Home / Journals / CMC / Vol.79, No.2, 2024
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  • Open AccessOpen Access

    ARTICLE

    Low-Brightness Object Recognition Based on Deep Learning

    Shu-Yin Chiang*, Ting-Yu Lin
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1757-1773, 2024, DOI:10.32604/cmc.2024.049477
    (This article belongs to the Special Issue: Advanced Machine Learning and Optimization for Practical Solutions in Complex Real-world Systems)
    Abstract This research focuses on addressing the challenges associated with image detection in low-light environments, particularly by applying artificial intelligence techniques to machine vision and object recognition systems. The primary goal is to tackle issues related to recognizing objects with low brightness levels. In this study, the Intel RealSense Lidar Camera L515 is used to simultaneously capture color information and 16-bit depth information images. The detection scenarios are categorized into normal brightness and low brightness situations. When the system determines a normal brightness environment, normal brightness images are recognized using deep learning methods. In low-brightness situations,… More >

  • Open AccessOpen Access

    ARTICLE

    Blood Pressure Estimation with Phonocardiogram on CNN-Based Approach

    Kasidit Kokkhunthod1, Khomdet Phapatanaburi2, Wongsathon Pathonsuwan1, Talit Jumphoo1, Patikorn Anchuen3, Porntip Nimkuntod4, Monthippa Uthansakul1, Peerapong Uthansakul1,*
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1775-1794, 2024, DOI:10.32604/cmc.2024.049276
    Abstract Monitoring blood pressure is a critical aspect of safeguarding an individual’s health, as early detection of abnormal blood pressure levels facilitates timely medical intervention, ultimately leading to a reduction in mortality rates associated with cardiovascular diseases. Consequently, the development of a robust and continuous blood pressure monitoring system holds paramount significance. In the context of this research paper, we introduce an innovative deep learning regression model that harnesses phonocardiogram (PCG) data to achieve precise blood pressure estimation. Our novel approach incorporates a convolutional neural network (CNN)-based regression model, which not only enhances its adaptability to… More >

  • Open AccessOpen Access

    REVIEW

    Federated Learning on Internet of Things: Extensive and Systematic Review

    Meenakshi Aggarwal1, Vikas Khullar1, Sunita Rani2, Thomas André Prola3,4,5, Shyama Barna Bhattacharjee6, Sarowar Morshed Shawon7, Nitin Goyal8,*
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1795-1834, 2024, DOI:10.32604/cmc.2024.049846
    Abstract The proliferation of IoT devices requires innovative approaches to gaining insights while preserving privacy and resources amid unprecedented data generation. However, FL development for IoT is still in its infancy and needs to be explored in various areas to understand the key challenges for deployment in real-world scenarios. The paper systematically reviewed the available literature using the PRISMA guiding principle. The study aims to provide a detailed overview of the increasing use of FL in IoT networks, including the architecture and challenges. A systematic review approach is used to collect, categorize and analyze FL-IoT-based articles.… More >

  • Open AccessOpen Access

    ARTICLE

    The Impact of Network Topologies and Radio Duty Cycle Mechanisms on the RPL Routing Protocol Power Consumption

    Amal Hkiri1,*, Hamzah Faraj2, Omar Ben Bahri2, Mouna Karmani1, Sami Alqurashi2, Mohsen Machhout1
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1835-1854, 2024, DOI:10.32604/cmc.2024.049207
    (This article belongs to the Special Issue: Advanced Machine Learning and Optimization for Practical Solutions in Complex Real-world Systems)
    Abstract The Internet of Things (IoT) has witnessed a significant surge in adoption, particularly through the utilization of Wireless Sensor Networks (WSNs), which comprise small internet-connected devices. These deployments span various environments and offer a multitude of benefits. However, the widespread use of battery-powered devices introduces challenges due to their limited hardware resources and communication capabilities. In response to this, the Internet Engineering Task Force (IETF) has developed the IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) to address the unique requirements of such networks. Recognizing the critical role of RPL in maintaining high performance,… More >

  • Open AccessOpen Access

    ARTICLE

    A Cooperated Imperialist Competitive Algorithm for Unrelated Parallel Batch Machine Scheduling Problem

    Deming Lei*, Heen Li
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1855-1874, 2024, DOI:10.32604/cmc.2024.049480
    (This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
    Abstract This study focuses on the scheduling problem of unrelated parallel batch processing machines (BPM) with release times, a scenario derived from the moulding process in a foundry. In this process, a batch is initially formed, placed in a sandbox, and then the sandbox is positioned on a BPM for moulding. The complexity of the scheduling problem increases due to the consideration of BPM capacity and sandbox volume. To minimize the makespan, a new cooperated imperialist competitive algorithm (CICA) is introduced. In CICA, the number of empires is not a parameter, and four empires are maintained More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Approach to Breast Tumor Detection: Enhanced Speckle Reduction and Hybrid Classification in Ultrasound Imaging

    K. Umapathi1,*, S. Shobana1, Anand Nayyar2, Judith Justin3, R. Vanithamani3, Miguel Villagómez Galindo4, Mushtaq Ahmad Ansari5, Hitesh Panchal6,*
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1875-1901, 2024, DOI:10.32604/cmc.2024.047961
    Abstract Breast cancer detection heavily relies on medical imaging, particularly ultrasound, for early diagnosis and effective treatment. This research addresses the challenges associated with computer-aided diagnosis (CAD) of breast cancer from ultrasound images. The primary challenge is accurately distinguishing between malignant and benign tumors, complicated by factors such as speckle noise, variable image quality, and the need for precise segmentation and classification. The main objective of the research paper is to develop an advanced methodology for breast ultrasound image classification, focusing on speckle noise reduction, precise segmentation, feature extraction, and machine learning-based classification. A unique approach… More >

