,2, Manh-Tuyen Vi3,*, Duc-Tan Tran3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062590
Abstract This study focuses on the design and validation of a behavior classification system for cattle using behavioral data collected through accelerometer sensors. Data collection and behavioral analysis are achieved using machine learning (ML) algorithms through accelerometer sensors. However, behavioral analysis poses challenges due to the complexity of cow activities. The task becomes more challenging in a real-time behavioral analysis system with the requirement for shorter data windows and energy constraints. Shorter windows may lack sufficient information, reducing algorithm performance. Additionally, the sensor’s position on the cows may shift during practical use, altering the collected accelerometer… More >
Open Access
ARTICLE
Xuejing Li*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062161
Abstract Few-shot point cloud 3D object detection (FS3D) aims to identify and locate objects of novel classes within point clouds using knowledge acquired from annotated base classes and a minimal number of samples from the novel classes. Due to imbalanced training data, existing FS3D methods based on fully supervised learning can lead to overfitting toward base classes, which impairs the network’s ability to generalize knowledge learned from base classes to novel classes and also prevents the network from extracting distinctive foreground and background representations for novel class objects. To address these issues, this thesis proposes a… More >
Open Access
ARTICLE
Medhat A. Tawfeek1,*, Ibrahim Alrashdi1, Madallah Alruwaili2, Fatma M. Talaat3,4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061773
（This article belongs to the Special Issue: AI-Assisted Energy Harvesting Techniques and its Applications in Wireless Sensor Networks)
Abstract Wireless Sensor Networks (WSNs) are one of the best technologies of the 21st century and have seen tremendous growth over the past decade. Much work has been put into its development in various aspects such as architectural attention, routing protocols, location exploration, time exploration, etc. This research aims to optimize routing protocols and address the challenges arising from conflicting objectives in WSN environments, such as balancing energy consumption, ensuring routing reliability, distributing network load, and selecting the shortest path. Many optimization techniques have shown success in achieving one or two objectives but struggle to achieve… More >
Open Access
ARTICLE
Jun Li1,*, Yawei Dong1, Liang Ni1, Guopeng Feng1, Fangfang Shan1,2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.059325
Abstract With the development of vehicle networks and the construction of roadside units, Vehicular Ad Hoc Networks (VANETs) are increasingly promoting cooperative computing patterns among vehicles. Vehicular edge computing (VEC) offers an effective solution to mitigate resource constraints by enabling task offloading to edge cloud infrastructure, thereby reducing the computational burden on connected vehicles. However, this sharing-based and distributed computing paradigm necessitates ensuring the credibility and reliability of various computation nodes. Existing vehicular edge computing platforms have not adequately considered the misbehavior of vehicles. We propose a practical task offloading algorithm based on reputation assessment to More >
Open Access
ARTICLE
Khulud Salem Alshudukhi1, Mamoona Humayun2,*, Ghadah Naif Alwakid1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063077
Abstract The Internet of Things (IoT) and edge computing have substantially contributed to the development and growth of smart cities. It handled time-constrained services and mobile devices to capture the observing environment for surveillance applications. These systems are composed of wireless cameras, digital devices, and tiny sensors to facilitate the operations of crucial healthcare services. Recently, many interactive applications have been proposed, including integrating intelligent systems to handle data processing and enable dynamic communication functionalities for crucial IoT services. Nonetheless, most solutions lack optimizing relaying methods and impose excessive overheads for maintaining devices’ connectivity. Alternatively, data More >
Open Access
ARTICLE
Huiling Yu1, Xibei Jia2, Yongfeng Niu1, Yizhuo Zhang1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061882
Abstract The counterflow burner is a combustion device used for research on combustion. By utilizing deep convolutional models to identify the combustion state of a counterflow burner through visible flame images, it facilitates the optimization of the combustion process and enhances combustion efficiency. Among existing deep convolutional models, InceptionNeXt is a deep learning architecture that integrates the ideas of the Inception series and ConvNeXt. It has garnered significant attention for its computational efficiency, remarkable model accuracy, and exceptional feature extraction capabilities. However, since this model still has limitations in the combustion state recognition task, we propose… More >
Open Access
ARTICLE
Lingqiu Zeng1, Peibing Sa1, Qingwen Han2, Lei Ye2,*, Letian Yang1, Cheng Zhang1, Jiqiang Cheng2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.060392
（This article belongs to the Special Issue: Advanced Communication and Networking Technologies for Internet of Things and Internet of Vehicles)
