Future forecasting prediction of Covid-19 using hybrid deep learning algorithm

Abstract

Due the quick spread of coronavirus disease 2019 , identification of that disease, prediction of mortality rate and recovery rate are considered as one of the critical challenges in the whole world. The occurrence of COVID-19 dissemination beyond the world is analyzed in this research and an artificial-intelligence (AI) based deep learning algorithm is suggested to detect positive cases of COVID19 patients, mortality rate and recovery rate using real-world datasets. Initially, the unwanted data like prepositions, links, hashtags etc., are removed using some pre-processing techniques. After that, term frequency inverse-term frequency (TF-IDF) andBag of Words (BoW) techniques are utilized to extract the features from pre-processed dataset. Then, Mayfly Optimization (MO) algorithm is performed to pick the relevant features from the set of features. Finally, two deep learning procedures, ResNet model and GoogleNet model, are hybridized to achieve the prediction process. Our system examines two different kinds of publicly available text datasets to identify COVID-19 disease as well as to predict mortality rate and recovery rate using those datasets. There are four different datasets are taken to analyse the performance, in which the proposed method achieves 97.56% accuracy which is 1.40% greater than Linear Regression (LR) and Multinomial Naive Bayesian (MNB), 3.39% higher than Random Forest (RF) and Stochastic gradient boosting (SGB) as well as 5.32% higher than Decision tree (DT) and Bagging techniques if first dataset. When compared to existing machine learning models, the simulation result indicates that a proposed hybrid deep learning method is valuable in corona virus identification and future mortality forecast study.

Post Covid-19 Complications Detection Using ML Review Paper

“…Future forecasting prediction of Covid-19 using hybrid deep learning algorithm On four distinct datasets, a performance analysis revealed that the proposed method outperforms Linear Regression (LR), Multinomial Naive Bayesian (MNB), Random Forest (RF), Stochastic gradient boosting (SGB), and Decision Tree (DT) by 1.40 percentage points, 3.39 percentage points, and 5.32 percentage points, respectively. These results indicate that the suggested method outperforms other techniques across multiple datasets, as demonstrated in the study (Yenurkar & Mal, 2022). In this study, a novel approach was developed by modifying the Inception transfer-learning model, which was then validated internally and externally.…”

Multifactor data analysis to forecast an individual's severity over novel COVID‐19 pandemic using extreme gradient boosting and random forest classifier algorithms

“…Peak period, transmission rate, mortality, and positivity may all be predicted using the given mathematical formulae 32 . Using two hybridized deep learning processes, ResNet model and GoogleNet model, as well as a deep learning model using the mayfly optimization approach, Yenurkar et al, 33 offered future forecasting prediction of COVID‐19.…”

An Integrated Framework for Early Differential Diagnosis of COVID-19 using Improved Fuzzy Cognitive Map Approaches

“…However, most studies in the literature have focused on computational approaches using Computed Tomography (CT) images and X-ray images, with Deep Neural Networks being the most commonly used classifier (13) . These image-based approaches may not be suitable for early diagnosis of mild COVID-19 cases, where symptoms overlap with other respiratory diseases like influenza, pneumonia, and tuberculosis (14) .…”