Stacked Bi-directional Long Short Term Memory model for Multi-Class Arabic Sentiment Analysis on Covid-19

The world has been affected by the Covid-19 epidemic during the last three years. During that period, most people tended to use social networks, where by searching for topics related to Covid-19, information could be provided to manage decisions by organizations or governments about public health. With the importance of the Arabic language, despite the lack of research targeting it, using Arabic language as a source of data and analyzing it due to the large number of users on social networks gives an impetus to understand people's feelings about the Covid-19 pandemic. One of the challenges facing sentiment analysis in Arabic is the use of dialects. The most common and existing methods used have been quite ineffective as they are oblivious to contextual information and cannot handle long-distance word dependencies. The Iraqi Arabic dialect is one of the Arabic dialects that still suffers from a lack of research in sentiment analysis. In this study, the official page of the Iraqi Ministry of Health on Facebook was used to collect and analysis comments. Word2vec model is incorporated to extract words semantic characteristics. To capture contextual features, Stacked Bi-directional Long Short Term Memory model (Stacked Bi-LSTM) utilizes sequential word vectors derived from the Continuous Bag of Words model. When compared to most common and existing approaches, the proposed method performed well. © 2022 IEEE.

A Comparison of LSTM and BiLSTM for Forecasting the Air Pollution Index and Meteorological Conditions in Jakarta

Jakarta is the capital city of Indonesia where air pollution becomes one of the problems that must be properly handled. The historical data of the air pollution index is beneficial for developing models for forecasting future values. One of the advantages of forecasting air pollution is to help people to arrange future plans to reduce the dangerous effect on health. Analyzing a record of meteorological conditions can be used to understand climate change. This paper reports the comparison of Long Short Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models for multivariate forecasting of the air pollution index and meteorological conditions in Jakarta. It also informs the performance of those algorithms for forecasting the observed variables before and during the Coronavirus disease (Covid-19) outbreak to analyze the effect of the pandemic on the environment. The experiments use a historical time series dataset from 2010-2021. The experimental results show that LSTM and BiLSTM work well to forecast PM10, temperature, humidity, and wind speed. In this case study, there are no significant differences in the performance of LSTM and BiLSTM. © 2022 IEEE.

Sentiment Analysis of COVID-19 Tweets Using BiLSTM and CNN-BiLSTM

In a society where people express almost every thought they have on social media, analysing social media for sentiment has become very significant in order to understand what the masses are thinking. Especially microblogging website like twitter, where highly opinionated individuals come together to discuss ongoing socioeconomic and political events happening in their respective countries or happening around the world. For analysing such vast amounts of data generated every day, a model with high efficiency, i.e., less running time and high accuracy, is needed. Sentiment analysis has become extremely useful in this regard. A model trained on a dataset of tweets can help determine the general sentiment of people towards a particular topic. This paper proposes a bidirectional long short-term memory (BiLSTM) and a convolutional bidirectional long short-term memory (CNN-BiLSTM) to classify tweet sentiment;the tweets were divided into three categories—positive, neutral and negative. Specialized word embeddings such as Word2Vec or term frequency—inverse document frequency (tf-idf) were avoided. The aim of this paper is to analyse the performance of deep neural network (DNN) models where traditional classifiers like logistic regression and decision trees fail. The results show that the BiLSTM model can predict with an accuracy of 0.84, and the CNN-BiLSTM model can predict with an accuracy of 0.80. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Smart Short-Term Load Forecasting through Coordination of LSTM-Based Models and Feature Engineering Methods during the COVID-19 Pandemic

