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  • Open Access

    ARTICLE

    Research on SQL Injection Detection Technology Based on Content Matching and Deep Learning

    Yuqi Chen1,2, Guangjun Liang1,2,3,*, Qun Wang1,2,3

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1145-1167, 2025, DOI:10.32604/cmc.2025.063319 - 09 June 2025

    Abstract Structured Query Language (SQL) injection attacks have become the most common means of attacking Web applications due to their simple implementation and high degree of harm. Traditional injection attack detection techniques struggle to accurately identify various types of SQL injection attacks. This paper presents an enhanced SQL injection detection method that utilizes content matching technology to improve the accuracy and efficiency of detection. Features are extracted through content matching, effectively avoiding the loss of valid information, and an improved deep learning model is employed to enhance the detection effect of SQL injections. Considering that grammar More >

  • Open Access

    ARTICLE

    Detection and Classification of Fig Plant Leaf Diseases Using Convolution Neural Network

    Rahim Khan1, Ihsan Rabbi1, Umar Farooq1, Jawad Khan2,*, Fahad Alturise3,*

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 827-842, 2025, DOI:10.32604/cmc.2025.063303 - 09 June 2025

    Abstract Leaf disease identification is one of the most promising applications of convolutional neural networks (CNNs). This method represents a significant step towards revolutionizing agriculture by enabling the quick and accurate assessment of plant health. In this study, a CNN model was specifically designed and tested to detect and categorize diseases on fig tree leaves. The researchers utilized a dataset of 3422 images, divided into four classes: healthy, fig rust, fig mosaic, and anthracnose. These diseases can significantly reduce the yield and quality of fig tree fruit. The objective of this research is to develop a… More >

  • Open Access

    ARTICLE

    URLLC Service in UAV Rate-Splitting Multiple Access: Adapting Deep Learning Techniques for Wireless Network

    Reem Alkanhel1,#, Abuzar B. M. Adam2,#, Samia Allaoua Chelloug1, Dina S. M. Hassan1,*, Mohammed Saleh Ali Muthanna3, Ammar Muthanna4

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 607-624, 2025, DOI:10.32604/cmc.2025.063206 - 09 June 2025

    Abstract The 3GPP standard defines the requirements for next-generation wireless networks, with particular attention to Ultra-Reliable Low-Latency Communications (URLLC), critical for applications such as Unmanned Aerial Vehicles (UAVs). In this context, Non-Orthogonal Multiple Access (NOMA) has emerged as a promising technique to improve spectrum efficiency and user fairness by allowing multiple users to share the same frequency resources. However, optimizing key parameters–such as beamforming, rate allocation, and UAV trajectory–presents significant challenges due to the nonconvex nature of the problem, especially under stringent URLLC constraints. This paper proposes an advanced deep learning-driven approach to address the resulting… More >

  • Open Access

    ARTICLE

    Intelligent Management of Resources for Smart Edge Computing in 5G Heterogeneous Networks Using Blockchain and Deep Learning

    Mohammad Tabrez Quasim1,*, Khair Ul Nisa1, Mohammad Shahid Husain2, Abakar Ibraheem Abdalla Aadam1, Mohammed Waseequ Sheraz1, Mohammad Zunnun Khan1

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1169-1187, 2025, DOI:10.32604/cmc.2025.062989 - 09 June 2025

    Abstract Smart edge computing (SEC) is a novel paradigm for computing that could transfer cloud-based applications to the edge network, supporting computation-intensive services like face detection and natural language processing. A core feature of mobile edge computing, SEC improves user experience and device performance by offloading local activities to edge processors. In this framework, blockchain technology is utilized to ensure secure and trustworthy communication between edge devices and servers, protecting against potential security threats. Additionally, Deep Learning algorithms are employed to analyze resource availability and optimize computation offloading decisions dynamically. IoT applications that require significant resources… More >

  • Open Access

    ARTICLE

    Optimized Feature Selection for Leukemia Diagnosis Using Frog-Snake Optimization and Deep Learning Integration

    Reza Goodarzi1, Ali Jalali1,*, Omid Hashemi Pour Tafreshi1, Jalil Mazloum1, Peyman Beygi2

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 653-679, 2025, DOI:10.32604/cmc.2025.062803 - 09 June 2025

    Abstract Acute lymphoblastic leukemia (ALL) is characterized by overgrowth of immature lymphoid cells in the bone marrow at the expense of normal hematopoiesis. One of the most prioritized tasks is the early and correct diagnosis of this malignancy; however, manual observation of the blood smear is very time-consuming and requires labor and expertise. Transfer learning in deep neural networks is of growing importance to intricate medical tasks such as medical imaging. Our work proposes an application of a novel ensemble architecture that puts together Vision Transformer and EfficientNetV2. This approach fuses deep and spatial features to… More >

  • Open Access

    ARTICLE

    A Data-Enhanced Deep Learning Approach for Emergency Domain Question Intention Recognition in Urban Rail Transit