Abstract Congestion control is an inherent challenge of V2X (Vehicle to Everything) technologies. Due to the use of a broadcasting mechanism, channel congestion becomes severe with the increase in vehicle density. The researchers suggested reducing the frequency of packet dissemination to relieve congestion, which caused a rise in road driving risk. Obviously, high-risk vehicles should be able to send messages timely to alarm surrounding vehicles. Therefore, packet dissemination frequency should be set according to the corresponding vehicle’s risk level, which is hard to evaluate. In this paper, a two-stage fuzzy inference model is constructed to evaluate More >
Open Access
ARTICLE
Huyong Yan1, Huidong Zhou2,*, Jian Zheng1, Zhaozhe Zhou1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062807
（This article belongs to the Special Issue: AI and Data Security for the Industrial Internet)
Abstract As smart manufacturing and Industry 4.0 continue to evolve, fault diagnosis of mechanical equipment has become crucial for ensuring production safety and optimizing equipment utilization. To address the challenge of cross-domain adaptation in intelligent diagnostic models under varying operational conditions, this paper introduces the CNN-1D-KAN model, which combines a 1D Convolutional Neural Network (1D-CNN) with a Kolmogorov–Arnold Network (KAN). The novelty of this approach lies in replacing the traditional 1D-CNN’s final fully connected layer with a KANLinear layer, leveraging KAN’s advanced nonlinear processing and function approximation capabilities while maintaining the simplicity of linear transformations. Experimental… More >
Open Access
ARTICLE
Min Yao1,*, Guangjie Hu1, Yaozu Zhang2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.060860
Abstract Semantic segmentation of remote sensing images is a critical research area in the field of remote sensing. Despite the success of Convolutional Neural Networks (CNNs), they often fail to capture inter-layer feature relationships and fully leverage contextual information, leading to the loss of important details. Additionally, due to significant intra-class variation and small inter-class differences in remote sensing images, CNNs may experience class confusion. To address these issues, we propose a novel Category-Guided Feature Collaborative Learning Network (CG-FCLNet), which enables fine-grained feature extraction and adaptive fusion. Specifically, we design a Feature Collaborative Learning Module (FCLM)… More >
Open Access
ARTICLE
Xuewen Mu*, Bingcong Zhao
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.060739
Abstract When dealing with imbalanced datasets, the traditional support vector machine (SVM) tends to produce a classification hyperplane that is biased towards the majority class, which exhibits poor robustness. This paper proposes a high-performance classification algorithm specifically designed for imbalanced datasets. The proposed method first uses a biased second-order cone programming support vector machine (B-SOCP-SVM) to identify the support vectors (SVs) and non-support vectors (NSVs) in the imbalanced data. Then, it applies the synthetic minority over-sampling technique (SV-SMOTE) to oversample the support vectors of the minority class and uses the random under-sampling technique (NSV-RUS) multiple times More >
Open Access
ARTICLE
Martina Rinaldi1,2, Stefano Valvano1,*, Francesco Tornabene2, Rossana Dimitri2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.060672
（This article belongs to the Special Issue: Advanced Modeling of Smart and Composite Materials and Structures)
Abstract This study conducts a thorough examination of honeycomb sandwich panels with a lattice core, adopting advanced computational techniques for their modeling. The research extends its analysis to investigate the natural frequency behavior of sandwich panels, encompassing the comprehensive assessment of the entire panel structure. At its core, the research applies the Representative Volume Element (RVE) theory to establish the equivalent material properties, thereby enhancing the predictive capabilities of lattice structure simulations. The methodology applies these properties in the core of infinite panels, which are modeled using double periodic boundary conditions to explore their natural frequencies.… More >
Open Access
ARTICLE
Qiming Li1, Mengcheng Wu1, Daozheng Chen1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062969
（This article belongs to the Special Issue: The Latest Deep Learning Architectures for Artificial Intelligence Applications)
Abstract Image style transfer is a research hotspot in the field of computer vision. For this job, many approaches have been put forth. These techniques do, however, still have some drawbacks, such as high computing complexity and content distortion caused by inadequate stylization. To address these problems, PhotoGAN, a new Generative Adversarial Network (GAN) model is proposed in this paper. A deeper feature extraction network has been designed to capture global information and local details better. Introducing multi-scale attention modules helps the generator focus on important feature areas at different scales, further enhancing the effectiveness of More >
Open Access
ARTICLE
Hui Xu, Jiahui Chen*, Zhonghao Hu
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062189
（This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
Abstract Nowadays, abnormal traffic detection for Software-Defined Networking (SDN) faces the challenges of large data volume and high dimensionality. Since traditional machine learning-based detection methods have the problem of data redundancy, the Metaheuristic Algorithm (MA) is introduced to select features before machine learning to reduce the dimensionality of data. Since a Tyrannosaurus Optimization Algorithm (TROA) has the advantages of few parameters, simple implementation, and fast convergence, and it shows better results in feature selection, TROA can be applied to abnormal traffic detection for SDN. However, TROA suffers from insufficient global search capability, is easily trapped in… More >
