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

<p>This study on the fusion of deep convolutional neural network (CNN) and extended short-term memory network (LSTM) aims to improve the efficiency and accuracy of broken information recovery. The challenges faced by traditional information recovery techniques are addressed through improved algorithms. The research methodology includes constructing CNN models to automatically extract features and combining LSTM networks to process complex time-series data. We conducted a detailed experimental evaluation of the CNN-LSTM fusion algorithm, including recovery of different types of corrupted data, and compared it with other algorithms. The results show that the CNN-LSTM fusion algorithm has the highest structural similarity (0.9545) and the most minor normalized mean square error (0.0016) for recovering broken video information, outperforming the methods using CNN or LSTM alone. The fusion algorithm dramatically reduces computation time and resource consumption for processing complex datasets. The combination of CNN and LSTM significantly improves the performance of broken information recovery, especially in processing video and audio data, and provides new ideas for future information processing techniques.</p>
</abstract>

This study on the fusion of deep convolutional neural network (CNN) and extended short-term memory network (LSTM) aims to improve the efficiency and accuracy of broken information recovery. The challenges faced by traditional information recovery techniques are addressed through improved algorithms. The research methodology includes constructing CNN models to automatically extract features and combining LSTM networks to process complex time-series data. We conducted a detailed experimental evaluation of the CNN-LSTM fusion algorithm, including recovery of different types of corrupted data, and compared it with other algorithms. The results show that the CNN-LSTM fusion algorithm has the highest structural similarity (0.9545) and the most minor normalized mean square error (0.0016) for recovering broken video information, outperforming the methods using CNN or LSTM alone. The fusion algorithm dramatically reduces computation time and resource consumption for processing complex datasets. The combination of CNN and LSTM significantly improves the performance of broken information recovery, especially in processing video and audio data, and provides new ideas for future information processing techniques.

Study on the Application of Improved Deep Convolutional Neural Network Algorithm in Broken Information Recovery

Applied Mathematics and Nonlinear Sciences

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

This study on the fusion of deep convolutional neural network (CNN) and extended short-term memory network (LSTM) aims to improve the efficiency and accuracy...

Study on the Application of Improved Deep Convolutional Neural Network Algorithm in Broken Information Recovery

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 12 gen 2024

Accettato: 21 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0526

Parole chiave
Deep convolutional neural network (CNN), Long short-term memory network (LSTM), Information recovery, Fusion algorithm

© 2024 Sheng Zhou, published by Sciendo

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

Study on the Application of Improved Deep Convolutional Neural Network Algorithm in Broken Information Recovery

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 12 gen 2024

Accettato: 21 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0526

Parole chiaveDeep convolutional neural network (CNN), Long short-term memory network (LSTM), Information recovery, Fusion algorithm

© 2024 Sheng Zhou, published by Sciendo

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

Parole chiave
Deep convolutional neural network (CNN), Long short-term memory network (LSTM), Information recovery, Fusion algorithm