Shivani Sood¹, Harjeet Singh^2,*, Surbhi Bhatia Khan^3,4,5,*, 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 >

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

Tawfeeq Shawly^1,2, Ahmed A. Alsheikhy^3,*

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 >

Thaer Thaher^1,*, 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 >

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 >

Tarek Muallim¹, Haluk Kucuk^2,*, 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 >

Jungpil Shin^1,*, 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

Francisco J. Soler Mora^1,*, Adrián Peidró Vidal¹, Marc Fabregat-Jaén¹, Luis Payá Castelló^1,2, 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 >

Deepak Mahto¹, Sudhakar Kumar¹, Sunil K. Singh¹, Amit Chhabra¹, Irfan Ahmad Khan², Varsha Arya^3,4, Wadee Alhalabi⁵, Brij B. Gupta^6,7,8,9,*, 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 >

Smita Khairnar^1,2, Shilpa Gite^1,3,*, Biswajeet Pradhan^4,*, Sudeep D. Thepade^2,5, 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