Open Access
ARTICLE
Jiahao Wu1,2, Jinzhong Xu1, Xiaoming Liu1,*, Guan Yang1,3, Jie Liu4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061359
Abstract Large language models cross-domain named entity recognition task in the face of the scarcity of large language labeled data in a specific domain, due to the entity bias arising from the variation of entity information between different domains, which makes large language models prone to spurious correlations problems when dealing with specific domains and entities. In order to solve this problem, this paper proposes a cross-domain named entity recognition method based on causal graph structure enhancement, which captures the cross-domain invariant causal structural representations between feature representations of text sequences and annotation sequences by establishing… More >
Open Access
ARTICLE
Zengyu Cai1,*, Yuming Dai1, Jianwei Zhang2,3,*, Yuan Feng4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061206
Abstract The rapid development and widespread adoption of Internet technology have significantly increased Internet traffic, highlighting the growing importance of network security. Intrusion Detection Systems (IDS) are essential for safeguarding network integrity. To address the low accuracy of existing intrusion detection models in identifying network attacks, this paper proposes an intrusion detection method based on the fusion of Spatial Attention mechanism and Residual Neural Network (SA-ResNet). Utilizing residual connections can effectively capture local features in the data; by introducing a spatial attention mechanism, the global dependency relationships of intrusion features can be extracted, enhancing the intrusion More >
Open Access
ARTICLE
Jianxiang Cao1, Jinyang Wu1, Wenqian Shang1,*, Chunhua Wang1, Kang Song1, Tong Yi2,*, Jiajun Cai1, Haibin Zhu3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.060025
Abstract With the rapid growth of social media, the spread of fake news has become a growing problem, misleading the public and causing significant harm. As social media content is often composed of both images and text, the use of multimodal approaches for fake news detection has gained significant attention. To solve the problems existing in previous multi-modal fake news detection algorithms, such as insufficient feature extraction and insufficient use of semantic relations between modes, this paper proposes the MFFFND-Co (Multimodal Feature Fusion Fake News Detection with Co-Attention Block) model. First, the model deeply explores the More >
Open Access
ARTICLE
Changlong Wang1, Jiawei Jiang1, Chong Han1,2,*, Hengyi Ren3, Lijuan Sun1,2, Jian Guo1,2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063295
Abstract Existing through-wall human activity recognition methods often rely on Doppler information or reflective signal characteristics of the human body. However, static individuals, lacking prominent motion features, do not generate Doppler information. Moreover, radar signals experience significant attenuation due to absorption and scattering effects as they penetrate walls, limiting recognition performance. To address these challenges, this study proposes a novel through-wall human activity recognition method based on MIMO radar. Utilizing a MIMO radar operating at 1–2 GHz, we capture activity data of individuals through walls and process it into range-angle maps to represent activity features. To… More >
Open Access
ARTICLE
Xinyi Wang*, Wanru Song, Chuanyan Hao, Feng Liu
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062922
（This article belongs to the Special Issue: Applications of Artificial Intelligence for Information Security)
Abstract As Deepfake technology continues to evolve, the distinction between real and fake content becomes increasingly blurred. Most existing Deepfake video detection methods rely on single-frame facial image features, which limits their ability to capture temporal differences between frames. Current methods also exhibit limited generalization capabilities, struggling to detect content generated by unknown forgery algorithms. Moreover, the diversity and complexity of forgery techniques introduced by Artificial Intelligence Generated Content (AIGC) present significant challenges for traditional detection frameworks, which must balance high detection accuracy with robust performance. To address these challenges, we propose a novel Deepfake detection… More >
Open Access
ARTICLE
Qinghai Ou1, Min Yang1, Jingcai Kong1, Yang Yang2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062067
Abstract The lack of communication infrastructure in remote regions presents significant obstacles to gathering data from smart power sensors (SPSs) in smart grid networks. In such cases, a space-air-ground integrated network serves as an effective emergency solution. This study addresses the challenge of optimizing the energy efficiency of data transmission from SPSs to low Earth orbit (LEO) satellites through unmanned aerial vehicles (UAVs), considering both effective capacity and fronthaul link capacity constraints. Due to the non-convex nature of the problem, the objective function is reformulated, and a delay-aware energy-efficient power allocation and UAV trajectory design (DEPATD)… More >
Open Access
ARTICLE
Junzhe Zhang1, Lei Tang1,*, Xin Zhou2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061690
