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  • Open Access

    ARTICLE

    Securing Forwarding Layers from Eavesdropping Attacks Using Proactive Approaches

    Jiajun Yan, Ying Zhou*, Anchen Dai, Tao Wang
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048922
    Abstract As an emerging network paradigm, the software-defined network (SDN) finds extensive application in areas such as smart grids, the Internet of Things (IoT), and edge computing. The forwarding layer in software-defined networks is susceptible to eavesdropping attacks. Route hopping is a moving target defense (MTD) technology that is frequently employed to resist eavesdropping attacks. In the traditional route hopping technology, both request and reply packets use the same hopping path. If an eavesdropping attacker monitors the nodes along this path, the risk of 100% data leakage becomes substantial. In this paper, we present an effective route hopping approach, called two-day… More >

  • Open Access

    ARTICLE

    A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

    Tsu-Yang Wu1,2, Haonan Li2, Saru Kumari3, Chien-Ming Chen1,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048347
    Abstract Hyperspectral image classification stands as a pivotal task within the field of remote sensing, yet achieving high-precision classification remains a significant challenge. In response to this challenge, a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm (AFLA-SCNN) is proposed. The Adaptive Fick’s Law Algorithm (AFLA) constitutes a novel metaheuristic algorithm introduced herein, encompassing three new strategies: Adaptive weight factor, Gaussian mutation, and probability update policy. With adaptive weight factor, the algorithm can adjust the weights according to the change in the number of iterations to improve the performance of the algorithm. Gaussian mutation helps the algorithm avoid… More >

  • Open Access

    ARTICLE

    HCSP-Net: A Novel Model of Age-Related Macular Degeneration Classification Based on Color Fundus Photography

    Cheng Wan1, Jiani Zhao1, Xiangqian Hong2, Weihua Yang2,*, Shaochong Zhang2,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048307
    (This article belongs to this Special Issue: Deep Learning in Computer-Aided Diagnosis Based on Medical Image)
    Abstract Age-related macular degeneration (AMD) ranks third among the most common causes of blindness. As the most conventional and direct method for identifying AMD, color fundus photography has become prominent owing to its consistency, ease of use, and good quality in extensive clinical practice. In this study, a convolutional neural network (CSPDarknet53) was combined with a transformer to construct a new hybrid model, HCSP-Net. This hybrid model was employed to tri-classify color fundus photography into the normal macula (NM), dry macular degeneration (DMD), and wet macular degeneration (WMD) based on clinical classification manifestations, thus identifying and resolving AMD as early as… More >

  • Open Access

    ARTICLE

    Securing Cloud-Encrypted Data: Detecting Ransomware-as-a-Service (RaaS) Attacks through Deep Learning Ensemble

    Amardeep Singh1, Hamad Ali Abosaq2, Saad Arif3, Zohaib Mushtaq4,*, Muhammad Irfan5, Ghulam Abbas6, Arshad Ali7, Alanoud Al Mazroa8
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048036
    (This article belongs to this Special Issue: Multimedia Encryption and Information Security)
    Abstract Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries, especially in light of the growing number of cybersecurity threats. A major and ever-present threat is Ransomware-as-a-Service (RaaS) assaults, which enable even individuals with minimal technical knowledge to conduct ransomware operations. This study provides a new approach for RaaS attack detection which uses an ensemble of deep learning models. For this purpose, the network intrusion detection dataset “UNSW-NB15” from the Intelligent Security Group of the University of New South Wales, Australia is analyzed. In the initial phase, the rectified linear… More >

  • Open Access

    ARTICLE

    MSC-YOLO: Improved YOLOv7 Based on Multi-Scale Spatial Context for Small Object Detection in UAV-View

