Panyu Liu¹, Tongqing Zhou^1,*, Guofeng Lu², Huaizhe Zhou³, Zhiping Cai¹

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2913-2934, 2025, DOI:10.32604/cmc.2025.069175 - 23 September 2025

Abstract The isolation of healthcare data among worldwide hospitals and institutes forms barriers for fully realizing the data-hungry artificial intelligence (AI) models promises in renewing medical services. To overcome this, privacy-preserving distributed learning frameworks, represented by swarm learning and federated learning, have been investigated recently with the sensitive healthcare data retaining in its local premises. However, existing frameworks use a one-size-fits-all mode that tunes one model for all healthcare situations, which could hardly fit the usually diverse disease prediction in practice. This work introduces the idea of ensemble learning into privacy-preserving distributed learning and presents the More >

Noman Khan¹, Afnan², Mi Young Lee^3,*, Jakyoung Min^4,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2935-2958, 2025, DOI:10.32604/cmc.2025.069058 - 23 September 2025

Abstract Fine-grained dog breed classification presents significant challenges due to subtle inter-class differences, pose variations, and intra-class diversity. To address these complexities and limitations of traditional handcrafted approaches, a novel and efficient two-stage Deep Learning (DL) framework tailored for robust fine-grained classification is proposed. In the first stage, a lightweight object detector, YOLO v8N (You Only Look Once Version 8 Nano), is fine-tuned to localize both the head and full body of the dog from each image. In the second stage, a dual-stream Vision Transformer (ViT) architecture independently processes the detected head and body regions, enabling… More >

Atta Rahman^*, Fahad Abdullah Alatallah, Abdullah Jafar Almubarak, Haider Ali Alkhazal, Hasan Ali Alzayer, Younis Zaki Shaaban, Nasro Min-Allah, Aghiad Bakry, Khalid Aloup

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3507-3525, 2025, DOI:10.32604/cmc.2025.067547 - 23 September 2025

Abstract This study presents an automated system for monitoring Personal Protective Equipment (PPE) compliance using advanced computer vision techniques in industrial settings. Despite strict safety regulations, manual monitoring of PPE compliance remains inefficient and prone to human error, particularly in harsh environmental conditions like in Saudi Arabia’s Eastern Province. The proposed solution leverages the state-of-the-art YOLOv11 deep learning model to detect multiple safety equipment classes, including safety vests, hard hats, safety shoes, gloves, and their absence (no_hardhat, no_safety_vest, no_safety_shoes, no_gloves) along with person detection. The system is designed to perform real-time detection of safety gear while… More >

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 >

Xi Li^1,2, Runpu Nie^1,*, Zhaoyong Fan², Lianying Zou², Zhenhua Xiao², Kaile Dong¹

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2959-2984, 2025, DOI:10.32604/cmc.2025.066740 - 23 September 2025

Abstract With the rapid development of robotics, grasp prediction has become fundamental to achieving intelligent physical interactions. To enhance grasp detection accuracy in unstructured environments, we propose a novel Cross-Multiscale Adaptive Collaborative and Fusion Grasp Detection Network (CMACF-Net). Addressing the limitations of conventional methods in capturing multi-scale spatial features, CMACF-Net introduces the Quantized Multi-scale Global Attention Module (QMGAM), which enables precise multi-scale spatial calibration and adaptive spatial-channel interaction, ultimately yielding a more robust and discriminative feature representation. To reduce the degradation of local features and the loss of high-frequency information, the Cross-scale Context Integration Module (CCI) More >

Hassan Javed¹, Labiba Gillani Fahad¹, Syed Fahad Tahir^2,*, Mehdi Hassan², Hani Alquhayz³

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3981-3996, 2025, DOI:10.32604/cmc.2025.064359 - 23 September 2025

Abstract Accurate plant species classification is essential for many applications, such as biodiversity conservation, ecological research, and sustainable agricultural practices. Traditional morphological classification methods are inherently slow, labour-intensive, and prone to inaccuracies, especially when distinguishing between species exhibiting visual similarities or high intra-species variability. To address these limitations and to overcome the constraints of image-only approaches, we introduce a novel Artificial Intelligence-driven framework. This approach integrates robust Vision Transformer (ViT) models for advanced visual analysis with a multi-modal data fusion strategy, incorporating contextual metadata such as precise environmental conditions, geographic location, and phenological traits. This combination… More >

Geoffry Mutiso^*, John Ndia

Journal on Artificial Intelligence, Vol.7, pp. 275-288, 2025, DOI:10.32604/jai.2025.069768 - 05 September 2025

Abstract Tomato leaf diseases significantly reduce crop yield; therefore, early and accurate disease detection is required. Traditional detection methods are laborious and error-prone, particularly in large-scale farms, whereas existing hybrid deep learning models often face computational inefficiencies and poor generalization over diverse environmental and disease conditions. This study presents a unified U-Net-Vision Mamba Model with Hierarchical Bottleneck Attention Mechanism (U-net-Vim-HBAM), which integrates U-Net’s high-resolution segmentation, Vision Mamba’s efficient contextual processing, and a Hierarchical Bottleneck Attention Mechanism to address the challenges of disease detection accuracy, computational complexity, and efficiency in existing models. The model was trained on More >

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 >