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  • Open Access

    ARTICLE

    Chinese Cyber Threat Intelligence Named Entity Recognition via RoBERTa-wwm-RDCNN-CRF

    Zhen Zhen1, Jian Gao1,2,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.042090
    Abstract In recent years, cyber attacks have been intensifying and causing great harm to individuals, companies, and countries. The mining of cyber threat intelligence (CTI) can facilitate intelligence integration and serve well in combating cyber attacks. Named Entity Recognition (NER), as a crucial component of text mining, can structure complex CTI text and aid cybersecurity professionals in effectively countering threats. However, current CTI NER research has mainly focused on studying English CTI. In the limited studies conducted on Chinese text, existing models have shown poor performance. To fully utilize the power of Chinese pre-trained language models (PLMs) and conquer the problem… More >

  • Open Access

    ARTICLE

    Efficient Technique for Image Cryptography Using Sudoku Keys

    M. A. P. Manimekalai1, M. Karthikeyan1, I. Thusnavis Bella Mary1, K. Martin Sagayam1, Ahmed A Elngar2, Unai Fernandez-Gamiz3, Hatıra Günerhan4,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.035856
    Abstract This paper proposes a cryptographic technique on images based on the Sudoku solution. Sudoku is a number puzzle, which needs applying defined protocols and filling the empty boxes with numbers. Given a small size of numbers as input, solving the sudoku puzzle yields an expanded big size of numbers, which can be used as a key for the Encryption/Decryption of images. In this way, the given small size of numbers can be stored as the prime key, which means the key is compact. A prime key clue in the sudoku puzzle always leads to only one solution, which means the… More >

  • Open Access

    ARTICLE

    Linguistic Knowledge Representation in DPoS Consensus Scheme for Blockchain

    Yixia Chen1,2, Mingwei Lin1,2,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.040970
    Abstract The consensus scheme is an essential component in the real blockchain environment. The Delegated Proof of Stake (DPoS) is a competitive consensus scheme that can decrease energy costs, promote decentralization, and increase eciency, respectively. However, how to study the knowledge representation of the collective voting information and then select delegates is a new open problem. To ensure the fairness and eectiveness of transactions in the blockchain, in this paper, we propose a novel More >

  • Open Access

    ARTICLE

    A Machine Learning-Based Attack Detection and Prevention System in Vehicular Named Data Networking

    Arif Hussain Magsi1,*, Ali Ghulam2, Saifullah Memon1, Khalid Javeed3, Musaed Alhussein4, Imad Rida5
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.040290
    (This article belongs to this Special Issue: Innovations in Pervasive Computing and Communication Technologies)
    Abstract Named Data Networking (NDN) is gaining a significant attention in Vehicular Ad-hoc Networks (VANET) due to its in-network content caching, name-based routing, and mobility-supporting characteristics. Nevertheless, existing NDN faces three significant challenges, including security, privacy, and routing. In particular, security attacks, such as Content Poisoning Attacks (CPA), can jeopardize legitimate vehicles with malicious content. For instance, attacker host vehicles can serve consumers with invalid information, which has dire consequences, including road accidents. In such a situation, trust in the content-providing vehicles brings a new challenge. On the other hand, ensuring privacy and preventing unauthorized access in vehicular (VNDN) is another… More >

  • Open Access

    ARTICLE

    A New S-Box Design System for Data Encryption Using Artificial Bee Colony Algorithm

    Yazeed Yasin Ghadi1, Mohammed S. Alshehri2, Sultan Almakdi2, Oumaima Saidani3,*, Nazik Alturki3, Fawad Masood4, Muhammad Shahbaz Khan5
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.042777
    (This article belongs to this Special Issue: Multimedia Encryption and Information Security)
    Abstract Securing digital image data is a key concern in today’s information-driven society. Effective encryption techniques are required to protect sensitive image data, with the Substitution-box (S-box) often playing a pivotal role in many symmetric encryption systems. This study introduces an innovative approach to creating S-boxes for encryption algorithms. The proposed S-boxes are tested for validity and non-linearity by incorporating them into an image encryption scheme. The nonlinearity measure of the proposed S-boxes is 112. These qualities significantly enhance its resistance to common cryptographic attacks, ensuring high image data security. Furthermore, to assess the robustness of the S-boxes, an encryption system… More >

  • Open Access

    ARTICLE

    VeriFace: Defending against Adversarial Attacks in Face Verification Systems

    Awny Sayed1, Sohair Kinlany2, Alaa Zaki2, Ahmed Mahfouz2,3,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.040256
    Abstract Face verification systems are critical in a wide range of applications, such as security systems and biometric authentication. However, these systems are vulnerable to adversarial attacks, which can significantly compromise their accuracy and reliability. Adversarial attacks are designed to deceive the face verification system by adding subtle perturbations to the input images. These perturbations can be imperceptible to the human eye but can cause the system to misclassify or fail to recognize the person in the image. To address this issue, we propose a novel system called VeriFace that comprises two defense mechanisms, adversarial detection, and adversarial removal. The first… More >

