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

Due the quick spread of coronavirus disease 2019 (COVID-19), 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.

Epidemic Prediction using Machine Learning and Deep Learning Models on COVID-19 Data

A catastrophic epidemic of Severe Acute Respiratory Syndrome-Coronavirus, commonly recognised as COVID-19, introduced a worldwide vulnerability to human community. All nations around the world are making enormous effort to tackle the outbreak towards this deadly virus through various aspects such as technology, economy, relevant data, protective gear, lives-risk medications and all other instruments. The artificial intelligence-based researchers apply knowledge, experience and skill set on national level data to create computational and statistical models for investigating such a pandemic condition. In order to make a contribution to this worldwide human community, this paper recommends using machine-learning and deep-learning models to understand its daily accelerating actions together with predicting the future reachability of COVID-19 across nations by using the real-time information from the Johns Hopkins dashboard. In this work, a novel Exponential Smoothing Long-Short-Term Memory Networks Model (ESLSTM) learning model is proposed to predict the virus spread in the near future. The results are evaluated using RMSE and R-Squared values.

ADPT: An Automated Disease Prognosis Tool Towards Classifying Medical Disease Using Hybrid Architecture of Deep Learning Paradigm

The Covid 19 beta coronavirus, commonly known as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently one of the most significant RNA-type viruses in human health. However, more such epidemics occurred beforehand because they were not limited. Much research has recently been carried out on classifying the disease. Still, no automated diagnostic tools have been developed to identify multiple diseases using X-ray, Computed Tomography (CT) scan, or Magnetic Resonance Imaging (MRI) images. In this research, several Tate-of-the-art techniques have been applied to the Chest-Xray, CT scan, and MRI segmented images' datasets and trained them simultaneously. Deep learning models based on VGG16, VGG19, InceptionV3, ResNet50, Capsule Network, DenseNet architecture, Exception and Optimized Convolutional Neural Network (Optimized CNN) were applied to the detecting of Covid-19 contaminated situation, Alzheimer's disease, and Lung infected tissues. Due to efforts taken to reduce model losses and overfitting, the models' performances have improved in terms of accuracy. With the use of image augmentation techniques like flip-up, flip-down, flip-left, flip-right, etc., the size of the training dataset was further increased. In addition, we have proposed a mobile application by integrating a deep learning model to make the diagnosis faster. Eventually, we applied the Image fusion technique to analyze the medical images by extracting meaningful insights from the multimodal imaging modalities. © 2022 IEEE.

Evolution of the Perceptions about Tourist Destinations Affected by Risk Events Using a PANAS-tDL Deep Learning Model

The tourism industry has dynamized the economy of the countries by offering places, as well as related tourism experiences, products, and services. In the context of the COVID-19 pandemic, some of these tourist destinations were affected by subjective perceptions of users on social networks, within stands out Twitter. To achieve an objective perception from user comments posted on Twitter in front of a tourist destination, we propose a PANAS-tDL (Positive and Negative Affect Schedule - Deep Learning) model which integrates into a single structure a neural model inspired by a Stacked neural deep learning model (SDL), as well as the PANAS-t methodology. For this process, a database of comments was available for four destinations (Colombia, Italy, Spain, USA), and its tourist's products and services, before and in the context of COVID-19 pandemic throughout the year 2020. The proposed model made it possible to generate objective perceptions of the tourist destinations and their products and services using an automatic classification of comments in each category defined by the PANAS-t methodology (11-sentiments). The results show how users' perceptions were towards the negative sentiment zone defined by this methodology, according to the evolution of the COVID-19 pandemic worldwide throughout the year 2020. The proposed model also integrated an automatic process of normalisation, lemmatisation and tokenisation (Natural language process - NLP) for the objective characterization of perceptions, and due to its capacity for adaption and learning, it can be extended for the evaluation of new tourist destinations, products or services using comments from different social networks. Copyright © 2021 by Author/s and Licensed by Veritas Publications Ltd., UK.

Multivariate Time Series Forecasting using ARIMAX, SARIMAX, and RNN-based Deep Learning Models on Electricity Consumption

Growing energy consumption has been a contemporary problem, especially in the climate crisis and the COVID-19 pandemic. Many statistical reports have stated that there is an increase in energy consumption from residential households to the industrial sector. Electricity consumption forecasting is extremely important as it supports power system decision-making and management. In this paper, traditional ARIMAX and SARIMAX forecasting models and RNN-based deep learning models were used to model the electricity consumption historical data of a two-storied house located in Houston, Texas, USA. The features used in the modeling process include the daily-average electricity consumption historical data of the two-storied house, day category (weekday, weekend, vacation day, and COVID-lockdown), and weather-related variables. Each model's respective error performance on the testing dataset is compared. The result showed that RNN-based deep learning models outperformed the traditional ARIMAX and SARIMAX models in forecasting the daily-average electricity consumption of the two-storied house and that the performance of the RNN-based deep learning models doesn't differ significantly from each other. © 2022 IEEE.

