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  • Open Access

    ARTICLE

    Tannins from Acacia mangium Bark as Natural Dyes for Textiles: Characteristics and Applications

    Maya Ismayati1,*, Nissa Nurfajrin Solihat1, Fifi Melinda Setiawati2, Wasrin Syafii2, Yuki Tobimatsu3, Deni Zulfiana4

    Journal of Renewable Materials, Vol.12, No.9, pp. 1625-1637, 2024, DOI:10.32604/jrm.2024.054739 - 25 September 2024

    Abstract Tannins are capable of producing natural dyes with antioxidant and antibacterial propertis, while synthetic dyes are commonly used in the textile industry, causing environmental issues like water pollution. This research aims to utilize waste tannins as natural dyes as an alternative to synthetic dyes. This study examined the effect of the extraction method on tannin properties such as phenolic content, antioxidants, and antibacterial activity. In addition, Pyrolysis Gas Chromatography‒Mass Spectrometry (Py-GCMS) analysis was used to identify the effect of extraction temperature on the chemical elucidation of tannin. The effect of tannin concentration was evaluated against… More > Graphic Abstract

    Tannins from <i>Acacia mangium</i> Bark as Natural Dyes for Textiles: Characteristics and Applications

  • Open Access

    ARTICLE

    SteelGuard-yolo: Steel surface defect detection network based on improved YOLOv5s

    Zheng Zhou1, Min Yuan1

    Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, Vol.40, No.2, pp. 1-7, 2024, DOI:10.23967/j.rimni.2024.05.011 - 10 June 2024

    Abstract Steel is playing an increasingly important role in industry, and the detection of defects on its surface is also of great significance. The complex and diverse defects on the steel surface bring great challenges to the detection. In this paper, we propose a SteelGuard-yolo based steel surface defect detection model, whose main role is to improve the existing algorithms for detecting steel surface defects. First, we design the C2f module with weight aggregation and introduce the BiFormer attention mechanism to improve the feature extraction capability of the model. Second, we design a new up-sampling structure More >

  • Open Access

    ARTICLE

    HWD-YOLO: A New Vision-Based Helmet Wearing Detection Method

    Licheng Sun1, Heping Li2,3, Liang Wang1,4,*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4543-4560, 2024, DOI:10.32604/cmc.2024.055115 - 12 September 2024

    Abstract It is crucial to ensure workers wear safety helmets when working at a workplace with a high risk of safety accidents, such as construction sites and mine tunnels. Although existing methods can achieve helmet detection in images, their accuracy and speed still need improvements since complex, cluttered, and large-scale scenes of real workplaces cause server occlusion, illumination change, scale variation, and perspective distortion. So, a new safety helmet-wearing detection method based on deep learning is proposed. Firstly, a new multi-scale contextual aggregation module is proposed to aggregate multi-scale feature information globally and highlight the details… More >

  • Open Access

    ARTICLE

    Joint Biomedical Entity and Relation Extraction Based on Multi-Granularity Convolutional Tokens Pairs of Labeling

    Zhaojie Sun1, Linlin Xing1,*, Longbo Zhang1, Hongzhen Cai2, Maozu Guo3

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4325-4340, 2024, DOI:10.32604/cmc.2024.053588 - 12 September 2024

    Abstract Extracting valuable information from biomedical texts is one of the current research hotspots of concern to a wide range of scholars. The biomedical corpus contains numerous complex long sentences and overlapping relational triples, making most generalized domain joint modeling methods difficult to apply effectively in this field. For a complex semantic environment in biomedical texts, in this paper, we propose a novel perspective to perform joint entity and relation extraction; existing studies divide the relation triples into several steps or modules. However, the three elements in the relation triples are interdependent and inseparable, so we… More >

  • Open Access

    ARTICLE

    A Graph with Adaptive Adjacency Matrix for Relation Extraction

    Run Yang1,2,3, Yanping Chen1,2,3,*, Jiaxin Yan1,2,3, Yongbin Qin1,2,3

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4129-4147, 2024, DOI:10.32604/cmc.2024.051675 - 12 September 2024

    Abstract The relation is a semantic expression relevant to two named entities in a sentence. Since a sentence usually contains several named entities, it is essential to learn a structured sentence representation that encodes dependency information specific to the two named entities. In related work, graph convolutional neural networks are widely adopted to learn semantic dependencies, where a dependency tree initializes the adjacency matrix. However, this approach has two main issues. First, parsing a sentence heavily relies on external toolkits, which can be error-prone. Second, the dependency tree only encodes the syntactical structure of a sentence,… More >

