Home / Journals / CMC / Vol.78, No.3, 2024
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  • Open AccessOpen Access

    REVIEW

    A Review on the Recent Trends of Image Steganography for VANET Applications

    Arshiya S. Ansari*
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 2865-2892, 2024, DOI:10.32604/cmc.2024.045908 - 26 March 2024
    Abstract Image steganography is a technique of concealing confidential information within an image without dramatically changing its outside look. Whereas vehicular ad hoc networks (VANETs), which enable vehicles to communicate with one another and with roadside infrastructure to enhance safety and traffic flow provide a range of value-added services, as they are an essential component of modern smart transportation systems. VANETs steganography has been suggested by many authors for secure, reliable message transfer between terminal/hope to terminal/hope and also to secure it from attack for privacy protection. This paper aims to determine whether using steganography is… More >

  • Open AccessOpen Access

    ARTICLE

    Restoration of the JPEG Maximum Lossy Compressed Face Images with Hourglass Block-GAN

    Jongwook Si1, Sungyoung Kim2,*
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 2893-2908, 2024, DOI:10.32604/cmc.2023.046081 - 26 March 2024
    (This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
    Abstract In the context of high compression rates applied to Joint Photographic Experts Group (JPEG) images through lossy compression techniques, image-blocking artifacts may manifest. This necessitates the restoration of the image to its original quality. The challenge lies in regenerating significantly compressed images into a state in which these become identifiable. Therefore, this study focuses on the restoration of JPEG images subjected to substantial degradation caused by maximum lossy compression using Generative Adversarial Networks (GAN). The generator in this network is based on the U-Net architecture. It features a new hourglass structure that preserves the characteristics… More >

  • Open AccessOpen Access

    REVIEW

    A Review of Computing with Spiking Neural Networks

    Jiadong Wu, Yinan Wang*, Zhiwei Li*, Lun Lu, Qingjiang Li
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 2909-2939, 2024, DOI:10.32604/cmc.2024.047240 - 26 March 2024
    Abstract Artificial neural networks (ANNs) have led to landmark changes in many fields, but they still differ significantly from the mechanisms of real biological neural networks and face problems such as high computing costs, excessive computing power, and so on. Spiking neural networks (SNNs) provide a new approach combined with brain-like science to improve the computational energy efficiency, computational architecture, and biological credibility of current deep learning applications. In the early stage of development, its poor performance hindered the application of SNNs in real-world scenarios. In recent years, SNNs have made great progress in computational performance… More >

  • Open AccessOpen Access

    REVIEW

    Trends in Event Understanding and Caption Generation/Reconstruction in Dense Video: A Review

    Ekanayake Mudiyanselage Chulabhaya Lankanatha Ekanayake1,2, Abubakar Sulaiman Gezawa3,*, Yunqi Lei1
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 2941-2965, 2024, DOI:10.32604/cmc.2024.046155 - 26 March 2024
    Abstract Video description generates natural language sentences that describe the subject, verb, and objects of the targeted Video. The video description has been used to help visually impaired people to understand the content. It is also playing an essential role in devolving human-robot interaction. The dense video description is more difficult when compared with simple Video captioning because of the object’s interactions and event overlapping. Deep learning is changing the shape of computer vision (CV) technologies and natural language processing (NLP). There are hundreds of deep learning models, datasets, and evaluations that can improve the gaps… More >

  • Open AccessOpen Access

    REVIEW

    A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

    Usman Khan1, Muhammad Khalid Khan1, Muhammad Ayub Latif1, Muhammad Naveed1,2,*, Muhammad Mansoor Alam2,3,4, Salman A. Khan1, Mazliham Mohd Su’ud2,*
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 2967-3000, 2024, DOI:10.32604/cmc.2024.045101 - 26 March 2024
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract Recently, there has been a notable surge of interest in scientific research regarding spectral images. The potential of these images to revolutionize the digital photography industry, like aerial photography through Unmanned Aerial Vehicles (UAVs), has captured considerable attention. One encouraging aspect is their combination with machine learning and deep learning algorithms, which have demonstrated remarkable outcomes in image classification. As a result of this powerful amalgamation, the adoption of spectral images has experienced exponential growth across various domains, with agriculture being one of the prominent beneficiaries. This paper presents an extensive survey encompassing multispectral and… More >

  • Open AccessOpen Access

    ARTICLE

    A Cover-Independent Deep Image Hiding Method Based on Domain Attention Mechanism

    Nannan Wu1, Xianyi Chen1,*, James Msughter Adeke2, Junjie Zhao2
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3001-3019, 2024, DOI:10.32604/cmc.2023.045311 - 26 March 2024
    Abstract Recently, deep image-hiding techniques have attracted considerable attention in covert communication and high-capacity information hiding. However, these approaches have some limitations. For example, a cover image lacks self-adaptability, information leakage, or weak concealment. To address these issues, this study proposes a universal and adaptable image-hiding method. First, a domain attention mechanism is designed by combining the Atrous convolution, which makes better use of the relationship between the secret image domain and the cover image domain. Second, to improve perceived human similarity, perceptual loss is incorporated into the training process. The experimental results are promising, with More >