Deep learning based masked face recognition in the era of the COVID-19 pandemic

During the coronavirus disease 2019 (COVID-19) pandemic, monitoring for wearing masks obtains a crucial attention due to the effect of wearing masks to prevent the spread of coronavirus. This work introduces two deep learning models, the former based on pre-trained convolutional neural network (CNN) which called MobileNetv2, and the latter is a new CNN architecture. These two models have been used to detect masked face with three classes (correct, not correct, and no mask). The experiments conducted on benchmark dataset which is face mask detection dataset from Kaggle. Moreover, the comparison between two models is driven to evaluate the results of these two proposed models. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

Masked Face Recognition Using FaceNet

The global epidemic of COVID-19 has seriously affected people's life. To prevent and control the outbreak, people are required to wear masks, which poses a formidable challenge to the existing face recognition system. A masked face recognition method based on FaceNet is proposed to tackle the problems. In this paper, a smaller model based on the Inception-ResN et Vl model is proposed. The main idea is to reduce filter numbers in each inception block while maintaining the whole structure. The reduced version has much fewer parameters to compute and can recognize faces with and without masks. Comprehensive experiments on both masked and unmasked datasets have been conducted. With 99.79% test accuracy in the masked MS-Celeb-1M dataset, the model trained in this paper can be integrated into existing face recognition programs designed to recognize faces for verification purposes. © 2022 IEEE.

Efficient Artificial Intelligence-based Models for COVID-19 Disease Detection and Diagnosis from CT-Scans

COVID-19 is contagious virus that first emerged in China in 2019's last month. It mainly infects the both the lungs and the respiratory system. The virus has severely impacted life and the economy, which exposed threats to governments worldwide to manage it. Early diagnosis of COVID-19 could help with treatment planning and disease prevention strategies. In this study, we use CT-Scanned images of the lungs to show how COVID-19 may be identified using transfer learning model and investigate which model achieved the best and fastest results. Our primary focus was to detect structural anomalies to distinguish among COVID-19 positive, negative, and normal cases with deep learning methods. Every model received training with and without transfer learning and results were compared for various versions of DenseNet and EfficientNet. Optimal results were obtained using DenseNet201 (99.75%). When transfer learning was applied, all models produced almost similar results. © 2022 IEEE.

COVID-19 Forecasting Using Deep Learning Models

COVID-19, responsible for infecting billions of people and the economy across the globe, requires a detailed study of the trend it follows to develop adequate short-term prediction models for forecasting the number of future cases. In this perspective, it is possible to develop strategic planning in the public health system to avoid deaths as well as managing patients. In this paper, forecast models comprising various artificial intelligence approaches such as support vector regression (SVR), long short term memory (LSTM), bidirectional long short term memory (Bi-LSTM) are assessed for time series prediction of confirmed cases, deaths, and recoveries in ten major countries affected due to COVID-19. The paper also reviewed a deep learning model to forecast the range of increase in COVID-19 infected cases in future days to present a novel method to compute multidimensional representations of multivariate time series and multivariate spatial time series data. The paper enables the researchers to consider a large number of heterogeneous features, such as census data, intra-county mobility, inter-county mobility, social distancing data, past growth of the infection, among others, and learn complex interactions between these features. To fast-track further development and experimentation, the analyzed code could be used to implement the AI in an efficient way. The paper discusses existing theories and research that provide a better understanding of the spread pattern recognition which will help to tackle any future pandemic of similar intensity. We encourage others to further develop a novel modeling paradigm for infectious disease based on GNNs and high resolution mobility data. © 2021 River Publishers. All rights reserved.

Epidemic Prediction using Machine Learning and Deep Learning Models on COVID-19 Data

A catastrophic epidemic of Severe Acute Respiratory Syndrome-Coronavirus, commonly recognised as COVID-19, introduced a worldwide vulnerability to human community. All nations around the world are making enormous effort to tackle the outbreak towards this deadly virus through various aspects such as technology, economy, relevant data, protective gear, lives-risk medications and all other instruments. The artificial intelligence-based researchers apply knowledge, experience and skill set on national level data to create computational and statistical models for investigating such a pandemic condition. In order to make a contribution to this worldwide human community, this paper recommends using machine-learning and deep-learning models to understand its daily accelerating actions together with predicting the future reachability of COVID-19 across nations by using the real-time information from the Johns Hopkins dashboard. In this work, a novel Exponential Smoothing Long-Short-Term Memory Networks Model (ESLSTM) learning model is proposed to predict the virus spread in the near future. The results are evaluated using RMSE and R-Squared values. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

