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  • Open Access

    ARTICLE

    PCA-LSTM: An Impulsive Ground-Shaking Identification Method Based on Combined Deep Learning

    Yizhao Wang*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3029-3045, 2024, DOI:10.32604/cmes.2024.046270

    Abstract Near-fault impulsive ground-shaking is highly destructive to engineering structures, so its accurate identification ground-shaking is a top priority in the engineering field. However, due to the lack of a comprehensive consideration of the ground-shaking characteristics in traditional methods, the generalization and accuracy of the identification process are low. To address these problems, an impulsive ground-shaking identification method combined with deep learning named PCA-LSTM is proposed. Firstly, ground-shaking characteristics were analyzed and ground-shaking the data was annotated using Baker’s method. Secondly, the Principal Component Analysis (PCA) method was used to extract the most relevant features related More >

  • Open Access

    ARTICLE

    Network Intrusion Traffic Detection Based on Feature Extraction

    Xuecheng Yu1, Yan Huang2, Yu Zhang1, Mingyang Song1, Zhenhong Jia1,3,*

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 473-492, 2024, DOI:10.32604/cmc.2023.044999

    Abstract With the increasing dimensionality of network traffic, extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems (IDS). However, both unsupervised and semisupervised anomalous traffic detection methods suffer from the drawback of ignoring potential correlations between features, resulting in an analysis that is not an optimal set. Therefore, in order to extract more representative traffic features as well as to improve the accuracy of traffic identification, this paper proposes a feature dimensionality reduction method combining principal component analysis and Hotelling’s T2 and a multilayer convolutional bidirectional… More >

  • Open Access

    ARTICLE

    Modeling and Validation of Base Pressure for Aerodynamic Vehicles Based on Machine Learning Models

    Jaimon Dennis Quadros1, Sher Afghan Khan2, Abdul Aabid3,*, Muneer Baig3

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2331-2352, 2023, DOI:10.32604/cmes.2023.028925

    Abstract The application of abruptly enlarged flows to adjust the drag of aerodynamic vehicles using machine learning models has not been investigated previously. The process variables (Mach number (M), nozzle pressure ratio (η), area ratio (α), and length to diameter ratio (γ )) were numerically explored to address several aspects of this process, namely base pressure (β) and base pressure with cavity (βcav). In this work, the optimal base pressure is determined using the PCA-BAS-ENN based algorithm to modify the base pressure presetting accuracy, thereby regulating the base drag required for smooth flow of aerodynamic vehicles. Based… More > Graphic Abstract

    Modeling and Validation of Base Pressure for Aerodynamic Vehicles Based on Machine Learning Models

  • Open Access

    ARTICLE

    Intelligent Intrusion Detection System for the Internet of Medical Things Based on Data-Driven Techniques

    Okba Taouali1,*, Sawcen Bacha2, Khaoula Ben Abdellafou1, Ahamed Aljuhani1, Kamel Zidi3, Rehab Alanazi1, Mohamed Faouzi Harkat4

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1593-1609, 2023, DOI:10.32604/csse.2023.039984

    Abstract Introducing IoT devices to healthcare fields has made it possible to remotely monitor patients’ information and provide a proper diagnosis as needed, resulting in the Internet of Medical Things (IoMT). However, obtaining good security features that ensure the integrity and confidentiality of patient’s information is a significant challenge. However, due to the computational resources being limited, an edge device may struggle to handle heavy detection tasks such as complex machine learning algorithms. Therefore, designing and developing a lightweight detection mechanism is crucial. To address the aforementioned challenges, a new lightweight IDS approach is developed to… More >

  • Open Access

    ARTICLE

    An Optimized Chinese Filtering Model Using Value Scale Extended Text Vector

    Siyu Lu1, Ligao Cai1, Zhixin Liu2, Shan Liu1, Bo Yang1, Lirong Yin3, Mingzhe Liu4, Wenfeng Zheng1,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1881-1899, 2023, DOI:10.32604/csse.2023.034853

    Abstract With the development of Internet technology, the explosive growth of Internet information presentation has led to difficulty in filtering effective information. Finding a model with high accuracy for text classification has become a critical problem to be solved by text filtering, especially for Chinese texts. This paper selected the manually calibrated Douban movie website comment data for research. First, a text filtering model based on the BP neural network has been built; Second, based on the Term Frequency-Inverse Document Frequency (TF-IDF) vector space model and the doc2vec method, the text word frequency vector and the More >

