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Evolutionary algorithms have been successfully employed to find the best structure for many learning algorithms including neural networks. Due to their flexibility and promising results, Convolutional Neural Networks (CNNs) have found their application in many image processing applications. The structure of CNNs greatly affects the performance of these algorithms both in terms of accuracy and computational cost, thus, finding the best architecture for these networks is a crucial task before they are employed. In this paper, we develop a genetic programming approach for the optimization of CNN structure in diagnosing COVID-19 cases via X-ray images. A graph representation for CNN architecture is proposed and evolutionary operators including crossover and mutation are specifically designed for the proposed representation. The proposed architecture of CNNs is defined by two sets of parameters, one is the skeleton which determines the arrangement of the convolutional and pooling operators and their connections and one is the numerical parameters of the operators which determine the properties of these operators like filter size and kernel size. The proposed algorithm in this paper optimizes the skeleton and the numerical parameters of the CNN architectures in a co-evolutionary scheme. The proposed algorithm is used to identify covid-19 cases via X-ray images. • A novel optimization algorithm is proposed for image thresholding. • Three different fitness functions are used for optimization. • Experimental analysis are performed. [ABSTRACT FROM AUTHOR]

Coronavirus (COVID-19) is a newly discovered viral disease from the SARS-CoV-2 family. This has caused a moral panic resulting in the spread of informative and uninformative information about COVID-19 and its effects. Twitter is a popular social media platform used extensively during the current outbreak. This paper aims to predict informative tweets related to COVID-19 on Twitter using a novel set of fuzzy rules involving deep learning techniques. This study focuses on identifying informative tweets during the pandemic to provide the public with trustworthy information and forecast how quickly diseases could spread. In this case, we have implemented RoBERTa and CT-BERT models using the fuzzy methodology to identify COVID-19 patient tweets. The proposed architecture combines deep learning transformer models RoBERTa and CT-BERT with the fuzzy technique to categorize posts as INFORMATIVE or UNINFORMATIVE. We performed a comparative analysis of our method with machine learning models and deep learning approaches. The results show that our proposed model can classify informative and uninformative tweets with an accuracy of 91.40% and an F1-score of 91.94% using the COVID-19 English tweet dataset. The proposed model is accurate and ready for real-world application. • The article presents a fuzzy ensemble deep learning model. • The combination of pre-trained transformers RoBERTa, and CT-BERT, were used. • The latest COVID-19 dataset was classified using the proposed model. • The model has shown 91.40 percent accuracy and 91.94 percent F1-score. • The proposed model can be helpful in real-time applications. [ABSTRACT FROM AUTHOR]