Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks.

The novel human coronavirus disease COVID-19 has become the fifth documented pandemic since the 1918 flu pandemic. COVID-19 was first reported in Wuhan, China, and subsequently spread worldwide. Almost all of the countries of the world are facing this natural challenge. We present forecasting models to estimate and predict COVID-19 outbreak in Asia Pacific countries, particularly Pakistan, Afghanistan, India, and Bangladesh. We have utilized the latest deep learning techniques such as Long Short Term Memory networks (LSTM), Recurrent Neural Network (RNN), and Gated Recurrent Units (GRU) to quantify the intensity of pandemic for the near future. We consider the time variable and data non-linearity when employing neural networks. Each model's salient features have been evaluated to foresee the number of COVID-19 cases in the next 10 days. The forecasting performance of employed deep learning models shown up to July 01, 2020, is more than 90% accurate, which shows the reliability of the proposed study. We hope that the present comparative analysis will provide an accurate picture of pandemic spread to the government officials so that they can take appropriate mitigation measures.

Deep learning based hybrid prediction model for predicting the spread of COVID-19 in the world's most populous countries.

COVID-19 has a disease and health phenomenon and has sociological and economic adverse effects. Accurate prediction of the spread of the epidemic will help in the planning of health management and the development of economic and sociological action plans. In the literature, there are many studies to analyse and predict the spread of COVID-19 in cities and countries. However, there is no study to predict and analyse the cross-country spread in the world's most populous countries. In this study, it was aimed to predict the spread of the COVID-19 epidemic. The motivation of this study is to reduce the workload of health workers, take preventive measures and optimize health processes by predicting the spread of the COVID-19 epidemic. A hybrid deep learning model was developed to predict and analyse COVID-19 cross-country spread and a case study was carried out for the world's most populous countries. The developed model was tested extensively using RMSE, MAE and R2. The experimental results showed that the developed model was more successful in predicting and analysis of COVID-19 cross-country spread in the world's most populous countries than LR, RF, SVM, MLP, CNN, GRU, LSTM and base CNN-GRU. In the developed model, CNN performs convolution and pooling operations to extract spatial features from the input data. GRU provides learning of long-term and non-linear relationships inferred by CNN. The developed hybrid model was more successful than the other models compared, as it enabled the effective features of the CNN and GRU models to be used together. The prediction and analysis of the cross-country spread of COVID-19 in the world's most populated countries can be presented as a novelty of this study.

Modelling Electricity Consumption During the COVID19 Pandemic: Datasets, Models, Results and a Research Agenda.

The COVID19 pandemic has impacted the global economy, social activities, and Electricity Consumption (EC), affecting the performance of historical data-based Electricity Load Forecasting (ELF) algorithms. This study thoroughly analyses the pandemic's impact on these models and develop a hybrid model with better prediction accuracy using COVID19 data. Existing datasets are reviewed, and their limited generalization potential for the COVID19 period is highlighted. A dataset of 96 residential customers, comprising 36 and six months before and after the pandemic, is collected, posing significant challenges for current models. The proposed model employs convolutional layers for feature extraction, gated recurrent nets for temporal feature learning, and a self-attention module for feature selection, leading to better generalization for predicting EC patterns. Our proposed model outperforms existing models, as demonstrated by a detailed ablation study using our dataset. For instance, it achieves an average reduction of 0.56% & 3.46% in MSE, 1.5% & 5.07% in RMSE, and 11.81% & 13.19% in MAPE over the pre- and post-pandemic data, respectively. However, further research is required to address the varied nature of the data. These findings have significant implications for improving ELF algorithms during pandemics and other significant events that disrupt historical data patterns.

A Session-Based Recommender System for Learning Resources

Learning resource recommendation systems can help learners find suitable resources (e.g., books, journals,.) for learning and research. In particular, in the context of online learning due to the impact of the COVID-19 pandemic, the learning resource recommendation is very necessary. In this study, we propose using session-based recommendation systems to suggest the learning resources to the learners. Experiments are performed on a learning resource dataset collected at a local university and a public dataset. After preprocessing the data to convert it to session form, the Neural Attentive Session-based Recommendation (NARM) and Recurrent Neural Networks (GRU4Rec) models were used for training, testing, and comparison. The results show that recommending learning resources according to the NARM model is more effective than that of the GRU4Rec model, and thus, using the session-based recommendation system would be a promising approach for learning resource recommendation.

