Abdullah G. M. Almansour^1,*, Faisal Alshomrani², Abdulaziz T. M. Almutairi³, Easa Alalwany⁴, Mohammed S. Alshuhri¹, Hussein Alshaari⁵, Abdullah Alfahaid⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2395-2418, 2025, DOI:10.32604/cmes.2025.069661 - 31 August 2025

Abstract The early and precise identification of Alzheimer’s Disease (AD) continues to pose considerable clinical difficulty due to subtle structural alterations and overlapping symptoms across the disease phases. This study presents a novel Deformable Attention Vision Transformer (DA-ViT) architecture that integrates deformable Multi-Head Self-Attention (MHSA) with a Multi-Layer Perceptron (MLP) block for efficient classification of Alzheimer’s disease (AD) using Magnetic resonance imaging (MRI) scans. In contrast to traditional vision transformers, our deformable MHSA module preferentially concentrates on spatially pertinent patches through learned offset predictions, markedly diminishing processing demands while improving localized feature representation. DA-ViT contains only More >

Vinh Truong Hoang^*, Viet-Tuan Le, Nghia Dinh, Kiet Tran-Trung, Bay Nguyen Van, Ha Duong Thi Hong, Thien Ho Huong

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1495-1514, 2025, DOI:10.32604/cmc.2025.066506 - 29 August 2025

Abstract Improving consumer satisfaction with the appearance and surface quality of wood-based products requires inspection methods that are both accurate and efficient. The adoption of artificial intelligence (AI) for surface evaluation has emerged as a promising solution. Since the visual appeal of wooden products directly impacts their market value and overall business success, effective quality control is crucial. However, conventional inspection techniques often fail to meet performance requirements due to limited accuracy and slow processing times. To address these shortcomings, the authors propose a real-time deep learning-based system for evaluating surface appearance quality. The method integrates… More >

MD Tausif Mallick¹, Saptarshi Banerjee², Nityananda Thakur³, Himadri Nath Saha^4,*, Amlan Chakrabarti¹

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 121-180, 2025, DOI:10.32604/cmc.2025.065250 - 29 August 2025

Abstract Addressing plant diseases and pests is not just crucial; it’s a matter of utmost importance for enhancing crop production and preventing economic losses. Recent advancements in artificial intelligence, machine learning, and deep learning have revolutionised the precision and efficiency of this process, surpassing the limitations of manual identification. This study comprehensively reviews modern computer-based techniques, including recent advances in artificial intelligence, for detecting diseases and pests through images. This paper uniquely categorises methodologies into hyperspectral imaging, non-visualisation techniques, visualisation approaches, modified deep learning architectures, and transformer models, helping researchers gain detailed, insightful understandings. The exhaustive… More >

Asaad Algarni¹, Aysha Naseer ², Mohammed Alshehri³, Yahya AlQahtani⁴, Abdulmonem Alshahrani⁴, Jeongmin Park^5,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1981-1998, 2025, DOI:10.32604/cmc.2025.064268 - 29 August 2025

Abstract Remote sensing plays a pivotal role in environmental monitoring, disaster relief, and urban planning, where accurate scene classification of aerial images is essential. However, conventional convolutional neural networks (CNNs) struggle with long-range dependencies and preserving high-resolution features, limiting their effectiveness in complex aerial image analysis. To address these challenges, we propose a Hybrid HRNet-Swin Transformer model that synergizes the strengths of HRNet-W48 for high-resolution segmentation and the Swin Transformer for global feature extraction. This hybrid architecture ensures robust multi-scale feature fusion, capturing fine-grained details and broader contextual relationships in aerial imagery. Our methodology begins with… More >

Zilu Liu^1,#, Hongjin Zhu^2,#,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 681-694, 2025, DOI:10.32604/cmc.2025.064191 - 29 August 2025

