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      Disease Control and Prevention in Rare Plants Based on the Dominant Population Selection Method in Opportunistic Social Networks

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      1 , 1 , 1 , 2 ,
      Computational Intelligence and Neuroscience
      Hindawi

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          Abstract

          The spread of seeds of rare and dangerous plants affects the regeneration, pattern, genetic structure, invasion, and settlement of plant populations. However, seed transmission is a relatively weak research link. The spread of plant seeds is not controlled by the communicator. Rather, this event results from the interaction between the host and the external environment determined by the mother. The way plants transmit and accept seeds is similar to how user nodes accept data transmission requests in social networks. Plants select the characteristics including seed size, maturity time, and gene matching, which are consistent with the size, delay, and keywords of the data received by the user. In this study, we selected rare and endangered Pterospermum heterophyllum as the research object and applied them to a social network. All plants were considered nodes and all seeds as transmitted data. This method avoids the influence of errors in actual sampling and statistical laws. By using historical information to record the reception of seeds, the Infection and Immunity Algorithm (IAIA) in opportunistic social networks was established. This method selects healthy plants through plant social populations and reduces the number of diseased plants. The experimental results show that the IAIA algorithm has a good effect in distinguishing dominant seedlings from seedlings with disease genes and realizes the selection of dominant plants in social networks.

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          Detection of plant leaf diseases using image segmentation and soft computing techniques

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            A novel PCA–whale optimization-based deep neural network model for classification of tomato plant diseases using GPU

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              Hand gesture classification using a novel CNN-crow search algorithm

              Human–computer interaction (HCI) and related technologies focus on the implementation of interactive computational systems. The studies in HCI emphasize on system use, creation of new techniques that support user activities, access to information, and ensures seamless communication. The use of artificial intelligence and deep learning-based models has been extensive across various domains yielding state-of-the-art results. In the present study, a crow search-based convolution neural networks model has been implemented in gesture recognition pertaining to the HCI domain. The hand gesture dataset used in the study is a publicly available one, downloaded from Kaggle. In this work, a one-hot encoding technique is used to convert the categorical data values to binary form. This is followed by the implementation of a crow search algorithm (CSA) for selecting optimal hyper-parameters for training of dataset using the convolution neural networks. The irrelevant parameters are eliminated from consideration, which contributes towards enhancement of accuracy in classifying the hand gestures. The model generates 100 percent training and testing accuracy that justifies the superiority of the model against traditional state-of-the-art models.
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                Author and article information

                Contributors
                Journal
                Comput Intell Neurosci
                Comput Intell Neurosci
                cin
                Computational Intelligence and Neuroscience
                Hindawi
                1687-5265
                1687-5273
                2022
                18 January 2022
                : 2022
                Affiliations
                1School of Computer Science and Engineering, Central South University, Changsha 410083, China
                2Hunan Botanical Garden, Changsha 410116, China
                Author notes

                Academic Editor: Thippa Reddy G

                Article
                10.1155/2022/1489988
                8789441
                35087578
                e8c1c664-ccc6-4981-839b-377efd3c8401
                Copyright © 2022 Jia Wu et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Funding
                Funded by: Natural Science Foundation of Hunan Province
                Award ID: 2018JJ3299
                Award ID: 2018JJ3682
                Funded by: Hunan Forestry Science and Technology Innovation Plan Project
                Award ID: XLK202106-2
                Categories
                Research Article

                Neurosciences
                Neurosciences

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