  • Open AccessOpen Access

    ARTICLE

    Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile Ad Hoc Networks (MANETS)

    Ahmed Alhussen1, Arshiya S. Ansari2,*
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1903-1923, 2024, DOI:10.32604/cmc.2024.049260
    Abstract Traffic in today’s cities is a serious problem that increases travel times, negatively affects the environment, and drains financial resources. This study presents an Artificial Intelligence (AI) augmented Mobile Ad Hoc Networks (MANETs) based real-time prediction paradigm for urban traffic challenges. MANETs are wireless networks that are based on mobile devices and may self-organize. The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts. This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network (CSFPNN) technique to assess real-time data… More >

  • Open AccessOpen Access

    ARTICLE

    Robust Information Hiding Based on Neural Style Transfer with Artificial Intelligence

    Xiong Zhang1,2, Minqing Zhang1,2,3,*, Xu An Wang1,2,3, Wen Jiang1,2, Chao Jiang1,2, Pan Yang1,4
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1925-1938, 2024, DOI:10.32604/cmc.2024.050899
    (This article belongs to the Special Issue: Security, Privacy, and Robustness for Trustworthy AI Systems)
    Abstract This paper proposes an artificial intelligence-based robust information hiding algorithm to address the issue of confidential information being susceptible to noise attacks during transmission. The algorithm we designed aims to mitigate the impact of various noise attacks on the integrity of secret information during transmission. The method we propose involves encoding secret images into stylized encrypted images and applies adversarial transfer to both the style and content features of the original and embedded data. This process effectively enhances the concealment and imperceptibility of confidential information, thereby improving the security of such information during transmission and… More >

  • Open AccessOpen Access

    ARTICLE

    Enhancing Deep Learning Semantics: The Diffusion Sampling and Label-Driven Co-Attention Approach

    Chunhua Wang1,2, Wenqian Shang1,2,*, Tong Yi3,*, Haibin Zhu4
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1939-1956, 2024, DOI:10.32604/cmc.2024.048135
    (This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
    Abstract The advent of self-attention mechanisms within Transformer models has significantly propelled the advancement of deep learning algorithms, yielding outstanding achievements across diverse domains. Nonetheless, self-attention mechanisms falter when applied to datasets with intricate semantic content and extensive dependency structures. In response, this paper introduces a Diffusion Sampling and Label-Driven Co-attention Neural Network (DSLD), which adopts a diffusion sampling method to capture more comprehensive semantic information of the data. Additionally, the model leverages the joint correlation information of labels and data to introduce the computation of text representation, correcting semantic representation biases in the data, and More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Scheduling Framework for Multi-Programming Quantum Computing in Cloud Environment

    Danyang Zheng, Jinchen Xv, Feng Yue, Qiming Du, Zhiheng Wang, Zheng Shan*
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1957-1974, 2024, DOI:10.32604/cmc.2024.048956
    Abstract As cloud quantum computing gains broader acceptance, a growing quantity of researchers are directing their focus towards this domain. Nevertheless, the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity, which in turn hampers users from achieving optimal satisfaction. Therefore, cloud quantum computing service providers require a unified analysis and scheduling framework for their quantum resources and user jobs to meet the ever-growing usage demands. This paper introduces a new multi-programming scheduling framework for quantum computing in a cloud environment. The framework addresses the issue of limited quantum computing resources More >

  • Open AccessOpen Access

    ARTICLE

    L-Smooth SVM with Distributed Adaptive Proximal Stochastic Gradient Descent with Momentum for Fast Brain Tumor Detection

    Chuandong Qin1,2, Yu Cao1,*, Liqun Meng1
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1975-1994, 2024, DOI:10.32604/cmc.2024.049228
    (This article belongs to the Special Issue: Advanced Machine Learning and Optimization for Practical Solutions in Complex Real-world Systems)
    Abstract Brain tumors come in various types, each with distinct characteristics and treatment approaches, making manual detection a time-consuming and potentially ambiguous process. Brain tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment outcomes. Machine learning models have become key players in automating brain tumor detection. Gradient descent methods are the mainstream algorithms for solving machine learning models. In this paper, we propose a novel distributed proximal stochastic gradient descent approach to solve the L-Smooth Support Vector Machine (SVM) classifier for brain tumor detection. Firstly, the smooth hinge loss is… More >

  • Open AccessOpen Access

    ARTICLE

    Customized Convolutional Neural Network for Accurate Detection of Deep Fake Images in Video Collections

    Dmitry Gura1,2, Bo Dong3,*, Duaa Mehiar4, Nidal Al Said5
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1995-2014, 2024, DOI:10.32604/cmc.2024.048238
    Abstract The motivation for this study is that the quality of deep fakes is constantly improving, which leads to the need to develop new methods for their detection. The proposed Customized Convolutional Neural Network method involves extracting structured data from video frames using facial landmark detection, which is then used as input to the CNN. The customized Convolutional Neural Network method is the date augmented-based CNN model to generate ‘fake data’ or ‘fake images’. This study was carried out using Python and its libraries. We used 242 films from the dataset gathered by the Deep Fake… More >

  • Open AccessOpen Access

    ARTICLE

    Improving the Segmentation of Arabic Handwriting Using Ligature Detection Technique

