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

  • Autor:

    Feriz, A.H. / Jalali, M. / Forghani, Y. (2024)


  • Quelle:

    Concurrency Computat Pract Exper., 2024, e8123, doi 10.1002/cpe.8123

  • Datum: April 2024
  • Abstract

    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.