    Xiangyan Tang1,2, Chengchun Ruan1,2,*, Xiulai Li2,3, Binbin Li1,2, Cebin Fu1,2
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047541
    Abstract Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in the field of small object detection on unmanned aerial vehicles (UAVs). This task is challenging due to variations in UAV flight altitude, differences in object scales, as well as factors like flight speed and motion blur. To enhance the detection efficacy of small targets in drone aerial imagery, we propose an enhanced You Only Look Once version 7 (YOLOv7) algorithm based on multi-scale spatial context. We build the MSC-YOLO model, which incorporates an additional prediction head, denoted as P2, to improve adaptability for small objects.… More >

  • Open Access

    ARTICLE

    A Hybrid Level Set Optimization Design Method of Functionally Graded Cellular Structures Considering Connectivity

    Yan Dong1,2, Kang Zhao1, Liang Gao1, Hao Li1,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048870
    (This article belongs to this Special Issue: Multiscale Computational Methods for Advanced Materials and Structures)
    Abstract With the continuous advancement in topology optimization and additive manufacturing (AM) technology, the capability to fabricate functionally graded materials and intricate cellular structures with spatially varying microstructures has grown significantly. However, a critical challenge is encountered in the design of these structures–the absence of robust interface connections between adjacent microstructures, potentially resulting in diminished efficiency or macroscopic failure. A Hybrid Level Set Method (HLSM) is proposed, specifically designed to enhance connectivity among non-uniform microstructures, contributing to the design of functionally graded cellular structures. The HLSM introduces a pioneering algorithm for effectively blending heterogeneous microstructure interfaces. Initially, an interpolation algorithm is… More >

  • Open Access

    ARTICLE

    Efficient Route Planning for Real-Time Demand-Responsive Transit

    Hongle Li1, SeongKi Kim2,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048402
    (This article belongs to this Special Issue: Intelligent Computing Techniques and Their Real Life Applications)
    Abstract Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetables and determines the stop and the start according to the demands. This study explores the optimization of dynamic vehicle scheduling and real-time route planning in urban public transportation systems, with a focus on bus services. It addresses the limitations of current shared mobility routing algorithms, which are primarily designed for simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. The research introduces an route planning algorithm designed to dynamically accommodate passenger travel needs and enable real-time route modifications.… More >

  • Open Access

    ARTICLE

    Complementary-Label Adversarial Domain Adaptation Fault Diagnosis Network under Time-Varying Rotational Speed and Weakly-Supervised Conditions

    Siyuan Liu1,*, Jinying Huang2, Jiancheng Ma1, Licheng Jing2, Yuxuan Wang2
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049484
    (This article belongs to this Special Issue: Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems)
    Abstract Recent research in cross-domain intelligence fault diagnosis of machinery still has some problems, such as relatively ideal speed conditions and sample conditions. In engineering practice, the rotational speed of the machine is often transient and time-varying, which makes the sample annotation increasingly expensive. Meanwhile, the number of samples collected from different health states is often unbalanced. To deal with the above challenges, a complementary-label (CL) adversarial domain adaptation fault diagnosis network (CLADAN) is proposed under time-varying rotational speed and weakly-supervised conditions. In the weakly supervised learning condition, machine prior information is used for sample annotation via cost-friendly complementary label learning.… More >

  • Open Access

    ARTICLE

    Mobile Crowdsourcing Task Allocation Based on Dynamic Self-Attention GANs

    Kai Wei1, Song Yu2, Qingxian Pan1,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048240
    (This article belongs to this Special Issue: Edge Computing in Advancing the Capabilities of Smart Cities)
    Abstract Crowdsourcing technology is widely recognized for its effectiveness in task scheduling and resource allocation. While traditional methods for task allocation can help reduce costs and improve efficiency, they may encounter challenges when dealing with abnormal data flow nodes, leading to decreased allocation accuracy and efficiency. To address these issues, this study proposes a novel two-part invalid detection task allocation framework. In the first step, an anomaly detection model is developed using a dynamic self-attentive GAN to identify anomalous data. Compared to the baseline method, the model achieves an approximately 4% increase in the F1 value on the public dataset. In… More >

  • Open Access

    ARTICLE

    Reinforcement Learning Based Quantization Strategy Optimal Assignment Algorithm for Mixed Precision

