COVID-19 outbreak: An ensemble pre-trained deep learning model for detecting informative tweets

DeepCov19Net: Automated COVID-19 Disease Detection with a Robust and Effective Technique Deep Learning Approach

“…In Ref. [ 14 ], deep features were extracted from fully connected and convolutional layers of the AlexNet model. A total of 10,568 deep features were reduced to 1500 deep features with the Relief algorithm.…”

“…Up to now, deep learning, which is a subset of machine learning based on neural networks that enable a machine to train itself to carry out a duty, has been used in many tasks such as image classification, environmental sound classification, and biomedical signal classification [ 6 – 12 ]. Generally, deep learning approaches have outperformed hand-crafted-based machine learning approaches [ 13 , 14 ]. Therefore, deep learning techniques have frequently been preferred for automated COVID-19 virus detection.…”

Twitter sentiment analysis using ensemble based deep learning model towards COVID-19 in India and European countries

“…The obtained experimental outcome indicates that the 8% of the tweets in the Philippines were negative against COVID-19 vaccines, 9% people were neutral, and the remaining 83% of the tweets were positive against COVID-19 vaccines. [25] used a dataset of 226,668 tweets related to COVID-19 that was collected between December 2019 to May 2020. In this literature study, the majority voting based ensemble deep learning model was used for sentimental analysis.…”

Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments

“…Pinter et al [106] Multi-layered perceptron Predictions of mortality rate and infected cases Aminu et al [107] Deep neural networks Detection of people with COVID-19 Magar et al [108] Ensemble techniques Virus-antibody sequence analysis and patients' Identification Zeng et al [109] Extreme Gradient Boosting (XGBoost) Forecasting of patient survival probability Ashraf et al [110] Machine & deep learning models Predict the severity of disease or chances of death Shah et al [111] Convolutional neural network (CNN) COVID-19 detection from X-ray images Prakash et al [112] Autoregressive Integrated Moving Average Impact analysis of various policies Rathod et al [113] AI Prediction models Effective crisis preparedness and management Ullah et al [114] Logistic Regression and Support Vector Machine Classification of patients with/without COVID-19 Rathod et al [115] SVM, RProp, and Decision tree Detection of abnormal data for effective analysis Hu et al [116] Spectral Clustering (SC) algorithm Feasible analysis model for the treatment & diagnosis Rashed et al [117] Long short-term memory (LSTM) network Provides public awareness about the risks of COVID-19 Singh et al [118] ResNet152V2 and VGG16 CNN Reduce the high false-negative results of the RT-PCR Saverino et al [119] Digital and artificial intelligence platform (DAIP) Changes implementation in rehabilitation services Peddinti et al [120] Convolutional Neural Network (CNN) Detection of COVID-19 cases in public places Malla et al [121] Ensemble deep learning model Real-time sentiment analysis of COVID-19 data Lella et al [122] Convolutional Neural Network (CNN) model Respiratory sound classification for patient identification Haleem et al [123] Artificial neuronal networks (ANN) Predictions of survival of COVID-19 patients Hashimi et al [124] Deep learning models Tracking and identifying potential virus spreaders Amaral et al [125] Artificial neuronal networks (ANN) forecasting and monitoring the progress of Covid-19 Zgheib et al [126] Collection of ensemble learning methods Detecting COVID-19 virus based on patient's demographics Ferrari et al [127] Bayesian framework Predictions about the behavior of the COVID-19 epidemic Almalki et al [128] COVID Inception-ResNet model (CoVIRNet) Automatic diagnosis of the COVID-19 patients Umair et al …”