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Improving Crisis Events Detection Using DistilBERT with Hunger Games Search Algorithm

Author

Listed:
  • Hadeer Adel

    (Department of Computer Science, Faculty of Computer Science, Nahda University, Beni Suef 62511, Egypt)

  • Abdelghani Dahou

    (Mathematics and Computer Science Department, University of Ahmed DRAIA, Adrar 01000, Algeria)

  • Alhassan Mabrouk

    (Mathematics and Computer Science Department, Faculty of Science, Beni-Suef University, Beni Suef 62511, Egypt)

  • Mohamed Abd Elaziz

    (Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt
    Artificial Intelligence Research Center (AIRC), Ajman University, Ajman P.O. Box 346, United Arab Emirates
    Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt)

  • Mohammed Kayed

    (Computer Science Department, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni Suef 62511, Egypt)

  • Ibrahim Mahmoud El-Henawy

    (Department of Computer Science, Faculty of Computer Science, Zagazig University, Zagazig 44519, Egypt)

  • Samah Alshathri

    (Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

  • Abdelmgeid Amin Ali

    (Faculty of Computer Science and Information, Minia University, Minia 61519, Egypt)

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.

Suggested Citation

  • Hadeer Adel & Abdelghani Dahou & Alhassan Mabrouk & Mohamed Abd Elaziz & Mohammed Kayed & Ibrahim Mahmoud El-Henawy & Samah Alshathri & Abdelmgeid Amin Ali, 2022. "Improving Crisis Events Detection Using DistilBERT with Hunger Games Search Algorithm," Mathematics, MDPI, vol. 10(3), pages 1-22, January.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:3:p:447-:d:738737
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    References listed on IDEAS

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    1. Jedsada Phengsuwan & Tejal Shah & Nipun Balan Thekkummal & Zhenyu Wen & Rui Sun & Divya Pullarkatt & Hemalatha Thirugnanam & Maneesha Vinodini Ramesh & Graham Morgan & Philip James & Rajiv Ranjan, 2021. "Use of Social Media Data in Disaster Management: A Survey," Future Internet, MDPI, vol. 13(2), pages 1-24, February.
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    Cited by:

    1. Zhiyuan Hao & Jie Ma & Wenjing Sun, 2022. "The Technology-Oriented Pathway for Auxiliary Diagnosis in the Digital Health Age: A Self-Adaptive Disease Prediction Model," IJERPH, MDPI, vol. 19(19), pages 1-23, September.
    2. Mohamed Abd Elaziz & Abdelghani Dahou & Dina Ahmed Orabi & Samah Alshathri & Eman M. Soliman & Ahmed A. Ewees, 2023. "A Hybrid Multitask Learning Framework with a Fire Hawk Optimizer for Arabic Fake News Detection," Mathematics, MDPI, vol. 11(2), pages 1-15, January.
    3. Abdelghani Dahou & Samia Allaoua Chelloug & Mai Alduailij & Mohamed Abd Elaziz, 2023. "Improved Feature Selection Based on Chaos Game Optimization for Social Internet of Things with a Novel Deep Learning Model," Mathematics, MDPI, vol. 11(4), pages 1-17, February.

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