  • Open AccessOpen Access

    ARTICLE

    Part-Whole Relational Few-Shot 3D Point Cloud Semantic Segmentation

    Shoukun Xu1, Lujun Zhang1, Guangqi Jiang1, Yining Hua2, Yi Liu1,*
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3021-3039, 2024, DOI:10.32604/cmc.2023.045853 - 26 March 2024
    Abstract This paper focuses on the task of few-shot 3D point cloud semantic segmentation. Despite some progress, this task still encounters many issues due to the insufficient samples given, e.g., incomplete object segmentation and inaccurate semantic discrimination. To tackle these issues, we first leverage part-whole relationships into the task of 3D point cloud semantic segmentation to capture semantic integrity, which is empowered by the dynamic capsule routing with the module of 3D Capsule Networks (CapsNets) in the embedding network. Concretely, the dynamic routing amalgamates geometric information of the 3D point cloud data to construct higher-level feature… More >

  • Open AccessOpen Access

    ARTICLE

    A Novel 6G Scalable Blockchain Clustering-Based Computer Vision Character Detection for Mobile Images

    Yuejie Li1,2,*, Shijun Li3
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3041-3070, 2024, DOI:10.32604/cmc.2023.045741 - 26 March 2024
    (This article belongs to the Special Issue: Secure Blockchain Clustering for 6G Wireless Technology with Advanced Artificial Intelligence Techniques)
    Abstract 6G is envisioned as the next generation of wireless communication technology, promising unprecedented data speeds, ultra-low Latency, and ubiquitous Connectivity. In tandem with these advancements, blockchain technology is leveraged to enhance computer vision applications’ security, trustworthiness, and transparency. With the widespread use of mobile devices equipped with cameras, the ability to capture and recognize Chinese characters in natural scenes has become increasingly important. Blockchain can facilitate privacy-preserving mechanisms in applications where privacy is paramount, such as facial recognition or personal healthcare monitoring. Users can control their visual data and grant or revoke access as needed.… More >

  • Open AccessOpen Access

    ARTICLE

    Unmanned Ship Identification Based on Improved YOLOv8s Algorithm

    Chun-Ming Wu1, Jin Lei1,*, Wu-Kai Liu1, Mei-Ling Ren1, Ling-Li Ran2
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3071-3088, 2024, DOI:10.32604/cmc.2023.047062 - 26 March 2024
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract Aiming at defects such as low contrast in infrared ship images, uneven distribution of ship size, and lack of texture details, which will lead to unmanned ship leakage misdetection and slow detection, this paper proposes an infrared ship detection model based on the improved YOLOv8 algorithm (R_YOLO). The algorithm incorporates the Efficient Multi-Scale Attention mechanism (EMA), the efficient Reparameterized Generalized-feature extraction module (CSPStage), the small target detection header, the Repulsion Loss function, and the context aggregation block (CABlock), which are designed to improve the model’s ability to detect targets at multiple scales and the speed… More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

    Navaneetha Krishnan Muthunambu1, Senthil Prabakaran2, Balasubramanian Prabhu Kavin3, Kishore Senthil Siruvangur4, Kavitha Chinnadurai1, Jehad Ali5,*
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3089-3127, 2024, DOI:10.32604/cmc.2023.043172 - 26 March 2024
    Abstract The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the Internet. Regrettably, this development has expanded the potential targets that hackers might exploit. Without adequate safeguards, data transmitted on the internet is significantly more susceptible to unauthorized access, theft, or alteration. The identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious attacks. This research paper introduces a novel intrusion detection framework that utilizes Recurrent… More >

  • Open AccessOpen Access

    ARTICLE

    Enhancing ChatGPT’s Querying Capability with Voice-Based Interaction and CNN-Based Impair Vision Detection Model

    Awais Ahmad1, Sohail Jabbar1,*, Sheeraz Akram1, Anand Paul2, Umar Raza3, Nuha Mohammed Alshuqayran1
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3129-3150, 2024, DOI:10.32604/cmc.2024.045385 - 26 March 2024
    (This article belongs to the Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
    Abstract This paper presents an innovative approach to enhance the querying capability of ChatGPT, a conversational artificial intelligence model, by incorporating voice-based interaction and a convolutional neural network (CNN)-based impaired vision detection model. The proposed system aims to improve user experience and accessibility by allowing users to interact with ChatGPT using voice commands. Additionally, a CNN-based model is employed to detect impairments in user vision, enabling the system to adapt its responses and provide appropriate assistance. This research tackles head-on the challenges of user experience and inclusivity in artificial intelligence (AI). It underscores our commitment to… More >

  • Open AccessOpen Access

    ARTICLE

    Intrusion Detection Model Using Chaotic MAP for Network Coding Enabled Mobile Small Cells

    Chanumolu Kiran Kumar, Nandhakumar Ramachandran*
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3151-3176, 2024, DOI:10.32604/cmc.2023.043534 - 26 March 2024
    (This article belongs to the Special Issue: Intelligent Technologies and Applications for Future Wireless Communications)
    Abstract Wireless Network security management is difficult because of the ever-increasing number of wireless network malfunctions, vulnerabilities, and assaults. Complex security systems, such as Intrusion Detection Systems (IDS), are essential due to the limitations of simpler security measures, such as cryptography and firewalls. Due to their compact nature and low energy reserves, wireless networks present a significant challenge for security procedures. The features of small cells can cause threats to the network. Network Coding (NC) enabled small cells are vulnerable to various types of attacks. Avoiding attacks and performing secure “peer” to “peer” data transmission is… More >