    Husam Ahmad Al Hamad*, Mohammad Shehab*
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2015-2034, 2024, DOI:10.32604/cmc.2024.048527
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract Recognizing handwritten characters remains a critical and formidable challenge within the realm of computer vision. Although considerable strides have been made in enhancing English handwritten character recognition through various techniques, deciphering Arabic handwritten characters is particularly intricate. This complexity arises from the diverse array of writing styles among individuals, coupled with the various shapes that a single character can take when positioned differently within document images, rendering the task more perplexing. In this study, a novel segmentation method for Arabic handwritten scripts is suggested. This work aims to locate the local minima of the vertical… More >

  • Open AccessOpen Access

    REVIEW

    Towards Blockchain-Based Secure BGP Routing, Challenges and Future Research Directions

    Qiong Yang1, Li Ma1,2,*, Shanshan Tu1, Sami Ullah3, Muhammad Waqas4,5, Hisham Alasmary6
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2035-2062, 2024, DOI:10.32604/cmc.2024.049970
    Abstract Border Gateway Protocol (BGP) is a standard inter-domain routing protocol for the Internet that conveys network layer reachability information and establishes routes to different destinations. The BGP protocol exhibits security design defects, such as an unconditional trust mechanism and the default acceptance of BGP route announcements from peers by BGP neighboring nodes, easily triggering prefix hijacking, path forgery, route leakage, and other BGP security threats. Meanwhile, the traditional BGP security mechanism, relying on a public key infrastructure, faces issues like a single point of failure and a single point of trust. The decentralization, anti-tampering, and More >

  • Open AccessOpen Access

    ARTICLE

    Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

    Xiaoqin Ma1,2, Jun Wang1, Wenchang Yu1, Qinli Zhang1,2,*
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2063-2083, 2024, DOI:10.32604/cmc.2024.049147
    Abstract The presence of numerous uncertainties in hybrid decision information systems (HDISs) renders attribute reduction a formidable task. Currently available attribute reduction algorithms, including those based on Pawlak attribute importance, Skowron discernibility matrix, and information entropy, struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values, and attributes with fuzzy boundaries and abnormal values. In order to address the aforementioned issues, this paper delves into the study of attribute reduction within HDISs. First of all, a novel metric based on the decision attribute is introduced to solve… More >

  • Open AccessOpen Access

    ARTICLE

    A HEVC Video Steganalysis Method Using the Optimality of Motion Vector Prediction

    Jun Li1,2, Minqing Zhang1,2,*, Ke Niu1, Yingnan Zhang1, Xiaoyuan Yang1,2
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2085-2103, 2024, DOI:10.32604/cmc.2024.048095
    (This article belongs to the Special Issue: Multimedia Encryption and Information Security)
    Abstract Among steganalysis techniques, detection against MV (motion vector) domain-based video steganography in the HEVC (High Efficiency Video Coding) standard remains a challenging issue. For the purpose of improving the detection performance, this paper proposes a steganalysis method that can perfectly detect MV-based steganography in HEVC. Firstly, we define the local optimality of MVP (Motion Vector Prediction) based on the technology of AMVP (Advanced Motion Vector Prediction). Secondly, we analyze that in HEVC video, message embedding either using MVP index or MVD (Motion Vector Difference) may destroy the above optimality of MVP. And then, we define More >

  • Open AccessOpen Access

    ARTICLE

    Lightweight Res-Connection Multi-Branch Network for Highly Accurate Crowd Counting and Localization

    Mingze Li, Diwen Zheng, Shuhua Lu*
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2105-2122, 2024, DOI:10.32604/cmc.2024.048928
    Abstract Crowd counting is a promising hotspot of computer vision involving crowd intelligence analysis, achieving tremendous success recently with the development of deep learning. However, there have been still many challenges including crowd multi-scale variations and high network complexity, etc. To tackle these issues, a lightweight Res-connection multi-branch network (LRMBNet) for highly accurate crowd counting and localization is proposed. Specifically, using improved ShuffleNet V2 as the backbone, a lightweight shallow extractor has been designed by employing the channel compression mechanism to reduce enormously the number of network parameters. A light multi-branch structure with different expansion rate… More >

  • Open AccessOpen Access

    ARTICLE

    MoBShield: A Novel XML Approach for Securing Mobile Banking

    Saeed Seraj1, Ali Safaa Sadiq1,*, Omprakash Kaiwartya1, Mohammad Aljaidi2, Alexandros Konios1, Mohammed Ali3, Mohammed Abazeed3
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2123-2149, 2024, DOI:10.32604/cmc.2024.048914
    Abstract Mobile banking security has witnessed significant R&D attention from both financial institutions and academia. This is due to the growing number of mobile baking applications and their reachability and usefulness to society. However, these applications are also attractive prey for cybercriminals, who use a variety of malware to steal personal banking information. Related literature in mobile banking security requires many permissions that are not necessary for the application’s intended security functionality. In this context, this paper presents a novel efficient permission identification approach for securing mobile banking (MoBShield) to detect and prevent malware. A permission-based… More >

  • Open AccessOpen Access

    ARTICLE

    Hyperspectral Image Based Interpretable Feature Clustering Algorithm

    Yaming Kang1,*, Peishun Ye1, Yuxiu Bai1, Shi Qiu2
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2151-2168, 2024, DOI:10.32604/cmc.2024.049360
    (This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)
    Abstract Hyperspectral imagery encompasses spectral and spatial dimensions, reflecting the material properties of objects. Its application proves crucial in search and rescue, concealed target identification, and crop growth analysis. Clustering is an important method of hyperspectral analysis. The vast data volume of hyperspectral imagery, coupled with redundant information, poses significant challenges in swiftly and accurately extracting features for subsequent analysis. The current hyperspectral feature clustering methods, which are mostly studied from space or spectrum, do not have strong interpretability, resulting in poor comprehensibility of the algorithm. So, this research introduces a feature clustering algorithm for hyperspectral… More >

