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  • Open Access

    ARTICLE

    Local Adaptive Gradient Variance Attack for Deep Fake Fingerprint Detection

    Chengsheng Yuan1,2, Baojie Cui1,2, Zhili Zhou3, Xinting Li4,*, Qingming Jonathan Wu5

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 899-914, 2024, DOI:10.32604/cmc.2023.045854

    Abstract In recent years, deep learning has been the mainstream technology for fingerprint liveness detection (FLD) tasks because of its remarkable performance. However, recent studies have shown that these deep fake fingerprint detection (DFFD) models are not resistant to attacks by adversarial examples, which are generated by the introduction of subtle perturbations in the fingerprint image, allowing the model to make fake judgments. Most of the existing adversarial example generation methods are based on gradient optimization, which is easy to fall into local optimal, resulting in poor transferability of adversarial attacks. In addition, the perturbation added to the blank area of… More >

  • Open Access

    ARTICLE

    Network Configuration Entity Extraction Method Based on Transformer with Multi-Head Attention Mechanism

    Yang Yang1, Zhenying Qu1, Zefan Yan1, Zhipeng Gao1,*, Ti Wang2

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 735-757, 2024, DOI:10.32604/cmc.2023.045807

    Abstract Nowadays, ensuring the quality of network services has become increasingly vital. Experts are turning to knowledge graph technology, with a significant emphasis on entity extraction in the identification of device configurations. This research paper presents a novel entity extraction method that leverages a combination of active learning and attention mechanisms. Initially, an improved active learning approach is employed to select the most valuable unlabeled samples, which are subsequently submitted for expert labeling. This approach successfully addresses the problems of isolated points and sample redundancy within the network configuration sample set. Then the labeled samples are utilized to train the model… More >

  • Open Access

    ARTICLE

    A Strengthened Dominance Relation NSGA-III Algorithm Based on Differential Evolution to Solve Job Shop Scheduling Problem

    Liang Zeng1,2, Junyang Shi1, Yanyan Li1, Shanshan Wang1,2,*, Weigang Li3

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 375-392, 2024, DOI:10.32604/cmc.2023.045803

    Abstract The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems. It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives. The Non-dominated Sorting Genetic Algorithm III (NSGA-III) is an effective approach for solving the multi-objective job shop scheduling problem. Nevertheless, it has some limitations in solving scheduling problems, including inadequate global search capability, susceptibility to premature convergence, and challenges in balancing convergence and diversity. To enhance its performance, this paper introduces a strengthened dominance relation NSGA-III algorithm based on differential evolution… More >

  • Open Access

    ARTICLE

    Enhancing Identity Protection in Metaverse-Based Psychological Counseling System

    Jun Lee1, Hanna Lee2, Seong Chan Lee2, Hyun Kwon3,*

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 617-632, 2024, DOI:10.32604/cmc.2023.045768

    Abstract Non-face-to-face psychological counseling systems rely on network technologies to anonymize information regarding client identity. However, these systems often face challenges concerning voice data leaks and the suboptimal communication of the client’s non-verbal expressions, such as facial cues, to the counselor. This study proposes a metaverse-based psychological counseling system designed to enhance client identity protection while ensuring efficient information delivery to counselors during non-face-to-face counseling. The proposed system incorporates a voice modulation function that instantly modifies/masks the client’s voice to safeguard their identity. Additionally, it employs real-time client facial expression recognition using an ensemble of decision trees to mirror the client’s… More >

  • Open Access

    ARTICLE

    An Improved Solov2 Based on Attention Mechanism and Weighted Loss Function for Electrical Equipment Instance Segmentation

    Junpeng Wu1,2,*, Zhenpeng Liu2, Xingfan Jiang2, Xinguang Tao2, Ye Zhang3

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 677-694, 2024, DOI:10.32604/cmc.2023.045759

    Abstract The current existing problem of deep learning framework for the detection and segmentation of electrical equipment is dominantly related to low precision. Because of the reliable, safe and easy-to-operate technology provided by deep learning-based video surveillance for unmanned inspection of electrical equipment, this paper uses the bottleneck attention module (BAM) attention mechanism to improve the Solov2 model and proposes a new electrical equipment segmentation mode. Firstly, the BAM attention mechanism is integrated into the feature extraction network to adaptively learn the correlation between feature channels, thereby improving the expression ability of the feature map; secondly, the weighted sum of CrossEntropy… More >