  • Open AccessOpen Access

    ARTICLE

    Privacy-Preserving Multi-Keyword Fuzzy Adjacency Search Strategy for Encrypted Graph in Cloud Environment

    Bin Wu1,2, Xianyi Chen3, Jinzhou Huang4,*, Caicai Zhang5, Jing Wang6, Jing Yu1,2, Zhiqiang Zhao7, Zhuolin Mei1,2
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3177-3194, 2024, DOI:10.32604/cmc.2023.047147 - 26 March 2024
    Abstract In a cloud environment, outsourced graph data is widely used in companies, enterprises, medical institutions, and so on. Data owners and users can save costs and improve efficiency by storing large amounts of graph data on cloud servers. Servers on cloud platforms usually have some subjective or objective attacks, which make the outsourced graph data in an insecure state. The issue of privacy data protection has become an important obstacle to data sharing and usage. How to query outsourcing graph data safely and effectively has become the focus of research. Adjacency query is a basic… More >

  • Open AccessOpen Access

    ARTICLE

    A Hybrid Machine Learning Approach for Improvised QoE in Video Services over 5G Wireless Networks

    K. B. Ajeyprasaath, P. Vetrivelan*
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3195-3213, 2024, DOI:10.32604/cmc.2023.046911 - 26 March 2024
    (This article belongs to the Special Issue: Intelligent Computing Techniques and Their Real Life Applications)
    Abstract Video streaming applications have grown considerably in recent years. As a result, this becomes one of the most significant contributors to global internet traffic. According to recent studies, the telecommunications industry loses millions of dollars due to poor video Quality of Experience (QoE) for users. Among the standard proposals for standardizing the quality of video streaming over internet service providers (ISPs) is the Mean Opinion Score (MOS). However, the accurate finding of QoE by MOS is subjective and laborious, and it varies depending on the user. A fully automated data analytics framework is required to… More >

  • Open AccessOpen Access

    ARTICLE

    Audio-Text Multimodal Speech Recognition via Dual-Tower Architecture for Mandarin Air Traffic Control Communications

    Shuting Ge1,2, Jin Ren2,3,*, Yihua Shi4, Yujun Zhang1, Shunzhi Yang2, Jinfeng Yang2
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3215-3245, 2024, DOI:10.32604/cmc.2023.046746 - 26 March 2024
    Abstract In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues,… More >

  • Open AccessOpen Access

    ARTICLE

    Boosting Adversarial Training with Learnable Distribution

    Kai Chen1,2, Jinwei Wang3, James Msughter Adeke1,2, Guangjie Liu1,2,*, Yuewei Dai1,4
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3247-3265, 2024, DOI:10.32604/cmc.2024.046082 - 26 March 2024
    Abstract In recent years, various adversarial defense methods have been proposed to improve the robustness of deep neural networks. Adversarial training is one of the most potent methods to defend against adversarial attacks. However, the difference in the feature space between natural and adversarial examples hinders the accuracy and robustness of the model in adversarial training. This paper proposes a learnable distribution adversarial training method, aiming to construct the same distribution for training data utilizing the Gaussian mixture model. The distribution centroid is built to classify samples and constrain the distribution of the sample features. The… More >

  • Open AccessOpen Access

    ARTICLE

    Deep Learning-Based Secure Transmission Strategy with Sensor-Transmission-Computing Linkage for Power Internet of Things

    Bin Li*, Linghui Kong, Xiangyi Zhang, Bochuo Kou, Hui Yu, Bowen Liu
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3267-3282, 2024, DOI:10.32604/cmc.2024.047193 - 26 March 2024
    Abstract The automatic collection of power grid situation information, along with real-time multimedia interaction between the front and back ends during the accident handling process, has generated a massive amount of power grid data. While wireless communication offers a convenient channel for grid terminal access and data transmission, it is important to note that the bandwidth of wireless communication is limited. Additionally, the broadcast nature of wireless transmission raises concerns about the potential for unauthorized eavesdropping during data transmission. To address these challenges and achieve reliable, secure, and real-time transmission of power grid data, an intelligent… More >

  • Open AccessOpen Access

    ARTICLE

    Secure Transmission of Compressed Medical Image Sequences on Communication Networks Using Motion Vector Watermarking

    Rafi Ullah1,*, Mohd Hilmi bin Hasan1, Sultan Daud Khan2, Mussadiq Abdul Rahim3
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3283-3301, 2024, DOI:10.32604/cmc.2024.046305 - 26 March 2024
    (This article belongs to the Special Issue: Multimedia Encryption and Information Security)
    Abstract Medical imaging plays a key role within modern hospital management systems for diagnostic purposes. Compression methodologies are extensively employed to mitigate storage demands and enhance transmission speed, all while upholding image quality. Moreover, an increasing number of hospitals are embracing cloud computing for patient data storage, necessitating meticulous scrutiny of server security and privacy protocols. Nevertheless, considering the widespread availability of multimedia tools, the preservation of digital data integrity surpasses the significance of compression alone. In response to this concern, we propose a secure storage and transmission solution for compressed medical image sequences, such as… More >

  • Open AccessOpen Access

    ARTICLE

    Federated Machine Learning Based Fetal Health Prediction Empowered with Bio-Signal Cardiotocography