  • Open Access

    ARTICLE

    PCATNet: Position-Class Awareness Transformer for Image Captioning

    Ziwei Tang1, Yaohua Yi2,*, Changhui Yu2, Aiguo Yin3

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6007-6022, 2023, DOI:10.32604/cmc.2023.037861

    Abstract Existing image captioning models usually build the relation between visual information and words to generate captions, which lack spatial information and object classes. To address the issue, we propose a novel Position-Class Awareness Transformer (PCAT) network which can serve as a bridge between the visual features and captions by embedding spatial information and awareness of object classes. In our proposal, we construct our PCAT network by proposing a novel Grid Mapping Position Encoding (GMPE) method and refining the encoder-decoder framework. First, GMPE includes mapping the regions of objects to grids, calculating the relative distance among… More >

  • Open Access

    ARTICLE

    Whale Optimization Algorithm-Based Deep Learning Model for Driver Identification in Intelligent Transport Systems

    Yuzhou Li*, Chuanxia Sun, Yinglei Hu

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3497-3515, 2023, DOI:10.32604/cmc.2023.035878

    Abstract Driver identification in intelligent transport systems has immense demand, considering the safety and convenience of traveling in a vehicle. The rapid growth of driver assistance systems (DAS) and driver identification system propels the need for understanding the root causes of automobile accidents. Also, in the case of insurance, it is necessary to track the number of drivers who commonly drive a car in terms of insurance pricing. It is observed that drivers with frequent records of paying “fines” are compelled to pay higher insurance payments than drivers without any penalty records. Thus driver identification act… More >

  • Open Access

    ARTICLE

    Infrared Spectroscopy-Based Chemometric Analysis for Lard Differentiation in Meat Samples

    Muhammad Aadil Siddiqui1,*, M. H. Md Khir1, Zaka Ullah2, Muath Al Hasan2, Abdul Saboor3, Saeed Ahmed Magsi1

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2859-2871, 2023, DOI:10.32604/cmc.2023.034164

    Abstract One of the most pressing concerns for the consumer market is the detection of adulteration in meat products due to their preciousness. The rapid and accurate identification mechanism for lard adulteration in meat products is highly necessary, for developing a mechanism trusted by consumers and that can be used to make a definitive diagnosis. Fourier Transform Infrared Spectroscopy (FTIR) is used in this work to identify lard adulteration in cow, lamb, and chicken samples. A simplified extraction method was implied to obtain the lipids from pure and adulterated meat. Adulterated samples were obtained by mixing… More >

  • Open Access

    ARTICLE

    Changes of protein expression during tumorosphere formation of small cell lung cancer circulating tumor cells

    SANDRA STICKLER1, BARBARA RATH1, MAXIMILIAN HOCHMAIR2, CLEMENS LANG3, LUKAS WEIGL4, GERHARD HAMILTON1,*

    Oncology Research, Vol.31, No.1, pp. 13-22, 2023, DOI:10.32604/or.2022.027281

    Abstract Small cell lung cancer (SCLC) is frequently disseminated and has a dismal prognosis with survival times of approximately two years. This cancer responds well to initial chemotherapy but recurs within a short time as a globally chemoresistant tumor. Circulating tumor cells (CTCs) are held responsible for metastasis, the extremely high numbers of these cells in advanced SCLC allowed us to establish several permanent CTC cell lines. These CTCs are distinguished by the spontaneous formation of large spheroids, termed tumorospheres, in regular tissue culture. These contain quiescent and hypoxic cells in their interior and are associated… More >

  • Open Access

    ARTICLE

    An Improved LSTM-PCA Ensemble Classifier for SQL Injection and XSS Attack Detection

    Deris Stiawan1, Ali Bardadi1, Nurul Afifah1, Lisa Melinda1, Ahmad Heryanto1, Tri Wanda Septian1, Mohd Yazid Idris2, Imam Much Ibnu Subroto3, Lukman4, Rahmat Budiarto5,*

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1759-1774, 2023, DOI:10.32604/csse.2023.034047

    Abstract The Repository Mahasiswa (RAMA) is a national repository of research reports in the form of final assignments, student projects, theses, dissertations, and research reports of lecturers or researchers that have not yet been published in journals, conferences, or integrated books from the scientific repository of universities and research institutes in Indonesia. The increasing popularity of the RAMA Repository leads to security issues, including the two most widespread, vulnerable attacks i.e., Structured Query Language (SQL) injection and cross-site scripting (XSS) attacks. An attacker gaining access to data and performing unauthorized data modifications is extremely dangerous. This… More >

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