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Enhancing CNN‐LSTM neural networks using jellyfish search algorithm for pandemic modeling

Feriz, Azade Hashemi; Jalali, Mehrdad ORCID iD icon 1; Forghani, Yahya
1 Institut für Funktionelle Grenzflächen (IFG), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

This paper presents a comprehensive three-step approach (CNN-JSO-LSTM) for predictive modeling using a pandemic such as COVID-19 as a test case. Initially, a Convolutional Neural Network (CNN) is employed to extract crucial features pertinent to the pandemic. Subsequently, the Jellyfish Search Optimizer (JSO) algorithm is applied for feature selection, identifying the most relevant factors. These chosen features are then inputted into a Long Short-Term Memory (LSTM) network, responsible for classifying samples into “healthy” and “diseased” categories. Our method enhances LSTM performance using the Jellyfish Search optimizer, resulting in exceptional prediction accuracy. Our experiments achieved remarkable metrics, with an accuracy of 95.32%, high sensitivity (94.87%), and precision (94.28%), surpassing alternative methods. In conclusion, our study presents a promising and highly accurate approach for pandemic prediction, harnessing deep learning and swarm intelligence techniques. These findings suggest a potential for more effective pandemic management and intervention strategies.


Verlagsausgabe §
DOI: 10.5445/IR/1000169922
Veröffentlicht am 15.04.2024
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Funktionelle Grenzflächen (IFG)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 12.04.2024
Sprache Englisch
Identifikator ISSN: 1532-0626, 1532-0634
KITopen-ID: 1000169922
HGF-Programm 43.33.11 (POF IV, LK 01) Adaptive and Bioinstructive Materials Systems
Erschienen in Concurrency and Computation: Practice and Experience
Verlag John Wiley and Sons
Band 36
Heft 17
Seiten e8123
Vorab online veröffentlicht am 11.04.2024
Nachgewiesen in Web of Science
Dimensions
Scopus
Globale Ziele für nachhaltige Entwicklung Ziel 3 – Gesundheit und Wohlergehen
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