Cong Hou¹, Kang Ning¹, Xiuye Wei¹, Yufei Cheng¹, Huatao Yu¹, Haibin Yu², Xia Liu^1,*, Linlin Dong^1,*

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 >

Jianbo Lu^1,2,*, Ruxin Shi², Jin Tong³, Wenqi Cheng⁴, Xiaoya Ma^1,3, 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 >

Aoqi Xu¹, Khalid A. Alattas², Nasreen Kausar³, Ardashir Mohammadzadeh⁴, Ebru Ozbilge^5,*, 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 >

Atheer Joudah^1,*, 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 >

Shujing Sun^1,3, Jiale Wu², Jian Yao¹, Yang Cheng⁴, Xin Zhang¹, Zhihua Lu³, Pengjiang Qian^1,*

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 >

Yigang Wang¹, Hong Li¹, Wanbin Pan^1,*, 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 >

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 >

Hongxiu Gao¹, Lin Zhu², Tianqi Liu¹, Xueyu Leng¹, Zhenxing Zhu³, Wei Xie¹, Haitao Lv¹, Zhengxun Jin¹, Ping Wu^4,#, Zhongchen Zhang^1,*

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 >

Yeyao Du^1,#, Ye Zhang^2,#, Yang Song¹, Zhuo Zhang¹, Sujie Fan¹, Hanzhu Zhang¹, Piwu Wang^1,*

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 >

Khaled M. Alalayah¹, Fatma S. Alrayes², Jaber S. Alzahrani³, Khadija M. Alaidarous¹, Ibrahim M. Alwayle¹, Heba Mohsen⁴, Ibrahim Abdulrab Ahmed⁵, Mesfer Al Duhayyim^6,*

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 >