Abstract Fisheye cameras offer a significantly larger field of view compared to conventional cameras, making them valuable tools in the field of computer vision. However, their unique optical characteristics often lead to image distortions, which pose challenges for object detection tasks. To address this issue, we propose Yolo-CaSKA (Yolo with Contrastive Learning and Selective Kernel Attention), a novel training method that enhances object detection on fisheye camera images. The standard image and the corresponding distorted fisheye image pairs are used as positive samples, and the rest of the image pairs are used as negative samples, which More >
Open Access
ARTICLE
Kamil Yasen1,#, Heyan Jin2,#, Sijie Yang2, Li Zhan2, Xuyang Zhang2, Ke Qin1,3, Ye Li2,3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061037
Abstract Research on mass gathering events is critical for ensuring public security and maintaining social order. However, most of the existing works focus on crowd behavior analysis areas such as anomaly detection and crowd counting, and there is a relative lack of research on mass gathering behaviors. We believe real-time detection and monitoring of mass gathering behaviors are essential for migrating potential security risks and emergencies. Therefore, it is imperative to develop a method capable of accurately identifying and localizing mass gatherings before disasters occur, enabling prompt and effective responses. To address this problem, we propose… More >
Open Access
ARTICLE
Hongyang Wang1,2, Yichen Shi3, Jun Feng1,2,*, Zitong Yu4, Zhuofu Tao5
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061019
（This article belongs to the Special Issue: Multimedia Security in Deep Learning)
Abstract Face Presentation Attack Detection (fPAD) plays a vital role in securing face recognition systems against various presentation attacks. While supervised learning-based methods demonstrate effectiveness, they are prone to overfitting to known attack types and struggle to generalize to novel attack scenarios. Recent studies have explored formulating fPAD as an anomaly detection problem or one-class classification task, enabling the training of generalized models for unknown attack detection. However, conventional anomaly detection approaches encounter difficulties in precisely delineating the boundary between bonafide samples and unknown attacks. To address this challenge, we propose a novel framework focusing on… More >
Open Access
ARTICLE
Yuxin Yan, Huirui Wang, Jingyi Wen, Zerong Lan, Liang Wang*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057949
Abstract The development of artificial intelligence (AI) technologies creates a great chance for the iteration of railway monitoring. This paper proposes a comprehensive method for railway utility pole detection. The framework of this paper on railway systems consists of two parts: point cloud preprocessing and railway utility pole detection. This method overcomes the challenges of dynamic environment adaptability, reliance on lighting conditions, sensitivity to weather and environmental conditions, and visual occlusion issues present in 2D images and videos, which utilize mobile LiDAR (Laser Radar) acquisition devices to obtain point cloud data. Due to factors such as… More >
Open Access
ARTICLE
Jianyin Tang, Zhenglin Yu*, Changshun Shao
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063287
Abstract In the field of automated fruit harvesting, precise and efficient fruit target recognition and localization play a pivotal role in enhancing the efficiency of harvesting robots. However, this domain faces two core challenges: firstly, the dynamic nature of the automatic picking process requires fruit target detection algorithms to adapt to multi-view characteristics, ensuring effective recognition of the same fruit from different perspectives. Secondly, fruits in natural environments often suffer from interference factors such as overlapping, occlusion, and illumination fluctuations, which increase the difficulty of image capture and recognition. To address these challenges, this study conducted… More >
Open Access
ARTICLE
Yumei Hao1,2, Haiyan Wang1,2,*, Dong Zhang3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063008
（This article belongs to the Special Issue: Advances in Deep Learning and Neural Networks: Architectures, Applications, and Challenges)
Abstract Named entity recognition (NER) in musk deer domain is the extraction of specific types of entities from unstructured texts, constituting a fundamental component of the knowledge graph, Q&A system, and text summarization system of musk deer domain. Due to limited annotated data, diverse entity types, and the ambiguity of Chinese word boundaries in musk deer domain NER, we present a novel NER model, CAELF-GP, which is based on cross-attention mechanism enhanced lexical features (CAELF). Specifically, we employ BERT as a character encoder and advocate the integration of external lexical information at the character representation layer.… More >
Open Access
ARTICLE
Shu-Chuan Chu1,2, Libin Fu2, Jeng-Shyang Pan2,3, Xingsi Xue4, Min Liu5,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062469
（This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
Abstract Evolutionary algorithms have been extensively utilized in practical applications. However, manually designed population updating formulas are inherently prone to the subjective influence of the designer. Genetic programming (GP), characterized by its tree-based solution structure, is a widely adopted technique for optimizing the structure of mathematical models tailored to real-world problems. This paper introduces a GP-based framework (GP-EAs) for the autonomous generation of update formulas, aiming to reduce human intervention. Partial modifications to tree-based GP have been instigated, encompassing adjustments to its initialization process and fundamental update operations such as crossover and mutation within the algorithm.… More >