Short-term load forecasting is essential for power companies because it is necessary to ensure sufficient capacity. This article proposes a smart load forecasting scheme to forecast the short-term load for an actual sample network in the presence of uncertainties such as weather and the COVID-19 epidemic. The studied electric load data with hourly resolution from the beginning of 2020 to the first seven days of 2021 for the New York Independent Operator is the basis for the modeling. The new components used in this article include the coordination of stacked long short-term memory-based models and feature engineering methods. Also, more accurate and realistic modeling of the problem has been implemented according to the existing conditions through COVID-19 epidemic data. The influential variables for short-term load forecasting through various feature engineering methods have contributed to the problem. The achievements of this research include increasing the accuracy and speed of short-term electric load forecasting, reducing the probability of overfitting during model training, and providing an analytical comparison between different feature engineering methods. Through an analytical comparison between different feature engineering methods, the findings of this article show an increase in the accuracy and speed of short-term load forecasting. The results indicate that combining the stacked long short-term memory model and feature engineering methods based on extra-trees and principal component analysis performs well. The RMSE index for day-ahead load forecasting in the best engineering method for the proposed stacked long short-term memory model is 0.1071. © 2023 Taylor & Francis Group, LLC.

Forecasting domestic waste generation during successive COVID-19 lockdowns by Bidirectional LSTM super learner neural network

Accurate prediction of domestic waste generation is a challenging task for municipalities to implement sustainable waste management strategies. In the present study, domestic waste generation in the Kingdom of Bahrain, representing a Small Island Developing State (SIDS) case study, has been investigated during successive COVID-19 lockdowns due to the pandemic in 2020. Temporal trends of daily domestic waste generation between 2019 and 2020 and their statistical analyses exhibited remarkable variations highlighting the impact of consecutive COVID-19 lockdowns on domestic waste generation. Machine learning has great potential for predicting solid waste generation rates, but only a few studies utilized deep learning approaches. The state-of-the-art Bidirectional Long Short-Term Memory (BiLSTM) network model as a deep learning method is applied to forecast daily domestic waste data in 2020. Bayesian optimization algorithm (BOA) was hybridized with BiLSTM to generate a super learner approach. The performance of the BOA-BiLSTM super learner model was further compared with the statistical ARIMA model. Performance indicators of the developed models using ARIMA and BiLSTM showed that the latter yielded superior performance for short-term forecasts of domestic waste generation. The MAE, RMSE, MAPE, and R2 were 47.38, 60.73, 256.43, and 0.46, respectively, for the ARIMA model, compared to 3.67, 12.57, 0.24, and 0.96, respectively, for the BiLSTM model. Additionally, the relative errors for the BiLSTM model were lower than those of the ARIMA model. This study highlights that the BiLSTM can be a reliable forecasting tool for solid waste management policymakers during public health emergencies. © 2022 Elsevier B.V.

Stock Market Prediction during COVID Using Stacked LSTM

In the field of computation, the art of predicting the stock market has always been a tough nut to crack for researchers. This is because stock prices are highly influential values. The prices depend on many factors, ranging from physical to physiological, rational and irrational, from geopolitical stability to the sentiments of the investors - all play a crucial role. Investors anticipate market conditions in the future for a successful investment. Hence considering the past stock prices as an embodiment of the factors mentioned above, we propose a stacked long-short-term-memory (LSTM) model to predict the closing index of stock prices during this highly uncertain pandemic period using root mean square error (RSME) as the performance indicator. The model is optimized to improve the prediction accuracy in order to achieve high performance stock forecasting. The dataset considered is from NIFTY 50 scaling across four sectors, namely - auto, bank, healthcare and metal from a duration of 30th January 2020 to 31st March 2022. This paper aims to consider the historical data to analyze future patterns and insights. © 2022 IEEE.

District Level Covid Case Prediction using LSTM Models

Covid-19 has had an adverse effect on the world, with more than 440 million cases recorded so far. The outbreak has hampered the country's healthcare and economy. This calls for an accurate prediction model for the prediction of Covid Cases, so that it gives some time to the hospitals and administration, to make the necessary arrangement. For population-dense countries like India, the covid case dynamics of every district is different, hence this requires a district-wise case prediction of Covid Cases. In this paper, we perform prediction of covid cases across all districts of India using different architectures of Long short-term memory (LSTM) and performed a comparative analysis between them. To the best of our knowledge, this is the first such attempt at the district level. Bidirectional LSTM encoder-decoder outperformed other LSTM-based models and, gave a test set MAPE of 15.44, followed by LSTM Encoder Decoder, giving a MAPE of 19.72. © 2022 IEEE.