Model for movement of individuals in society after the corona pandemic using deep learning algorithms

The health crisis that attributed to the rapid spread of the COVID-19 has impacted the globe negatively in terms of economy, education and transport and led to the global lockdown. The risk of the COVID-19 infection has been increased due to a lack of successful cure for the disease. Thus, social distancing is considered as the most appropriate precaution measureto control the viral spread throughout the world. Social distancing means that physical contact between individuals can be prevented to reduce the viral transmission effectively. The purpose of this work is to provide a deep learning model capable of predicting the movement of people in the pandemic to take precautions and control the COVID-19 infection. This model is based on twoLSTMand GRU algorithms. The results show that the GRU is better than LSTM in terms of prediction error rate and duration. © 2023 Author(s).

Outbreak prediction of COVID-19 using recurrent neural network with gated recurrent units.

Respiratory infections corona virus 2-caused inflammatory disorders are CORONAVIRUS DISEASE 2019 (COVID-19) (SARS-CoV-2). A serious corona virus acute disease arose in 2019. Wuhan, China, was the first location to find the virus in December 2019, which has now been spreading all over the world. Recurrent neural networks, together with the use of LSTMs, fail to provide solutions to numerous issues (RNNs). So this paper has proposed RNN with Gated Recurrent Units for the COVID-19 prediction. This paper utilizes system, which was developed to assist nations (the Czech Republic, the United States, India, and Russia) combat the early stages of a newly emerging infection. For instance, the system tracks confirmed and reported cases, and monitors cures and deaths on a daily basis. This was done to allow the relevant parties to have an early grasp of the disastrous damage the lethal virus will bring. The implemented is an ensemble approach of RNN and GRU that work has computed the RMSE value for the different cases such as infected, cure and death across the four different countries.

COVID-19 vaccine rejection causes based on Twitter people's opinions analysis using deep learning.

According to the World Health Organization, vaccine hesitancy was one of the ten major threats to global health in 2019, including the COVID-19 vaccine. The availability of vaccines does not always mean utilization. This is because, people have less confidence in vaccines, which resulted in vaccination hesitancy and developing global decline in vaccine intake and has caused viral disease outbreaks worldwide. Therefore, there is a need to understand people's perceptions about the COVID-19 vaccine to help the manufacturing companies of the vaccine to improve their marketing strategy based on the rejection causes. In this paper, we used multi-class Sentiment Analysis to classify people's opinions from extracted tweets about COVID-19 vaccines, using firstly different Machine Learning (ML) classifiers such as Logistic Regression (LR), Stochastic Gradient Descent, Support Vector Machine, K-Nearest Neighbors, Decision Tree (DT), Multinomial Naïve Bayes, Random Forest and Gradient Boosting and secondly various Deep Learning (DL) models such as Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), RNN-LSTM and RNN-GRU. Then, we investigated the analysis of the negative tweets to identify the causes of rejection using the Latent Dirichlet Allocation (LDA) technique. Finally, we classified these negative tweets according to the rejection causes for all the vaccines using the same selected ML and DL models. The result of SA showed that DT gives the best performance with an accuracy of 92.26% and for DL models, GRU achieved 96.83%. Then, we identified five causes: Lack of safety, Side effect, Production problem, Fake news and Misinformation, and Cost. Furthermore, for the classification of the negative tweets according to the identified rejection causes, the LR achieved the best result with an accuracy of 89.97%. For DL models, the LSTM model showed the best result with an accuracy of 91.66%.

Centralized CNN-GRU Model by Federated Learning for COVID-19 Prediction in India

In 2019, the corona virus was found in Wuhan, China. The corona virus has traveled several countries in the world from the beginning of 2020. The early estimation of COVID-19 cases is one of the efficient approaches to control the pandemic. Many researchers had proposed the deep learning model for the efficient estimation of COVID-19 cases for different provinces in the world. The research work had not focused on the discussion of robustness in the model. In this study, centralized federated-convolutional neural network–gated recurrent unit (Fed-CNN–GRU) model is proposed for the estimation of active cases per day in different provinces of India. In India, the uneven transmission of COVID-19 virus was seen in 36 provinces due to the different geographical areas and population densities. So, the methodology of this study had focused on the development of single deep learning algorithm, which is robust and reliable to estimate the active cases of COVID-19 in different provinces of India. The concept of transfer and federated learning is involved to enhance the estimation of active cases of COVID-19 by the CNN–GRU model. The study had considered the active cases per day dataset for 36 provinces in India from 12 March, 2020 to 17 January, 2022. Based on the study, it is proven that the centralized CNN–GRU model by federated learning had captured the transmission dynamics of COVID-19 in different provinces with an enhanced result. IEEE