Open Access
ARTICLE
Qian Zhu1, Qian Kang1, Tao Xu2, Dengxiu Yu3,*, Zhen Wang1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061913
Abstract In this study, we present a deterministic convergence analysis of Gated Recurrent Unit (GRU) networks enhanced by a smoothing regularization technique. While GRU architectures effectively mitigate gradient vanishing/exploding issues in sequential modeling, they remain prone to overfitting, particularly under noisy or limited training data. Traditional regularization, despite enforcing sparsity and accelerating optimization, introduces non-differentiable points in the error function, leading to oscillations during training. To address this, we propose a novel smoothing regularization framework that replaces the non-differentiable absolute function with a quadratic approximation, ensuring gradient continuity and stabilizing the optimization landscape. Theoretically, we rigorously… More >
Open Access
ARTICLE
Xinyi Song1, Haixiang Zhang1,*, Shaohua Li2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.059006
（This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
Abstract In recent years, Transformer has achieved remarkable results in the field of computer vision, with its built-in attention layers effectively modeling global dependencies in images by transforming image features into token forms. However, Transformers often face high computational costs when processing large-scale image data, which limits their feasibility in real-time applications. To address this issue, we propose Token Masked Pose Transformers (TMPose), constructing an efficient Transformer network for pose estimation. This network applies semantic-level masking to tokens and employs three different masking strategies to optimize model performance, aiming to reduce computational complexity. Experimental results show More >
Open Access
ARTICLE
Shenjian Xiao1, Xiaoli Qin1, Yanzhao Tian1,*, Zhongkai Dang2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062324
Abstract The advent of the digital age has consistently provided impetus for facilitating global trade, as evidenced by the numerous customs clearance documents and participants involved in the international trade process, including enterprises, agents, and government departments. However, the urgent issue that requires immediate attention is how to achieve secure and efficient cross-border data sharing among these government departments and enterprises in complex trade processes. In addressing this need, this paper proposes a data exchange architecture employing Multi-Authority Attribute-Based Encryption (MA-ABE) in combination with blockchain technology. This scheme supports proxy decryption, attribute revocation, and policy update,… More >
Open Access
ARTICLE
Buwei Dang, Huanming Chen*, Heng Zhang, Jixian Wang, Jian Zhou
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062653
（This article belongs to the Special Issue: Intelligent Manufacturing, Robotics and Control Engineering)
Abstract This paper introduces a lane-changing strategy aimed at trajectory planning and tracking control for intelligent vehicles navigating complex driving environments. A fifth-degree polynomial is employed to generate a set of potential lane-changing trajectories in the Frenet coordinate system. These trajectories are evaluated using non-cooperative game theory, considering the interaction between the target vehicle and its surroundings. Models considering safety payoffs, speed payoffs, comfort payoffs, and aggressiveness are formulated to obtain a Nash equilibrium solution. This way, collision avoidance is ensured, and an optimal lane change trajectory is planned. Three game scenarios are discussed, and the More >
Open Access
ARTICLE
Feng Zhu*, Kailin Wu, Jie Ding
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062007
Abstract With the widespread application of Internet of Things (IoT) technology, the processing of massive real-time streaming data poses significant challenges to the computational and data-processing capabilities of systems. Although distributed streaming data processing frameworks such as Apache Flink and Apache Spark Streaming provide solutions, meeting stringent response time requirements while ensuring high throughput and resource utilization remains an urgent problem. To address this, the study proposes a formal modeling approach based on Performance Evaluation Process Algebra (PEPA), which abstracts the core components and interactions of cloud-based distributed streaming data processing systems. Additionally, a generic service… More >
Open Access
ARTICLE
Kaiyang Han1, Xiaoqiang Jia1, Yangfei Lin2, Tsutomu Yoshinaga2, Yalong Li2, Jiale Wu2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061563
（This article belongs to the Special Issue: Privacy-Preserving Deep Learning and its Advanced Applications)
Abstract With the rapid development of artificial intelligence and the Internet of Things, along with the growing demand for privacy-preserving transmission, the need for efficient and secure communication systems has become increasingly urgent. Traditional communication methods transmit data at the bit level without considering its semantic significance, leading to redundant transmission overhead and reduced efficiency. Semantic communication addresses this issue by extracting and transmitting only the most meaningful semantic information, thereby improving bandwidth efficiency. However, despite reducing the volume of data, it remains vulnerable to privacy risks, as semantic features may still expose sensitive information. To… More >
Open Access
ARTICLE
Hongxiao Wang1, Hongshen Liu1, Dingsen Zhang1,*, Ziye Zhang1, Yonghui Yue1, Jie Chen2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.060384
