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 >

Saad Sadiq¹, Kashif Sultan¹, Muhammad Sheraz², Teong Chee Chuah^2,*, Muhammad Usman Hashmi³

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3945-3963, 2025, DOI:10.32604/cmc.2025.067538 - 23 September 2025

Abstract Vehicle recognition plays a vital role in intelligent transportation systems, law enforcement, access control, and security operations—domains that are becoming increasingly dynamic and complex. Despite advancements, most existing solutions remain siloed, addressing individual tasks such as vehicle make and model recognition (VMMR), automatic number plate recognition (ANPR), and color classification separately. This fragmented approach limits real-world efficiency, leading to slower processing, reduced accuracy, and increased operational costs, particularly in traffic monitoring and surveillance scenarios. To address these limitations, we present a unified framework that consolidates all three recognition tasks into a single, lightweight system. The More >

Nareshkumar Raveendhran, Nimala Krishnan^*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3253-3265, 2025, DOI:10.32604/cmc.2025.067490 - 23 September 2025

Abstract Social media outlets deliver customers a medium for communication, exchange, and expression of their thoughts with others. The advent of social networks and the fast escalation of the quantity of data have created opportunities for textual evaluation. Utilising the user corpus, characteristics of social platform users, and other data, academic research may accurately discern the personality traits of users. This research examines the traits of consumer personalities. Usually, personality tests administered by psychological experts via interviews or self-report questionnaires are costly, time-consuming, complex, and labour-intensive. Currently, academics in computational linguistics are increasingly focused on predicting… More >

Zihan Jia, Bowen Ma, Dongyue Chen^*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2873-2891, 2025, DOI:10.32604/cmc.2025.067460 - 23 September 2025

Abstract In real-world scenarios, dual-view X-ray machines have outnumbered single-view X-ray machines due to their ability to provide comprehensive internal information about the baggage, which is important for identifying prohibited items that are not visible in one view due to rotation or overlap. However, existing work still focuses mainly on single-view, and the limited dual-view based work only performs simple information fusion at the feature or decision level and lacks effective utilization of the complementary information hidden in dual view. To this end, this paper proposes an end-to-end dual-view prohibited item detection method, the core of… More >

Sarab Almuhaideb^1,*, Najwa Altwaijry¹, Isra Al-Turaiki¹, Ahmad Raza Khan², Hamza Ali Rizvi³

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3095-3128, 2025, DOI:10.32604/cmc.2025.067390 - 23 September 2025

Abstract Many bioinformatics applications require determining the class of a newly sequenced Deoxyribonucleic acid (DNA) sequence, making DNA sequence classification an integral step in performing bioinformatics analysis, where large biomedical datasets are transformed into valuable knowledge. Existing methods rely on a feature extraction step and suffer from high computational time requirements. In contrast, newer approaches leveraging deep learning have shown significant promise in enhancing accuracy and efficiency. In this paper, we investigate the performance of various deep learning architectures: Convolutional Neural Network (CNN), CNN-Long Short-Term Memory (CNN-LSTM), CNN-Bidirectional Long Short-Term Memory (CNN-BiLSTM), Residual Network (ResNet), and… 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 >

Hazem Farah¹, Akram Bennour^1,*, Hama Soltani¹, Mouaaz Nahas², Rashiq Rafiq Marie³, Mohammed Al-Sarem^3,4,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3335-3348, 2025, DOI:10.32604/cmc.2025.067226 - 23 September 2025

Abstract This study presents an advanced method for post-mortem person identification using the segmentation of skeletal structures from chest X-ray images. The proposed approach employs the Attention U-Net architecture, enhanced with gated attention mechanisms, to refine segmentation by emphasizing spatially relevant anatomical features while suppressing irrelevant details. By isolating skeletal structures which remain stable over time compared to soft tissues, this method leverages bones as reliable biometric markers for identity verification. The model integrates custom-designed encoder and decoder blocks with attention gates, achieving high segmentation precision. To evaluate the impact of architectural choices, we conducted an… More >

Omar Sabri¹, Bassam Al-Shargabi^2,*, Abdelrahman Abuarqoub²

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2443-2486, 2025, DOI:10.32604/cmc.2025.066987 - 23 September 2025

Abstract This review comprehensively analyzes advancements in artificial intelligence, particularly machine learning and deep learning, in medical imaging, focusing on their transformative role in enhancing diagnostic accuracy. Our in-depth analysis of 138 selected studies reveals that artificial intelligence (AI) algorithms frequently achieve diagnostic performance comparable to, and often surpassing, that of human experts, excelling in complex pattern recognition. Key findings include earlier detection of conditions like skin cancer and diabetic retinopathy, alongside radiologist-level performance for pneumonia detection on chest X-rays. These technologies profoundly transform imaging by significantly improving processes in classification, segmentation, and sequential analysis across… More >

Peng Xiao¹, Haiyu Xu¹, Peng Xu¹, Zhiwei Guo^1,*, Amr Tolba^2,*, Osama Alfarraj²

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2999-3022, 2025, DOI:10.32604/cmc.2025.066937 - 23 September 2025

Abstract Medical image analysis based on deep learning has become an important technical requirement in the field of smart healthcare. In view of the difficulties in collaborative modeling of local details and global features in multimodal image analysis of ophthalmology, as well as the existence of information redundancy in cross-modal data fusion, this paper proposes a multimodal fusion framework based on cross-modal collaboration and weighted attention mechanism. In terms of feature extraction, the framework collaboratively extracts local fine-grained features and global structural dependencies through a parallel dual-branch architecture, overcoming the limitations of traditional single-modality models in… More >

Sunawar Khan¹, Tehseen Mazhar^1,2,*, Naila Sammar Naz¹, Fahed Ahmed¹, Tariq Shahzad³, Atif Ali⁴, Muhammad Adnan Khan^5,*, Habib Hamam^6,7,8,9

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2347-2401, 2025, DOI:10.32604/cmc.2025.066932 - 23 September 2025

Abstract Feature selection (FS) plays a crucial role in medical imaging by reducing dimensionality, improving computational efficiency, and enhancing diagnostic accuracy. Traditional FS techniques, including filter, wrapper, and embedded methods, have been widely used but often struggle with high-dimensional and heterogeneous medical imaging data. Deep learning-based FS methods, particularly Convolutional Neural Networks (CNNs) and autoencoders, have demonstrated superior performance but lack interpretability. Hybrid approaches that combine classical and deep learning techniques have emerged as a promising solution, offering improved accuracy and explainability. Furthermore, integrating multi-modal imaging data (e.g., Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron… More >