  • Open AccessOpen Access

    ARTICLE

    CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features

    Mahmood Ul Haq1, Muhammad Athar Javed Sethi1, Najib Ben Aoun2,3, Ala Saleh Alluhaidan4,*, Sadique Ahmad5,6, Zahid farid7
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2169-2186, 2024, DOI:10.32604/cmc.2024.049645
    (This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
    Abstract Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security, authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neural networks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since they do not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networks as a more robust design capable of retaining pose information and spatial correlations to recognize objects more like the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, and so on, which are… More >

  • Open AccessOpen Access

    ARTICLE

    Improved Particle Swarm Optimization for Parameter Identification of Permanent Magnet Synchronous Motor

    Shuai Zhou1, Dazhi Wang1,*, Yongliang Ni2, Keling Song2, Yanming Li2
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2187-2207, 2024, DOI:10.32604/cmc.2024.048859
    (This article belongs to the Special Issue: Intelligent Computing Techniques and Their Real Life Applications)
    Abstract In the process of identifying parameters for a permanent magnet synchronous motor, the particle swarm optimization method is prone to being stuck in local optima in the later stages of iteration, resulting in low parameter accuracy. This work proposes a fuzzy particle swarm optimization approach based on the transformation function and the filled function. This approach addresses the topic of particle swarm optimization in parameter identification from two perspectives. Firstly, the algorithm uses a transformation function to change the form of the fitness function without changing the position of the extreme point of the fitness… More >

  • Open AccessOpen Access

    ARTICLE

    Relational Turkish Text Classification Using Distant Supervised Entities and Relations

    Halil Ibrahim Okur1,2,*, Kadir Tohma1, Ahmet Sertbas2
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2209-2228, 2024, DOI:10.32604/cmc.2024.050585
    Abstract Text classification, by automatically categorizing texts, is one of the foundational elements of natural language processing applications. This study investigates how text classification performance can be improved through the integration of entity-relation information obtained from the Wikidata (Wikipedia database) database and BERT-based pre-trained Named Entity Recognition (NER) models. Focusing on a significant challenge in the field of natural language processing (NLP), the research evaluates the potential of using entity and relational information to extract deeper meaning from texts. The adopted methodology encompasses a comprehensive approach that includes text preprocessing, entity detection, and the integration of… More >

  • Open AccessOpen Access

    ARTICLE

    MAIPFE: An Efficient Multimodal Approach Integrating Pre-Emptive Analysis, Personalized Feature Selection, and Explainable AI

    Moshe Dayan Sirapangi1, S. Gopikrishnan1,*
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2229-2251, 2024, DOI:10.32604/cmc.2024.047438
    Abstract Medical Internet of Things (IoT) devices are becoming more and more common in healthcare. This has created a huge need for advanced predictive health modeling strategies that can make good use of the growing amount of multimodal data to find potential health risks early and help individuals in a personalized way. Existing methods, while useful, have limitations in predictive accuracy, delay, personalization, and user interpretability, requiring a more comprehensive and efficient approach to harness modern medical IoT devices. MAIPFE is a multimodal approach integrating pre-emptive analysis, personalized feature selection, and explainable AI for real-time health… More >

  • Open AccessOpen Access

    ARTICLE

    CMAES-WFD: Adversarial Website Fingerprinting Defense Based on Covariance Matrix Adaptation Evolution Strategy

    Di Wang, Yuefei Zhu, Jinlong Fei*, Maohua Guo
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2253-2276, 2024, DOI:10.32604/cmc.2024.049504
    Abstract Website fingerprinting, also known as WF, is a traffic analysis attack that enables local eavesdroppers to infer a user’s browsing destination, even when using the Tor anonymity network. While advanced attacks based on deep neural network (DNN) can perform feature engineering and attain accuracy rates of over 98%, research has demonstrated that DNN is vulnerable to adversarial samples. As a result, many researchers have explored using adversarial samples as a defense mechanism against DNN-based WF attacks and have achieved considerable success. However, these methods suffer from high bandwidth overhead or require access to the target… More >

  • Open AccessOpen Access

    ARTICLE

    A Heuristic Radiomics Feature Selection Method Based on Frequency Iteration and Multi-Supervised Training Mode

    Zhigao Zeng1,2, Aoting Tang1,2, Shengqiu Yi1,2, Xinpan Yuan1,2, Yanhui Zhu1,2,*
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2277-2293, 2024, DOI:10.32604/cmc.2024.047989
    (This article belongs to the Special Issue: Recent Advances in Ensemble Framework of Meta-heuristics and Machine Learning: Methods and Applications)
    Abstract Radiomics is a non-invasive method for extracting quantitative and higher-dimensional features from medical images for diagnosis. It has received great attention due to its huge application prospects in recent years. We can know that the number of features selected by the existing radiomics feature selection methods is basically about ten. In this paper, a heuristic feature selection method based on frequency iteration and multiple supervised training mode is proposed. Based on the combination between features, it decomposes all features layer by layer to select the optimal features for each layer, then fuses the optimal features More >

  • Open AccessOpen Access

    ARTICLE

    Nonlinear Registration of Brain Magnetic Resonance Images with Cross Constraints of Intensity and Structure

