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  • Open Access

    ARTICLE

    Diabetic Retinopathy Detection: A Hybrid Intelligent Approach

    Atta Rahman1,*, Mustafa Youldash2, Ghaida Alshammari2, Abrar Sebiany2, Joury Alzayat2, Manar Alsayed2, Mona Alqahtani2, Noor Aljishi2

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4561-4576, 2024, DOI:10.32604/cmc.2024.055106 - 12 September 2024

    Abstract Diabetes is a serious health condition that can cause several issues in human body organs such as the heart and kidney as well as a serious eye disease called diabetic retinopathy (DR). Early detection and treatment are crucial to prevent complete blindness or partial vision loss. Traditional detection methods, which involve ophthalmologists examining retinal fundus images, are subjective, expensive, and time-consuming. Therefore, this study employs artificial intelligence (AI) technology to perform faster and more accurate binary classifications and determine the presence of DR. In this regard, we employed three promising machine learning models namely, support… More >

  • Open Access

    ARTICLE

    Rail-PillarNet: A 3D Detection Network for Railway Foreign Object Based on LiDAR

    Fan Li1,2, Shuyao Zhang3, Jie Yang1,2,*, Zhicheng Feng1,2, Zhichao Chen1,2

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3819-3833, 2024, DOI:10.32604/cmc.2024.054525 - 12 September 2024

    Abstract Aiming at the limitations of the existing railway foreign object detection methods based on two-dimensional (2D) images, such as short detection distance, strong influence of environment and lack of distance information, we propose Rail-PillarNet, a three-dimensional (3D) LIDAR (Light Detection and Ranging) railway foreign object detection method based on the improvement of PointPillars. Firstly, the parallel attention pillar encoder (PAPE) is designed to fully extract the features of the pillars and alleviate the problem of local fine-grained information loss in PointPillars pillars encoder. Secondly, a fine backbone network is designed to improve the feature extraction… More >

  • Open Access

    ARTICLE

    Abnormal Action Detection Based on Parameter-Efficient Transfer Learning in Laboratory Scenarios

    Changyu Liu1, Hao Huang1, Guogang Huang2,*, Chunyin Wu1, Yingqi Liang3

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4219-4242, 2024, DOI:10.32604/cmc.2024.053625 - 12 September 2024

    Abstract Laboratory safety is a critical area of broad societal concern, particularly in the detection of abnormal actions. To enhance the efficiency and accuracy of detecting such actions, this paper introduces a novel method called TubeRAPT (Tubelet Transformer based on Adapter and Prefix Training Module). This method primarily comprises three key components: the TubeR network, an adaptive clustering attention mechanism, and a prefix training module. These components work in synergy to address the challenge of knowledge preservation in models pre-trained on large datasets while maintaining training efficiency. The TubeR network serves as the backbone for spatio-temporal… More >

  • Open Access

    ARTICLE

    Explainable AI-Based DDoS Attacks Classification Using Deep Transfer Learning

    Ahmad Alzu’bi1,*, Amjad Albashayreh2, Abdelrahman Abuarqoub3, Mai A. M. Alfawair4

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3785-3802, 2024, DOI:10.32604/cmc.2024.052599 - 12 September 2024

    Abstract In the era of the Internet of Things (IoT), the proliferation of connected devices has raised security concerns, increasing the risk of intrusions into diverse systems. Despite the convenience and efficiency offered by IoT technology, the growing number of IoT devices escalates the likelihood of attacks, emphasizing the need for robust security tools to automatically detect and explain threats. This paper introduces a deep learning methodology for detecting and classifying distributed denial of service (DDoS) attacks, addressing a significant security concern within IoT environments. An effective procedure of deep transfer learning is applied to utilize More >

  • Open Access

    ARTICLE

    Spatial Attention Integrated EfficientNet Architecture for Breast Cancer Classification with Explainable AI

    Sannasi Chakravarthy1, Bharanidharan Nagarajan2, Surbhi Bhatia Khan3,7,*, Vinoth Kumar Venkatesan2, Mahesh Thyluru Ramakrishna4, Ahlam Al Musharraf5, Khursheed Aurungzeb6

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 5029-5045, 2024, DOI:10.32604/cmc.2024.052531 - 12 September 2024

    Abstract Breast cancer is a type of cancer responsible for higher mortality rates among women. The cruelty of breast cancer always requires a promising approach for its earlier detection. In light of this, the proposed research leverages the representation ability of pretrained EfficientNet-B0 model and the classification ability of the XGBoost model for the binary classification of breast tumors. In addition, the above transfer learning model is modified in such a way that it will focus more on tumor cells in the input mammogram. Accordingly, the work proposed an EfficientNet-B0 having a Spatial Attention Layer with More >

