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      Research on the Relationship between Human Resource Management Activities and Enterprise Performance Based on the Supervised Learning Model

      1 , 2
      Discrete Dynamics in Nature and Society
      Hindawi Limited

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          Abstract

          HRMS is a very critical tool for companies. The recruitment text contains rich information that can provide strong information support for the company’s recruitment work and also improve the efficiency of job seekers in finding job opportunities. To this end, for the problem of multilabel text classification of recruitment information, this paper provides two algorithms for multilayer classification based on supported SVM. First, the same learning subclass method is used for text sorting subclass acquisition, and then, the class of the text is determined. Second, the hemispherical support SVM is used to find the smallest hypersphere in the feature space that contains the most text of that class and segment the text of that class from other texts. For the text to be classified, the distance from it to the center of each hypersphere is used to determine the class of the text. Experimental results on recruitment data demonstrate that the algorithm in this paper has a high check-all rate, check-accuracy rate, and F1. And, the relationship between HRM activities and corporate performance is discussed.

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          Positioning optimisation based on particle quality prediction in wireless sensor networks

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            Forest Fire Recognition Based on Feature Extraction from Multi-View Images

            Forest fire recognition is important to the protection of forest resources. To effectively monitor forest fires, it is necessary to deploy multiple monitors from different angles. However, most of the traditional recognition models can only recognize single-source images. The neglection of multi-view images leads to a high false positive/negative rate. To improve the accuracy of forest fire recognition, this paper proposes a graph neural network (GNN) model based on the feature similarity of multi-view images. Specifically, the correlations (nodes) between multi-view images and library images were established to convert the input features of graph nodes into the correlation features between different images. Based on feature relationships, the image features in the library were updated to estimate the node similarity in the GNN model, improving the image recognition rate of our model. Furthermore, a fire area feature extraction method was designed based on image segmentation, aiming to simplify the complex preprocessing of images, and effectively extract the key features from images. By setting the threshold in the hue-saturation-value (HSV) color space, the fire area was extracted from the images, and the dynamic features were extracted from the continuous frames of the fire area. Experimental results show that our method recognized forest fires more effectively than the baselines, improving the recognition accuracy by 4%. In addition, the multi-source forest fire data experiment also confirms that our method could adapt to different forest fire scenes, and boast a strong generalization ability and anti-interference ability.
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              Utilizing Active Sensor Nodes in Smart Environments for Optimal Communication Coverage

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                Author and article information

                Contributors
                Journal
                Discrete Dynamics in Nature and Society
                Discrete Dynamics in Nature and Society
                Hindawi Limited
                1607-887X
                1026-0226
                November 28 2021
                November 28 2021
                : 2021
                : 1-7
                Affiliations
                [1 ]School of Business, Hunan International Economics University, Changsha 410000, Hunan, China
                [2 ]School of Business Administration, Hunan University of Finance and Economics, Changsha 410205, Hunan, China
                Article
                10.1155/2021/4094704
                4360fbd8-4e3d-4c33-ac52-ee122efacd2d
                © 2021

                https://creativecommons.org/licenses/by/4.0/


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