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  • Open Access

    ARTICLE

    Leveraging the WFD2020 Dataset for Multi-Class Detection of Wheat Fungal Diseases with YOLOv8 and Faster R-CNN

    Shivani Sood1, Harjeet Singh2,*, Surbhi Bhatia Khan3,4,5,*, Ahlam Almusharraf6

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2751-2787, 2025, DOI:10.32604/cmc.2025.060185 - 03 July 2025

    Abstract Wheat fungal infections pose a danger to the grain quality and crop productivity. Thus, prompt and precise diagnosis is essential for efficient crop management. This study used the WFD2020 image dataset, which is available to everyone, to look into how deep learning models could be used to find powdery mildew, leaf rust, and yellow rust, which are three common fungal diseases in Punjab, India. We changed a few hyperparameters to test TensorFlow-based models, such as SSD and Faster R-CNN with ResNet50, ResNet101, and ResNet152 as backbones. Faster R-CNN with ResNet50 achieved a mean average precision More >

  • Open Access

    ARTICLE

    The Blockchain Neural Network Superior to Deep Learning for Improving the Trust of Supply Chain

    Hsiao-Chun Han, Der-Chen Huang*

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3921-3941, 2025, DOI:10.32604/cmes.2025.065627 - 30 June 2025

    Abstract With the increasing importance of supply chain transparency, blockchain-based data has emerged as a valuable and verifiable source for analyzing procurement transaction risks. This study extends the mathematical model and proof of ‘the Overall Performance Characteristics of the Supply Chain’ to encompass multiple variables within blockchain data. Utilizing graph theory, the model is further developed into a single-layer neural network, which serves as the foundation for constructing two multi-layer deep learning neural network models, Feedforward Neural Network (abbreviated as FNN) and Deep Clustering Network (abbreviated as DCN). Furthermore, this study retrieves corporate data from the… More > Graphic Abstract

    The Blockchain Neural Network Superior to Deep Learning for Improving the Trust of Supply Chain

  • Open Access

    ARTICLE

    A Neural ODE-Enhanced Deep Learning Framework for Accurate and Real-Time Epilepsy Detection

    Tawfeeq Shawly1,2, Ahmed A. Alsheikhy3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3033-3064, 2025, DOI:10.32604/cmes.2025.065264 - 30 June 2025

    Abstract Epilepsy is a long-term neurological condition marked by recurrent seizures, which result from abnormal electrical activity in the brain that disrupts its normal functioning. Traditional methods for detecting epilepsy through machine learning typically utilize discrete-time models, which inadequately represent the continuous dynamics of electroencephalogram (EEG) signals. To overcome this limitation, we introduce an innovative approach that employs Neural Ordinary Differential Equations (NODEs) to model EEG signals as continuous-time systems. This allows for effective management of irregular sampling and intricate temporal patterns. In contrast to conventional techniques, such as Convolutional Neural Networks (CNNs) and Recurrent Neural… More >

  • Open Access

    REVIEW

    A Comprehensive Review of Face Detection Techniques for Occluded Faces: Methods, Datasets, and Open Challenges

    Thaer Thaher1,*, Majdi Mafarja2, Muhammed Saffarini3, Abdul Hakim H. M. Mohamed4, Ayman A. El-Saleh5

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2615-2673, 2025, DOI:10.32604/cmes.2025.064857 - 30 June 2025

    Abstract Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks, sunglasses, and other obstructions. Addressing this issue is crucial for applications such as surveillance, biometric authentication, and human-computer interaction. This paper provides a comprehensive review of face detection techniques developed to handle occluded faces. Studies are categorized into four main approaches: feature-based, machine learning-based, deep learning-based, and hybrid methods. We analyzed state-of-the-art studies within each category, examining their methodologies, strengths, and limitations based on widely used benchmark datasets, highlighting their adaptability to partial and severe occlusions. The review… More >

  • Open Access

    ARTICLE

    Aerial Object Tracking with Attention Mechanisms: Accurate Motion Path Estimation under Moving Camera Perspectives

    Yu-Shiuan Tsai*, Yuk-Hang Sit

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3065-3090, 2025, DOI:10.32604/cmes.2025.064783 - 30 June 2025

    Abstract To improve small object detection and trajectory estimation from an aerial moving perspective, we propose the Aerial View Attention-PRB (AVA-PRB) model. AVA-PRB integrates two attention mechanisms—Coordinate Attention (CA) and the Convolutional Block Attention Module (CBAM)—to enhance detection accuracy. Additionally, Shape-IoU is employed as the loss function to refine localization precision. Our model further incorporates an adaptive feature fusion mechanism, which optimizes multi-scale object representation, ensuring robust tracking in complex aerial environments. We evaluate the performance of AVA-PRB on two benchmark datasets: Aerial Person Detection and VisDrone2019-Det. The model achieves 60.9% mAP@0.5 on the Aerial Person… More >

  • Open Access

    ARTICLE

    Lightweight Deep Learning Model and Novel Dataset for Restoring Damaged Barcodes and QR Codes in Logistics Applications

