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  • Open Access

    ARTICLE

    A Novel Approach to Enhanced Cancelable Multi-Biometrics Personal Identification Based on Incremental Deep Learning

    Ali Batouche1, Souham Meshoul2,*, Hadil Shaiba3, Mohamed Batouche2,*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1727-1752, 2025, DOI:10.32604/cmc.2025.063227 - 16 April 2025

    Abstract The field of biometric identification has seen significant advancements over the years, with research focusing on enhancing the accuracy and security of these systems. One of the key developments is the integration of deep learning techniques in biometric systems. However, despite these advancements, certain challenges persist. One of the most significant challenges is scalability over growing complexity. Traditional methods either require maintaining and securing a growing database, introducing serious security challenges, or relying on retraining the entire model when new data is introduced—a process that can be computationally expensive and complex. This challenge underscores the… More >

  • Open Access

    ARTICLE

    Deep Learning Algorithm for Person Re-Identification Based on Dual Network Architecture

    Meng Zhu1,2, Xingyue Wang3, Honge Ren3,4,*, Abeer Hakeem5, Linda Mohaisen5,*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2889-2905, 2025, DOI:10.32604/cmc.2025.061421 - 16 April 2025

    Abstract Changing a person’s posture and low resolution are the key challenges for person re-identification (ReID) in various deep learning applications. In this paper, we introduce an innovative architecture using a dual attention network that includes an attention module and a joint measurement module of spatial-temporal information. The proposed approach can be classified into two main tasks. Firstly, the spatial attention feature map is formed by aggregating features in the spatial dimension. Additionally, the same operation is carried out on the channel dimension to form channel attention feature maps. Therefore, the receptive field size is adjusted… More >

  • Open Access

    ARTICLE

    VPM-Net: Person Re-ID Network Based on Visual Prompt Technology and Multi-Instance Negative Pooling

    Haitao Xie, Yuliang Chen, Yunjie Zeng, Lingyu Yan, Zhizhi Wang, Zhiwei Ye*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3389-3410, 2025, DOI:10.32604/cmc.2025.060783 - 16 April 2025

    Abstract With the rapid development of intelligent video surveillance technology, pedestrian re-identification has become increasingly important in multi-camera surveillance systems. This technology plays a critical role in enhancing public safety. However, traditional methods typically process images and text separately, applying upstream models directly to downstream tasks. This approach significantly increases the complexity of model training and computational costs. Furthermore, the common class imbalance in existing training datasets limits model performance improvement. To address these challenges, we propose an innovative framework named Person Re-ID Network Based on Visual Prompt Technology and Multi-Instance Negative Pooling (VPM-Net). First, we… More >

  • Open Access

    ARTICLE

    Leveraging Unlabeled Corpus for Arabic Dialect Identification

    Mohammed Abdelmajeed1,*, Jiangbin Zheng1, Ahmed Murtadha1, Youcef Nafa1, Mohammed Abaker2, Muhammad Pervez Akhter3

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3471-3491, 2025, DOI:10.32604/cmc.2025.059870 - 16 April 2025

    Abstract Arabic Dialect Identification (DID) is a task in Natural Language Processing (NLP) that involves determining the dialect of a given piece of text in Arabic. The state-of-the-art solutions for DID are built on various deep neural networks that commonly learn the representation of sentences in response to a given dialect. Despite the effectiveness of these solutions, the performance heavily relies on the amount of labeled examples, which is labor-intensive to attain and may not be readily available in real-world scenarios. To alleviate the burden of labeling data, this paper introduces a novel solution that leverages… More >

  • Open Access

    ARTICLE

    Identification of Secondary Metabolites of Lycium ruthenicum Murray by UPLC-QTOF/MS and Network Pharmacology of Its Anti-Inflammatory Properties

    Chen Chen#,*, Chunli Li#, Tengfei Li, Qianhong Li, Luyao Li, Fengqin Liu

    Phyton-International Journal of Experimental Botany, Vol.94, No.3, pp. 793-807, 2025, DOI:10.32604/phyton.2025.063549 - 31 March 2025

    Abstract Lycium ruthenicum Murray, a plant widely cultivated in northwestern China, is integral to traditional Chinese medicine, with applications in treating menstrual disorders, cardiovascular diseases, and menopausal symptoms. Despite its recognized medicinal value and use as a functional food, comprehensive knowledge of its metabolites and their pharmacological effects remains limited. This study presents an innovative approach using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC–QTOF/MS) to conduct a detailed analysis of both wild and cultivated L. ruthenicum samples. A total of 62 peaks were detected in the total ion current profile, with 59 metabolites identified based… More >

  • Open Access

    ARTICLE

    Leveraging Deep Learning for Precise Chronic Bronchitis Identification in X-Ray Modalities

