Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (349)
  • Open Access


    Identification and Expression Analysis of Abscisic Acid Signal Transduction Genes in Hemp Seeds

    Cong Hou1, Kang Ning1, Xiuye Wei1, Yufei Cheng1, Huatao Yu1, Haibin Yu2, Xia Liu1,*, Linlin Dong1,*

    Phyton-International Journal of Experimental Botany, Vol.92, No.7, pp. 2087-2103, 2023, DOI:10.32604/phyton.2023.029041

    Abstract Abscisic acid (ABA) is involved in regulating diverse biological processes, but its signal transduction genes and roles in hemp seed germination are not well known. Here, the ABA signaling pathway members, PYL, PP2C and SnRK2 gene families, were identified from the hemp reference genome, including 7 CsPYL (pyrab-actin resistance1-like, ABA receptor), 8 CsPP2CA (group A protein phosphatase 2c), and 7 CsSnRK2 (sucrose nonfermenting1-related protein kinase 2). The content of ABA in hemp seeds in germination stage is lower than that in non-germination stage. Exogenous ABA (1 or 10 μM) treatment had a significant regulatory effect on the selected PYL, PP2C,… More >

  • Open Access


    Lightweight Method for Plant Disease Identification Using Deep Learning

    Jianbo Lu1,2,*, Ruxin Shi2, Jin Tong3, Wenqi Cheng4, Xiaoya Ma1,3, Xiaobin Liu2

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 525-544, 2023, DOI:10.32604/iasc.2023.038287

    Abstract In the deep learning approach for identifying plant diseases, the high complexity of the network model, the large number of parameters, and great computational effort make it challenging to deploy the model on terminal devices with limited computational resources. In this study, a lightweight method for plant diseases identification that is an improved version of the ShuffleNetV2 model is proposed. In the proposed model, the depthwise convolution in the basic module of ShuffleNetV2 is replaced with mixed depthwise convolution to capture crop pest images with different resolutions; the efficient channel attention module is added into the ShuffleNetV2 model network structure… More >

  • Open Access


    A Non-singleton Type-3 Fuzzy Modeling: Optimized by Square-Root Cubature Kalman Filter

    Aoqi Xu1, Khalid A. Alattas2, Nasreen Kausar3, Ardashir Mohammadzadeh4, Ebru Ozbilge5,*, Tonguc Cagin5

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 17-32, 2023, DOI:10.32604/iasc.2023.036623

    Abstract In many problems, to analyze the process/metabolism behavior, a model of the system is identified. The main gap is the weakness of current methods vs. noisy environments. The primary objective of this study is to present a more robust method against uncertainties. This paper proposes a new deep learning scheme for modeling and identification applications. The suggested approach is based on non-singleton type-3 fuzzy logic systems (NT3-FLSs) that can support measurement errors and high-level uncertainties. Besides the rule optimization, the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalman filter (SCKF).… More >

  • Open Access


    Survey on Segmentation and Classification Techniques of Satellite Images by Deep Learning Algorithm

    Atheer Joudah1,*, Souheyl Mallat2, Mounir Zrigui1

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4973-4984, 2023, DOI:10.32604/cmc.2023.036483

    Abstract This survey paper aims to show methods to analyze and classify field satellite images using deep learning and machine learning algorithms. Users of deep learning-based Convolutional Neural Network (CNN) technology to harvest fields from satellite images or generate zones of interest were among the planned application scenarios (ROI). Using machine learning, the satellite image is placed on the input image, segmented, and then tagged. In contemporary categorization, field size ratio, Local Binary Pattern (LBP) histograms, and color data are taken into account. Field satellite image localization has several practical applications, including pest management, scene analysis, and field tracking. The relationship… More >

  • Open Access


    Rectal Cancer Stages T2 and T3 Identification Based on Asymptotic Hybrid Feature Maps

    Shujing Sun1,3, Jiale Wu2, Jian Yao1, Yang Cheng4, Xin Zhang1, Zhihua Lu3, Pengjiang Qian1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 923-938, 2023, DOI:10.32604/cmes.2023.027356

    Abstract Many existing intelligent recognition technologies require huge datasets for model learning. However, it is not easy to collect rectal cancer images, so the performance is usually low with limited training samples. In addition, traditional rectal cancer staging is time-consuming, error-prone, and susceptible to physicians’ subjective awareness as well as professional expertise. To settle these deficiencies, we propose a novel deep-learning model to classify the rectal cancer stages of T2 and T3. First, a novel deep learning model (RectalNet) is constructed based on residual learning, which combines the squeeze-excitation with the asymptotic output layer and new cross-convolution layer links in the… More >

