Atef Gharbi¹, Mohamed Ayari², Nasser Albalawi³, Ahmad Alshammari³, Nadhir Ben Halima^4,*, Zeineb Klai³

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075474 - 12 March 2026

Abstract This paper presents Dual Adaptive Neural Topology (Dual ANT), a distributed dual-network meta-adaptive framework that enhances ant-colony-based multi-agent coordination with online introspection, adaptive parameter control, and privacy-preserving interactions. This approach improves standard Ant Colony Optimization (ACO) with two lightweight neural components: a forward network that estimates swarm efficiency in real time and an inverse network that converts these descriptors into parameter adaptations. To preserve the privacy of individual trajectories in shared pheromone maps, we introduce a locally differentially private pheromone update mechanism that adds calibrated noise to each agent’s pheromone deposit while preserving the efficacy More >

Shtwai Alsubai¹, Abdullah Al Hejaili², Najib Ben Aoun^3,4,*, Amina Salhi⁵, Vincent Karovič^6,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074057 - 12 March 2026

Abstract The concept of Human Activity Recognition (HAR) is integral to applications based on Internet of Things (IoT)-enabled devices, particularly in healthcare, fitness tracking, and smart environments. The streams of data from wearable sensors are rich in information, yet their high dimensionality and variability pose a significant challenge to proper classification. To address this problem, this paper proposes hybrid architectures that integrate traditional machine learning models with a deep neural network (DNN) to deliver improved performance and enhanced capabilities for HAR tasks. Multi-sensor HAR data were used to systematically test several hybrid models, including: RF +… More >

Syed Nisar Hussain Bukhari^*, Sehar Altaf

Journal on Artificial Intelligence, Vol.8, pp. 137-152, 2026, DOI:10.32604/jai.2026.077943 - 11 March 2026

Abstract Knee osteoarthritis is a progressive degenerative joint disorder that leads to pain, stiffness, and reduced mobility, significantly affecting quality of life. Early and reliable diagnosis is essential for effective disease management, yet conventional radiographic assessment remains time-consuming and subject to inter-observer variability. This study presents a comparative deep learning (DL) based approach for automated severity classification of knee osteoarthritis using plain radiographic images. Multiple pretrained convolutional neural network architectures, including EfficientNetB3, InceptionNet, VGG19, ResNet, and EfficientNetV2S, were evaluated within a transfer learning paradigm. All models were trained and assessed on a publicly available dataset to More >

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 >