    Fahad Ahmad1,2,*, Saad Awadh Alanazi3, Kashaf Junaid4, Maryam Shabbir5, Asim Ali1

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 381-405, 2025, DOI:10.32604/cmc.2025.062452 - 26 March 2025

    Abstract Image processing plays a vital role in various fields such as autonomous systems, healthcare, and cataloging, especially when integrated with deep learning (DL). It is crucial in medical diagnostics, including the early detection of diseases like chronic obstructive pulmonary disease (COPD), which claimed 3.2 million lives in 2015. COPD, a life-threatening condition often caused by prolonged exposure to lung irritants and smoking, progresses through stages. Early diagnosis through image processing can significantly improve survival rates. COPD encompasses chronic bronchitis (CB) and emphysema; CB particularly increases in smokers and generally affects individuals between 50 and 70… More >

  • Open Access

    REVIEW

    A Comprehensive Review of Pill Image Recognition

    Linh Nguyen Thi My1,2,*, Viet-Tuan Le3, Tham Vo1, Vinh Truong Hoang3,*

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3693-3740, 2025, DOI:10.32604/cmc.2025.060793 - 06 March 2025

    Abstract Pill image recognition is an important field in computer vision. It has become a vital technology in healthcare and pharmaceuticals due to the necessity for precise medication identification to prevent errors and ensure patient safety. This survey examines the current state of pill image recognition, focusing on advancements, methodologies, and the challenges that remain unresolved. It provides a comprehensive overview of traditional image processing-based, machine learning-based, deep learning-based, and hybrid-based methods, and aims to explore the ongoing difficulties in the field. We summarize and classify the methods used in each article, compare the strengths and More >

  • Open Access

    ARTICLE

    Lightweight YOLOM-Net for Automatic Identification and Real-Time Detection of Fatigue Driving

    Shanmeng Zhao1,2, Yaxue Peng1,*, Yaqing Wang3, Gang Li3,*, Mohammed Al-Mahbashi1

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4995-5017, 2025, DOI:10.32604/cmc.2025.059972 - 06 March 2025

    Abstract In recent years, the country has spent significant workforce and material resources to prevent traffic accidents, particularly those caused by fatigued driving. The current studies mainly concentrate on driver physiological signals, driving behavior, and vehicle information. However, most of the approaches are computationally intensive and inconvenient for real-time detection. Therefore, this paper designs a network that combines precision, speed and lightweight and proposes an algorithm for facial fatigue detection based on multi-feature fusion. Specifically, the face detection model takes YOLOv8 (You Only Look Once version 8) as the basic framework, and replaces its backbone network… More >

  • Open Access

    ARTICLE

    Pseudo Label Purification with Dual Contrastive Learning for Unsupervised Vehicle Re-Identification

    Jiyang Xu1, Qi Wang1,*, Xin Xiong2, Weidong Min1,3, Jiang Luo4, Di Gai1, Qing Han1,3

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3921-3941, 2025, DOI:10.32604/cmc.2024.058586 - 06 March 2025

    Abstract The unsupervised vehicle re-identification task aims at identifying specific vehicles in surveillance videos without utilizing annotation information. Due to the higher similarity in appearance between vehicles compared to pedestrians, pseudo-labels generated through clustering are ineffective in mitigating the impact of noise, and the feature distance between inter-class and intra-class has not been adequately improved. To address the aforementioned issues, we design a dual contrastive learning method based on knowledge distillation. During each iteration, we utilize a teacher model to randomly partition the entire dataset into two sub-domains based on clustering pseudo-label categories. By conducting contrastive… More >

  • Open Access

    ARTICLE

    DaC-GANSAEBF: Divide and Conquer-Generative Adversarial Network—Squeeze and Excitation-Based Framework for Spam Email Identification

    Tawfeeq Shawly1, Ahmed A. Alsheikhy2,*, Yahia Said3, Shaaban M. Shaaban3, Husam Lahza4, Aws I. AbuEid5, Abdulrahman Alzahrani6

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3181-3212, 2025, DOI:10.32604/cmes.2025.061608 - 03 March 2025

    Abstract Email communication plays a crucial role in both personal and professional contexts; however, it is frequently compromised by the ongoing challenge of spam, which detracts from productivity and introduces considerable security risks. Current spam detection techniques often struggle to keep pace with the evolving tactics employed by spammers, resulting in user dissatisfaction and potential data breaches. To address this issue, we introduce the Divide and Conquer-Generative Adversarial Network Squeeze and Excitation-Based Framework (DaC-GANSAEBF), an innovative deep-learning model designed to identify spam emails. This framework incorporates cutting-edge technologies, such as Generative Adversarial Networks (GAN), Squeeze and… More >

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