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Deep Learning-Based Mathematical Modelling For Predictive Analysis in Media Consumer Behaviour |
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PP: 433-443 |
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doi:10.18576/amis/180217
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Author(s) |
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Abd Elmotaleb A. M. A. Elamin,
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Abstract |
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| Advanced predictive models are required to understand and predict consumer behavior due to the rapid evolution of media consumption patterns. This work aims to improve the accuracy of predictive analyses in media consumer behavior by introducing a new method as Bayesian optimized Long-Short Term Memory (LSTM)-Based Mathematical Modelling. The proposed model uses Bayesian optimization techniques to improve performance in capturing temporal dependencies within media consumption data by optimizing LSTM networks for hyperparameter tuning. Models based on long short-term dependencies in sequential data are based on recurrent neural networks, a class of networks well-known for this capacity. To ensure that the LSTM model is precisely tuned to the particular features of media consumption datasets, the Bayesian optimization framework makes it easier to tune hyperparameters automatically. A more accurate and efficient representation of the complex patterns present in media consumer behavior is made possible by combining LSTM networks and Bayesian optimization. The mathematical model based on Bayesian optimized LSTM is increased the accuracy with 99%, which is 9.62% higher than the accuracy of Random Forests, RNN Based Click Stream Model and Gradient Tree Boosting Method. In an era of constant technological and content evolution, the results of this work adds to the growing field of predictive analytics by providing a potent tool for comprehending and forecasting the dynamic nature of media consumer behavior.
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