Dong Hyun Lee¹, Na Kyung Lee², Young Seo Lee^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-17, 2026, DOI:10.32604/cmc.2025.069976 - 09 December 2025

Abstract There has been an increasing emphasis on performing deep neural network (DNN) inference locally on edge devices due to challenges such as network congestion and security concerns. However, as DRAM process technology continues to scale down, the bit-flip errors in the memory of edge devices become more frequent, thereby leading to substantial DNN inference accuracy loss. Though several techniques have been proposed to alleviate the accuracy loss in edge environments, they require complex computations and additional parity bits for error correction, thus resulting in significant performance and storage overheads. In this paper, we propose FeatherGuard,… More >

Hanyu Tao^1,2, Dongye Sun^1,2, Tao Fang^1,2, Wenhu Zhao^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 521-536, 2025, DOI:10.32604/cmes.2025.072089 - 30 October 2025

Abstract Structural internal flaws often weaken the performance and integral stability, while traditional nondestructive testing or inversion methods face challenges of high cost and low efficiency in quantitative flaw identification. To quickly identify internal flaws within structures, a deep learning model for flaw detection is proposed based on the image quadtree scaled boundary finite element method (SBFEM) combined with a deep neural network (DNN). The training dataset is generated from the numerical simulations using the balanced quadtree algorithm and SBFEM, where the structural domain is discretized based on recursive decomposition principles and mesh refinement is automatically… More >

Farhan A. Alenizi¹, Faten Khalid Karim^2,*, Alaa R. Al-Shamasneh³, Mohammad Hossein Shakoor⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1793-1829, 2025, DOI:10.32604/cmes.2025.066023 - 31 August 2025

Abstract Deep neural networks provide accurate results for most applications. However, they need a big dataset to train properly. Providing a big dataset is a significant challenge in most applications. Image augmentation refers to techniques that increase the amount of image data. Common operations for image augmentation include changes in illumination, rotation, contrast, size, viewing angle, and others. Recently, Generative Adversarial Networks (GANs) have been employed for image generation. However, like image augmentation methods, GAN approaches can only generate images that are similar to the original images. Therefore, they also cannot generate new classes of data.… More >

Ahmad Mwfaq Bataineh^1,2,*, Ahmad Sufril Azlan Mohamed¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 93-124, 2025, DOI:10.32604/cmc.2025.064250 - 09 June 2025

Abstract Advancements in deep learning have considerably enhanced techniques for Rapid Entire Body Assessment (REBA) pose estimation by leveraging progress in three-dimensional human modeling. This survey provides an extensive overview of recent advancements, particularly emphasizing monocular image-based methodologies and their incorporation into ergonomic risk assessment frameworks. By reviewing literature from 2016 to 2024, this study offers a current and comprehensive analysis of techniques, existing challenges, and emerging trends in three-dimensional human pose estimation. In contrast to traditional reviews organized by learning paradigms, this survey examines how three-dimensional pose estimation is effectively utilized within musculoskeletal disorder (MSD)… More >

Waleed Khalid Al-Ghanem¹, Emad Ul Haq Qazi^2,*, Tanveer Zia^2,3, Muhammad Hamza Faheem², Muhammad Imran⁴, Iftikhar Ahmad⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1009-1027, 2025, DOI:10.32604/cmes.2025.058352 - 11 April 2025

Abstract In the current digital era, new technologies are becoming an essential part of our lives. Consequently, the number of malicious software or malware attacks is rapidly growing. There is no doubt, the majority of malware attacks can be detected by most antivirus programs. However, such types of antivirus programs are one step behind malicious software. Due to these dilemmas, deep learning become popular in the detection and classification of malicious data. Therefore, researchers have significantly focused on finding solutions for malware attacks by analyzing malicious samples with the help of different techniques and models. In More >

Can Hu¹, Shanqing Zhang^2,*, Kewei Tao², Gaoming Yang¹, Li Li²

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4913-4930, 2025, DOI:10.32604/cmc.2025.059770 - 06 March 2025

Abstract The surge of large-scale models in recent years has led to breakthroughs in numerous fields, but it has also introduced higher computational costs and more complex network architectures. These increasingly large and intricate networks pose challenges for deployment and execution while also exacerbating the issue of network over-parameterization. To address this issue, various network compression techniques have been developed, such as network pruning. A typical pruning algorithm follows a three-step pipeline involving training, pruning, and retraining. Existing methods often directly set the pruned filters to zero during retraining, significantly reducing the parameter space. However, this… More >

Asma Aldrees¹, Hong Min^2,*, Ashit Kumar Dutta³, Yousef Ibrahim Daradkeh⁴, Mohd Anjum⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2487-2511, 2025, DOI:10.32604/cmes.2025.061103 - 03 March 2025

Abstract Fundoscopic diagnosis involves assessing the proper functioning of the eye’s nerves, blood vessels, retinal health, and the impact of diabetes on the optic nerves. Fundus disorders are a major global health concern, affecting millions of people worldwide due to their widespread occurrence. Fundus photography generates machine-based eye images that assist in diagnosing and treating ocular diseases such as diabetic retinopathy. As a result, accurate fundus detection is essential for early diagnosis and effective treatment, helping to prevent severe complications and improve patient outcomes. To address this need, this article introduces a Derivative Model for Fundus… More >

Saad Akbar^1,2, Humera Azam¹, Sulaiman Sulmi Almutairi^3,*, Omar Alqahtani⁴, Habib Shah⁴, Aliya Aleryani⁴

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1075-1104, 2024, DOI:10.32604/cmc.2024.050913 - 18 July 2024

Abstract The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic tools. In this article, a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19, pneumonia, and normal conditions in chest X-ray images (CXIs) is proposed coupled with Explainable Artificial Intelligence (XAI). Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3, VGG16, and VGG19 that excel in the task of feature extraction. The methodology is further enhanced by the inclusion of the t-SNE (t-Distributed… More >

Jiajie Shen¹, Yan Wang^1,*, Dongxu Zhang²

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4349-4370, 2024, DOI:10.32604/cmc.2024.047903 - 20 June 2024

Abstract Locomotor intent classification has become a research hotspot due to its importance to the development of assistive robotics and wearable devices. Previous work have achieved impressive performance in classifying steady locomotion states. However, it remains challenging for these methods to attain high accuracy when facing transitions between steady locomotion states. Due to the similarities between the information of the transitions and their adjacent steady states. Furthermore, most of these methods rely solely on data and overlook the objective laws between physical activities, resulting in lower accuracy, particularly when encountering complex locomotion modes such as transitions.… More >

Aigerim Baimakhanova^1,*, Ainur Zhumadillayeva², Bigul Mukhametzhanova³, Natalya Glazyrina², Rozamgul Niyazova², Nurseit Zhunissov¹, Aizhan Sambetbayeva⁴

Computer Systems Science and Engineering, Vol.48, No.3, pp. 793-811, 2024, DOI:10.32604/csse.2024.043273 - 20 May 2024

Abstract As digital technologies have advanced more rapidly, the number of paper documents recently converted into a digital format has exponentially increased. To respond to the urgent need to categorize the growing number of digitized documents, the classification of digitized documents in real time has been identified as the primary goal of our study. A paper classification is the first stage in automating document control and efficient knowledge discovery with no or little human involvement. Artificial intelligence methods such as Deep Learning are now combined with segmentation to study and interpret those traits, which were not… More >