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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 - 11 March 2024

    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

    Transcriptome-Wide Identification and Functional Analysis of PgSQE08-01 Gene in Ginsenoside Biosynthesis in Panax ginseng C. A. Mey.

    Lei Zhu1,#, Lihe Hou1,3,#, Yu Zhang1, Yang Jiang1, Yi Wang1,2, Meiping Zhang1,2, Mingzhu Zhao1,2,*, Kangyu Wang1,2,*

    Phyton-International Journal of Experimental Botany, Vol.93, No.2, pp. 313-327, 2024, DOI:10.32604/phyton.2024.047938 - 27 February 2024

    Abstract Panax ginseng C. A. Mey. is an important plant species used in traditional Chinese medicine, whose primary active ingredient is a ginsenoside. Ginsenoside biosynthesis is not only regulated by transcription factors but also controlled by a variety of structural genes. Nonetheless, the molecular mechanism underlying ginsenoside biosynthesis has always been a topic in the discussion of ginseng secondary metabolites. Squalene epoxidase (SQE) is a key enzyme in the mevalonic acid pathway, which affects the biosynthesis of secondary metabolites such as terpenoid. Using ginseng transcriptome, expression, and ginsenoside content databases, this study employed bioinformatic methods to systematically… More >

  • Open Access

    ARTICLE

    Identification of Important FPGA Modules Based on Complex Network

    Senjie Zhang1,2, Jinbo Wang2,*, Shan Zhou2, Jingpei Wang2,3, Zhenyong Zhang4,*, Ruixue Wang2

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1027-1047, 2024, DOI:10.32604/cmc.2023.046355 - 30 January 2024

    Abstract The globalization of hardware designs and supply chains, as well as the integration of third-party intellectual property (IP) cores, has led to an increased focus from malicious attackers on computing hardware. However, existing defense or detection approaches often require additional circuitry to perform security verification, and are thus constrained by time and resource limitations. Considering the scale of actual engineering tasks and tight project schedules, it is usually difficult to implement designs for all modules in field programmable gate array (FPGA) circuits. Some studies have pointed out that the failure of key modules tends to… More >

  • Open Access

    ARTICLE

    A New Encrypted Traffic Identification Model Based on VAE-LSTM-DRN

    Haizhen Wang1,2,*, Jinying Yan1,*, Na Jia1

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 569-588, 2024, DOI:10.32604/cmc.2023.046055 - 30 January 2024

    Abstract Encrypted traffic identification pertains to the precise acquisition and categorization of data from traffic datasets containing imbalanced and obscured content. The extraction of encrypted traffic attributes and their subsequent identification presents a formidable challenge. The existing models have predominantly relied on direct extraction of encrypted traffic data from imbalanced datasets, with the dataset’s imbalance significantly affecting the model’s performance. In the present study, a new model, referred to as UD-VLD (Unbalanced Dataset-VAE-LSTM-DRN), was proposed to address above problem. The proposed model is an encrypted traffic identification model for handling unbalanced datasets. The encoder of the… More >

  • Open Access

    REVIEW

    A Review on the Application of Deep Learning Methods in Detection and Identification of Rice Diseases and Pests

    Xiaozhong Yu1,2,*, Jinhua Zheng1,2

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 197-225, 2024, DOI:10.32604/cmc.2023.043943 - 30 January 2024

    Abstract In rice production, the prevention and management of pests and diseases have always received special attention. Traditional methods require human experts, which is costly and time-consuming. Due to the complexity of the structure of rice diseases and pests, quickly and reliably recognizing and locating them is difficult. Recently, deep learning technology has been employed to detect and identify rice diseases and pests. This paper introduces common publicly available datasets; summarizes the applications on rice diseases and pests from the aspects of image recognition, object detection, image segmentation, attention mechanism, and few-shot learning methods according to More >

  • Open Access

    ARTICLE

    Deep Learning-Based Classification of Rotten Fruits and Identification of Shelf Life

    S. Sofana Reka1, Ankita Bagelikar2, Prakash Venugopal2,*, V. Ravi2, Harimurugan Devarajan3

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 781-794, 2024, DOI:10.32604/cmc.2023.043369 - 30 January 2024