Forecasting Covid-19 Time Series Data using the Long Short-Term Memory (LSTM)

Confirmed statistical data of Covid-19 cases that have accumulated sourced from (https://corona.riau.go.id/data-statistik/) in Riau Province on June 7, 2021, there were 63441 cases, on June 14, 2021, it increased to 65883 cases, on June 21, 2021, it increased to 67910, and on June 28, 2021, it increased to 69830 cases. Since the beginning of this pandemic outbreak, it has been observed that the case data continues to increase every week until this July. This study predicts cases of Covid-19 time series data in Riau Province using the LSTM algorithm, with a dataset of 64 lines. Long-Short Term Memory has the ability to store memory information for patterns in the data for a long time at the same time. Tests predicting historical data for Covid-19 cases in Riau Province resulted in the lowest RMSE value in the training data, which was 8.87, and the test data, which was 13.00, in the death column. The evaluation of the best MAPE value in the training data, which is 0.23%, is in the recovered column, and the evaluation of the best MAPE value in the test data, which is 0.27%, in the positive_number column. In the test to predict the next 30 days using the LSTM model that has been trained, it was found that the performance evaluation of the prediction results for the positive_number column and the death column was very good, the recovery column was categorized as good, the independent_isolation column and the care_rs column were categorized as poor. © 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Hand-Hygiene Education and Facilitation System for Children

Hand hygiene has become even more im-portant in light of the COVID-19 pandemic, where hands are one of the high-risk transmission routes. Existing hand-hygiene education is focused on one-time training and does not ensure that correct handwashing procedures are undertaken. Our study, therefore, proposes a hand-hygiene education and facilitation system. Compared to previous systems, through an external RGB camera with our proposed image preprocessing and use the 3-D convo-lution and convolutional long short-term memory (Con-vLSTM) models to detect correctness of handwashing postures, which also facilitates children's ability to wash their hands properly through an on-screen tutorial. It also encourages children to develop good handwashing habits through a positive competition and reward system, and helps teachers to understand children's learning pro-gresses. The experimental results showed that the model was able to identify handwashing postures in real-time with 95.12% accuracy in a realistic and variable environ-ment. © 2022 IEEE.

Research on Time Performance Optimization of LSTM Model for Pedestrian Volume Prediction

The prediction of pedestrian volume in public area is of great significance to maintain personnel safety and improve the level of public management. Especially under the situation of COVID-19 prevention and control, the prediction of pedestrian volume within closed public spaces has a higher demand. While long short-term memory (LSTM), is often used to establish the prediction model of time series, for this purpose, taking the pedestrian flow prediction as the application background, the influence of the activation function on the time performance of LSTM model is studied, and an optimized scheme of the activation function, which can significantly improve the time performance while ensuring the prediction accuracy is proposed in this paper. The experimental results based on pedestrian flow prediction show that the time performance of the optimized LSTM model is improved by about 12.8% compared with the traditional model, and the prediction accuracy is even slightly increased. © 2022 IEEE.

COVID-19 Cases Prediction Using Different LSTM Models and Comparison of Effectiveness of Different Models

Large infectivity and transmissibility of COVID-19 caused severe damage to the economy, education and health of many countries. Due to the increasing number of COVID-19 cases in the world, some predictive methods are therefore needed to forecast the number of cases of COVID-19 in the future. Long short-term memory (LSTM) predicts the correlation between confirmed cases and predicts COVID-19 spread over time. The system shall be trained using training data containing confirmed cases. Various parameters considered are the no of positive cases, the number of recovered cases and the no of deaths every day. LSTM models in different types are evaluated for the time series forecasting confirmed cases, deaths and recovery and the accuracy of the prediction is compared. Different LSTM models like bidirectional LSTM, Gated Recurrent unit, W-LSTM and simple LSTM are helps to predict the no of cases in each country. Model performance is measured using the root mean square error, mean absolute percentage error and r2-score indices. Proposed method can be used to predict other types of pandemics for improved planning. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Predicting Future, Past, and Misinterpreted COVID-19 Cases Using Bidirectional LSTM Model for Proper Health Monitoring: Advances in Data Analytics

