Siham Essahraui¹, Ismail Lamaakal¹, Yassine Maleh^2,*, Khalid El Makkaoui¹, Mouncef Filali Bouami¹, Ibrahim Ouahbi¹, May Almousa³, Ali Abdullah S. AlQahtani⁴, Ahmed A. Abd El-Latif^5,6

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3151-3183, 2025, DOI:10.32604/cmc.2025.067359 - 23 September 2025

Abstract The rapid shift to online education has introduced significant challenges to maintaining academic integrity in remote assessments, as traditional proctoring methods fall short in preventing cheating. The increase in cheating during online exams highlights the need for efficient, adaptable detection models to uphold academic credibility. This paper presents a comprehensive analysis of various deep learning models for cheating detection in online proctoring systems, evaluating their accuracy, efficiency, and adaptability. We benchmark several advanced architectures, including EfficientNet, MobileNetV2, ResNet variants and more, using two specialized datasets (OEP and OP) tailored for online proctoring contexts. Our findings More >

Thaer Thaher^1,*, Muhammed Saffarini², Majdi Mafarja³, Abdulaziz Alashbi⁴, Abdul Hakim Mohamed⁵, Ayman A. El-Saleh⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2359-2394, 2025, DOI:10.32604/cmes.2025.065388 - 31 August 2025

Abstract Face detection is a critical component in modern security, surveillance, and human-computer interaction systems, with widespread applications in smartphones, biometric access control, and public monitoring. However, detecting faces with high levels of occlusion, such as those covered by masks, veils, or scarves, remains a significant challenge, as traditional models often fail to generalize under such conditions. This paper presents a hybrid approach that combines traditional handcrafted feature extraction technique called Histogram of Oriented Gradients (HOG) and Canny edge detection with modern deep learning models. The goal is to improve face detection accuracy under occlusions. The… More >

Irfan Abbas, Robertas Damaševičius^*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 483-502, 2025, DOI:10.32604/cmc.2025.066578 - 29 August 2025

Abstract Forests are vital ecosystems that play a crucial role in sustaining life on Earth and supporting human well-being. Traditional forest mapping and monitoring methods are often costly and limited in scope, necessitating the adoption of advanced, automated approaches for improved forest conservation and management. This study explores the application of deep learning-based object detection techniques for individual tree detection in RGB satellite imagery. A dataset of 3157 images was collected and divided into training (2528), validation (495), and testing (134) sets. To enhance model robustness and generalization, data augmentation was applied to the training part… More >

Zhiwei Chen^#, Runze Liu^#, Shitao Huang, Yangyang Guo^*, Yongjun Ren

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 37-81, 2025, DOI:10.32604/cmes.2025.067809 - 31 July 2025

Abstract The rapid advancement of artificial intelligence technology is driving transformative changes in medical diagnosis, treatment, and management systems through large-scale deep learning models—a process that brings both groundbreaking opportunities and multifaceted challenges. This study focuses on the medical and healthcare applications of large-scale deep learning architectures, conducting a comprehensive survey to categorize and analyze their diverse uses. The survey results reveal that current applications of large models in healthcare encompass medical data management, healthcare services, medical devices, and preventive medicine, among others. Concurrently, large models demonstrate significant advantages in the medical domain, especially in high-precision More >

Junling Zhong¹, Furong Geng², Zhixiao Chen¹, Wenbin Hou^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1-27, 2025, DOI:10.32604/cmes.2025.065664 - 31 July 2025

Abstract This study presents an interpretable surrogate framework for predicting pedestrian-leg injury severity that integrates high-fidelity finite-element (FE) simulations with a TabNet-based deep-learning model. We generated a parametric dataset of 3000 impact scenarios—covering ten vehicle types and various legform impactors—using automated FE runs configured via Latin hypercube sampling. After preprocessing and one-hot encoding of categorical features, we trained TabNet alongside Support-Vector Regression, Random Forest, and Decision-Tree ensembles. All models underwent hyperparameter tuning via Optuna’s Bayesian optimization coupled with repeated four-fold cross-validation (20 trials per model). TabNet achieved the best balance of explanatory power and predictive accuracy, More > Graphic Abstract

