Human resource attendance mechanism based on the internet of things: A method based on data fusion
Qiong Wu, Ying Shen · Internet Technology Letters · 2023
Abstract The development of Internet of Things technology makes people need to deal with an increasing amount of data, which brings the poor data quality, fast data generation speed and other problems. These problems have become one of the main obstacles restricting the development of human resource attendance. At present, data fusion methods as important methods in data fusion technology. This article fully utilizes human resource attendance data and proposes the LSTM‐Attention data fusion method. This method uses LSTM to model the data, extract potential features of the data, and filter out meaningful data in the human resource attendance mechanism through the Attention mechanism to achieve data fusion. Finally, experimental results in this article showed that the LSTM‐Attention data fusion method designed in this article has higher prediction accuracy. It fully mines the correlation between data, and further improves the accuracy of analysis and prediction in the human resource attendance mechanism based on the Internet of Things.