  • Open Access

    ARTICLE

    A Deep Transfer Learning Approach for Addressing Yaw Pose Variation to Improve Face Recognition Performance

    M. Jayasree1, K. A. Sunitha2,*, A. Brindha1, Punna Rajasekhar3, G. Aravamuthan3, G. Joselin Retnakumar1

    Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 745-764, 2024, DOI:10.32604/iasc.2024.052983 - 06 September 2024

    Abstract Identifying faces in non-frontal poses presents a significant challenge for face recognition (FR) systems. In this study, we delved into the impact of yaw pose variations on these systems and devised a robust method for detecting faces across a wide range of angles from 0° to ±90°. We initially selected the most suitable feature vector size by integrating the Dlib, FaceNet (Inception-v2), and “Support Vector Machines (SVM)” + “K-nearest neighbors (KNN)” algorithms. To train and evaluate this feature vector, we used two datasets: the “Labeled Faces in the Wild (LFW)” benchmark data and the “Robust… More >

  • Open Access

    ARTICLE

    Importance-Weighted Transfer Learning for Fault Classification under Covariate Shift

    Yi Pan1, Lei Xie2,*, Hongye Su2

    Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 683-696, 2024, DOI:10.32604/iasc.2023.038543 - 06 September 2024

    Abstract In the process of fault detection and classification, the operation mode usually drifts over time, which brings great challenges to the algorithms. Because traditional machine learning based fault classification cannot dynamically update the trained model according to the probability distribution of the testing dataset, the accuracy of these traditional methods usually drops significantly in the case of covariate shift. In this paper, an importance-weighted transfer learning method is proposed for fault classification in the nonlinear multi-mode industrial process. It effectively alters the drift between the training and testing dataset. Firstly, the mutual information method is… More >

  • Open Access

    REVIEW

    Deep Transfer Learning Techniques in Intrusion Detection System-Internet of Vehicles: A State-of-the-Art Review

    Wufei Wu1, Javad Hassannataj Joloudari2,3,4, Senthil Kumar Jagatheesaperumal5, Kandala N. V. P. S. Rajesh6, Silvia Gaftandzhieva7,*, Sadiq Hussain8, Rahimullah Rabih9, Najibullah Haqjoo10, Mobeen Nazar11, Hamed Vahdat-Nejad9, Rositsa Doneva12

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2785-2813, 2024, DOI:10.32604/cmc.2024.053037 - 15 August 2024

    Abstract The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles (IoV) technology. The functional advantages of IoV include online communication services, accident prevention, cost reduction, and enhanced traffic regularity. Despite these benefits, IoV technology is susceptible to cyber-attacks, which can exploit vulnerabilities in the vehicle network, leading to perturbations, disturbances, non-recognition of traffic signs, accidents, and vehicle immobilization. This paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning (DTL) models for Intrusion Detection Systems in the Internet of Vehicles (IDS-IoV) based on anomaly… More >

  • Open Access

    ARTICLE

    Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

    Atif Mehmood1,2, Zhonglong Zheng1,*, Rizwan Khan1, Ahmad Al Smadi3, Farah Shahid1,2, Shahid Iqbal4, Mutasem K. Alsmadi5, Yazeed Yasin Ghadi6, Syed Aziz Shah8, Mostafa M. Ibrahim7

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2081-2098, 2024, DOI:10.32604/cmc.2024.046869 - 15 August 2024

    Abstract Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease (AD). Mild cognitive impairment (MCI) is a condition that falls between the spectrum of normal cognitive function and AD. However, previous studies have mainly used handcrafted features to classify MCI, AD, and normal control (NC) individuals. This paper focuses on using gray matter (GM) scans obtained through magnetic resonance imaging (MRI) for the diagnosis of individuals with MCI, AD, and NC. To improve classification performance, we developed two transfer learning strategies with data augmentation (i.e., shear range, rotation, zoom… More >

  • Open Access

    ARTICLE

    Deep Transfer Learning Models for Mobile-Based Ocular Disorder Identification on Retinal Images

    Roseline Oluwaseun Ogundokun1,2, Joseph Bamidele Awotunde3, Hakeem Babalola Akande4, Cheng-Chi Lee5,6,*, Agbotiname Lucky Imoize7,8

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 139-161, 2024, DOI:10.32604/cmc.2024.052153 - 18 July 2024

    Abstract Mobile technology is developing significantly. Mobile phone technologies have been integrated into the healthcare industry to help medical practitioners. Typically, computer vision models focus on image detection and classification issues. MobileNetV2 is a computer vision model that performs well on mobile devices, but it requires cloud services to process biometric image information and provide predictions to users. This leads to increased latency. Processing biometrics image datasets on mobile devices will make the prediction faster, but mobiles are resource-restricted devices in terms of storage, power, and computational speed. Hence, a model that is small in size,… More >

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