    Muhammad Umar Nasir1, Omar Kassem Khalil2, Karamath Ateeq3, Bassam SaleemAllah Almogadwy4, Muhammad Adnan Khan5, Muhammad Hasnain Azam6, Khan Muhammad Adnan7,*
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3303-3321, 2024, DOI:10.32604/cmc.2024.048035 - 26 March 2024
    Abstract Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or pathologic. Various cardiotocography measures infer wrongly and give wrong predictions because of human error. The traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as well. Fetal condition is very important to measure at numerous stages and give proper medications to the fetus for its well-being. In… More >

  • Open AccessOpen Access

    ARTICLE

    Unsupervised Color Segmentation with Reconstructed Spatial Weighted Gaussian Mixture Model and Random Color Histogram

    Umer Sadiq Khan1,2,*, Zhen Liu1,2,*, Fang Xu1,2, Muhib Ullah Khan3,4, Lerui Chen5, Touseef Ahmed Khan4,6, Muhammad Kashif Khattak7, Yuquan Zhang8
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3323-3348, 2024, DOI:10.32604/cmc.2024.046094 - 26 March 2024
    (This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
    Abstract Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture model. Although the Gaussian mixture model enhances the flexibility of image segmentation, it does not reflect spatial information and is sensitive to the segmentation parameter. In this study, we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model (GMM) without parameter estimation. The proposed model highlights the residual region with considerable information and constructs color saliency. Second, we incorporate the content-based color saliency as spatial information in the Gaussian mixture model. The segmentation is performed by clustering… More >

  • Open AccessOpen Access

    ARTICLE

    Missing Value Imputation for Radar-Derived Time-Series Tracks of Aerial Targets Based on Improved Self-Attention-Based Network

    Zihao Song, Yan Zhou*, Wei Cheng, Futai Liang, Chenhao Zhang
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3349-3376, 2024, DOI:10.32604/cmc.2024.047034 - 26 March 2024
    Abstract The frequent missing values in radar-derived time-series tracks of aerial targets (RTT-AT) lead to significant challenges in subsequent data-driven tasks. However, the majority of imputation research focuses on random missing (RM) that differs significantly from common missing patterns of RTT-AT. The method for solving the RM may experience performance degradation or failure when applied to RTT-AT imputation. Conventional autoregressive deep learning methods are prone to error accumulation and long-term dependency loss. In this paper, a non-autoregressive imputation model that addresses the issue of missing value imputation for two common missing patterns in RTT-AT is proposed.… More >

  • Open AccessOpen Access

    ARTICLE

    Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

    Umar Rashid1,2,*, Arfan Jaffar1,2, Muhammad Rashid3, Mohammed S. Alshuhri4, Sheeraz Akram1,4,5
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3377-3390, 2024, DOI:10.32604/cmc.2024.046320 - 26 March 2024
    (This article belongs to the Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)
    Abstract Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as More >

  • Open AccessOpen Access

    ARTICLE

    Improve Chinese Aspect Sentiment Quadruplet Prediction via Instruction Learning Based on Large Generate Models

    Zhaoliang Wu1, Yuewei Wu1,2, Xiaoli Feng1, Jiajun Zou3, Fulian Yin1,2,*
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3391-3412, 2024, DOI:10.32604/cmc.2024.047076 - 26 March 2024
    Abstract Aspect-Based Sentiment Analysis (ABSA) is a fundamental area of research in Natural Language Processing (NLP). Within ABSA, Aspect Sentiment Quad Prediction (ASQP) aims to accurately identify sentiment quadruplets in target sentences, including aspect terms, aspect categories, corresponding opinion terms, and sentiment polarity. However, most existing research has focused on English datasets. Consequently, while ASQP has seen significant progress in English, the Chinese ASQP task has remained relatively stagnant. Drawing inspiration from methods applied to English ASQP, we propose Chinese generation templates and employ prompt-based instruction learning to enhance the model’s understanding of the task, ultimately More >

  • Open AccessOpen Access

    ARTICLE

    Adaptation of Federated Explainable Artificial Intelligence for Efficient and Secure E-Healthcare Systems

    Rabia Abid1, Muhammad Rizwan2, Abdulatif Alabdulatif3,*, Abdullah Alnajim4, Meznah Alamro5, Mourade Azrour6
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3413-3429, 2024, DOI:10.32604/cmc.2024.046880 - 26 March 2024
    Abstract Explainable Artificial Intelligence (XAI) has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning (ML) and Deep Learning (DL) based algorithms. In this paper, we chose e-healthcare systems for efficient decision-making and data classification, especially in data security, data handling, diagnostics, laboratories, and decision-making. Federated Machine Learning (FML) is a new and advanced technology that helps to maintain privacy for Personal Health Records (PHR) and handle a large amount of medical data effectively. In this context, XAI, along with FML, increases efficiency and improves the More >

  • Open AccessOpen Access

    ARTICLE

    Olive Leaf Disease Detection via Wavelet Transform and Feature Fusion of Pre-Trained Deep Learning Models