Comparative Predictive Analysis of Mortality Rate after Covid-19 Vaccination Using Various Machine Learning Approaches

This work has mainly targeted in performing comparative real time predictive analysis of mortality rate after having COVID-19 vaccination using different machine learning approaches. In this paper various deep learning models viz. RNN, LSTM and CNN have been utilized to make future prediction on mortality rate on the basis of administered vaccine doses. Firstly, the dataset of confirmed active cases, death cases and administered vaccine doses have been converted from time-series format to supervised learning format, and secondly different deep learning models have been trained and compared based on the transformed dataset. The prediction analysis is performed strictly based on the newest COVID-19 Delta Variant infected cases. The predictive analysis has resulted 15.53% of reduction in mortality rate and 24.67% of reduction in confirmed active cases with increase in vaccination rate. © 2022 IEEE.

Identification of with Face Mask and without Face Mask using Face Recognition Model

In this paper, we present a method for automatically detecting mask elements from faces and synthesizing the affected region with fine details while preserving the original structure of the face. Wearing face masks appears to be a promising approach for reducing COVID-19 spread. Effective recognition technologies are crucial in this situation to keep people's faces hidden in limited places. As a result, in order to train deep learning models to distinguish between those wearing masks and those who aren't, a huge dataset of masked faces is required. A technique has been devised for researching COVID-19 related behaviours and contamination processes. A potential method for minimising COVID-19 transmission through health education has been identified. With 96.70% accuracy, the suggested approach recognised faces with and without masks. They will give you a decent detection result with or without a mask. © 2021 IEEE.

COVID-19 X-ray Prediction Using Machine Learning Models

In this paper, we aim to predict whether the patients are COVID-inflected or not using machine learning models, including AlexNet, VGG, YOLOv5, and ResNet18. Since the X-ray images are large for processing, we utilize image decomposition and selective search to decouple the images and then send them to the network to predict the label. We analyze the available dataset, which concludes that the VGG network obtains the best performance with 86.84%. Furthermore, we find that the detection process in image pre-processing can help the classification method to handle the prediction task. © 2021 IEEE.

An Efficient Deep Learning Model to Detect COVID-19 Using Chest X-ray Images.

The tragic pandemic of COVID-19, due to the Severe Acute Respiratory Syndrome coronavirus-2 or SARS-CoV-2, has shaken the entire world, and has significantly disrupted healthcare systems in many countries. Because of the existing challenges and controversies to testing for COVID-19, improved and cost-effective methods are needed to detect the disease. For this purpose, machine learning (ML) has emerged as a strong forecasting method for detecting COVID-19 from chest X-ray images. In this paper, we used a Deep Learning Method (DLM) to detect COVID-19 using chest X-ray (CXR) images. Radiographic images are readily available and can be used effectively for COVID-19 detection compared to other expensive and time-consuming pathological tests. We used a dataset of 10,040 samples, of which 2143 had COVID-19, 3674 had pneumonia (but not COVID-19), and 4223 were normal (not COVID-19 or pneumonia). Our model had a detection accuracy of 96.43% and a sensitivity of 93.68%. The area under the ROC curve was 99% for COVID-19, 97% for pneumonia (but not COVID-19 positive), and 98% for normal cases. In conclusion, ML approaches may be used for rapid analysis of CXR images and thus enable radiologists to filter potential candidates in a time-effective manner to detect COVID-19.

Prediction and Analysis of Covid-19 Positive Cases Using Deep Learning Model

At the end of December 2019, the COVID-19 virus was the initial report case in China Wuhan City. On March 11, 2020. The Department of Health (WHO) announced COVID-19, a global pandemic. The COVID-19 spread rapidly out all over the world within a few weeks. We will propose to develop a forecasting model of COV-19 positive case predict outbreak in Pakistan using Deep Learning (DL) models. We assessed the main features to forecast patterns and indicated The new COVID-19 disease pattern in Pakistan and other countries of the world. This research will use the deep learning model to measure several COVID-19 positive case reports in Pakistan. LSTM cell to process time-series data forecasts is very efficient. Recurrent neural network processes to handle time-dependent and involve hidden layers are confirmed and predict positive cases and weekly cases reported in the future. Bidirectional LSTM (Bi-LSTM) processes data and information in one direction to predict and analyze the weekly 6-9 days readily forecast the number of positive cases of COVID-19 © 2021 IEEE.