    Tarek Muallim1, Haluk Kucuk2,*, Muhammet Bareket1, Metin Kahraman1

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3557-3581, 2025, DOI:10.32604/cmes.2025.064733 - 30 June 2025

    Abstract This study introduces a lightweight deep learning model and a novel synthetic dataset designed to restore damaged one-dimensional (1D) barcodes and Quick Response (QR) codes, addressing critical challenges in logistics operations. The proposed solution leverages an efficient Pix2Pix-based framework, a type of conditional Generative Adversarial Network (GAN) optimized for image-to-image translation tasks, enabling the recovery of degraded barcodes and QR codes with minimal computational overhead. A core contribution of this work is the development of a synthetic dataset that simulates realistic damage scenarios frequently encountered in logistics environments, such as low contrast, misalignment, physical wear,… More >

  • Open Access

    ARTICLE

    Video-Based Human Activity Recognition Using Hybrid Deep Learning Model

    Jungpil Shin1,*, Md. Al Mehedi Hasan2, Md. Maniruzzaman3, Satoshi Nishimura1, Sultan Alfarhood4

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3615-3638, 2025, DOI:10.32604/cmes.2025.064588 - 30 June 2025

    Abstract Activity recognition is a challenging topic in the field of computer vision that has various applications, including surveillance systems, industrial automation, and human-computer interaction. Today, the demand for automation has greatly increased across industries worldwide. Real-time detection requires edge devices with limited computational time. This study proposes a novel hybrid deep learning system for human activity recognition (HAR), aiming to enhance the recognition accuracy and reduce the computational time. The proposed system combines a pre-trained image classification model with a sequence analysis model. First, the dataset was divided into a training set (70%), validation set… More > Graphic Abstract

    Video-Based Human Activity Recognition Using Hybrid Deep Learning Model

  • Open Access

    ARTICLE

    Methods for the Segmentation of Reticular Structures Using 3D LiDAR Data: A Comparative Evaluation

    Francisco J. Soler Mora1,*, Adrián Peidró Vidal1, Marc Fabregat-Jaén1, Luis Payá Castelló1,2, Óscar Reinoso García 1,2

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3167-3195, 2025, DOI:10.32604/cmes.2025.064510 - 30 June 2025

    Abstract Reticular structures are the basis of major infrastructure projects, including bridges, electrical pylons and airports. However, inspecting and maintaining these structures is both expensive and hazardous, traditionally requiring human involvement. While some research has been conducted in this field of study, most efforts focus on faults identification through images or the design of robotic platforms, often neglecting the autonomous navigation of robots through the structure. This study addresses this limitation by proposing methods to detect navigable surfaces in truss structures, thereby enhancing the autonomous capabilities of climbing robots to navigate through these environments. The paper… More >

  • Open Access

    ARTICLE

    Cardiovascular Sound Classification Using Neural Architectures and Deep Learning for Advancing Cardiac Wellness

    Deepak Mahto1, Sudhakar Kumar1, Sunil K. Singh1, Amit Chhabra1, Irfan Ahmad Khan2, Varsha Arya3,4, Wadee Alhalabi5, Brij B. Gupta6,7,8,9,*, Bassma Saleh Alsulami10

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3743-3767, 2025, DOI:10.32604/cmes.2025.063427 - 30 June 2025

    Abstract Cardiovascular diseases (CVDs) remain one of the foremost causes of death globally; hence, the need for several must-have, advanced automated diagnostic solutions towards early detection and intervention. Traditional auscultation of cardiovascular sounds is heavily reliant on clinical expertise and subject to high variability. To counter this limitation, this study proposes an AI-driven classification system for cardiovascular sounds whereby deep learning techniques are engaged to automate the detection of an abnormal heartbeat. We employ FastAI vision-learner-based convolutional neural networks (CNNs) that include ResNet, DenseNet, VGG, ConvNeXt, SqueezeNet, and AlexNet to classify heart sound recordings. Instead of… More >

  • Open Access

    ARTICLE

    Optimizing CNN Architectures for Face Liveness Detection: Performance, Efficiency, and Generalization across Datasets

    Smita Khairnar1,2, Shilpa Gite1,3,*, Biswajeet Pradhan4,*, Sudeep D. Thepade2,5, Abdullah Alamri6

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3677-3707, 2025, DOI:10.32604/cmes.2025.058855 - 30 June 2025

    Abstract Face liveness detection is essential for securing biometric authentication systems against spoofing attacks, including printed photos, replay videos, and 3D masks. This study systematically evaluates pre-trained CNN models— DenseNet201, VGG16, InceptionV3, ResNet50, VGG19, MobileNetV2, Xception, and InceptionResNetV2—leveraging transfer learning and fine-tuning to enhance liveness detection performance. The models were trained and tested on NUAA and Replay-Attack datasets, with cross-dataset generalization validated on SiW-MV2 to assess real-world adaptability. Performance was evaluated using accuracy, precision, recall, FAR, FRR, HTER, and specialized spoof detection metrics (APCER, NPCER, ACER). Fine-tuning significantly improved detection accuracy, with DenseNet201 achieving the highest… More > Graphic Abstract

    Optimizing CNN Architectures for Face Liveness Detection: Performance, Efficiency, and Generalization across Datasets

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