<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, we first constructed a deep learning model, optimized the LSTM model to get the BiLSTM model based on the long and short-term memory network, and used the generative adversarial network to calculate the probability distribution of data. Then, the advantages of deep learning in intelligent big data visualization and analysis are explored from the dimensions of data preprocessing, dimension anchor layout, coordinate expansion and data analysis. Finally, the efficiency of the deep learning model is compared with that of other algorithms using indicators such as accuracy and recall, and the feasibility of this paper’s method is verified by empirical analysis using intelligent transportation data as an example. The results show that the model in this paper achieves an accuracy rate of 95.5%, the loss rate is stable at 0.2% to 0.4%, and the average running time is maintained at 20ms, which are all better than other models. The predicted and real values of traffic data for the Deep-STCL model using deep learning basically match, indicating that the deep learning model has obvious advantages in data visualization and analysis.</p>
</abstract>

In this paper, we first constructed a deep learning model, optimized the LSTM model to get the BiLSTM model based on the long and short-term memory network, and used the generative adversarial network to calculate the probability distribution of data. Then, the advantages of deep learning in intelligent big data visualization and analysis are explored from the dimensions of data preprocessing, dimension anchor layout, coordinate expansion and data analysis. Finally, the efficiency of the deep learning model is compared with that of other algorithms using indicators such as accuracy and recall, and the feasibility of this paper’s method is verified by empirical analysis using intelligent transportation data as an example. The results show that the model in this paper achieves an accuracy rate of 95.5%, the loss rate is stable at 0.2% to 0.4%, and the average running time is maintained at 20ms, which are all better than other models. The predicted and real values of traffic data for the Deep-STCL model using deep learning basically match, indicating that the deep learning model has obvious advantages in data visualization and analysis.

Intelligent big data visual analytics based on deep learning

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Intelligent big data visual analytics based on deep learning"}

In this paper, we first constructed a deep learning model, optimized the LSTM model to get the BiLSTM model based on the long and short-term memory network,...

Intelligent big data visual analytics based on deep learning

Data publikacji: 18 gru 2023

Zakres stron: -

Otrzymano: 26 mar 2023

Przyjęty: 28 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01539

Słowa kluczowe
Deep learning, LSTM model, Generative adversarial network, Long short-term memory network, Data visualization

© 2023 Ruixiang Guo, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Intelligent big data visual analytics based on deep learning

Data publikacji: 18 gru 2023

Zakres stron: -

Otrzymano: 26 mar 2023

Przyjęty: 28 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01539

Słowa kluczoweDeep learning, LSTM model, Generative adversarial network, Long short-term memory network, Data visualization

© 2023 Ruixiang Guo, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Deep learning, LSTM model, Generative adversarial network, Long short-term memory network, Data visualization