Sentiment Analysis of COVID-19 Tweets Using Deep Learning and Lexicon-Based Approaches

Social media is a platform where people communicate, share content, and build relationships. Due to the current pandemic, many people are turning to social networks such as Facebook, WhatsApp, Twitter, etc., to express their feelings. In this paper, we analyse the sentiments of Indian citizens about the COVID-19 pandemic and vaccination drive using text messages posted on the Twitter platform. The sentiments were classified using deep learning and lexicon-based techniques. A lexicon-based approach was used to classify the polarity of the tweets using the tools VADER and NRCLex. A recurrent neural network was trained using Bi-LSTM and GRU techniques, achieving 92.70% and 91.24% accuracy on the COVID-19 dataset. Accuracy values of 92.48% and 93.03% were obtained for the vaccination tweets classification with Bi-LSTM and GRU, respectively. The developed models can assist healthcare workers and policymakers to make the right decisions in the upcoming pandemic outbreaks. © 2023 by the authors.

Sentiment Analysis of Government Policy Regarding PPKM on Twitter Using LSTM

The Policy of PPKM Covid from the government has become a popular topic to be discussed among the public, especially on Twitter. Due to the many responses or opinions about the PPKM that has been implemented by the government in Indonesia. Sentiment Analysis is the basis for research on the issue of Indonesian PPKM by using a deep learning model, namely LSTM. The data collection of tweets is obtained through crawling the data of Twitter API using the 'snscrape' module with the keyword 'PPKM COVID' and the target data is 15,001 tweets. The data is processed and divided into two parts become 80% training data, 20% testing data and using the GRU, BiLSTM and RNN comparison models. Accuracy performance obtained from the four models include LSTM 90%, GRU 89%, BiLSTM 90% and RNN 85%. The comparison of the best accuracy results is obtained from the LSTM and BilSTM models. Furthermore, the result of sentiment obtained a high percentage for negative sentiment with a total percentage of 54.6%, while the positive sentiment had a percentage of 37.0% and neutral sentiment is 8.5%. © 2022 IEEE.

Biological Sequence Classification Using Deep Learning Architectures

Finding similar biological sequences to categorize into respective families is an important task. The present works attempt to use machine learning-based approaches to find the family of a given sequence. The first task in this direction is to convert the sequences to vector representations and then train a model using a suitable machine learning architecture. The second task is to find which family the sequence belongs to. In this work, deep learning-based architectures are proposed to do the task. A comparative study on how effective various deep learning architectures for this problem is also discussed in this work. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Intelligent IoT-BOTNET attack detection model with optimized hybrid classification model

The botnet have developed into a severe risk to Internet of Things (IoT) systems as a result of manufacturers ‘insufficient security policies and end users' lack of security awareness. By default, several ports are open and user credentials are left unmodified. ML and DL strategies have been suggested in numerous latest research for identifying and categorising botnet assaults in the IoT context, but still, it has a few issues like high error susceptibility, working only with a large amount of data, poor quality, and data acquisition. This research provided use of a brand-new IoT botnet detector built on an improved hybrid classifier. The proposed work's main components are "pre-processing, feature extraction, feature selection, and attack detection." Following that, the improved Information Gain (IIG) model is used to choose the most reliable characteristics from the received information. To detect an attack, a hybrid classifier is utilized which can be constructed by integrating the optimized Bi-GRU with the Recurrent Neural Network (RNN). To increase the detection accuracy of IoT-BOTNETS, a novel hybrid optimization approach called SMIE (Slime Mould with Immunity Evolution) is created by conceptually integrating two conventional optimization modes: Coronavirus herd immunity optimizer (CHIO) and the Slime mould algorithm. The final output of the hybrid classifier displays the presence or absence of IoT-BOTNET attacks. The projected model's accuracy is 97%, which is 22.6%, 18.5%, 27.8%, 22.6%, and 24.8% higher than the previous models like GWO+ HC, SSO+ HC, WOA+ HC, SMA+ HC, and CHIO+ HC, respectively. © 2022