    Yuejiao Wang, Zhong Ma*, Chaojie Yang, Yu Yang, Lu Wei
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047108
    (This article belongs to this Special Issue: Development and Industrial Application of AI Technologies)
    Abstract The quantization algorithm compresses the original network by reducing the numerical bit width of the model, which improves the computation speed. Because different layers have different redundancy and sensitivity to data bit width. Reducing the data bit width will result in a loss of accuracy. Therefore, it is difficult to determine the optimal bit width for different parts of the network with guaranteed accuracy. Mixed precision quantization can effectively reduce the amount of computation while keeping the model accuracy basically unchanged. In this paper, a hardware-aware mixed precision quantization strategy optimal assignment algorithm adapted to low bit width is proposed,… More >

  • Open Access

    ARTICLE

    Anomaly Detection Algorithm of Power System Based on Graph Structure and Anomaly Attention

    Yifan Gao*, Jieming Zhang, Zhanchen Chen, Xianchao Chen
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048615
    (This article belongs to this Special Issue: AI and Data Security for the Industrial Internet)
    Abstract In this paper, we propose a novel anomaly detection method for data centers based on a combination of graph structure and abnormal attention mechanism. The method leverages the sensor monitoring data from target power substations to construct multidimensional time series. These time series are subsequently transformed into graph structures, and corresponding adjacency matrices are obtained. By incorporating the adjacency matrices and additional weights associated with the graph structure, an aggregation matrix is derived. The aggregation matrix is then fed into a pre-trained graph convolutional neural network (GCN) to extract graph structure features. Moreover, both the multidimensional time series segments and… More >

  • Open Access

    ARTICLE

    Weakly Supervised Network with Scribble-Supervised and Edge-Mask for Road Extraction from High-Resolution Remote Sensing Images

    Supeng Yu1, Fen Huang1,*, Chengcheng Fan2,3,4,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048608
    Abstract Significant advancements have been achieved in road surface extraction based on high-resolution remote sensing image processing. Most current methods rely on fully supervised learning, which necessitates enormous human effort to label the image. Within this field, other research endeavors utilize weakly supervised methods. These approaches aim to reduce the expenses associated with annotation by leveraging sparsely annotated data, such as scribbles. This paper presents a novel technique called a weakly supervised network using scribble-supervised and edge-mask (WSSE-net). This network is a three-branch network architecture, whereby each branch is equipped with a distinct decoder module dedicated to road extraction tasks. One… More >

  • Open Access

    ARTICLE

    A Deep Learning Framework for Mass-Forming Chronic Pancreatitis and Pancreatic Ductal Adenocarcinoma Classification Based on Magnetic Resonance Imaging

    Luda Chen1, Kuangzhu Bao2, Ying Chen2, Jingang Hao2,*, Jianfeng He1,3,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048507
    (This article belongs to this Special Issue: Deep Learning in Computer-Aided Diagnosis Based on Medical Image)
    Abstract Pancreatic diseases, including mass-forming chronic pancreatitis (MFCP) and pancreatic ductal adenocarcinoma (PDAC), present with similar imaging features, leading to diagnostic complexities. Deep Learning (DL) methods have been shown to perform well on diagnostic tasks. Existing DL pancreatic lesion diagnosis studies based on Magnetic Resonance Imaging (MRI) utilize the prior information to guide models to focus on the lesion region. However, over-reliance on prior information may ignore the background information that is helpful for diagnosis. This study verifies the diagnostic significance of the background information using a clinical dataset. Consequently, the Prior Difference Guidance Network (PDGNet) is proposed, merging decoupled lesion… More >

  • Open Access

    ARTICLE

    KurdSet: A Kurdish Handwritten Characters Recognition Dataset Using Convolutional Neural Network