（This article belongs to the Special Issue: Enhancing IoT Cyber-Resilience: Convergence of AI, Digital Twins, and the Metaverse )
Abstract Despite its immense potential, the application of digital twin technology in real industrial scenarios still faces numerous challenges. This study focuses on industrial assembly lines in sectors such as microelectronics, pharmaceuticals, and food packaging, where precision and speed are paramount, applying digital twin technology to the robotic assembly process. The innovation of this research lies in the development of a digital twin architecture and system for Delta robots that is suitable for real industrial environments. Based on this system, a deep reinforcement learning algorithm for obstacle avoidance path planning in Delta robots has been developed, More >
Open Access
ARTICLE
Xiaoying Qiu1,*, Xiaoyu Ma1, Guangxu Zhao1, Jinwei Yu2, Wenbao Jiang1, Zhaozhong Guo1, Maozhi Xu3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061869
（This article belongs to the Special Issue: Applications of Artificial Intelligence for Information Security)
Abstract Physical layer authentication (PLA) in the context of the Internet of Things (IoT) has gained significant attention. Compared with traditional encryption and blockchain technologies, PLA provides a more computationally efficient alternative to exploiting the properties of the wireless medium itself. Some existing PLA solutions rely on static mechanisms, which are insufficient to address the authentication challenges in fifth generation (5G) and beyond wireless networks. Additionally, with the massive increase in mobile device access, the communication security of the IoT is vulnerable to spoofing attacks. To overcome the above challenges, this paper proposes a lightweight deep More >
Open Access
ARTICLE
Panru Liang1, Guojiang Xin1,*, Xiaolei Yi2, Hao Liang3, Changsong Ding1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.060961
Abstract Automatic pancreas segmentation plays a pivotal role in assisting physicians with diagnosing pancreatic diseases, facilitating treatment evaluations, and designing surgical plans. Due to the pancreas’s tiny size, significant variability in shape and location, and low contrast with surrounding tissues, achieving high segmentation accuracy remains challenging. To improve segmentation precision, we propose a novel network utilizing EfficientNetV2 and multi-branch structures for automatically segmenting the pancreas from CT images. Firstly, an EfficientNetV2 encoder is employed to extract complex and multi-level features, enhancing the model’s ability to capture the pancreas’s intricate morphology. Then, a residual multi-branch dilated attention… More >
Open Access
ARTICLE
Min Yao1,*, Guofeng Liu2, Yaozu Zhang3, Guangjie Hu1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.060609
Abstract Knowledge distillation (KD) is an emerging model compression technique for learning compact object detector models. Previous KD often focused solely on distilling from the logits layer or the feature intermediate layers, which may limit the comprehensive learning of the student network. Additionally, the imbalance between the foreground and background also affects the performance of the model. To address these issues, this paper employs feature-based distillation to enhance the detection performance of the bounding box localization part, and logit-based distillation to improve the detection performance of the category prediction part. Specifically, for the intermediate layer feature… More >
Open Access
ARTICLE
Liang Zhou1, Qiyao Hu1,2,3,*, Xianlin Peng4,5, Qianlong Liu6
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061894
（This article belongs to the Special Issue: Collaborative Edge Intelligence and Its Emerging Applications)
Abstract Over 1.3 million people die annually in traffic accidents, and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control systems. In modern industrial and technological applications and collaborative edge intelligence, control systems are crucial for ensuring efficiency and safety. However, deficiencies in these systems can lead to significant operational risks. This paper uses edge intelligence to address the challenges of achieving target speeds and improving efficiency in vehicle control, particularly the limitations of traditional Proportional-Integral-Derivative (PID) controllers in managing nonlinear and time-varying dynamics, such as varying road conditions… More >
Open Access
ARTICLE
Dawei Qiu1, Peng Wu1,*, Xiaoming Zhang2,*, Renjie Xu3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062599
（This article belongs to the Special Issue: Advances in Deep Learning and Neural Networks: Architectures, Applications, and Challenges)
Abstract Recently, many Sequential Recommendation methods adopt self-attention mechanisms to model user preferences. However, these methods tend to focus more on low-frequency information while neglecting high-frequency information, which makes them ineffective in balancing users’ long- and short-term preferences. At the same time, many methods overlook the potential of frequency domain methods, ignoring their efficiency in processing frequency information. To overcome this limitation, we shift the focus to the combination of time and frequency domains and propose a novel Hybrid Time-Frequency Dual-Branch Transformer for Sequential Recommendation, namely HyTiFRec. Specifically, we design two hybrid filter modules: the learnable… More >
Open Access
ARTICLE
Vijaya Krishna Akula1,*, Tan Kuan Tak2, Pravin Ramdas Kshirsagar3, Shrikant Vijayrao Sonekar4, Gopichand Ginnela5
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061486