Abstract Real-time detection of surface defects on cables is crucial for ensuring the safe operation of power systems. However, existing methods struggle with small target sizes, complex backgrounds, low-quality image acquisition, and interference from contamination. To address these challenges, this paper proposes the Real-time Cable Defect Detection Network (RC2DNet), which achieves an optimal balance between detection accuracy and computational efficiency. Unlike conventional approaches, RC2DNet introduces a small object feature extraction module that enhances the semantic representation of small targets through feature pyramids, multi-level feature fusion, and an adaptive weighting mechanism. Additionally, a boundary feature enhancement module More >

Anthony Frank Obeng, Yongyue Zhu^*

Journal of Psychology in Africa, Vol.35, No.4, pp. 505-512, 2025, DOI:10.32604/jpa.2025.070121 - 17 August 2025

Abstract The study examined work engagement and coping strategies in the relationship between abusive supervision and helping behaviors among hospitality employees. Participants were 386 frontline hospitality employees (50.8% females; 38.9% with 1–5 years of experience; 78.3% in the 18–40 age range). They self-reported coping strategies, abusive supervision, work engagement, and helping behaviors. Structural equation model results showed that abusive supervision to be associated with lower employee helping behaviors. Work engagement was higher with employees’ helping behaviors. Engaged employees would unleash helping behaviors. Work engagement mediated the relationship between abusive supervision and helping behaviors, lowering the abusive More >

Ahmad Taher Azar^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 29-36, 2025, DOI:10.32604/cmes.2025.069309 - 31 July 2025

Abstract This article has no abstract. More >

Osama M. Alshehri¹, Ahmad Shaf^2,*, Muhammad Irfan^3,*, Mohammed M. Jalal⁴, Malik A. Altayar⁴, Mohammed H. Abu-Alghayth⁵, Humood Al Shmrany⁶, Tariq Ali⁷, Toufique A. Soomro⁸, Ali G. Alkhathami⁹

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1165-1196, 2025, DOI:10.32604/cmes.2025.067150 - 31 July 2025

Abstract Background: Accurate classification of normal blood cells is a critical foundation for automated hematological analysis, including the detection of pathological conditions like leukemia. While convolutional neural networks (CNNs) excel in local feature extraction, their ability to capture global contextual relationships in complex cellular morphologies is limited. This study introduces a hybrid CNN-Transformer framework to enhance normal blood cell classification, laying the groundwork for future leukemia diagnostics. Methods: The proposed architecture integrates pre-trained CNNs (ResNet50, EfficientNetB3, InceptionV3, CustomCNN) with Vision Transformer (ViT) layers to combine local and global feature modeling. Four hybrid models were evaluated on… More >

Ziyu Cui, Zijun Wei, Xiaohui Yuan, Pei Li^*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1045-1070, 2025, DOI:10.32604/cmes.2025.066659 - 31 July 2025

Abstract At present, noise reduction has become an urgent challenge across various fields. Whether in the context of household appliances in daily life or in the enhancement of stealth performance in military equipment, noise control technologies play a critical role. This study introduces a computational framework for simulating Helmholtz equation-governed acoustic scattering using a boundary element method (BEM) integrated with Loop subdivision surfaces. By adopting the Loop subdivision scheme—a widely used computer-aided design (CAD) technique—the framework unifies geometric representation and physical field discretization, ensuring seamless compatibility with industrial CAD workflows. The core innovation lies in the More >

Hussain Dawood¹, Marriam Nawaz², Tahira Nazir³, Ali Javed², Abdul Khader Jilani Saudagar^4,*, Hatoon S. AlSagri⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 429-459, 2025, DOI:10.32604/cmes.2025.066415 - 31 July 2025

Abstract Reliable human action recognition (HAR) in video sequences is critical for a wide range of applications, such as security surveillance, healthcare monitoring, and human-computer interaction. Several automated systems have been designed for this purpose; however, existing methods often struggle to effectively integrate spatial and temporal information from input samples such as 2-stream networks or 3D convolutional neural networks (CNNs), which limits their accuracy in discriminating numerous human actions. Therefore, this study introduces a novel deep-learning framework called the ARNet, designed for robust HAR. ARNet consists of two main modules, namely, a refined InceptionResNet-V2-based CNN and… More >