    Sardar Hasen Ali*, Maiwan Bahjat Abdulrazzaq
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048356
    (This article belongs to this Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
    Abstract Handwritten character recognition (HCR) involves identifying characters in images, documents, and various sources such as forms surveys, questionnaires, and signatures, and transforming them into a machine-readable format for subsequent processing. Successfully recognizing complex and intricately shaped handwritten characters remains a significant obstacle. The use of convolutional neural network (CNN) in recent developments has notably advanced HCR, leveraging the ability to extract discriminative features from extensive sets of raw data. Because of the absence of pre-existing datasets in the Kurdish language, we created a Kurdish handwritten dataset called (KurdSet). The dataset consists of Kurdish characters, digits, texts, and symbols. The dataset… More >

  • Open Access

    ARTICLE

    MIDNet: Deblurring Network for Material Microstructure Images

    Jiaxiang Wang1, Zhengyi Li1, Peng Shi1, Hongying Yu2, Dongbai Sun1,3,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.046929
    (This article belongs to this Special Issue: Advances and Applications in Signal, Image and Video Processing)
    Abstract Scanning electron microscopy (SEM) is a crucial tool in the field of materials science, providing valuable insights into the microstructural characteristics of materials. Unfortunately, SEM images often suffer from blurriness caused by improper hardware calibration or imaging automation errors, which present challenges in analyzing and interpreting material characteristics. Consequently, rectifying the blurring of these images assumes paramount significance to enable subsequent analysis. To address this issue, we introduce a Material Images Deblurring Network (MIDNet) built upon the foundation of the Nonlinear Activation Free Network (NAFNet). MIDNet is meticulously tailored to address the blurring in images capturing the microstructure of materials.… More >

  • Open Access

    ARTICLE

    Safety-Constrained Multi-Agent Reinforcement Learning for Power Quality Control in Distributed Renewable Energy Networks

    Yongjiang Zhao, Haoyi Zhong, Chang Cyoon Lim*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048771
    (This article belongs to this Special Issue: Edge Computing in Advancing the Capabilities of Smart Cities)
    Abstract This paper examines the difficulties of managing distributed power systems, notably due to the increasing use of renewable energy sources, and focuses on voltage control challenges exacerbated by their variable nature in modern power grids. To tackle the unique challenges of voltage control in distributed renewable energy networks, researchers are increasingly turning towards multi-agent reinforcement learning (MARL). However, MARL raises safety concerns due to the unpredictability in agent actions during their exploration phase. This unpredictability can lead to unsafe control measures. To mitigate these safety concerns in MARL-based voltage control, our study introduces a novel approach: Safety-Constrained Multi-Agent Reinforcement Learning… More >

  • Open Access

    ARTICLE

    U-Net Inspired Deep Neural Network-Based Smoke Plume Detection in Satellite Images

    Ananthakrishnan Balasundaram1,2, Ayesha Shaik1,2,*, Japmann Kaur Banga2, Aman Kumar Singh2
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048362
    (This article belongs to this Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract Industrial activities, through the human-induced release of Green House Gas (GHG) emissions, have been identified as the primary cause of global warming. Accurate and quantitative monitoring of these emissions is essential for a comprehensive understanding of their impact on the Earth’s climate and for effectively enforcing emission regulations at a large scale. This work examines the feasibility of detecting and quantifying industrial smoke plumes using freely accessible geo-satellite imagery. The existing system has so many lagging factors such as limitations in accuracy, robustness, and efficiency and these factors hinder the effectiveness in supporting timely response to industrial fires. In this… More >

  • Open Access

    ARTICLE

    Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

    Hala AlShamlan*, Halah AlMazrua*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048146
    Abstract In this study, our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization (GWO) with Harris Hawks Optimization (HHO) for feature selection. The motivation for utilizing GWO and HHO stems from their bio-inspired nature and their demonstrated success in optimization problems. We aim to leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification. We selected leave-one-out cross-validation (LOOCV) to evaluate the performance of both two widely used classifiers, k-nearest neighbors (KNN) and support vector machine (SVM), on high-dimensional cancer microarray… More >

  • Open Access

    ARTICLE

    Combined CNN-LSTM Deep Learning Algorithms for Recognizing Human Physical Activities in Large and Distributed Manners: A Recommendation System