Abstract The rapid expansion of Internet of Things (IoT) networks has introduced challenges in network management, primarily in maintaining energy efficiency and robust connectivity across an increasing array of devices. This paper introduces the Adaptive Blended Marine Predators Algorithm (AB-MPA), a novel optimization technique designed to enhance Quality of Service (QoS) in IoT systems by dynamically optimizing network configurations for improved energy efficiency and stability. Our results represent significant improvements in network performance metrics such as energy consumption, throughput, and operational stability, indicating that AB-MPA effectively addresses the pressing needs of modern IoT environments. Nodes are More >
Open Access
ARTICLE
Honglin Wang1, Yaolong Zhang2,*, Cheng Zhu3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061363
Abstract UAV-based object detection is rapidly expanding in both civilian and military applications, including security surveillance, disaster assessment, and border patrol. However, challenges such as small objects, occlusions, complex backgrounds, and variable lighting persist due to the unique perspective of UAV imagery. To address these issues, this paper introduces DAFPN-YOLO, an innovative model based on YOLOv8s (You Only Look Once version 8s). The model strikes a balance between detection accuracy and speed while reducing parameters, making it well-suited for multi-object detection tasks from drone perspectives. A key feature of DAFPN-YOLO is the enhanced Drone-AFPN (Adaptive Feature… More >
Open Access
ARTICLE
Ahmed A. Ewees1,2,*, Mohammed A. A. Al-Qaness3, Ali Alshahrani1, Mohamed Abd Elaziz4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061320
Abstract Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy output. This enhances the efficiency and reliability of renewable energy systems. Forecasting approaches inform energy management strategies, reduce reliance on fossil fuels, and support the broader transition to sustainable energy solutions. The primary goal of this study is to introduce an effective methodology for estimating wind power through temporal data analysis. This research advances an optimized Multilayer Perceptron (MLP) model using recently proposed metaheuristic optimization algorithms, namely the Fire Hawk Optimizer (FHO)… More >
Open Access
ARTICLE
Meng Zhu1,2, Xingyue Wang3, Honge Ren3,4,*, Abeer Hakeem5, Linda Mohaisen5,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061421
Abstract Changing a person’s posture and low resolution are the key challenges for person re-identification (ReID) in various deep learning applications. In this paper, we introduce an innovative architecture using a dual attention network that includes an attention module and a joint measurement module of spatial-temporal information. The proposed approach can be classified into two main tasks. Firstly, the spatial attention feature map is formed by aggregating features in the spatial dimension. Additionally, the same operation is carried out on the channel dimension to form channel attention feature maps. Therefore, the receptive field size is adjusted… More >
Open Access
ARTICLE
Cui Zhang1, Maoxin Ji2, Qiong Wu2,*, Pingyi Fan3, Qiang Fan4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061292
（This article belongs to the Special Issue: Advanced Communication and Networking Technologies for Internet of Things and Internet of Vehicles)
Abstract As Internet of Vehicles (IoV) technology continues to advance, edge computing has become an important tool for assisting vehicles in handling complex tasks. However, the process of offloading tasks to edge servers may expose vehicles to malicious external attacks, resulting in information loss or even tampering, thereby creating serious security vulnerabilities. Blockchain technology can maintain a shared ledger among servers. In the Raft consensus mechanism, as long as more than half of the nodes remain operational, the system will not collapse, effectively maintaining the system’s robustness and security. To protect vehicle information, we propose a… More >
Open Access
ARTICLE
Jiming Lan1, Bo Zeng1,*, Suiqun Li1, Weihan Zhang1, Xinyi Shi2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.060260
（This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
Abstract The Quadric Error Metrics (QEM) algorithm is a widely used method for mesh simplification; however, it often struggles to preserve high-frequency geometric details, leading to the loss of salient features. To address this limitation, we propose the Salient Feature Sampling Points-based QEM (SFSP-QEM)—also referred to as the Deep Learning-Based Salient Feature-Preserving Algorithm for Mesh Simplification—which incorporates a Salient Feature-Preserving Point Sampler (SFSP). This module leverages deep learning techniques to prioritize the preservation of key geometric features during simplification. Experimental results demonstrate that SFSP-QEM significantly outperforms traditional QEM in preserving geometric details. Specifically, for general models… More >
Open Access
ARTICLE
Lieping Zhang1,2, Hao Ma1, Jiancheng Huang3, Cui Zhang4,*, Xiaolin Gao2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061519
（This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
Abstract Detecting individuals wearing safety helmets in complex environments faces several challenges. These factors include limited detection accuracy and frequent missed or false detections. Additionally, existing algorithms often have excessive parameter counts, complex network structures, and high computational demands. These challenges make it difficult to deploy such models efficiently on resource-constrained devices like embedded systems. Aiming at this problem, this research proposes an optimized and lightweight solution called FGP-YOLOv8, an improved version of YOLOv8n. The YOLOv8 backbone network is replaced with the FasterNet model to reduce parameters and computational demands while local convolution layers are added.… More >