    Mahmood A. Mahmood1,2,*, Khalaf Alsalem1
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3431-3448, 2024, DOI:10.32604/cmc.2024.047604 - 26 March 2024
    (This article belongs to the Special Issue: Deep Learning in Computer-Aided Diagnosis Based on Medical Image)
    Abstract Olive trees are susceptible to a variety of diseases that can cause significant crop damage and economic losses. Early detection of these diseases is essential for effective management. We propose a novel transformed wavelet, feature-fused, pre-trained deep learning model for detecting olive leaf diseases. The proposed model combines wavelet transforms with pre-trained deep-learning models to extract discriminative features from olive leaf images. The model has four main phases: preprocessing using data augmentation, three-level wavelet transformation, learning using pre-trained deep learning models, and a fused deep learning model. In the preprocessing phase, the image dataset is… More >

  • Open AccessOpen Access

    ARTICLE

    Prediction of Bandwidth of Metamaterial Antenna Using Pearson Kernel-Based Techniques

    Sherly Alphonse1,*, S. Abinaya1, Sourabh Paul2
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3449-3467, 2024, DOI:10.32604/cmc.2024.046403 - 26 March 2024
    Abstract The use of metamaterial enhances the performance of a specific class of antennas known as metamaterial antennas. The radiation cost and quality factor of the antenna are influenced by the size of the antenna. Metamaterial antennas allow for the circumvention of the bandwidth restriction for small antennas. Antenna parameters have recently been predicted using machine learning algorithms in existing literature. Machine learning can take the place of the manual process of experimenting to find the ideal simulated antenna parameters. The accuracy of the prediction will be primarily dependent on the model that is used. In… More >

  • Open AccessOpen Access

    ARTICLE

    Nonparametric Statistical Feature Scaling Based Quadratic Regressive Convolution Deep Neural Network for Software Fault Prediction

    Sureka Sivavelu, Venkatesh Palanisamy*
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3469-3487, 2024, DOI:10.32604/cmc.2024.047407 - 26 March 2024
    Abstract The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes… More >

  • Open AccessOpen Access

    ARTICLE

    MOALG: A Metaheuristic Hybrid of Multi-Objective Ant Lion Optimizer and Genetic Algorithm for Solving Design Problems

    Rashmi Sharma1, Ashok Pal1, Nitin Mittal2, Lalit Kumar2, Sreypov Van3, Yunyoung Nam3,*, Mohamed Abouhawwash4,5
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3489-3510, 2024, DOI:10.32604/cmc.2024.046606 - 26 March 2024
    Abstract This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm (MOALO) which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm (ALO) and the Genetic Algorithm (GA). MOALO version has been employed to address those problems containing many objectives and an archive has been employed for retaining the non-dominated solutions. The uniqueness of the hybrid is that the operators like mutation and crossover of GA are employed in the archive to update the solutions and later those solutions go through the process of MOALO. A first-time hybrid of these… More >

  • Open AccessOpen Access

    ARTICLE

    CL2ES-KDBC: A Novel Covariance Embedded Selection Based on Kernel Distributed Bayes Classifier for Detection of Cyber-Attacks in IoT Systems

    Talal Albalawi, P. Ganeshkumar*
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3511-3528, 2024, DOI:10.32604/cmc.2024.046396 - 26 March 2024
    Abstract The Internet of Things (IoT) is a growing technology that allows the sharing of data with other devices across wireless networks. Specifically, IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks. In this framework, a Covariance Linear Learning Embedding Selection (CL2ES) methodology is used at first to extract the features highly associated with the IoT intrusions. Then, the Kernel Distributed Bayes Classifier (KDBC) is created to forecast attacks based on the probability distribution More >

  • Open AccessOpen Access

    ARTICLE

    Automated Machine Learning Algorithm Using Recurrent Neural Network to Perform Long-Term Time Series Forecasting

    Ying Su1, Morgan C. Wang1, Shuai Liu2,*
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3529-3549, 2024, DOI:10.32604/cmc.2024.047189 - 26 March 2024
    Abstract Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning (AutoML). At present, forecasting, whether rooted in machine learning or statistical learning, typically relies on expert input and necessitates substantial manual involvement. This manual effort spans model development, feature engineering, hyper-parameter tuning, and the intricate construction of time series models. The complexity of these tasks renders complete automation unfeasible, as they inherently demand human intervention at multiple junctures. To surmount these challenges, this article proposes leveraging Long Short-Term Memory, which is the variant of Recurrent Neural Networks, harnessing… More >

  • Open AccessOpen Access

    ARTICLE

    Multi-Branch High-Dimensional Guided Transformer-Based 3D Human Posture Estimation

    Xianhua Li1,2,*, Haohao Yu1, Shuoyu Tian1, Fengtao Lin3, Usama Masood1
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3551-3564, 2024, DOI:10.32604/cmc.2024.047336 - 26 March 2024
    (This article belongs to the Special Issue: Recognition Tasks with Transformers)
    Abstract The human pose paradigm is estimated using a transformer-based multi-branch multidimensional directed the three-dimensional (3D) method that takes into account self-occlusion, badly posedness, and a lack of depth data in the per-frame 3D posture estimation from two-dimensional (2D) mapping to 3D mapping. Firstly, by examining the relationship between the movements of different bones in the human body, four virtual skeletons are proposed to enhance the cyclic constraints of limb joints. Then, multiple parameters describing the skeleton are fused and projected into a high-dimensional space. Utilizing a multi-branch network, motion features between bones and overall motion More >

  • Open AccessOpen Access

    ARTICLE

    Video Summarization Approach Based on Binary Robust Invariant Scalable Keypoints and Bisecting K-Means