    Ameni Ellouze1, Nesrine Kadri2, Alaa Alaerjan3,*, Mohamed Ksantini1
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048061
    (This article belongs to this Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
    Abstract Recognizing human activity (HAR) from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases. Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not. Typically, smartphones and their associated sensing devices operate in distributed and unstable environments. Therefore, collecting their data and extracting useful information is a significant challenge. In this context, the aim of this paper is twofold: The first is to analyze human behavior based on the recognition of physical activities. Using the results of physical activity detection… More >

  • Open Access

    ARTICLE

    Cervical Cancer Prediction Empowered with Federated Machine Learning

    Muhammad Umar Nasir1, Omar Kassem Khalil2, Karamath Ateeq3, Bassam SaleemAllah Almogadwy4, M. A. Khan5, Khan Muhammad Adnan6,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047874
    Abstract Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in the fourth position because of the leading death cause in its premature stages. The cervix which is the lower end of the vagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumor in the cervix is deadly if it cannot be detected in the early stages. So, in this delineated study, the proposed approach uses federated machine learning with numerous machine learning solvers for the prediction of cervical cancer to train the weights with varying neurons… More >

  • Open Access

    ARTICLE

    An Enhanced Multiview Transformer for Population Density Estimation Using Cellular Mobility Data in Smart City

    Yu Zhou1, Bosong Lin1, Siqi Hu2, Dandan Yu3,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047836
    (This article belongs to this Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
    Abstract This paper addresses the problem of predicting population density leveraging cellular station data. As wireless communication devices are commonly used, cellular station data has become integral for estimating population figures and studying their movement, thereby implying significant contributions to urban planning. However, existing research grapples with issues pertinent to preprocessing base station data and the modeling of population prediction. To address this, we propose methodologies for preprocessing cellular station data to eliminate any irregular or redundant data. The preprocessing reveals a distinct cyclical characteristic and high-frequency variation in population shift. Further, we devise a multi-view enhancement model grounded on the… More >

  • Open Access

    ARTICLE

    Image Fusion Using Wavelet Transformation and XGboost Algorithm

    Shahid Naseem1, Tariq Mahmood2,3, Amjad Rehman Khan2, Umer Farooq1, Samra Nawazish4, Faten S. Alamri5,*, Tanzila Saba2
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047623
    (This article belongs to this Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
    Abstract Recently, there have been several uses for digital image processing. Image fusion has become a prominent application in the domain of imaging processing. To create one final image that proves more informative and helpful compared to the original input images, image fusion merges two or more initial images of the same item. Image fusion aims to produce, enhance, and transform significant elements of the source images into combined images for the sake of human visual perception. Image fusion is commonly employed for feature extraction in smart robots, clinical imaging, audiovisual camera integration, manufacturing process monitoring, electronic circuit design, advanced device… More >

  • Open Access

    ARTICLE

    ResNeSt-biGRU: An Intrusion Detection Model Based on Internet of Things

    Yan Xiang1,2, Daofeng Li1,2,*, Xinyi Meng1,2, Chengfeng Dong1,2, Guanglin Qin1,2
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047143
    (This article belongs to this Special Issue: Security and Privacy for Blockchain-empowered Internet of Things)
    Abstract The rapid expansion of Internet of Things (IoT) devices across various sectors is driven by steadily increasing demands for interconnected and smart technologies. Nevertheless, the surge in the number of IoT device has caught the attention of cyber hackers, as it provides them with expanded avenues to access valuable data. This has resulted in a myriad of security challenges, including information leakage, malware propagation, and financial loss, among others. Consequently, developing an intrusion detection system to identify both active and potential intrusion traffic in IoT networks is of paramount importance. In this paper, we propose ResNeSt-biGRU, a practical intrusion detection… More >

  • Open Access

    ARTICLE

    Multimodal Social Media Fake News Detection Based on Similarity Inference and Adversarial Networks

