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Volume 10
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10.3390/math10030447
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Article

Improving Crisis Events Detection Using DistilBERT with Hunger Games Search Algorithm

by
Hadeer Adel
1,
Abdelghani Dahou
2,
Alhassan Mabrouk
3,
Mohamed Abd Elaziz
4,5,6,
Mohammed Kayed
7,
Ibrahim Mahmoud El-Henawy
8,
Samah Alshathri
9,* and
Abdelmgeid Amin Ali
10
1
Department of Computer Science, Faculty of Computer Science, Nahda University, Beni Suef 62511, Egypt
2
Mathematics and Computer Science Department, University of Ahmed DRAIA, Adrar 01000, Algeria
3
Mathematics and Computer Science Department, Faculty of Science, Beni-Suef University, Beni Suef 62511, Egypt
4
Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt
5
Artificial Intelligence Research Center (AIRC), Ajman University, Ajman P.O. Box 346, United Arab Emirates
6
Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
7
Computer Science Department, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni Suef 62511, Egypt
8
Department of Computer Science, Faculty of Computer Science, Zagazig University, Zagazig 44519, Egypt
9
Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
10
Faculty of Computer Science and Information, Minia University, Minia 61519, Egypt
*
Author to whom correspondence should be addressed.
Mathematics 2022, 10(3), 447; https://doi.org/10.3390/math10030447
Submission received: 5 January 2022 / Revised: 24 January 2022 / Accepted: 27 January 2022 / Published: 30 January 2022
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Abstract

This paper presents an alternative event detection model based on the integration between the DistilBERT and a new meta-heuristic technique named the Hunger Games Search (HGS). The DistilBERT aims to extract features from the text dataset, while a binary version of HGS is developed as a feature selection (FS) approach, which aims to remove the irrelevant features from those extracted. To assess the developed model, a set of experiments are conducted using a set of real-world datasets. In addition, we compared the binary HGS with a set of well-known FS algorithms, as well as the state-of-the-art event detection models. The comparison results show that the proposed model is superior to other methods in terms of performance measures.
Keywords: event detection; deep learning; hunger game search; DistilBERT; feature selection optimization algorithms event detection; deep learning; hunger game search; DistilBERT; feature selection optimization algorithms

Share and Cite

MDPI and ACS Style

Adel, H.; Dahou, A.; Mabrouk, A.; Abd Elaziz, M.; Kayed, M.; El-Henawy, I.M.; Alshathri, S.; Amin Ali, A. Improving Crisis Events Detection Using DistilBERT with Hunger Games Search Algorithm. Mathematics 2022, 10, 447. https://doi.org/10.3390/math10030447

AMA Style

Adel H, Dahou A, Mabrouk A, Abd Elaziz M, Kayed M, El-Henawy IM, Alshathri S, Amin Ali A. Improving Crisis Events Detection Using DistilBERT with Hunger Games Search Algorithm. Mathematics. 2022; 10(3):447. https://doi.org/10.3390/math10030447

Chicago/Turabian Style

Adel, Hadeer, Abdelghani Dahou, Alhassan Mabrouk, Mohamed Abd Elaziz, Mohammed Kayed, Ibrahim Mahmoud El-Henawy, Samah Alshathri, and Abdelmgeid Amin Ali. 2022. "Improving Crisis Events Detection Using DistilBERT with Hunger Games Search Algorithm" Mathematics 10, no. 3: 447. https://doi.org/10.3390/math10030447

APA Style

Adel, H., Dahou, A., Mabrouk, A., Abd Elaziz, M., Kayed, M., El-Henawy, I. M., Alshathri, S., & Amin Ali, A. (2022). Improving Crisis Events Detection Using DistilBERT with Hunger Games Search Algorithm. Mathematics, 10(3), 447. https://doi.org/10.3390/math10030447

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