COVID-19 and Pneumonia Detection using Hybrid VGG-16 model using Chest X-rays

The time frame of 2020 to present day 2022 primarily highlights the COVID-19 pandemic. The humanity is being largely affected by SARS-CoV-2(The Severe Acute Respiratory Syndrome CoronaVirus 2) because of its highly infectious characteristic which can be even fatal in severe cases. The World Health Organization (WHO), have reported over 544.3 million verified cases of COVID-19 globally till date, including over 6.3 million deaths. The reason why SARS-CoV-2 is considered to be a dangerous illness is due to this relatively high mortality and contagious rates, in addition to asymptomatic individuals also being carriers of the virus. The only way to identify susceptible populations and to attempt to control the spread would be via RT-PCR COVID testing of all individuals, which is time consuming and expensive. The challenges of this testing mechanism and the prolonging end of the pandemic are the primary motivation to bring up an effective system over a large test cases with a reduced time constraints. This paper proposes a combination of the pretrained convolutional neural network, VGG-16(Visual Geometry Group-16) and GRU(Gated Recurrent Unit) to differentiate the Pneumonia and COVID-19 attack from chest X-rays(CXRs). The proposed model employs VGG-16 to extract features from the CXR inputs, and the GRU classifies it. We experimented this model over 6939 CXR images with 3 classes (COVID-19, Pneumonia, and Normal) and the training produced encouraging macro average precision, recall, and f1-score of 0.9525, 0.9524, and 0.9524 respectively. These results indicate hybrid deep learning systems can greatly aid in the early detection of COVID-19 using CXRs and thereby reduce the widespread of the pandemic. We believe that early diagnosis can be easily and effectively done using this model. © 2022 IEEE.

Evaluation of e-learners' concentration using recurrent neural networks.

Recently, interest in e-learning has increased rapidly owing to the lockdowns imposed by COVID-19. A major disadvantage of e-learning is the difficulty in maintaining concentration because of the limited interaction between teachers and students. The objective of this paper is to develop a methodology to predict e-learners' concentration by applying recurrent neural network models to eye gaze and facial landmark data extracted from e-learners' video data. One hundred eighty-four video data of ninety-two e-learners were obtained, and their frame data were extracted using the OpenFace 2.0 toolkit. Recurrent neural networks, long short-term memory, and gated recurrent units were utilized to predict the concentration of e-learners. A set of comparative experiments was conducted. As a result, gated recurrent units exhibited the best performance. The main contribution of this paper is to present a methodology to predict e-learners' concentration in a natural e-learning environment.

A Session-Based Recommender System for Learning Resources

Learning resource recommendation systems can help learners find suitable resources (e.g., books, journals, …) for learning and research. In particular, in the context of online learning due to the impact of the COVID-19 pandemic, the learning resource recommendation is very necessary. In this study, we propose using session-based recommendation systems to suggest the learning resources to the learners. Experiments are performed on a learning resource dataset collected at a local university and a public dataset. After preprocessing the data to convert it to session form, the Neural Attentive Session-based Recommendation (NARM) and Recurrent Neural Networks (GRU4Rec) models were used for training, testing, and comparison. The results show that recommending learning resources according to the NARM model is more effective than that of the GRU4Rec model, and thus, using the session-based recommendation system would be a promising approach for learning resource recommendation. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Intelligent IoT-BOTNET Attack Detection Model with optimized Hybrid Classification Model

The botnet have developed into a severe risk to Internet of Things (IoT) systems as a result of manufacturers ‘insufficient security policies and end users' lack of security awareness. By default, several ports are open and user credentials are left unmodified. ML and DL strategies have been suggested in numerous latest research for identifying and categorising botnet assaults in the IoT context, but still, it has a few issues like high error susceptibility, working only with a large amount of data, poor quality, and data acquisition. This research provided use of a brand-new IoT botnet detector built on an improved hybrid classifier. The proposed work's main components are "pre-processing, feature extraction, feature selection, and attack detection." Following that, the improved Information Gain (IIG) model is used to choose the most reliable characteristics from the received information. To detect an attack, a hybrid classifier is utilized which can be constructed by integrating the optimized Bi-GRU with the Recurrent Neural Network (RNN). To increase the detection accuracy of IoT-BOTNETS, a novel hybrid optimization approach called SMIE (Slime Mould with Immunity Evolution) is created by conceptually integrating two conventional optimization modes: Coronavirus herd immunity optimizer (CHIO) and the Slime mould algorithm. The final output of the hybrid classifier displays the presence or absence of IoT-BOTNET attacks. The projected model's accuracy is 97%, which is 22.6%, 18.5%, 27.8%, 22.6%, and 24.8% higher than the previous models like GWO+ HC, SSO+ HC, WOA+ HC, SMA+ HC, and CHIO+ HC, respectively.