    Fangfang Shan1,2,*, Huifang Sun1,2, Mengyi Wang1,2
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.046202
    Abstract As social networks become increasingly complex, contemporary fake news often includes textual descriptions of events accompanied by corresponding images or videos. Fake news in multiple modalities is more likely to create a misleading perception among users. While early research primarily focused on text-based features for fake news detection mechanisms, there has been relatively limited exploration of learning shared representations in multimodal (text and visual) contexts. To address these limitations, this paper introduces a multimodal model for detecting fake news, which relies on similarity reasoning and adversarial networks. The model employs Bidirectional Encoder Representation from Transformers (BERT) and Text Convolutional Neural… More >

  • Open Access

    ARTICLE

    Contrastive Consistency and Attentive Complementarity for Deep Multi-View Subspace Clustering

    Jiao Wang, Bin Wu*, Hongying Zhang
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.046011
    (This article belongs to this Special Issue: Development and Industrial Application of AI Technologies)
    Abstract Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention due to its outstanding performance and nonlinear application. However, most existing methods neglect that view-private meaningless information or noise may interfere with the learning of self-expression, which may lead to the degeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistency and Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple views and fuses them based on their discrimination, so that it can effectively explore consistent and complementary information for achieving precise clustering. Specifically, the view-specific self-expression is learned by… More >

  • Open Access

    ARTICLE

    Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

    Fangjun Luan1,2,3, Xuewen Mu1,2,3, Shuai Yuan1,2,3,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048502
    Abstract Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries. However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. To address these issues, we propose a novel approach for online signature verification, using a one-dimensional GhostACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolution with a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residual structure is introduced to leverage both self-attention and convolution mechanisms for capturing global feature information and extracting local information, effectively complementing whole and local signature features and mitigating… More >

  • Open Access

    ARTICLE

    YOLOv5ST: A Lightweight and Fast Scene Text Detector

    Yiwei Liu1, Yingnan Zhao1,*, Yi Chen1, Zheng Hu1, Min Xia2
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047901
    (This article belongs to this Special Issue: Deep Learning based Object Detection and Tracking in Videos)
    Abstract Scene text detection is an important task in computer vision. In this paper, we present YOLOv5 Scene Text (YOLOv5ST), an optimized architecture based on YOLOv5 v6.0 tailored for fast scene text detection. Our primary goal is to enhance inference speed without sacrificing significant detection accuracy, thereby enabling robust performance on resource-constrained devices like drones, closed-circuit television cameras, and other embedded systems. To achieve this, we propose key modifications to the network architecture to lighten the original backbone and improve feature aggregation, including replacing standard convolution with depth-wise convolution, adopting the C2 sequence module in place of C3, employing Spatial Pyramid… More >

  • Open Access

    ARTICLE

    An Adaptive Hate Speech Detection Approach Using Neutrosophic Neural Networks for Social Media Forensics

    Yasmine M. Ibrahim1,2, Reem Essameldin3, Saad M. Darwish1,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047840
    (This article belongs to this Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
    Abstract Detecting hate speech automatically in social media forensics has emerged as a highly challenging task due to the complex nature of language used in such platforms. Currently, several methods exist for classifying hate speech, but they still suffer from ambiguity when differentiating between hateful and offensive content and they also lack accuracy. The work suggested in this paper uses a combination of the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) to adjust the weights of two Multi-Layer Perceptron (MLPs) for neutrosophic sets classification. During the training process of the MLP, the WOA is employed to explore and determine… More >

  • Open Access

    ARTICLE

    The Effect of Key Nodes on the Malware Dynamics in the Industrial Control Network

    Qiang Fu1, Jun Wang1,*, Changfu Si1, Jiawei Liu2
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048117
    (This article belongs to this Special Issue: Recent Advances in Ensemble Framework of Meta-heuristics and Machine Learning: Methods and Applications)
    Abstract As industrialization and informatization become more deeply intertwined, industrial control networks have entered an era of intelligence. The connection between industrial control networks and the external internet is becoming increasingly close, which leads to frequent security accidents. This paper proposes a model for the industrial control network. It includes a malware containment strategy that integrates intrusion detection, quarantine, and monitoring. Based on this model, the role of key nodes in the spread of malware is studied, a comparison experiment is conducted to validate the impact of the containment strategy. In addition, the dynamic behavior of the model is analyzed, the… More >

