Zeeshan Ali¹, Jihoon Moon², Saira Gillani³, Sitara Afzal⁴, Maryam Bukhari⁵, Seungmin Rho^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2263-2286, 2025, DOI:10.32604/cmes.2025.067743 - 31 August 2025

Abstract Surveillance systems can take various forms, but gait-based surveillance is emerging as a powerful approach due to its ability to identify individuals without requiring their cooperation. In the existing studies, several approaches have been suggested for gait recognition; nevertheless, the performance of existing systems is often degraded in real-world conditions due to covariate factors such as occlusions, clothing changes, walking speed, and varying camera viewpoints. Furthermore, most existing research focuses on single-person gait recognition; however, counting, tracking, detecting, and recognizing individuals in dual-subject settings with occlusions remains a challenging task. Therefore, this research proposed a… 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 >

Manoharan Madhiarasan^*

Energy Engineering, Vol.122, No.8, pp. 2993-3011, 2025, DOI:10.32604/ee.2025.068358 - 24 July 2025

Abstract Accurate Global Horizontal Irradiance (GHI) forecasting has become vital for successfully integrating solar energy into the electrical grid because of the expanding demand for green power and the worldwide shift favouring green energy resources. Particularly considering the implications of the aggressive GHG emission targets, accurate GHI forecasting has become vital for developing, designing, and operational managing solar energy systems. This research presented the core concepts of modelling and performance analysis of the application of various forecasting models such as ARIMA (Autoregressive Integrated Moving Average), Elaman NN (Elman Neural Network), RBFN (Radial Basis Function Neural Network),… More >

Maheen Anwar¹, Saima Farhan¹, Yasin Ul Haq², Waqar Azeem³, Muhammad Ilyas⁴, Razvan Cristian Voicu^5,*, Muhammad Hassan Tanveer⁵

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3477-3502, 2025, DOI:10.32604/cmc.2025.065141 - 03 July 2025

Abstract Glaucoma, a chronic eye disease affecting millions worldwide, poses a substantial threat to eyesight and can result in permanent vision loss if left untreated. Manual identification of glaucoma is a complicated and time-consuming practice requiring specialized expertise and results may be subjective. To address these challenges, this research proposes a computer-aided diagnosis (CAD) approach using Artificial Intelligence (AI) techniques for binary and multiclass classification of glaucoma stages. An ensemble fusion mechanism that combines the outputs of three pre-trained convolutional neural network (ConvNet) models–ResNet-50, VGG-16, and InceptionV3 is utilized in this paper. This fusion technique enhances… 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 >