COVID-19 Prediction Model Based on Pre-training and Fine-tuning Strategy

The COVID-19 pandemic has had a serious impact on the global society. Building a mathematical model to predict the number of confirmed cases will help provide a basis for public health decision-making.In a complex and changeable external environment, the infectious disease prediction model based on deep learning has become commonly researched. However, the existing models have high requirements regarding the amount of data and cannot adapt to a scene with scarce data during supervised learning. This results in the reduction of model prediction accuracy.The COVID-19 prediction model P-GRU combined with pre-training and fine-tuning strategy is constructed in this study. By adopting the pre-training strategy on the dataset obtained from a specific region, the model is exposed to more epidemic data in advance. Consequently, it can learn the implicit evolution law of COVID-19, provide more sufficient prior knowledge for model prediction, and use the fixed length series containing recent historical information to predict the number of confirmed cases in the future.During the prediction process, the impact of local restrictive policies on the epidemic trend is considered to realize an accurate prediction of the dataset in the target area. The experimental results demonstrate that the pre-training strategy can effectively improve the prediction performance.Compared to Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Long and Short Term Memory (LSTM) network, and Gated Recurrent Unit (GRU) models, P-GRU model attains excellent performance regarding the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) evaluation indexes. Furthermore, it is more suitable for predicting the transmission trend of COVID-19. © 2022, Editorial Office of Computer Engineering. All rights reserved.

Pronóstico del COVID-19 en Colombia utilizando Redes Neuronales Recurrentes con celdas de gran memoria de corto plazo y unidades recurrentes cerradas

El 6 de marzo del 2020, el primer caso de COVID-19 fue reportado en Colombia, este virus, declarado como una emergencia de salud pública de importancia internacional ha afectado diferentes sectores. Existe un auge en cuanto al número de estudios que buscan hacer pronósticos en diversos aspectos que tienen que ver con este virus. El presente trabajo muestra los aspectos teóricos de las redes neuronales recurrentes y se utilizan para crear una predicción de 60 días sobre los casos acumulados, fallecidos acumulados y recuperados acumulados disponibles desde el 6 de marzo del 2020 hasta el 6 de marzo del 2022. Redes neuronales con celdas GRU y LSTM junto con las clásicas RNN fueron utilizadas para hacer estos pronósticos.Alternate : On march 6 of 2020, the first case of COVID-19 was reported in Colombia. This virus, declared a public health emergency of international importance, has affected different sectors. There is a boom in the number of studies that make forecasts in various aspects that have to do with this virus. The present work shows the theoretical aspects of recurrent neuronal networks and his use to create a 60-day forecast on cumulative cases, cumulative deaths and cumulative recovered, available from march 6 2020 to march 6 2022. Neural networks with GRU and LSTM cells along with the classic RNN were used to make these forecasts.

Vaccine supply chain management: An intelligent system utilizing blockchain, IoT and machine learning.

Vaccination offers health, economic, and social benefits. However, three major issues-vaccine quality, demand forecasting, and trust among stakeholders-persist in the vaccine supply chain (VSC), leading to inefficiencies. The COVID-19 pandemic has exacerbated weaknesses in the VSC, while presenting opportunities to apply digital technologies to manage it. For the first time, this study establishes an intelligent VSC management system that provides decision support for VSC management during the COVID-19 pandemic. The system combines blockchain, internet of things (IoT), and machine learning that effectively address the three issues in the VSC. The transparency of blockchain ensures trust among stakeholders. The real-time monitoring of vaccine status by the IoT ensures vaccine quality. Machine learning predicts vaccine demand and conducts sentiment analysis on vaccine reviews to help companies improve vaccine quality. The present study also reveals the implications for the management of supply chains, businesses, and government.

Covid-19 spread Forecast with respect to vaccination based on LSTM and GRU

All the studies proposed so far have either tackled the issue of Covid 19 spread around the world or the vaccination coverage of a specific region. The relation between Covid 19 and vaccination is not exploited in any of these studies to achieve better performance in the AI models they propose. Our solution is to learn and forecast the trend of Covid 19 spread across the world with respect to vaccination. Preprocessing the data to exempt any covid cases before vaccination would remove the unnecessary training of the machine learning model with patterns that can be considered as noise. Our study introduces each country to a model tailored for them. Our model would allow the researchers to approach Covid 19 spread prediction from a new perspective with respect to vaccination statistics and also to choose which model to presume according to their needs. © 2022 ACM.