Abstract: Currently, there are issues with low efficiency and outdated Internet of Things resource allocation. To study real Internet of Things user behavior data, a Bayesian optimization algorithm is used to automatically select hyperparameter combinations and construct an Internet of Things user behavior analysis model based on long short-term memory. The results showed that the prediction accuracy of the model reached 96.8% and 97.53% on the training and validation sets, while in the set 50 maximum iterations, the model achieved 80.78% on the test set. In comparing the performance between the research model and the traditional recurrent network model, it was found that the optimal prediction accuracy of the research model was 80.78%, which was better than the comparison model. The application results of the research model in short-term power load forecasting also indicated that the prediction accuracy of the Internet of Things user behavior analysis model based on the improved recurrent network has reached a good level, far superior to the comparative model. The results have important application value for allocating energy and resources in Internet of Things systems.

Keling Bi, “Internet of Things User Behavior Analysis Model Based on Improved RNN” International Journal of Advanced Computer Science and Applications(IJACSA), 15(11), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151120

@article{Bi2024,
title = {Internet of Things User Behavior Analysis Model Based on Improved RNN},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151120},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151120},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {Keling Bi}
}