  • Open Access

    ARTICLE

    Multi-Layer Feature Extraction with Deformable Convolution for Fabric Defect Detection

    Jielin Jiang1,2,3,4,*, Chao Cui1, Xiaolong Xu1,2,3,4, Yan Cui5

    Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 725-744, 2024, DOI:10.32604/iasc.2024.036897 - 06 September 2024

    Abstract In the textile industry, the presence of defects on the surface of fabric is an essential factor in determining fabric quality. Therefore, identifying fabric defects forms a crucial part of the fabric production process. Traditional fabric defect detection algorithms can only detect specific materials and specific fabric defect types; in addition, their detection efficiency is low, and their detection results are relatively poor. Deep learning-based methods have many advantages in the field of fabric defect detection, however, such methods are less effective in identifying multi-scale fabric defects and defects with complex shapes. Therefore, we propose… More >

  • Open Access

    ARTICLE

    Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

    Prasanalakshmi Balaji1,*, Omar Alqahtani1, Sangita Babu2, Mousmi Ajay Chaurasia3, Shanmugapriya Prakasam4

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 443-458, 2024, DOI:10.32604/cmes.2024.053158 - 20 August 2024

    Abstract Breast cancer is a significant threat to the global population, affecting not only women but also a threat to the entire population. With recent advancements in digital pathology, Eosin and hematoxylin images provide enhanced clarity in examining microscopic features of breast tissues based on their staining properties. Early cancer detection facilitates the quickening of the therapeutic process, thereby increasing survival rates. The analysis made by medical professionals, especially pathologists, is time-consuming and challenging, and there arises a need for automated breast cancer detection systems. The upcoming artificial intelligence platforms, especially deep learning models, play an More >

  • Open Access

    REVIEW

    Unlocking the Potential: A Comprehensive Systematic Review of ChatGPT in Natural Language Processing Tasks

    Ebtesam Ahmad Alomari*

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 43-85, 2024, DOI:10.32604/cmes.2024.052256 - 20 August 2024

    Abstract As Natural Language Processing (NLP) continues to advance, driven by the emergence of sophisticated large language models such as ChatGPT, there has been a notable growth in research activity. This rapid uptake reflects increasing interest in the field and induces critical inquiries into ChatGPT’s applicability in the NLP domain. This review paper systematically investigates the role of ChatGPT in diverse NLP tasks, including information extraction, Name Entity Recognition (NER), event extraction, relation extraction, Part of Speech (PoS) tagging, text classification, sentiment analysis, emotion recognition and text annotation. The novelty of this work lies in its… More >

  • Open Access

    ARTICLE

    Anomaly Detection in Imbalanced Encrypted Traffic with Few Packet Metadata-Based Feature Extraction

    Min-Gyu Kim1, Hwankuk Kim2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 585-607, 2024, DOI:10.32604/cmes.2024.051221 - 20 August 2024

    Abstract In the IoT (Internet of Things) domain, the increased use of encryption protocols such as SSL/TLS, VPN (Virtual Private Network), and Tor has led to a rise in attacks leveraging encrypted traffic. While research on anomaly detection using AI (Artificial Intelligence) is actively progressing, the encrypted nature of the data poses challenges for labeling, resulting in data imbalance and biased feature extraction toward specific nodes. This study proposes a reconstruction error-based anomaly detection method using an autoencoder (AE) that utilizes packet metadata excluding specific node information. The proposed method omits biased packet metadata such as… More >

  • Open Access

    ARTICLE

    Convolution-Transformer for Image Feature Extraction

    Lirong Yin1, Lei Wang1, Siyu Lu2,*, Ruiyang Wang2, Youshuai Yang2, Bo Yang2, Shan Liu2, Ahmed AlSanad3, Salman A. AlQahtani3, Zhengtong Yin4, Xiaolu Li5, Xiaobing Chen6, Wenfeng Zheng3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 87-106, 2024, DOI:10.32604/cmes.2024.051083 - 20 August 2024

    Abstract This study addresses the limitations of Transformer models in image feature extraction, particularly their lack of inductive bias for visual structures. Compared to Convolutional Neural Networks (CNNs), the Transformers are more sensitive to different hyperparameters of optimizers, which leads to a lack of stability and slow convergence. To tackle these challenges, we propose the Convolution-based Efficient Transformer Image Feature Extraction Network (CEFormer) as an enhancement of the Transformer architecture. Our model incorporates E-Attention, depthwise separable convolution, and dilated convolution to introduce crucial inductive biases, such as translation invariance, locality, and scale invariance, into the Transformer… More >

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