Developing An Interactive Web-Based Time Series Forecasting System With Deep Learning And LSTM For Student Enrollment Using Dash

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Published: Dec 30, 2023
DOI: https://doi.org/10.53555/jrtdd.v6i10s(2).2214
Keywords:
Deep Learning LSTM IDSS Forecasting Time series Dash

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Hassan Bousnguar
Lotfi Najdi
Amal Battou

Abstract

Forecasting methods are one of the most promising areas in the data analytics landscape. In this paper we demonstrate the added value of using the LSTM model to forecast the enrollment process in higher education context. We also proposed web-based systems that give the top university manager the ability to make correct decisions.
The system that we develop offers a dashboard to improve strategic decisions for the university to allow managers to make decisions for expansion to new sites or to create new courses.

Article Details

How to Cite
Hassan Bousnguar, Lotfi Najdi, & Amal Battou. (2023). Developing An Interactive Web-Based Time Series Forecasting System With Deep Learning And LSTM For Student Enrollment Using Dash. Journal for ReAttach Therapy and Developmental Diversities, 6(10s(2), 1649–1656. https://doi.org/10.53555/jrtdd.v6i10s(2).2214
Issue
Vol. 6 No. 10s(2) (2023)
Section
Articles
Author Biographies

Hassan Bousnguar

IRF-SIC, Faculty of Science, Ibn Zohr University, Agadir, Morocco, 

Lotfi Najdi

LIMA Laboratory, ENSA, Ibn Zohr University, Agadir, Morocco

Amal Battou

IRF-SIC, Faculty of Science, Ibn Zohr University, Agadir, Morocco

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