    Han Zhou1,2, Hongtao Xu1,2, Xinyue Chang1,2, Wei Zhang1,2, Heng Dong1,2,*
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2295-2313, 2024, DOI:10.32604/cmc.2024.047754
    Abstract Many deep learning-based registration methods rely on a single-stream encoder-decoder network for computing deformation fields between 3D volumes. However, these methods often lack constraint information and overlook semantic consistency, limiting their performance. To address these issues, we present a novel approach for medical image registration called the Dual-VoxelMorph, featuring a dual-channel cross-constraint network. This innovative network utilizes both intensity and segmentation images, which share identical semantic information and feature representations. Two encoder-decoder structures calculate deformation fields for intensity and segmentation images, as generated by the dual-channel cross-constraint network. This design facilitates bidirectional communication between grayscale More >

  • Open AccessOpen Access

    ARTICLE

    Fusion of Spiral Convolution-LSTM for Intrusion Detection Modeling

    Fei Wang, Zhen Dong*
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2315-2329, 2024, DOI:10.32604/cmc.2024.048443
    Abstract Aiming at the problems of low accuracy and slow convergence speed of current intrusion detection models, SpiralConvolution is combined with Long Short-Term Memory Network to construct a new intrusion detection model. The dataset is first preprocessed using solo thermal encoding and normalization functions. Then the spiral convolution-Long Short-Term Memory Network model is constructed, which consists of spiral convolution, a two-layer long short-term memory network, and a classifier. It is shown through experiments that the model is characterized by high accuracy, small model computation, and fast convergence speed relative to previous deep learning models. The model More >

  • Open AccessOpen Access

    ARTICLE

    LDAS&ET-AD: Learnable Distillation Attack Strategies and Evolvable Teachers Adversarial Distillation

    Shuyi Li, Hongchao Hu*, Xiaohan Yang, Guozhen Cheng, Wenyan Liu, Wei Guo
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2331-2359, 2024, DOI:10.32604/cmc.2024.047275
    Abstract Adversarial distillation (AD) has emerged as a potential solution to tackle the challenging optimization problem of loss with hard labels in adversarial training. However, fixed sample-agnostic and student-egocentric attack strategies are unsuitable for distillation. Additionally, the reliability of guidance from static teachers diminishes as target models become more robust. This paper proposes an AD method called Learnable Distillation Attack Strategies and Evolvable Teachers Adversarial Distillation (LDAS&ET-AD). Firstly, a learnable distillation attack strategies generating mechanism is developed to automatically generate sample-dependent attack strategies tailored for distillation. A strategy model is introduced to produce attack strategies that… More >

  • Open AccessOpen Access

    ARTICLE

    FL-EASGD: Federated Learning Privacy Security Method Based on Homomorphic Encryption

    Hao Sun*, Xiubo Chen, Kaiguo Yuan
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2361-2373, 2024, DOI:10.32604/cmc.2024.049159
    (This article belongs to the Special Issue: Security, Privacy, and Robustness for Trustworthy AI Systems)
    Abstract Federated learning ensures data privacy and security by sharing models among multiple computing nodes instead of plaintext data. However, there is still a potential risk of privacy leakage, for example, attackers can obtain the original data through model inference attacks. Therefore, safeguarding the privacy of model parameters becomes crucial. One proposed solution involves incorporating homomorphic encryption algorithms into the federated learning process. However, the existing federated learning privacy protection scheme based on homomorphic encryption will greatly reduce the efficiency and robustness when there are performance differences between parties or abnormal nodes. To solve the above… More >

  • Open AccessOpen Access

    ARTICLE

    An Intelligent Framework for Resilience Recovery of FANETs with Spatio-Temporal Aggregation and Multi-Head Attention Mechanism

    Zhijun Guo1, Yun Sun2,*, Ying Wang1, Chaoqi Fu3, Jilong Zhong4,*
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2375-2398, 2024, DOI:10.32604/cmc.2024.048112
    (This article belongs to the Special Issue: Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems)
    Abstract Due to the time-varying topology and possible disturbances in a conflict environment, it is still challenging to maintain the mission performance of flying Ad hoc networks (FANET), which limits the application of Unmanned Aerial Vehicle (UAV) swarms in harsh environments. This paper proposes an intelligent framework to quickly recover the cooperative coverage mission by aggregating the historical spatio-temporal network with the attention mechanism. The mission resilience metric is introduced in conjunction with connectivity and coverage status information to simplify the optimization model. A spatio-temporal node pooling method is proposed to ensure all node location features… More >

  • Open AccessOpen Access

    ARTICLE

    Appropriate Combination of Crossover Operator and Mutation Operator in Genetic Algorithms for the Travelling Salesman Problem

    Zakir Hussain Ahmed1,*, Habibollah Haron2, Abdullah Al-Tameem3
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2399-2425, 2024, DOI:10.32604/cmc.2024.049704
    Abstract Genetic algorithms (GAs) are very good metaheuristic algorithms that are suitable for solving NP-hard combinatorial optimization problems. A simple GA begins with a set of solutions represented by a population of chromosomes and then uses the idea of survival of the fittest in the selection process to select some fitter chromosomes. It uses a crossover operator to create better offspring chromosomes and thus, converges the population. Also, it uses a mutation operator to explore the unexplored areas by the crossover operator, and thus, diversifies the GA search space. A combination of crossover and mutation operators… More >

  • Open AccessOpen Access

    ARTICLE

    Faster Region Convolutional Neural Network (FRCNN) Based Facial Emotion Recognition