  • Open Access


    An Intelligent Identification Approach of Assembly Interface for CAD Models

    Yigang Wang1, Hong Li1, Wanbin Pan1,*, Weijuan Cao1, Jie Miao1, Xiaofei Ai1, Enya Shen2

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 859-878, 2023, DOI:10.32604/cmes.2023.027320

    Abstract Kinematic semantics is often an important content of a CAD model (it refers to a single part/solid model in this work) in many applications, but it is usually not the belonging of the model, especially for the one retrieved from a common database. Especially, the effective and automatic method to reconstruct the above information for a CAD model is still rare. To address this issue, this paper proposes a smart approach to identify each assembly interface on every CAD model since the assembly interface is the fundamental but key element of reconstructing kinematic semantics. First, as the geometry of an… More >

  • Open Access


    Identifying Industrial Control Equipment Based on Rule Matching and Machine Learning

    Yuhao Wang, Yuying Li, Yanbin Sun, Yu Jiang*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 577-605, 2023, DOI:10.32604/cmes.2023.026791

    Abstract To identify industrial control equipment is often a key step in network mapping, categorizing network resources, and attack defense. For example, if vulnerable equipment or devices can be discovered in advance and the attack path can be cut off, security threats can be effectively avoided and the stable operation of the Internet can be ensured. The existing rule-matching method for equipment identification has limitations such as relying on experience and low scalability. This paper proposes an industrial control device identification method based on PCA-Adaboost, which integrates rule matching and machine learning. We first build a rule base from network data… More >

  • Open Access


    Identification of a Novel OsCYP2 Allele that Was Involved in Rice Response to Low Temperature Stress

    Hongxiu Gao1, Lin Zhu2, Tianqi Liu1, Xueyu Leng1, Zhenxing Zhu3, Wei Xie1, Haitao Lv1, Zhengxun Jin1, Ping Wu4,#, Zhongchen Zhang1,*

    Phyton-International Journal of Experimental Botany, Vol.92, No.6, pp. 1743-1763, 2023, DOI:10.32604/phyton.2023.026516

    Abstract Cyclophilin (CYP) plays an important role in plant response to stress, and OsCYP2, one gene of cyclophlilin family, is involved in auxin signal transduction and stress signaling in rice. However, the mechanism that OsCYP2 is involved in rice response to low temperature is still unclear. We identified a new OsCYP2 allelic mutant, lrl3, with fewer lateral roots, and the differences in shoot height, primary root length and adventitious root length increased with the growth process compared to the wild-type plant. Auxin signaling pathway was also affected and became insensitive to gravity. The transgenic rice plants with over-expression of OsCYP2 were… More >

  • Open Access


    Identification of Resistance to Pathogenesis Related Protein GmPR1L in Tobacco Botrytis cinerea Infection

    Yeyao Du1,#, Ye Zhang2,#, Yang Song1, Zhuo Zhang1, Sujie Fan1, Hanzhu Zhang1, Piwu Wang1,*

    Phyton-International Journal of Experimental Botany, Vol.92, No.6, pp. 1907-1920, 2023, DOI:10.32604/phyton.2023.027607

    Abstract Soybean (Glycine max (Linn.) Merr.) annual leguminous crop is cultivated all over the world. The occurrence of diseases has a great impact on the yield and quality of soybean. In this study, based on the RNA-seq of soybean variety M18, a complete CDS (Coding sequence) GmPR1L of the pathogenesis-related protein 1 family was obtained, which has the ability to resist fungal diseases. The overexpression vector and interference expression vector were transferred into tobacco NC89, and the resistance of transgenic tobacco (Nicotiana tabacum L.) to Botrytis cinerea infection was identified. The results show that: Compared with the control, the activities of… More >

  • Open Access


    Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

    Khaled M. Alalayah1, Fatma S. Alrayes2, Jaber S. Alzahrani3, Khadija M. Alaidarous1, Ibrahim M. Alwayle1, Heba Mohsen4, Ibrahim Abdulrab Ahmed5, Mesfer Al Duhayyim6,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3121-3139, 2023, DOI:10.32604/csse.2023.036352

    Abstract With the increased advancements of smart industries, cybersecurity has become a vital growth factor in the success of industrial transformation. The Industrial Internet of Things (IIoT) or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether. In industry 4.0, powerful Intrusion Detection Systems (IDS) play a significant role in ensuring network security. Though various intrusion detection techniques have been developed so far, it is challenging to protect the intricate data of networks. This is because conventional Machine Learning (ML) approaches are inadequate and insufficient to address the demands of dynamic IIoT networks. Further, the existing Deep Learning (DL)… More >

Displaying 1-10 on page 1 of 349. Per Page  

Share Link