@Article{JICS-15-146,
author = {Lei Zhao},
title = {Research on user behavior recognition based on 2D-CNN},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {15},
number = {2},
pages = {146--153},
abstract = {In this paper, we studied the application of two-dimensional convolutional neural networks 
to the classification of multivariate time series. Time series sample data is usually a set of measurement 
values  of  a  single  attribute  or  multiple  attributes  at  continuous  time  points  separated  by  uniform  time 
intervals. It is a set of structured data, usually non-discrete, time-related between data Features such as 
sex, feature space, and large dimension. At present, most methods for time series classification problems 
need to go through an extremely complex data preprocessing process and related feature engineering and 
do not consider the long pattern information hidden in different time dimensions of time series data, and 
the different characteristics of multivariate time series data Relevant information in the space dimension 
between. By converting multivariate time series data into matrix form, this paper proposes an end-to-end 
deep learning model Pyramid-CNN based on two-dimensional convolutional neural networks, which uses 
two-dimensional convolution kernels to extract the spatial dimensions of multivariate time series data and 
the relevant information in the time dimension, and applied it to the user behavior recognition time series 
data set. The experimental results show that for this data set, compared with the existing methods, the 
model proposed in this paper has higher performance Accuracy and robustness, with a good classification 
effect. 
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22390.html}
}