    Sameh Zarif1,2,*, Eman Morad1, Khalid Amin1, Abdullah Alharbi3, Wail S. Elkilani4, Shouze Tang5
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3565-3583, 2024, DOI:10.32604/cmc.2024.046185 - 26 March 2024
    Abstract Due to the exponential growth of video data, aided by rapid advancements in multimedia technologies. It became difficult for the user to obtain information from a large video series. The process of providing an abstract of the entire video that includes the most representative frames is known as static video summarization. This method resulted in rapid exploration, indexing, and retrieval of massive video libraries. We propose a framework for static video summary based on a Binary Robust Invariant Scalable Keypoint (BRISK) and bisecting K-means clustering algorithm. The current method effectively recognizes relevant frames using BRISK… More >

  • Open AccessOpen Access

    ARTICLE

    An Enhanced Ensemble-Based Long Short-Term Memory Approach for Traffic Volume Prediction

    Duy Quang Tran1, Huy Q. Tran2,*, Minh Van Nguyen3
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3585-3602, 2024, DOI:10.32604/cmc.2024.047760 - 26 March 2024
    Abstract With the advancement of artificial intelligence, traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality. Traffic volume is an influential parameter for planning and operating traffic structures. This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems. A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process. The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal… More >

  • Open AccessOpen Access

    ARTICLE

    RoBGP: A Chinese Nested Biomedical Named Entity Recognition Model Based on RoBERTa and Global Pointer

    Xiaohui Cui1,2,#, Chao Song1,2,#, Dongmei Li1,2,*, Xiaolong Qu1,2, Jiao Long1,2, Yu Yang1,2, Hanchao Zhang3
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3603-3618, 2024, DOI:10.32604/cmc.2024.047321 - 26 March 2024
    (This article belongs to the Special Issue: Transfroming from Data to Knowledge and Applications in Intelligent Systems)
    Abstract Named Entity Recognition (NER) stands as a fundamental task within the field of biomedical text mining, aiming to extract specific types of entities such as genes, proteins, and diseases from complex biomedical texts and categorize them into predefined entity types. This process can provide basic support for the automatic construction of knowledge bases. In contrast to general texts, biomedical texts frequently contain numerous nested entities and local dependencies among these entities, presenting significant challenges to prevailing NER models. To address these issues, we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer… More >

  • Open AccessOpen Access

    ARTICLE

    Ethical Decision-Making Framework Based on Incremental ILP Considering Conflicts

    Xuemin Wang, Qiaochen Li, Xuguang Bao*
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3619-3643, 2024, DOI:10.32604/cmc.2024.047586 - 26 March 2024
    Abstract Humans are experiencing the inclusion of artificial agents in their lives, such as unmanned vehicles, service robots, voice assistants, and intelligent medical care. If the artificial agents cannot align with social values or make ethical decisions, they may not meet the expectations of humans. Traditionally, an ethical decision-making framework is constructed by rule-based or statistical approaches. In this paper, we propose an ethical decision-making framework based on incremental ILP (Inductive Logic Programming), which can overcome the brittleness of rule-based approaches and little interpretability of statistical approaches. As the current incremental ILP makes it difficult to… More >

  • Open AccessOpen Access

    ARTICLE

    Enhancing PDF Malware Detection through Logistic Model Trees

    Muhammad Binsawad*
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3645-3663, 2024, DOI:10.32604/cmc.2024.048183 - 26 March 2024
    (This article belongs to the Special Issue: AI and Data Security for the Industrial Internet)
    Abstract Malware is an ever-present and dynamic threat to networks and computer systems in cybersecurity, and because of its complexity and evasiveness, it is challenging to identify using traditional signature-based detection approaches. The study article discusses the growing danger to cybersecurity that malware hidden in PDF files poses, highlighting the shortcomings of conventional detection techniques and the difficulties presented by adversarial methodologies. The article presents a new method that improves PDF virus detection by using document analysis and a Logistic Model Tree. Using a dataset from the Canadian Institute for Cybersecurity, a comparative analysis is carried… More >

  • Open AccessOpen Access

    ARTICLE

    TSCND: Temporal Subsequence-Based Convolutional Network with Difference for Time Series Forecasting

    Haoran Huang, Weiting Chen*, Zheming Fan
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3665-3681, 2024, DOI:10.32604/cmc.2024.048008 - 26 March 2024
    Abstract Time series forecasting plays an important role in various fields, such as energy, finance, transport, and weather. Temporal convolutional networks (TCNs) based on dilated causal convolution have been widely used in time series forecasting. However, two problems weaken the performance of TCNs. One is that in dilated casual convolution, causal convolution leads to the receptive fields of outputs being concentrated in the earlier part of the input sequence, whereas the recent input information will be severely lost. The other is that the distribution shift problem in time series has not been adequately solved. To address… More >

  • Open AccessOpen Access

    ARTICLE

    Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

    Asifa Mehmood Qureshi1, Naif Al Mudawi2, Mohammed Alonazi3, Samia Allaoua Chelloug4, Jeongmin Park5,*
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3683-3701, 2024, DOI:10.32604/cmc.2024.043611 - 26 March 2024
    (This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
    Abstract Road traffic monitoring is an imperative topic widely discussed among researchers. Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides. However, aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area. To this end, different models have shown the ability to recognize and track vehicles. However, these methods are not mature enough to produce accurate results in complex road scenes. Therefore, this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with… More >