The first coronavirus case was reported in Hubei province of China, and within three months, it affected almost all the countries in the world. under such circumstances, World Health Organization (WHO) declared 2019 novel coronavirus as a global pandemic. Even though its fatality is low, the transmission rate makes it more dangerous. Similar to previous disease outbreaks in the human history, COVID-19 also exhibits certain transmission patterns. Mathematical models can be used to analyze these patterns and forecast the upcoming COVID-19 cases. Such forecasting methods could help governments to take further actions to stop those cases from occurring. Most of the previous studies used past infections to forecast future infections. However, they completely neglected the unreported cases while making predictions. By knowing the initially reported cases, we can understand the dynamics of the epidemic more precisely. In order to capture the transmission dynamics, we proposed a novel deep learning model called a B-LSTM (Bidirectional Long Short-Term Memory) model. In order to recalculate the past or missing infections, we applied a masking technique to our B-LSTM model. Results obtained from our model shows that end point of this pandemic in India will be around next year. However, by November the rate of infections will decrease linearly. In addition to that, we compared the forecasting accuracies of B-LSTM with statistical-based ARIMA and LSTM models. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID-19 fake news detection based on long short-Term memory model

Since a wide-range of fake news concerning the COVID-19 virus spreads fast and without restraint, many pessimistic effects come along with it, disturbing people's daily life and interfering with the real news' distribution. To improve the situation nowadays, our study tries to come up with an idea of limiting the spread of fake news through detecting, identifying and classifying it. Such an objective is realized using a dataset named COVID-19 Fake News Dataset from the website of Mendeley Data which was delivered in early 2021. LSTM is also applied to build a related model to do fake news detection. As to the study result, our performance parameters include the value of accuracy, precision, etc. Additionally, we use the loss curve and confusion matrix to analyze the results and discuss accordingly. In conclusion, our research provides strategic references based on the LSTM model to solve problems connected with fake news detection on the COVID-19 virus. © COPYRIGHT SPIE.

Forecasting COVID-19 Total Daily Cases in Indonesia Using LSTM Networks

The COVID-19 virus has taken over the course of the world for over two years;governments all over the world have been trying to mitigate its effects in several ways such as instilling most jobs to be done at home instead of working from the office. Thus, it is important to be able to see predictions of COVID-19 cases to better plan the intervention of the virus spreading. With the use of machine learning, our paper aims to propose and evaluate an LSTM (Long Short Term Memory) model that can forecast daily COVID-19 cases in Indonesia. Several tests show that 50 epochs and a batch size of eight are the best parameters to use for our model. Furthermore, after comparison with differing amounts of lookbacks, we have decided that 10 is best for our model as it consistently does better than other numbers of lookbacks. Based on our model, there will still be an increase of COVID-19 cases in the future. © 2022 IEEE.

The Effects of Weather Conditions on COVID-19 Forecasting, the United Arab Emirates as a Reliable Study Case

Predicting new COVID-19 cases was, and still is, of paramount importance to decision-makers in many countries. Due to its transmission nature, e.g., sneezing, coughing, and physical contact, researchers have developed prediction models that include weather features hoping to improve the forecasting models' predictions. The research did not show any conclusive evidence about the importance of including weather features in forecasting models. Thus, this paper addresses this problem by considering the United Arab Emirates (UAE) COVID-19 cases and weather conditions. Using long-short term memory (LSTM) models, a variant of artificial neural network used for forecasting, we compare a uni-variate, default forecasting model that only considers COVID-19 cases to other bi- and multi-variate models that relies on COVID-19 and weather features. The results show that including weather features in the forecasting models did not significantly improve the accuracy of the default LSTM model;the maximum increase in the coefficient of determination did not exceed 0.02. Moreover, humidity, if considered with other weather features, has a small influence on improving the prediction accuracy. © 2021 IEEE.