  • Open Access

    ARTICLE

    3D Kronecker Convolutional Feature Pyramid for Brain Tumor Semantic Segmentation in MR Imaging

    Kainat Nazir1, Tahir Mustafa Madni1, Uzair Iqbal Janjua1, Umer Javed2, Muhammad Attique Khan3, Usman Tariq4, Jae-Hyuk Cha5,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.039181
    Abstract Brain tumor significantly impacts the quality of life and changes everything for a patient and their loved ones. Diagnosing a brain tumor usually begins with magnetic resonance imaging (MRI). The manual brain tumor diagnosis from the MRO images always requires an expert radiologist. However, this process is time-consuming and costly. Therefore, a computerized technique is required for brain tumor detection in MRI images. Using the MRI, a novel mechanism of the three-dimensional (3D) Kronecker convolution feature pyramid (KCFP) is used to segment brain tumors, resolving the pixel loss and weak processing of multi-scale lesions. A single dilation rate was replaced… More >

  • Open Access

    ARTICLE

    A Credit Card Fraud Detection Model Based on Multi-Feature Fusion and Generative Adversarial Network

    Yalong Xie1, Aiping Li1,*, Biyin Hu2, Liqun Gao1, Hongkui Tu1
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.037039
    Abstract Credit Card Fraud Detection (CCFD) is an essential technology for banking institutions to control fraud risks and safeguard their reputation. Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD, which significantly impact classification models’ performance. To address these issues, this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks (MFGAN). The MFGAN model consists of two modules: a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space, and a balance module… More >

  • Open Access

    ARTICLE

    A New Strategy for Dynamic Channel Allocation in CR-WMN Based on RCA

    Kaleem Arshid1,*, Jianbiao Zhang1, Muhammad Yaqub1, Mohammad Daud Awan2, Habiba Ijaz3, Imran Shabir Chuhan4
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.035735
    Abstract Channel assignment has emerged as an essential study subject in Cognitive Radio-based Wireless Mesh Networks (CR-WMN). In an era of alarming increase in Multi-Radio Multi-Channel (MRMC) network expansion interference is decreased and network throughput is significantly increased when non-overlapping or partially overlapping channels are correctly integrated. Because of its ad hoc behavior, dynamic channel assignment outperforms static channel assignment. Interference reduces network throughput in the CR-WMN. As a result, there is an extensive research gap for an algorithm that dynamically distributes channels while accounting for all types of interference. This work presents a method for dynamic channel allocations using unsupervised… More >

  • Open Access

    ARTICLE

    New Fragile Watermarking Technique to Identify Inserted Video Objects Using H.264 and Color Features

    Raheem Ogla1,*, Eman Shakar Mahmood1, Rasha I. Ahmed1, Abdul Monem S. Rahma2
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.039818
    Abstract The transmission of video content over a network raises various issues relating to copyright authenticity, ethics, legality, and privacy. The protection of copyrighted video content is a significant issue in the video industry, and it is essential to find effective solutions to prevent tampering and modification of digital video content during its transmission through digital media. However, there are still many unresolved challenges. This paper aims to address those challenges by proposing a new technique for detecting moving objects in digital videos, which can help prove the credibility of video content by detecting any fake objects inserted by hackers. The… More >

  • Open Access

    ARTICLE

    Deep Learning with a Novel Concoction Loss Function for Identification of Ophthalmic Disease

    Sayyid Kamran Hussain1, Ali Haider Khan2,*, Malek Alrashidi3, Sajid Iqbal4, Qazi Mudassar Ilyas4, Kamran Shah5
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.041722
    (This article belongs to this Special Issue: Recent Advances in Ophthalmic Diseases Diagnosis using AI)
    Abstract As ocular computer-aided diagnostic (CAD) tools become more widely accessible, many researchers are developing deep learning (DL) methods to aid in ocular disease (OHD) diagnosis. Common eye diseases like cataracts (CATR), glaucoma (GLU), and age-related macular degeneration (AMD) are the focus of this study, which uses DL to examine their identification. Data imbalance and outliers are widespread in fundus images, which can make it difficult to apply many DL algorithms to accomplish this analytical assignment. The creation of effcient and reliable DL algorithms is seen to be the key to further enhancing detection performance. Using the analysis of images of… More >

  • Open Access

    ARTICLE

    Deep Learning Models Based on Weakly Supervised Learning and Clustering Visualization for Disease Diagnosis