    Abstract The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality, flavor and nutritional value. The primary need for identifying rotten fruits is to ensure that only fresh and high-quality fruits are sold to consumers. The impact of rotten fruits can foster harmful bacteria, molds and other microorganisms that can cause food poisoning and other illnesses to the consumers. The overall purpose of the study is to classify rotten fruits, which can affect the taste, texture, and appearance of other fresh fruits, thereby reducing their shelf life.… More >

  • Open Access

    REVIEW

    AI-Based UAV Swarms for Monitoring and Disease Identification of Brassica Plants Using Machine Learning: A Review

    Zain Anwar Ali1,2,*, Dingnan Deng1, Muhammad Kashif Shaikh3, Raza Hasan4, Muhammad Aamir Khan2

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 1-34, 2024, DOI:10.32604/csse.2023.041866 - 26 January 2024

    Abstract Technological advances in unmanned aerial vehicles (UAVs) pursued by artificial intelligence (AI) are improving remote sensing applications in smart agriculture. These are valuable tools for monitoring and disease identification of plants as they can collect data with no damage and effects on plants. However, their limited carrying and battery capacities restrict their performance in larger areas. Therefore, using multiple UAVs, especially in the form of a swarm is more significant for monitoring larger areas such as crop fields and forests. The diversity of research studies necessitates a literature review for more progress and contribution in… More >

  • Open Access

    ARTICLE

    Identification and Molecular Characterization of the Alkaloid Biosynthesis Gene Family in Dendrobium catenatum

    Liping Yang1,#, Xin Wan2,3,#, Runyang Zhou1, Yingdan Yuan1,*

    Phyton-International Journal of Experimental Botany, Vol.93, No.1, pp. 81-96, 2024, DOI:10.32604/phyton.2023.045389 - 26 January 2024

    Abstract As one of the main active components of Dendrobium catenatum, alkaloids have high medicinal value. The physicochemical properties, conserved domains and motifs, phylogenetic analysis, and cis-acting elements of the gene family members in the alkaloid biosynthesis pathway of D. catenatum were analyzed by bioinformatics, and the expression of the genes in different years and tissues was analyzed by qRT-PCR. There are 16 gene families, including 25 genes, in the D. catenatum alkaloid biosynthesis pathway. The analysis of conserved domains and motifs showed that the types, quantities, and orders of domains and motifs were similar among members of the More >

  • Open Access

    ARTICLE

    A Transient-Pressure-Based Numerical Approach for Interlayer Identification in Sand Reservoirs

    Hao Luo1, Haibo Deng1, Honglin Xiao1, Shaoyang Geng2,*, Fu Hou1, Gang Luo1, Yaqi Li2

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.3, pp. 641-659, 2024, DOI:10.32604/fdmp.2023.043565 - 12 January 2024

    Abstract Almost all sandstone reservoirs contain interlayers. The identification and characterization of these interlayers is critical for minimizing the uncertainty associated with oilfield development and improving oil and gas recovery. Identifying interlayers outside wells using identification methods based on logging data and machine learning is difficult and seismic-based identification techniques are expensive. Herein, a numerical model based on seepage and well-testing theories is introduced to identify interlayers using transient pressure data. The proposed model relies on the open-source MATLAB Reservoir Simulation Toolbox. The effects of the interlayer thickness, position, and width on the pressure response are More >

  • Open Access

    ARTICLE

    Identification and validation of novel prognostic fatty acid metabolic gene signatures in colon adenocarcinoma through systematic approaches

    HENG ZHANG1,#, WENJING CHENG2,#, HAIBO ZHAO2, WEIDONG CHEN2, QIUJIE ZHANG2,*, QING-QING YU2,*

    Oncology Research, Vol.32, No.2, pp. 297-308, 2024, DOI:10.32604/or.2023.043138 - 28 December 2023

    Abstract Background: Colorectal cancer (CRC) belongs to the class of significantly malignant tumors found in humans. Recently, dysregulated fatty acid metabolism (FAM) has been a topic of attention due to its modulation in cancer, specifically CRC. However, the regulatory FAM pathways in CRC require comprehensive elucidation. Methods: The clinical and gene expression data of 175 fatty acid metabolic genes (FAMGs) linked with colon adenocarcinoma (COAD) and normal cornerstone genes were gathered through The Cancer Genome Atlas (TCGA)-COAD corroborating with the Molecular Signature Database v7.2 (MSigDB). Initially, crucial prognostic genes were selected by uni- and multi-variate Cox… More >

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