    Yinuo Chen1, Xu Wu1, Jiaxin Fan1, Guangyu Zhu2,*

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1597-1613, 2025, DOI:10.32604/cmc.2025.062779 - 09 June 2025

    Abstract The consultation intention of emergency decision-makers in urban rail transit (URT) is input into the emergency knowledge base in the form of domain questions to obtain emergency decision support services. This approach facilitates the rapid collection of complete knowledge and rules to form effective decisions. However, the current structured degree of the URT emergency knowledge base remains low, and the domain questions lack labeled datasets, resulting in a large deviation between the consultation outcomes and the intended objectives. To address this issue, this paper proposes a question intention recognition model for the URT emergency domain,… More >

  • Open Access

    ARTICLE

    Salient Features Guided Augmentation for Enhanced Deep Learning Classification in Hematoxylin and Eosin Images

    Tengyue Li1,*, Shuangli Song1, Jiaming Zhou2, Simon Fong2,3, Geyue Li4, Qun Song3, Sabah Mohammed5, Weiwei Lin6, Juntao Gao7

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1711-1730, 2025, DOI:10.32604/cmc.2025.062489 - 09 June 2025

    Abstract Hematoxylin and Eosin (H&E) images, popularly used in the field of digital pathology, often pose challenges due to their limited color richness, hindering the differentiation of subtle cell features crucial for accurate classification. Enhancing the visibility of these elusive cell features helps train robust deep-learning models. However, the selection and application of image processing techniques for such enhancement have not been systematically explored in the research community. To address this challenge, we introduce Salient Features Guided Augmentation (SFGA), an approach that strategically integrates machine learning and image processing. SFGA utilizes machine learning algorithms to identify… More >

  • Open Access

    ARTICLE

    Enhanced Wheat Disease Detection Using Deep Learning and Explainable AI Techniques

    Hussam Qushtom, Ahmad Hasasneh*, Sari Masri

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1379-1395, 2025, DOI:10.32604/cmc.2025.061995 - 09 June 2025

    Abstract This study presents an enhanced convolutional neural network (CNN) model integrated with Explainable Artificial Intelligence (XAI) techniques for accurate prediction and interpretation of wheat crop diseases. The aim is to streamline the detection process while offering transparent insights into the model’s decision-making to support effective disease management. To evaluate the model, a dataset was collected from wheat fields in Kotli, Azad Kashmir, Pakistan, and tested across multiple data splits. The proposed model demonstrates improved stability, faster convergence, and higher classification accuracy. The results show significant improvements in prediction accuracy and stability compared to prior works,… More >

  • Open Access

    REVIEW

    A Contemporary and Comprehensive Bibliometric Exposition on Deepfake Research and Trends

    Akanbi Bolakale AbdulQudus1, Oluwatosin Ahmed Amodu2,3,*, Umar Ali Bukar4, Raja Azlina Raja Mahmood2, Anies Faziehan Zakaria5, Saki-Ogah Queen6, Zurina Mohd Hanapi2

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 153-236, 2025, DOI:10.32604/cmc.2025.061427 - 09 June 2025

    Abstract This paper provides a comprehensive bibliometric exposition on deepfake research, exploring the intersection of artificial intelligence and deepfakes as well as international collaborations, prominent researchers, organizations, institutions, publications, and key themes. We performed a search on the Web of Science (WoS) database, focusing on Artificial Intelligence and Deepfakes, and filtered the results across 21 research areas, yielding 1412 articles. Using VOSviewer visualization tool, we analyzed this WoS data through keyword co-occurrence graphs, emphasizing on four prominent research themes. Compared with existing bibliometric papers on deepfakes, this paper proceeds to identify and discuss some of the… More >

  • Open Access

    ARTICLE

    Enhanced Multimodal Physiological Signal Analysis for Pain Assessment Using Optimized Ensemble Deep Learning

    Karim Gasmi1, Olfa Hrizi1,*, Najib Ben Aoun2,3, Ibrahim Alrashdi1, Ali Alqazzaz4, Omer Hamid5, Mohamed O. Altaieb1, Alameen E. M. Abdalrahman1, Lassaad Ben Ammar6, Manel Mrabet6, Omrane Necibi1

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2459-2489, 2025, DOI:10.32604/cmes.2025.065817 - 30 May 2025

    Abstract The potential applications of multimodal physiological signals in healthcare, pain monitoring, and clinical decision support systems have garnered significant attention in biomedical research. Subjective self-reporting is the foundation of conventional pain assessment methods, which may be unreliable. Deep learning is a promising alternative to resolve this limitation through automated pain classification. This paper proposes an ensemble deep-learning framework for pain assessment. The framework makes use of features collected from electromyography (EMG), skin conductance level (SCL), and electrocardiography (ECG) signals. We integrate Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), Bidirectional Gated Recurrent Units (BiGRU),… More >

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