    J. Sheril Angel1, A. Diana Andrushia1,*, T. Mary Neebha1, Oussama Accouche2, Louai Saker2, N. Anand3
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2427-2448, 2024, DOI:10.32604/cmc.2024.047326
    Abstract Facial emotion recognition (FER) has become a focal point of research due to its widespread applications, ranging from human-computer interaction to affective computing. While traditional FER techniques have relied on handcrafted features and classification models trained on image or video datasets, recent strides in artificial intelligence and deep learning (DL) have ushered in more sophisticated approaches. The research aims to develop a FER system using a Faster Region Convolutional Neural Network (FRCNN) and design a specialized FRCNN architecture tailored for facial emotion recognition, leveraging its ability to capture spatial hierarchies within localized regions of facial… More >

  • Open AccessOpen Access

    ARTICLE

    Preserving Data Secrecy and Integrity for Cloud Storage Using Smart Contracts and Cryptographic Primitives

    Maher Alharby*
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2449-2463, 2024, DOI:10.32604/cmc.2024.050425
    Abstract Cloud computing has emerged as a viable alternative to traditional computing infrastructures, offering various benefits. However, the adoption of cloud storage poses significant risks to data secrecy and integrity. This article presents an effective mechanism to preserve the secrecy and integrity of data stored on the public cloud by leveraging blockchain technology, smart contracts, and cryptographic primitives. The proposed approach utilizes a Solidity-based smart contract as an auditor for maintaining and verifying the integrity of outsourced data. To preserve data secrecy, symmetric encryption systems are employed to encrypt user data before outsourcing it. An extensive More >

  • Open AccessOpen Access

    ARTICLE

    Curve Classification Based on Mean-Variance Feature Weighting and Its Application

    Zewen Zhang1, Sheng Zhou1, Chunzheng Cao1,2,*
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2465-2480, 2024, DOI:10.32604/cmc.2024.049605
    Abstract The classification of functional data has drawn much attention in recent years. The main challenge is representing infinite-dimensional functional data by finite-dimensional features while utilizing those features to achieve better classification accuracy. In this paper, we propose a mean-variance-based (MV) feature weighting method for classifying functional data or functional curves. In the feature extraction stage, each sample curve is approximated by B-splines to transfer features to the coefficients of the spline basis. After that, a feature weighting approach based on statistical principles is introduced by comprehensively considering the between-class differences and within-class variations of the… More >

  • Open AccessOpen Access

    ARTICLE

    Enhancing Relational Triple Extraction in Specific Domains: Semantic Enhancement and Synergy of Large Language Models and Small Pre-Trained Language Models

    Jiakai Li, Jianpeng Hu*, Geng Zhang
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2481-2503, 2024, DOI:10.32604/cmc.2024.050005
    Abstract In the process of constructing domain-specific knowledge graphs, the task of relational triple extraction plays a critical role in transforming unstructured text into structured information. Existing relational triple extraction models face multiple challenges when processing domain-specific data, including insufficient utilization of semantic interaction information between entities and relations, difficulties in handling challenging samples, and the scarcity of domain-specific datasets. To address these issues, our study introduces three innovative components: Relation semantic enhancement, data augmentation, and a voting strategy, all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks. We first… More >

  • Open AccessOpen Access

    ARTICLE

    Image Segmentation-P300 Selector: A Brain–Computer Interface System for Target Selection

    Hang Sun, Changsheng Li*, He Zhang
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2505-2522, 2024, DOI:10.32604/cmc.2024.049898
    (This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
    Abstract Brain–computer interface (BCI) systems, such as the P300 speller, enable patients to express intentions without necessitating extensive training. However, the complexity of operational instructions and the slow pace of character spelling pose challenges for some patients. In this paper, an image segmentation P300 selector based on YOLOv7-mask and DeepSORT is proposed. The proposed system utilizes a camera to capture real-world objects for classification and tracking. By applying predefined stimulation rules and object-specific masks, the proposed system triggers stimuli associated with the objects displayed on the screen, inducing the generation of P300 signals in the patient’s… More >

  • Open AccessOpen Access

    ARTICLE

    Cluster Detection Method of Endogenous Security Abnormal Attack Behavior in Air Traffic Control Network

    Ruchun Jia1, Jianwei Zhang1,*, Yi Lin1, Yunxiang Han1, Feike Yang2
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2523-2546, 2024, DOI:10.32604/cmc.2024.047543
    (This article belongs to the Special Issue: AI and Data Security for the Industrial Internet)
    Abstract In order to enhance the accuracy of Air Traffic Control (ATC) cybersecurity attack detection, in this paper, a new clustering detection method is designed for air traffic control network security attacks. The feature set for ATC cybersecurity attacks is constructed by setting the feature states, adding recursive features, and determining the feature criticality. The expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature data. An autoencoder is introduced into the AI (artificial intelligence) algorithm to encode and… More >

  • Open AccessOpen Access

    ARTICLE

    YOLO-MFD: Remote Sensing Image Object Detection with Multi-Scale Fusion Dynamic Head

    Zhongyuan Zhang, Wenqiu Zhu*
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2547-2563, 2024, DOI:10.32604/cmc.2024.048755
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract Remote sensing imagery, due to its high altitude, presents inherent challenges characterized by multiple scales, limited target areas, and intricate backgrounds. These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery. Additionally, these complexities contribute to inaccuracies in target localization and hinder precise target categorization. This paper addresses these challenges by proposing a solution: The YOLO-MFD model (YOLO-MFD: Remote Sensing Image Object Detection with Multi-scale Fusion Dynamic Head). Before presenting our method, we delve into the prevalent issues faced in remote sensing imagery… More >