E-Quarantine: Remote Real Time Monitoring of COVID-19 Patients

It's been more than a year since the world is struggling with the COVID-19 pandemic. Mutation of the virus leads to a new wave of infection in a lot of countries. The virus has a very high spreading rate, so all the infected patients won't be able to treat in the hospitals and chances of it spreading among healthcare workers is also high. So we propose a system to monitor COVID-19 patients undergoing quarantine from their own homes during the pandemic, so as to save the hospital bed spaces for the patients with a critical health condition, who need immediate medical attention. The proposed system helps us to avoid overcrowding in hospitals and thereby avoiding the spreading of the virus from highly infected patients to the unaffected individuals. The methodology utilizes LSTM model which is a recurrent neural network (RNN) architecture used in the field of deep learning. © 2021 IEEE.

COVID-19 Vaccination Prediction in India Using Deep Learning

The worldwide spread of COVID-19. © 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

Prediction of the COVID-19 Spread in China Based on Long Short-Term Memory Network

The sudden outbreak of COVID-19 has caused great losses to the economy and the life of the masses. Long short-term memory (LSTM) network is a time recursive neural network, which is suitable for processing and predicting important events with relatively long interval and delay in time series. Using LSTM network to predict and analyze the development trend of epidemic situation, it is imperative to prevent epidemic situation from causing secondary harm to China's development. In this paper, we first obtained the COVID-19 data published by China Health Net using crawler technology, which is the accurate value of infection trend after the outbreak of COVID-19 in China. Then, based on these data, the LSTM model is used to predict the development trend of the epidemic in one year, and the mean square error is used to calculate the error between the prediction and the real data. The experimental model is used to predict and analyze the development trend of COVID-19. The results show that the error between predicted data and real data is small and the effect is very good, which provides a reasonable basis and forecast for scientific prevention and control of epidemic situation. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by IOP Publishing Ltd

An Epidemic Spread Risk Prediction Model Coupled with LSTM Algorithm and Cloud Model

The COVID-19 epidemic poses a great threat to public health and people's lives, which has initiated new challenges to the prevention and control system of the epidemic in China. In all efforts for epidemic control and prevention, predicting the risk of epidemic spread is of great practical importance for scientific prevention and control, and precise strategies. To predict the risk of an epidemic rapidly and quantitatively, this paper fused multi-source spatiotemporal data and established a risk prediction model for epidemic transmission by coupling LSTM algorithm and cloud model. Firstly, a simulation model of the spatiotemporal spread of infectious diseases was built based on GIS and LSTM algorithm, which simulated the infectious disease's spatiotemporal transmission process by learning rules in historical epidemic data. At the same time, to improve the simulation accuracy, this paper took 1 km × 1 km for the spatial scale, and days for the temporal scale as the study scale. Secondly, this paper applied the simulated data of infectious cases and the spatiotemporal influence factors on the spread of the epidemic to construct risk evaluation indicators. Finally, the cloud model and adaptive strategies were applied to construct an epidemic risk assessment model. In this way, the epidemic risk assessment at multiple spatial scales was achieved. In the empirical study phase, based on the Beijing COVID-19 epidemic data from 11 June 2020 to 25 June 2020, this paper simulated the process of the spatial evolution of the epidemic from 26 June 2020 to 1 July 2020. To test the advantage of the LSTM model applied to simulate spatiotemporal spread of infectious diseases, four machine learning models were introduced for comparison, including GA-BP Neural Network, Decision Regression Tree, Random Forest, and Support Vector Machine. The results were as follows: ① Compared with other conventional machine learning models, the LSTM model with time-series relationship had higher simulation accuracy (MAE=0.002 61) and better fitting degree (R-Square=0.9455). This showed that the LSTM model considering the temporal relationship between epidemic data was more suitable for epidemic spatial evolution simulation. ② The application results showed that the coupled model can not only fully consider the influence of infection source factors, weather factors, epidemic spread factors and epidemic prevention factors on the spread of transmission risk and reflect the trend of risk evolution, but also quickly quantify regional risk levels. Therefore, the coupled model based on LSTM algorithm and cloud model can effectively predict the transmission risk of epidemic, and also provide a method reference for establishing spatial-temporal transmission models and assessing epidemic risk. 2021, Science Press. All right reserved.