  • Open AccessOpen Access

    ARTICLE

    Material-SAM: Adapting SAM for Material XCT

    Xuelong Wu1, Junsheng Wang1,*, Zhongyao Li1, Yisheng Miao1, Chengpeng Xue1, Yuling Lang2, Decai Kong2, Xiaoying Ma2, Haibao Qiao2
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3703-3720, 2024, DOI:10.32604/cmc.2024.047027 - 26 March 2024
    Abstract X-ray Computed Tomography (XCT) enables non-destructive acquisition of the internal structure of materials, and image segmentation plays a crucial role in analyzing material XCT images. This paper proposes an image segmentation method based on the Segment Anything model (SAM). We constructed a dataset of carbide in nickel-based single crystal superalloys XCT images and preprocessed the images using median filtering, histogram equalization, and gamma correction. Subsequently, SAM was fine-tuned to adapt to the task of material XCT image segmentation, resulting in Material-SAM. We compared the performance of threshold segmentation, SAM, U-Net model, and Material-SAM. Our method More >

  • Open AccessOpen Access

    ARTICLE

    Path Planning for AUVs Based on Improved APF-AC Algorithm

    Guojun Chen*, Danguo Cheng, Wei Chen, Xue Yang, Tiezheng Guo
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3721-3741, 2024, DOI:10.32604/cmc.2024.047325 - 26 March 2024
    Abstract With the increase in ocean exploration activities and underwater development, the autonomous underwater vehicle (AUV) has been widely used as a type of underwater automation equipment in the detection of underwater environments. However, nowadays AUVs generally have drawbacks such as weak endurance, low intelligence, and poor detection ability. The research and implementation of path-planning methods are the premise of AUVs to achieve actual tasks. To improve the underwater operation ability of the AUV, this paper studies the typical problems of path-planning for the ant colony algorithm and the artificial potential field algorithm. In response to… More >

  • Open AccessOpen Access

    ARTICLE

    Aspect-Level Sentiment Analysis Based on Deep Learning

    Mengqi Zhang1, Jiazhao Chai2, Jianxiang Cao3, Jialing Ji3, Tong Yi4,*
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3743-3762, 2024, DOI:10.32604/cmc.2024.048486 - 26 March 2024
    (This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
    Abstract In recent years, deep learning methods have developed rapidly and found application in many fields, including natural language processing. In the field of aspect-level sentiment analysis, deep learning methods can also greatly improve the performance of models. However, previous studies did not take into account the relationship between user feature extraction and contextual terms. To address this issue, we use data feature extraction and deep learning combined to develop an aspect-level sentiment analysis method. To be specific, we design user comment feature extraction (UCFE) to distill salient features from users’ historical comments and transform them More >

  • Open AccessOpen Access

    ARTICLE

    Hybrid Optimization Algorithm for Handwritten Document Enhancement

    Shu-Chuan Chu1, Xiaomeng Yang1, Li Zhang2, Václav Snášel3, Jeng-Shyang Pan1,4,*
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3763-3786, 2024, DOI:10.32604/cmc.2024.048594 - 26 March 2024
    (This article belongs to the Special Issue: Metaheuristics, Soft Computing, and Machine Learning in Image Processing and Computer Vision)
    Abstract The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance; however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. More >

  • Open AccessOpen Access

    ARTICLE

    Unmanned Aerial Vehicles General Aerial Person-Vehicle Recognition Based on Improved YOLOv8s Algorithm

    Zhijian Liu*
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3787-3803, 2024, DOI:10.32604/cmc.2024.048998 - 26 March 2024
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract Considering the variations in imaging sizes of the unmanned aerial vehicles (UAV) at different aerial photography heights, as well as the influence of factors such as light and weather, which can result in missed detection and false detection of the model, this paper presents a comprehensive detection model based on the improved lightweight You Only Look Once version 8s (YOLOv8s) algorithm used in natural light and infrared scenes (L_YOLO). The algorithm proposes a special feature pyramid network (SFPN) structure and substitutes most of the neck feature extraction module with the Special deformable convolution feature extraction… More >

  • Open AccessOpen Access

    ARTICLE

    A Hybrid and Lightweight Device-to-Server Authentication Technique for the Internet of Things

    Shaha Al-Otaibi1, Rahim Khan2,*, Hashim Ali2, Aftab Ahmed Khan2, Amir Saeed3, Jehad Ali4,*
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3805-3823, 2024, DOI:10.32604/cmc.2024.049017 - 26 March 2024
    (This article belongs to the Special Issue: Multimedia Encryption and Information Security)
    Abstract The Internet of Things (IoT) is a smart networking infrastructure of physical devices, i.e., things, that are embedded with sensors, actuators, software, and other technologies, to connect and share data with the respective server module. Although IoTs are cornerstones in different application domains, the device’s authenticity, i.e., of server(s) and ordinary devices, is the most crucial issue and must be resolved on a priority basis. Therefore, various field-proven methodologies were presented to streamline the verification process of the communicating devices; however, location-aware authentication has not been reported as per our knowledge, which is a crucial… More >