Ching-Lung Fan^1,*, Yu-Jen Chung², Shan-Min Yen^1,3

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4627-4653, 2025, DOI:10.32604/cmc.2025.066856 - 30 July 2025

Abstract Efficient banana crop detection is crucial for precision agriculture; however, traditional remote sensing methods often lack the spatial resolution required for accurate identification. This study utilizes low-altitude Unmanned Aerial Vehicle (UAV) images and deep learning-based object detection models to enhance banana plant detection. A comparative analysis of Faster Region-Based Convolutional Neural Network (Faster R-CNN), You Only Look Once Version 3 (YOLOv3), Retina Network (RetinaNet), and Single Shot MultiBox Detector (SSD) was conducted to evaluate their effectiveness. Results show that RetinaNet achieved the highest detection accuracy, with a precision of 96.67%, a recall of 71.67%, and… More >

Rachana Poongodan¹, Dayanand Lal Narayan², Deepika Gadakatte Lokeshwarappa³, Hirald Dwaraka Praveena⁴, Dae-Ki Kang^5,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5751-5772, 2025, DOI:10.32604/cmc.2025.066314 - 30 July 2025

Abstract In recent decades, brain tumors have emerged as a serious neurological disorder that often leads to death. Hence, Brain Tumor Segmentation (BTS) is significant to enable the visualization, classification, and delineation of tumor regions in Magnetic Resonance Imaging (MRI). However, BTS remains a challenging task because of noise, non-uniform object texture, diverse image content and clustered objects. To address these challenges, a novel model is implemented in this research. The key objective of this research is to improve segmentation accuracy and generalization in BTS by incorporating Switchable Normalization into Faster R-CNN, which effectively captures the… More >

Nawaf Waqas¹, Muhammad Islam^2,*, Muhammad Yahya³, Shabana Habib⁴, Mohammed Aloraini², Sheroz Khan⁵

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3029-3051, 2025, DOI:10.32604/cmc.2025.064660 - 03 July 2025

Abstract Deep learning (DL), derived from the domain of Artificial Neural Networks (ANN), forms one of the most essential components of modern deep learning algorithms. DL segmentation models rely on layer-by-layer convolution-based feature representation, guided by forward and backward propagation. A critical aspect of this process is the selection of an appropriate activation function (AF) to ensure robust model learning. However, existing activation functions often fail to effectively address the vanishing gradient problem or are complicated by the need for manual parameter tuning. Most current research on activation function design focuses on classification tasks using natural… More >

Samira Marhraoui^1,2,*, Basma Saad³, Hassan Silkan¹, Said Laasri², Asmaa El Hannani³

Energy Engineering, Vol.122, No.7, pp. 2653-2672, 2025, DOI:10.32604/ee.2025.064498 - 27 June 2025

Abstract Photovoltaic (PV) power forecasting is essential for balancing energy supply and demand in renewable energy systems. However, the performance of PV panels varies across different technologies due to differences in efficiency and how they process solar radiation. This study evaluates the effectiveness of deep learning models in predicting PV power generation for three panel technologies: Hybrid-Si, Mono-Si, and Poly-Si, across three forecasting horizons: 1-step, 12-step, and 24-step. Among the tested models, the Convolutional Neural Network—Long Short-Term Memory (CNN-LSTM) architecture exhibited superior performance, particularly for the 24-step horizon, achieving R² = 0.9793 and MAE = 0.0162 for More >

Xuezhi Wen¹, Eric Danso^2,*, Solomon Danso²

Journal of Cyber Security, Vol.7, pp. 45-69, 2025, DOI:10.32604/jcs.2025.063606 - 08 May 2025

Abstract Deep learning models have achieved remarkable success in healthcare, finance, and autonomous systems, yet their security vulnerabilities to adversarial attacks remain a critical challenge. This paper presents a novel dual-phase defense framework that combines progressive adversarial training with dynamic runtime protection to address evolving threats. Our approach introduces three key innovations: multi-stage adversarial training with TRADES (Tradeoff-inspired Adversarial Defense via Surrogate-loss minimization) loss that progressively scales perturbation strength, maintaining 85.10% clean accuracy on CIFAR-10 (Canadian Institute for Advanced Research 10-class dataset) while improving robustness; a hybrid runtime defense integrating feature manipulation, statistical anomaly detection, and… More >