    Jingyao Liu1,2, Qinghe Feng4, Jiashi Zhao2,3, Yu Miao2,3, Wei He2, Weili Shi2,3, Zhengang Jiang2,3,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.038891
    Abstract The coronavirus disease 2019 (COVID-19) has severely disrupted both human life and the health care system. Timely diagnosis and treatment have become increasingly important; however, the distribution and size of lesions vary widely among individuals, making it challenging to accurately diagnose the disease. This study proposed a deep-learning disease diagnosis model based on weakly supervised learning and clustering visualization (W_CVNet) that fused classification with segmentation. First, the data were preprocessed. An optimizable weakly supervised segmentation preprocessing method (O-WSSPM) was used to remove redundant data and solve the category imbalance problem. Second, a deep-learning fusion method was used for feature extraction… More >

  • Open Access

    ARTICLE

    Application of the Deep Convolutional Neural Network for the Classification of Auto Immune Diseases

    Fayaz Muhammad1, Jahangir Khan1, Asad Ullah1, Fasee Ullah1, Razaullah Khan2, Inayat Khan2, Mohammed ElAffendi3, Gauhar Ali3,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.038748
    (This article belongs to this Special Issue: Emerging Trends, Advances and Challenges of IoT in Healthcare and Education)
    Abstract IIF (Indirect Immune Florescence) has gained much attention recently due to its importance in medical sciences. The primary purpose of this work is to highlight a step-by-step methodology for detecting autoimmune diseases. The use of IIF for detecting autoimmune diseases is widespread in different medical areas. Nearly 80 different types of autoimmune diseases have existed in various body parts. The IIF has been used for image classification in both ways, manually and by using the Computer-Aided Detection (CAD) system. The data scientists conducted various research works using an automatic CAD system with low accuracy. The diseases in the human body… More >

  • Open Access

    ARTICLE

    Full Scale-Aware Balanced High-Resolution Network for Multi-Person Pose Estimation

    Shaohua Li, Haixiang Zhang*, Hanjie Ma, Jie Feng, Mingfeng Jiang
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.041538
    Abstract Scale variation is a major challenge in multi-person pose estimation. In scenes where persons are present at various distances, models tend to perform better on larger-scale persons, while the performance for smaller-scale persons often falls short of expectations. Therefore, effectively balancing the persons of different scales poses a significant challenge. So this paper proposes a new multi-person pose estimation model called FSA Net to improve the model’s performance in complex scenes. Our model utilizes High-Resolution Network (HRNet) as the backbone and feeds the outputs of the last stage’s four branches into the DCB module. The dilated convolution-based (DCB) module employs… More >

  • Open Access

    ARTICLE

    Convolutional Neural Network Model for Fire Detection in Real-Time Environment

    Abdul Rehman, Dongsun Kim*, Anand Paul
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.036435
    (This article belongs to this Special Issue: Recent Advances in Internet of Things and Emerging Technologies)
    Abstract Disasters such as conflagration, toxic smoke, harmful gas or chemical leakage, and many other catastrophes in the industrial environment caused by hazardous distance from the peril are frequent. The calamities are causing massive fiscal and human life casualties. However, Wireless Sensors Network-based adroit monitoring and early warning of these dangerous incidents will hamper fiscal and social fiasco. The authors have proposed an early fire detection system uses machine and/or deep learning algorithms. The article presents an Intelligent Industrial Monitoring System (IIMS) and introduces an Industrial Smart Social Agent (ISSA) in the Industrial SIoT (ISIoT) paradigm. The proffered ISSA empowers smart… More >

  • Open Access

    ARTICLE

    Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images

    Ali Haider Khan1,2,*, Hassaan Malik2, Wajeeha Khalil3, Sayyid Kamran Hussain4, Tayyaba Anees5, Muzammil Hussain2
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.039518
    Abstract To prevent irreversible damage to one’s eyesight, ocular diseases (ODs) need to be recognized and treated immediately. Color fundus imaging (CFI) is a screening technology that is both effective and economical. According to CFIs, the early stages of the disease are characterized by a paucity of observable symptoms, which necessitates the prompt creation of automated and robust diagnostic algorithms. The traditional research focuses on imagelevel diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of eyes. In addition, they usually only target one or a few different… More >

  • Open Access

    ARTICLE

    Characterization of Memory Access in Deep Learning and Its Implications in Memory Management

    Jeongha Lee1, Hyokyung Bahn2,*
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.039236
    Abstract Due to the recent trend of software intelligence in the Fourth Industrial Revolution, deep learning has become a mainstream workload for modern computer systems. Since the data size of deep learning increasingly grows, managing the limited memory capacity efficiently for deep learning workloads becomes important. In this paper, we analyze memory accesses in deep learning workloads and find out some unique characteristics differentiated from traditional workloads. First, when comparing instruction and data accesses, data access accounts for 96%–99% of total memory accesses in deep learning workloads, which is quite different from traditional workloads. Second, when comparing read and write accesses,… More >