  • Open AccessOpen Access

    ARTICLE

    An Implementation of Multiscale Line Detection and Mathematical Morphology for Efficient and Precise Blood Vessel Segmentation in Fundus Images

    Syed Ayaz Ali Shah1,*, Aamir Shahzad1,*, Musaed Alhussein2, Chuan Meng Goh3, Khursheed Aurangzeb2, Tong Boon Tang4, Muhammad Awais5
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2565-2583, 2024, DOI:10.32604/cmc.2024.047597
    (This article belongs to the Special Issue: Recent Advances in Ophthalmic Diseases Diagnosis using AI)
    Abstract Diagnosing various diseases such as glaucoma, age-related macular degeneration, cardiovascular conditions, and diabetic retinopathy involves segmenting retinal blood vessels. The task is particularly challenging when dealing with color fundus images due to issues like non-uniform illumination, low contrast, and variations in vessel appearance, especially in the presence of different pathologies. Furthermore, the speed of the retinal vessel segmentation system is of utmost importance. With the surge of now available big data, the speed of the algorithm becomes increasingly important, carrying almost equivalent weightage to the accuracy of the algorithm. To address these challenges, we present… More >

    Graphic Abstract

    An Implementation of Multiscale Line Detection and Mathematical Morphology for Efficient and Precise Blood Vessel Segmentation in Fundus Images

  • Open AccessOpen Access

    ARTICLE

    Automatic Finding of Brain-Tumour Group Using CNN Segmentation and Moth-Flame-Algorithm, Selected Deep and Handcrafted Features

    Imad Saud Al Naimi1,2,*, Syed Alwee Aljunid Syed Junid1, Muhammad lmran Ahmad1,*, K. Suresh Manic2,3
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2585-2608, 2024, DOI:10.32604/cmc.2024.046461
    (This article belongs to the Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)
    Abstract Augmentation of abnormal cells in the brain causes brain tumor (BT), and early screening and treatment will reduce its harshness in patients. BT’s clinical level screening is usually performed with Magnetic Resonance Imaging (MRI) due to its multi-modality nature. The overall aims of the study is to introduce, test and verify an advanced image processing technique with algorithms to automatically extract tumour sections from brain MRI scans, facilitating improved accuracy. The research intends to devise a reliable framework for detecting the BT region in the two-dimensional (2D) MRI slice, and identifying its class with improved… More >

  • Open AccessOpen Access

    ARTICLE

    Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

    Muhammad Ahmad Nawaz Ul Ghani1, Kun She1,*, Muhammad Arslan Rauf1, Shumaila Khan2, Javed Ali Khan3, Eman Abdullah Aldakheel4, Doaa Sami Khafaga4
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2609-2623, 2024, DOI:10.32604/cmc.2024.049611
    (This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
    Abstract The use of privacy-enhanced facial recognition has increased in response to growing concerns about data security and privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a variety of industries, including access control, law enforcement, surveillance, and internet communication. However, the growing usage of face recognition technology has created serious concerns about data monitoring and user privacy preferences, especially in context-aware systems. In response to these problems, this study provides a novel framework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain, and distributed computing… More >

  • Open AccessOpen Access

    ARTICLE

    Chaotic CS Encryption: An Efficient Image Encryption Algorithm Based on Chebyshev Chaotic System and Compressive Sensing

    Mingliang Sun, Jie Yuan*, Xiaoyong Li, Dongxiao Liu
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2625-2646, 2024, DOI:10.32604/cmc.2024.050337
    Abstract Images are the most important carrier of human information. Moreover, how to safely transmit digital images through public channels has become an urgent problem. In this paper, we propose a novel image encryption algorithm, called chaotic compressive sensing (CS) encryption (CCSE), which can not only improve the efficiency of image transmission but also introduce the high security of the chaotic system. Specifically, the proposed CCSE can fully leverage the advantages of the Chebyshev chaotic system and CS, enabling it to withstand various attacks, such as differential attacks, and exhibit robustness. First, we use a sparse… More >

  • Open AccessOpen Access

    ARTICLE

    Enhanced Hybrid Equilibrium Strategy in Fog-Cloud Computing Networks with Optimal Task Scheduling

    Muchang Rao, Hang Qin*
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2647-2672, 2024, DOI:10.32604/cmc.2024.050380
    (This article belongs to the Special Issue: Multi-Service and Resource Management in Intelligent Edge-Cloud Platform)
    Abstract More devices in the Intelligent Internet of Things (AIoT) result in an increased number of tasks that require low latency and real-time responsiveness, leading to an increased demand for computational resources. Cloud computing’s low-latency performance issues in AIoT scenarios have led researchers to explore fog computing as a complementary extension. However, the effective allocation of resources for task execution within fog environments, characterized by limitations and heterogeneity in computational resources, remains a formidable challenge. To tackle this challenge, in this study, we integrate fog computing and cloud computing. We begin by establishing a fog-cloud environment… More >

  • Open AccessOpen Access

    ARTICLE

    Posture Detection of Heart Disease Using Multi-Head Attention Vision Hybrid (MHAVH) Model

    Hina Naz1, Zuping Zhang1,*, Mohammed Al-Habib1, Fuad A. Awwad2, Emad A. A. Ismail2, Zaid Ali Khan3
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2673-2696, 2024, DOI:10.32604/cmc.2024.049186
    Abstract Cardiovascular disease is the leading cause of death globally. This disease causes loss of heart muscles and is also responsible for the death of heart cells, sometimes damaging their functionality. A person’s life may depend on receiving timely assistance as soon as possible. Thus, minimizing the death ratio can be achieved by early detection of heart attack (HA) symptoms. In the United States alone, an estimated 610,000 people die from heart attacks each year, accounting for one in every four fatalities. However, by identifying and reporting heart attack symptoms early on, it is possible to… More >

