# A Multi Hidden Recurrent Neural Network with a Modified Grey Wolf Optimizer

arXiv: https://arxiv.org/abs/1903.11712 · HTML: https://mlsanity.com/paper/1903.11712/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2019.

## Built on by (most cited first, 2 of 2)

- [Fitness Dependent Optimizer with Neural Networks for COVID-19 patients](https://mlsanity.com/paper/2302.02986/) (2023)
- [A New K means Grey Wolf Algorithm for Engineering Problems](https://mlsanity.com/paper/2103.05760/) (2021)

## Abstract (arXiv)

Identifying university students' weaknesses results in better learning and can function as an early warning system to enable students to improve. However, the satisfaction level of existing systems is not promising. New and dynamic hybrid systems are needed to imitate this mechanism. A hybrid system (a modified Recurrent Neural Network with an adapted Grey Wolf Optimizer) is used to forecast students' outcomes. This proposed system would improve instruction by the faculty and enhance the students' learning experiences. The results show that a modified recurrent neural network with an adapted Grey Wolf Optimizer has the best accuracy when compared with other models.