A multi hidden recurrent neural network with a modified grey wolf optimizer

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 s...

Full description

Saved in:
Bibliographic Details
Published in:PloS one Vol. 14; no. 3; p. e0213237
Main Authors: Rashid, Tarik A., Abbas, Dosti K., Turel, Yalin K.
Format: Journal Article
Language:English
Published: United States Public Library of Science 27.03.2019
Public Library of Science (PLoS)
Subjects:
Algorithms
Analysis
Area Under Curve
Artificial intelligence
Artificial neural networks
Biology and Life Sciences
College students
Colleges & universities
Comparative analysis
Computer and Information Sciences
Decision making
Early warning systems
Engineering and Technology
Humans
Hybrid systems
Logistic Models
Model accuracy
Neural networks
Neural Networks, Computer
Physical Sciences
Problems
Recurrent neural networks
Research and Analysis Methods
ROC Curve
Students
Taiwan
Kurdistan
Iraq
ISSN:1932-6203, 1932-6203
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0213237