Deep-Cov19-Hate: A Textual-Based Novel Approach for Automatic Detection of Hate Speech in Online Social Networks throughout COVID-19 with Shallow and Deep Learning Models

The use of various online social media platforms rising day by day caused an increase in the correct or incorrect information shared by users, especially during COVID-19. The introduction of COVID-19 on the world agenda gave rise to an overall bad reaction against East Asia (esp. China) in online social media platforms. The social media users who spread degrading, racist, disrespectful, abusive, discriminatory, critical, abuse, harsh, offensive, etc. posts accused the Asian people of being responsible for the outbreak of COVID-19. For this reason, the development of the Hate Speech Detection (HSD) system was necessary in order to prevent the spread of these posts about COVID-19. In this article, a textual-based study on COVID-19-related hate speech (HS) sharing in online social networks was carried out with Shallow Learning (SL) and Deep Learning (DL) methods. In the first step of this study, typical Natural Language Processing (NLP) pipeline was applied for gathered two different datasets. This NLP pipeline was performed using bag of words, term frequency, document matrix, etc. techniques for features extraction representing datasets. Then, ten different SL and DL models were fine-tuned for HS datasets related to COVID-19. Accuracy, precision, sensitivity, and F-score performance measurement criteria were calculated to compare the performance of the SL and DL algorithms for the problem of HSD. The RNN, one of the models proposed for the first and second dataset in HSD, prevailed with the highest accuracy values of 78.7% and 90.3%, respectively. Due to the promising results of all approaches operated in the HSD, they are forecasted to be chosen in the solution of many other social media and network problems related to COVID-19. © 2022, Strojarski Facultet. All rights reserved.

A Deep Learning Based Approach for Patient Pulmonary CT Image Screening to Predict Coronavirus (SARS-CoV-2) Infection.

The novel coronavirus (nCoV-2019) is responsible for the acute respiratory disease in humans known as COVID-19. This infection was found in the Wuhan and Hubei provinces of China in the month of December 2019, after which it spread all over the world. By March, 2020, this epidemic had spread to about 117 countries and its different variants continue to disturb human life all over the world, causing great damage to the economy. Through this paper, we have attempted to identify and predict the novel coronavirus from influenza-A viral cases and healthy patients without infection through applying deep learning technology over patient pulmonary computed tomography (CT) images, as well as by the model that has been evaluated. The CT image data used under this method has been collected from various radiopedia data from online sources with a total of 548 CT images, of which 232 are from 12 patients infected with COVID-19, 186 from 17 patients with influenza A virus, and 130 are from 15 healthy candidates without infection. From the results of examination of the reference data determined from the point of view of CT imaging cases in general, the accuracy of the proposed model is 79.39%. Thus, this deep learning model will help in establishing early screening of COVID-19 patients and thus prove to be an analytically robust method for clinical experts.

Microscopic segmentation and classification of COVID-19 infection with ensemble convolutional neural network.

The detection of biological RNA from sputum has a comparatively poor positive rate in the initial/early stages of discovering COVID-19, as per the World Health Organization. It has a different morphological structure as compared to healthy images, manifested by computer tomography (CT). COVID-19 diagnosis at an early stage can aid in the timely cure of patients, lowering the mortality rate. In this reported research, three-phase model is proposed for COVID-19 detection. In Phase I, noise is removed from CT images using a denoise convolutional neural network (DnCNN). In the Phase II, the actual lesion region is segmented from the enhanced CT images by using deeplabv3 and ResNet-18. In Phase III, segmented images are passed to the stack sparse autoencoder (SSAE) deep learning model having two stack auto-encoders (SAE) with the selected hidden layers. The designed SSAE model is based on both SAE and softmax layers for COVID19 classification. The proposed method is evaluated on actual patient data of Pakistan Ordinance Factories and other public benchmark data sets with different scanners/mediums. The proposed method achieved global segmentation accuracy of 0.96 and 0.97 for classification.

A novel deep learning based method for COVID-19 detection from CT image.

The novel Coronavirus named COVID-19 that World Health Organization (WHO) announced as a pandemic rapidly spread worldwide. Fast diagnosis of the virus infection is critical to prevent further spread of the virus, help identify the infected population, and cure the patients. Due to the increasing rate of infection and the limitations of the diagnosis kit, auxiliary detection tools are needed. Recent studies show that a deep learning model that comes up with the salient information of CT images can aid in the COVID-19 diagnosis. This study proposes a novel deep learning structure that the pooling layer of this model is a combination of pooling and the Squeeze Excitation Block (SE-block) layer. The proposed model uses Batch Normalization and Mish Function to optimize convergence time and performance of COVID-19 diagnosis. A dataset of two public hospitals was used to evaluate the proposed model. Moreover, it was compared to some different popular deep neural networks (DNN). The results expressed an accuracy of 99.03 with a recognition time of test mode of 0.069 ms in graphics processing unit (GPU). Furthermore, the best network results in classification metrics parameters and real-time applications belong to the proposed model.