  • Open Access

    ARTICLE

    Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing

    Shasha Zhao1,2,3,*, Huanwen Yan1,2, Qifeng Lin1,2, Xiangnan Feng1,2, He Chen1,2, Dengyin Zhang1,2

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1135-1156, 2024, DOI:10.32604/cmc.2024.045660

    Abstract Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment. Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios. In this work, the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm (HPSO-EABC) has been proposed, which hybrids our presented Evolutionary Artificial Bee Colony (EABC), and Hierarchical Particle Swarm Optimization (HPSO) algorithm. The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm. Comprehensive testing including evaluations of algorithm convergence speed, resource execution time, load balancing,… More >

  • Open Access

    ARTICLE

    Software Defect Prediction Method Based on Stable Learning

    Xin Fan1,2,3, Jingen Mao2,3,*, Liangjue Lian2,3, Li Yu1, Wei Zheng2,3, Yun Ge2,3

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 65-84, 2024, DOI:10.32604/cmc.2023.045522

    Abstract The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor. In previous software defect prediction studies, transfer learning was effective in solving the problem of inconsistent project data distribution. However, target projects often lack sufficient data, which affects the performance of the transfer learning model. In addition, the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model. To address these problems, this article propose a software defect prediction method based on stable learning (SDP-SL) that combines code… More >

  • Open Access

    ARTICLE

    Lightweight Malicious Code Classification Method Based on Improved SqueezeNet

    Li Li*, Youran Kong, Qing Zhang

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 551-567, 2024, DOI:10.32604/cmc.2023.045512

    Abstract With the growth of the Internet, more and more business is being done online, for example, online offices, online education and so on. While this makes people’s lives more convenient, it also increases the risk of the network being attacked by malicious code. Therefore, it is important to identify malicious codes on computer systems efficiently. However, most of the existing malicious code detection methods have two problems: (1) The ability of the model to extract features is weak, resulting in poor model performance. (2) The large scale of model data leads to difficulties deploying on devices with limited resources. Therefore,… More >

  • Open Access

    ARTICLE

    ProNet Adaptive Retinal Vessel Segmentation Algorithm Based on Improved UperNet Network

    Sijia Zhu1,*, Pinxiu Wang2, Ke Shen1

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 283-302, 2024, DOI:10.32604/cmc.2023.045506

    Abstract This paper proposes a new network structure, namely the ProNet network. Retinal medical image segmentation can help clinical diagnosis of related eye diseases and is essential for subsequent rational treatment. The baseline model of the ProNet network is UperNet (Unified perceptual parsing Network), and the backbone network is ConvNext (Convolutional Network). A network structure based on depth-separable convolution and 1 × 1 convolution is used, which has good performance and robustness. We further optimise ProNet mainly in two aspects. One is data enhancement using increased noise and slight angle rotation, which can significantly increase the diversity of data and help… More >

  • Open Access

    ARTICLE

    Deep Convolutional Neural Networks for Accurate Classification of Gastrointestinal Tract Syndromes

    Zahid Farooq Khan1, Muhammad Ramzan1,*, Mudassar Raza1, Muhammad Attique Khan2,3, Khalid Iqbal4, Taerang Kim5, Jae-Hyuk Cha5

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1207-1225, 2024, DOI:10.32604/cmc.2023.045491

    Abstract Accurate detection and classification of artifacts within the gastrointestinal (GI) tract frames remain a significant challenge in medical image processing. Medical science combined with artificial intelligence is advancing to automate the diagnosis and treatment of numerous diseases. Key to this is the development of robust algorithms for image classification and detection, crucial in designing sophisticated systems for diagnosis and treatment. This study makes a small contribution to endoscopic image classification. The proposed approach involves multiple operations, including extracting deep features from endoscopy images using pre-trained neural networks such as Darknet-53 and Xception. Additionally, feature optimization utilizes the binary dragonfly algorithm… More >

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