  • Open AccessOpen Access

    REVIEW

    Survey and Prospect for Applying Knowledge Graph in Enterprise Risk Management

    Pengjun Li1, Qixin Zhao1, Yingmin Liu1, Chao Zhong1, Jinlong Wang1,*, Zhihan Lyu2
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3825-3865, 2024, DOI:10.32604/cmc.2024.046851 - 26 March 2024
    Abstract Enterprise risk management holds significant importance in fostering sustainable growth of businesses and in serving as a critical element for regulatory bodies to uphold market order. Amidst the challenges posed by intricate and unpredictable risk factors, knowledge graph technology is effectively driving risk management, leveraging its ability to associate and infer knowledge from diverse sources. This review aims to comprehensively summarize the construction techniques of enterprise risk knowledge graphs and their prominent applications across various business scenarios. Firstly, employing bibliometric methods, the aim is to uncover the developmental trends and current research hotspots within the… More >

  • Open AccessOpen Access

    ARTICLE

    Systematic Security Guideline Framework through Intelligently Automated Vulnerability Analysis

    Dahyeon Kim1, Namgi Kim2, Junho Ahn2,*
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3867-3889, 2024, DOI:10.32604/cmc.2024.046871 - 26 March 2024
    Abstract This research aims to propose a practical framework designed for the automatic analysis of a product’s comprehensive functionality and security vulnerabilities, generating applicable guidelines based on real-world software. The existing analysis of software security vulnerabilities often focuses on specific features or modules. This partial and arbitrary analysis of the security vulnerabilities makes it challenging to comprehend the overall security vulnerabilities of the software. The key novelty lies in overcoming the constraints of partial approaches. The proposed framework utilizes data from various sources to create a comprehensive functionality profile, facilitating the derivation of real-world security guidelines.… More >

  • Open AccessOpen Access

    ARTICLE

    Differentially Private Support Vector Machines with Knowledge Aggregation

    Teng Wang, Yao Zhang, Jiangguo Liang, Shuai Wang, Shuanggen Liu*
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3891-3907, 2024, DOI:10.32604/cmc.2024.048115 - 26 March 2024
    (This article belongs to the Special Issue: Security, Privacy, and Robustness for Trustworthy AI Systems)
    Abstract With the widespread data collection and processing, privacy-preserving machine learning has become increasingly important in addressing privacy risks related to individuals. Support vector machine (SVM) is one of the most elementary learning models of machine learning. Privacy issues surrounding SVM classifier training have attracted increasing attention. In this paper, we investigate Differential Privacy-compliant Federated Machine Learning with Dimensionality Reduction, called FedDPDR-DPML, which greatly improves data utility while providing strong privacy guarantees. Considering in distributed learning scenarios, multiple participants usually hold unbalanced or small amounts of data. Therefore, FedDPDR-DPML enables multiple participants to collaboratively learn a global… More >

  • Open AccessOpen Access

    ARTICLE

    Enhancing Energy Efficiency with a Dynamic Trust Measurement Scheme in Power Distribution Network

    Yilei Wang1, Xin Sun1, Guiping Zheng2,3, Ahmar Rashid4, Sami Ullah5, Hisham Alasmary6, Muhammad Waqas7,8,*
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3909-3927, 2024, DOI:10.32604/cmc.2024.047767 - 26 March 2024
    Abstract The application of Intelligent Internet of Things (IIoT) in constructing distribution station areas strongly supports platform transformation, upgrade, and intelligent integration. The sensing layer of IIoT comprises the edge convergence layer and the end sensing layer, with the former using intelligent fusion terminals for real-time data collection and processing. However, the influx of multiple low-voltage in the smart grid raises higher demands for the performance, energy efficiency, and response speed of the substation fusion terminals. Simultaneously, it brings significant security risks to the entire distribution substation, posing a major challenge to the smart grid. In… More >

  • Open AccessOpen Access

    ARTICLE

    BSTFNet: An Encrypted Malicious Traffic Classification Method Integrating Global Semantic and Spatiotemporal Features

    Hong Huang1, Xingxing Zhang1,*, Ye Lu1, Ze Li1, Shaohua Zhou2
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3929-3951, 2024, DOI:10.32604/cmc.2024.047918 - 26 March 2024
    (This article belongs to the Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)
    Abstract While encryption technology safeguards the security of network communications, malicious traffic also uses encryption protocols to obscure its malicious behavior. To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic, we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features, called BERT-based Spatio-Temporal Features Network (BSTFNet). At the packet-level granularity, the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers (BERT)… More >

  • Open AccessOpen Access

    ARTICLE

    SAM Era: Can It Segment Any Industrial Surface Defects?

    Kechen Song1,2,*, Wenqi Cui2, Han Yu1, Xingjie Li1, Yunhui Yan2,*
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3953-3969, 2024, DOI:10.32604/cmc.2024.048451 - 26 March 2024
    (This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
    Abstract Segment Anything Model (SAM) is a cutting-edge model that has shown impressive performance in general object segmentation. The birth of the segment anything is a groundbreaking step towards creating a universal intelligent model. Due to its superior performance in general object segmentation, it quickly gained attention and interest. This makes SAM particularly attractive in industrial surface defect segmentation, especially for complex industrial scenes with limited training data. However, its segmentation ability for specific industrial scenes remains unknown. Therefore, in this work, we select three representative and complex industrial surface defect detection scenarios, namely strip steel More >

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