Open Access
ARTICLE
Dawei Xu1,2, Fan Huang1, Jiaxin Zhang1, Yunfang Liang1, Baokun Zheng3,*, Jian Zhao1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061455
（This article belongs to the Special Issue: Security and Privacy for Blockchain-empowered Internet of Things)
Abstract With the rapid proliferation of Internet of Things (IoT) devices, ensuring their communication security has become increasingly important. Blockchain and smart contract technologies, with their decentralized nature, provide strong security guarantees for IoT. However, at the same time, smart contracts themselves face numerous security challenges, among which reentrancy vulnerabilities are particularly prominent. Existing detection tools for reentrancy vulnerabilities often suffer from high false positive and false negative rates due to their reliance on identifying patterns related to specific transfer functions. To address these limitations, this paper proposes a novel detection method that combines pattern matching… More >
Open Access
ARTICLE
Xiaotong Han1,2, Yunqi Jiang2,3, Haitao Wang1,2, Yuan Tian1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.060134
Abstract Knowledge graphs (KGs), which organize real-world knowledge in triples, often suffer from issues of incompleteness. To address this, multi-hop knowledge graph reasoning (KGR) methods have been proposed for interpretable knowledge graph completion. The primary approaches to KGR can be broadly classified into two categories: reinforcement learning (RL)-based methods and sequence-to-sequence (seq2seq)-based methods. While each method has its own distinct advantages, they also come with inherent limitations. To leverage the strengths of each method while addressing their weaknesses, we propose a cyclical training method that alternates for several loops between the seq2seq training phase and the… More >
Open Access
ARTICLE
Xiangbin Wang1, Qingjun Yuan1,*, Weina Niu2, Qianwei Meng1, Yongjuan Wang1, Chunxiang Gu1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061767
Abstract With the rise of encrypted traffic, traditional network analysis methods have become less effective, leading to a shift towards deep learning-based approaches. Among these, multimodal learning-based classification methods have gained attention due to their ability to leverage diverse feature sets from encrypted traffic, improving classification accuracy. However, existing research predominantly relies on late fusion techniques, which hinder the full utilization of deep features within the data. To address this limitation, we propose a novel multimodal encrypted traffic classification model that synchronizes modality fusion with multiscale feature extraction. Specifically, our approach performs real-time fusion of modalities More >
Open Access
ARTICLE
Conghuan Ye1,*, Shenglong Tan1, Shi Li1, Jun Wang1, Qiankun Zuo1, Bing Xiong2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062051
Abstract With the fast development of multimedia social platforms, content dissemination on social media platforms is becoming more popular. Social image sharing can also raise privacy concerns. Image encryption can protect social images. However, most existing image protection methods cannot be applied to multimedia social platforms because of encryption in the spatial domain. In this work, the authors propose a secure social image-sharing method with watermarking/fingerprinting and encryption. First, the fingerprint code with a hierarchical community structure is designed based on social network analysis. Then, discrete wavelet transform (DWT) from block discrete cosine transform (DCT) directly… More >
Open Access
ARTICLE
Erlin Tian1, Zengchao Zhu2,*, Fangmei Liu2, Zuhe Li2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061145
Abstract Within the realm of multimodal neural machine translation (MNMT), addressing the challenge of seamlessly integrating textual data with corresponding image data to enhance translation accuracy has become a pressing issue. We saw that discrepancies between textual content and associated images can lead to visual noise, potentially diverting the model’s focus away from the textual data and so affecting the translation’s comprehensive effectiveness. To solve this visual noise problem, we propose an innovative KDNR-MNMT model. The model combines the knowledge distillation technique with an anti-noise interaction mechanism, which makes full use of the synthesized graphic knowledge… More >
Open Access
ARTICLE
Mengjiao Wei1,*, Wenyu Liu2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.060887
（This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
Abstract In recent years, decomposition-based evolutionary algorithms have become popular algorithms for solving multi-objective problems in real-life scenarios. In these algorithms, the reference vectors of the Penalty-Based boundary intersection (PBI) are distributed parallelly while those based on the normal boundary intersection (NBI) are distributed radially in a conical shape in the objective space. To improve the problem-solving effectiveness of multi-objective optimization algorithms in engineering applications, this paper addresses the improvement of the Collaborative Decomposition (CoD) method, a multi-objective decomposition technique that integrates PBI and NBI, and combines it with the Elephant Clan Optimization Algorithm, introducing the… More >
Login
Register
Submit a Paper
Propose a Special lssue