  • Open Access

    ARTICLE

    A Simple and Effective Surface Defect Detection Method of Power Line Insulators for Difficult Small Objects

    Xiao Lu1,*, Chengling Jiang1, Zhoujun Ma1, Haitao Li2, Yuexin Liu2
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047469
    (This article belongs to this Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract Insulator defect detection plays a vital role in maintaining the secure operation of power systems. To address the issues of the difficulty of detecting small objects and missing objects due to the small scale, variable scale, and fuzzy edge morphology of insulator defects, we construct an insulator dataset with 1600 samples containing flashovers and breakages. Then a simple and effective surface defect detection method of power line insulators for difficult small objects is proposed. Firstly, a high-resolution feature map is introduced and a small object prediction layer is added so that the model can detect tiny objects. Secondly, a simplified… More >

  • Open Access

    ARTICLE

    Analyze the Performance of Electroactive Anticorrosion Coating of Medical Magnesium Alloy Using Deep Learning

    Yashan Feng1, Yafang Tian1, Yongxin Yang1, Yufang Zhang1, Haiwei Guo1, Jing’an Li2,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047004
    (This article belongs to this Special Issue: Application of Soft Computing in Techniques in Materials Development)
    Abstract Electroactive anticorrosion coatings are specialized surface treatments that prevent or minimize corrosion. The study employs strategic thermodynamic equilibrium calculations to pioneer a novel factor in corrosion protection. A first-time proposal, the total acidity (TA) potential of the hydrogen (pH) concept significantly shapes medical magnesium alloys. These coatings are meticulously designed for robust corrosion resistance, blending theoretical insights and practical applications to enhance our grasp of corrosion prevention mechanisms and establish a systematic approach to coating design. The groundbreaking significance of this study lies in its innovative integration of the TA/pH concept, which encompasses the TA/pH ratio of the chemical environment.… More >

  • Open Access

    ARTICLE

    Blockchain-Based Key Management Scheme Using Rational Secret Sharing

    Xingfan Zhao1, Changgen Peng1,2,*, Weijie Tan2, Kun Niu1
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047975
    (This article belongs to this Special Issue: Security and Privacy for Blockchain-empowered Internet of Things)
    Abstract Traditional blockchain key management schemes store private keys in the same location, which can easily lead to security issues such as a single point of failure. Therefore, decentralized threshold key management schemes have become a research focus for blockchain private key protection. The security of private keys for blockchain user wallet is highly related to user identity authentication and digital asset security. The threshold blockchain private key management schemes based on verifiable secret sharing have made some progress, but these schemes do not consider participants’ self-interested behavior, and require trusted nodes to keep private key fragments, resulting in a narrow… More >

  • Open Access

    ARTICLE

    Coal/Gangue Volume Estimation with Convolutional Neural Network and Separation Based on Predicted Volume and Weight

    Zenglun Guan1,2, Murad S. Alfarzaeai1,3,*, Eryi Hu1,3,*, Taqiaden Alshmeri4, Wang Peng3
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047159
    (This article belongs to this Special Issue: Advanced Computer Technology for Materials Characterization, Properties Prediction, Design and Discovery)
    Abstract In the coal mining industry, the gangue separation phase imposes a key challenge due to the high visual similarity between coal and gangue. Recently, separation methods have become more intelligent and efficient, using new technologies and applying different features for recognition. One such method exploits the difference in substance density, leading to excellent coal/gangue recognition. Therefore, this study uses density differences to distinguish coal from gangue by performing volume prediction on the samples. Our training samples maintain a record of 3-side images as input, volume, and weight as the ground truth for the classification. The prediction process relies on a… More >