Human resource management involves a variety of data processing, and the process is complicated. In order to improve the effect of human resource management, this paper combines BP neural network and logistic regression analysis to construct an intelligent human resource management system and uses backpropagation learning to adjust training errors and determine connection weights. Moreover, this paper estimates the probability of a certain event through regression analysis, predicts and analyzes the human resource management process, and builds an intelligent human resource management system with the support of joint algorithms. In order to explore the reliability of the joint algorithm proposed in this paper, the effectiveness of the algorithm proposed in this paper is verified through simulation tests. The experimental research results show that the human resource management system based on BP neural network and logistic regression proposed in this paper has good practical effects.
One of the most pressing challenges in people's life is food safety. While many people prefer to purchase meals online since the dawn of the Internet era, regulating food safety online confronts numerous obstacles. A set of food safety evaluation data on violations and dangers was generated by analyzing feedback data from third-party operating systems. A distributed long-term and short-term memory network model was proposed to estimate trader risk values, and a quick warning system for or network attractors was constructed to find the association between opinion data and the amount of online food dangers. Using LSTMbased group learning, this research provides a method for categorizing food safety papers (long-term and short-term memory). First, due to the high cost of human annotation, the food safety document set only comprises one layer of the sample, and food safety document classification based on such a set is a one-layer classification. We propose an automatic body expansion strategy based on a large number of unlabelled web news reports (documents unrelated to food safety) and a binary-based food safety document collection. Select an LSTM-based group learning algorithm for document classification. Food safety documents can be automatically detected from high-performance websites using document classification algorithms based on LSTM-based group learning algorithms.
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