Research and Application of Rectified-NAdam Optimization Algorithm in Data Classification

Zhixuan, Zhu; Hou, Zai-En

doi:10.11648/j.ajcst.20210404.13

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…In contrast to the more traditional stochastic gradient descent approach, Adam is an optimization algorithm that can be used to iteratively update weights based on training data [21], [28]. Adam can be characterized as a stochastic gradient descent with momentum and the RMSprop model.…”

Section: Adam Optimizermentioning

confidence: 99%

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An LSTM-based prediction model for gradient-descending optimization in virtual learning environments

Ismanto,

Effendi

2024

Comput Sci Inf Technol

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A virtual learning environment (VLE) is an online learning platform that allows many students, even millions, to study according to their interests without being limited by space and time. Online learning environments have many benefits, but they also have some drawbacks, such as high dropout rates, low engagement, and students' self-regulated behavior. Evaluating and analyzing the students' data generated from online learning platforms can help instructors to understand and monitor students learning progress. In this study, we suggest a predictive model for assessing student success in online learning. We investigate the effect of hyperparameters on the prediction of student learning outcomes in VLEs by the long short-term memory (LSTM) model. A hyperparameter is a parameter that has an impact on prediction results. Two optimization algorithms, adaptive moment estimation (Adam) and Nesterov-accelerated adaptive moment estimation (Nadam), were used to modify the LSTM model's hyperparameters. Based on the findings of research done on the optimization of the LSTM model using the Adam and Nadam algorithm. The average accuracy of the LSTM model using Nadam optimization is 89%, with a maximum accuracy of 93%. The LSTM model with Nadam optimisation performs better than the model with Adam optimisation when predicting students in online learning.

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Section: Adam Optimizermentioning

confidence: 99%

“…To fine-tune the hyperparameters, we used the adaptive moment estimation (Adam) and Nesterov-accelerated adaptive moment estimation (Nadam) optimization algorithms. Adam and Nadam, are the two most effective gradient descent optimization algorithms [21], [22].…”

Section: Introductionmentioning

confidence: 99%