  • Open AccessOpen Access

    ARTICLE

    Optimizing Optical Fiber Faults Detection: A Comparative Analysis of Advanced Machine Learning Approaches

    Kamlesh Kumar Soothar1,2, Yuanxiang Chen1,2,*, Arif Hussain Magsi3, Cong Hu1, Hussain Shah1
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2697-2721, 2024, DOI:10.32604/cmc.2024.049607
    (This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
    Abstract Efficient optical network management poses significant importance in backhaul and access network communication for preventing service disruptions and ensuring Quality of Service (QoS) satisfaction. The emerging faults in optical networks introduce challenges that can jeopardize the network with a variety of faults. The existing literature witnessed various partial or inadequate solutions. On the other hand, Machine Learning (ML) has revolutionized as a promising technique for fault detection and prevention. Unlike traditional fault management systems, this research has three-fold contributions. First, this research leverages the ML and Deep Learning (DL) multi-classification system and evaluates their accuracy… More >

  • Open AccessOpen Access

    ARTICLE

    Cross-Modal Consistency with Aesthetic Similarity for Multimodal False Information Detection

    Weijian Fan1,*, Ziwei Shi2
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2723-2741, 2024, DOI:10.32604/cmc.2024.050344
    Abstract With the explosive growth of false information on social media platforms, the automatic detection of multimodal false information has received increasing attention. Recent research has significantly contributed to multimodal information exchange and fusion, with many methods attempting to integrate unimodal features to generate multimodal news representations. However, they still need to fully explore the hierarchical and complex semantic correlations between different modal contents, severely limiting their performance detecting multimodal false information. This work proposes a two-stage detection framework for multimodal false information detection, called ASMFD, which is based on image aesthetic similarity to segment and… More >

  • Open AccessOpen Access

    ARTICLE

    SCIRD: Revealing Infection of Malicious Software in Edge Computing-Enabled IoT Networks

    Jiehao Ye, Wen Cheng, Xiaolong Liu, Wenyi Zhu, Xuan’ang Wu, Shigen Shen*
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2743-2769, 2024, DOI:10.32604/cmc.2024.049985
    Abstract The Internet of Things (IoT) has characteristics such as node mobility, node heterogeneity, link heterogeneity, and topology heterogeneity. In the face of the IoT characteristics and the explosive growth of IoT nodes, which brings about large-scale data processing requirements, edge computing architecture has become an emerging network architecture to support IoT applications due to its ability to provide powerful computing capabilities and good service functions. However, the defense mechanism of Edge Computing-enabled IoT Nodes (ECIoTNs) is still weak due to their limited resources, so that they are susceptible to malicious software spread, which can compromise… More >

  • Open AccessOpen Access

    ARTICLE

    Developing Lexicons for Enhanced Sentiment Analysis in Software Engineering: An Innovative Multilingual Approach for Social Media Reviews

    Zohaib Ahmad Khan1, Yuanqing Xia1,*, Ahmed Khan2, Muhammad Sadiq2, Mahmood Alam3, Fuad A. Awwad4, Emad A. A. Ismail4
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2771-2793, 2024, DOI:10.32604/cmc.2024.046897
    (This article belongs to the Special Issue: Requirements Engineering: Bridging Theory, Research and Practice)
    Abstract Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significant source of user-generated content. The development of sentiment lexicons that can support languages other than English is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existing sentiment analysis systems focus on English, leaving a significant research gap in other languages due to limited resources and tools. This research aims to address this gap by building a sentiment lexicon for local languages, which is then used with a machine learning algorithm for efficient sentiment analysis.… More >

  • Open AccessOpen Access

    ARTICLE

    Model Agnostic Meta-Learning (MAML)-Based Ensemble Model for Accurate Detection of Wheat Diseases Using Vision Transformer and Graph Neural Networks

    Yasir Maqsood1, Syed Muhammad Usman1,*, Musaed Alhussein2, Khursheed Aurangzeb2,*, Shehzad Khalid3, Muhammad Zubair4
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2795-2811, 2024, DOI:10.32604/cmc.2024.049410
    (This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
    Abstract Wheat is a critical crop, extensively consumed worldwide, and its production enhancement is essential to meet escalating demand. The presence of diseases like stem rust, leaf rust, yellow rust, and tan spot significantly diminishes wheat yield, making the early and precise identification of these diseases vital for effective disease management. With advancements in deep learning algorithms, researchers have proposed many methods for the automated detection of disease pathogens; however, accurately detecting multiple disease pathogens simultaneously remains a challenge. This challenge arises due to the scarcity of RGB images for multiple diseases, class imbalance in existing… More >

  • Open AccessOpen Access

    ARTICLE

    Byzantine Robust Federated Learning Scheme Based on Backdoor Triggers

    Zheng Yang, Ke Gu*, Yiming Zuo
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2813-2831, 2024, DOI:10.32604/cmc.2024.050025
    Abstract Federated learning is widely used to solve the problem of data decentralization and can provide privacy protection for data owners. However, since multiple participants are required in federated learning, this allows attackers to compromise. Byzantine attacks pose great threats to federated learning. Byzantine attackers upload maliciously created local models to the server to affect the prediction performance and training speed of the global model. To defend against Byzantine attacks, we propose a Byzantine robust federated learning scheme based on backdoor triggers. In our scheme, backdoor triggers are embedded into benign data samples, and then malicious More >

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