Forecasting COVID-19 Pandemic using Prophet, LSTM, hybrid GRU-LSTM, CNN-LSTM, Bi-LSTM and Stacked-LSTM for India

The COVID-19 Pandemic has been around for four years and remains a health concern for everyone. Although things are somewhat returning to normal, increased incidence of COVID-19 cases in some regions of the world (such as China, Japan, France, South Korea, etc.) has bred worry and anxiety in world, including India. The scientific community, which includes governmental organizations and healthcare facilities, was eager to learn how the COVID-19 Pandemic would develop. The current work makes an attempt to address this question by employing cutting-edge machine learning and Deep Learning algorithms to anticipate the daily incidence of COVID-19 for India over the course of the next six months. For the purpose famous timeseries algorithms were implemented including LSTM, Bi-Directional LSTM and Stacked LSTM and Prophet. Owing to success of hybrid algorithms in specific problem domains- the present study also focuses on such algorithms like GRU-LSTM, CNN-LSTM and LSTM with Attention. All these models have been trained on timeseries dataset of COVID-19 for India and performance metrics are recorded. Of all the models, the simplistic algorithms have performed better than complex and hybrid ones. Owing to this best result was obtained with Prophet, Bidirectional LSTM and Vanilla LSTM. The forecast reveals flat nature of COVID-19 case load for India in future six months. . © 2023 IEEE.

A Cross-Corpus Deep Learning Approach to Social Media Emotion Classification

Emotion Detection refers to the identification of emotions from contextual data in the form of written text, such as comments, posts, reviews, publications, articles, recommendations, conversations, and so on. Because of the Internet's exponential uptake and the recent coronavirus outbreak, social media platforms have become a crucial means of sharing thoughts and ideas throughout the entire globe, creating rapid data growth through users' contributions on various platforms. The necessity to acquire knowledge of their behaviors is a matter of great concern for both internet safety and privacy. In this study, we categorize emotional sentiments using deep learning models along with hybrid approaches such as LSTM, Bi-LSTM, and CNN+LSTM. When compared to existing state-of-the-art methods, the experiments showed that the suggested strategy is more robust and achieves an expressively higher quality of emotion detection with an accuracy rate of 94.16%, including strong F1-scores on complex and difficult emotion categories such as Fear (93.85%) and Anger (94.66%) through CNN+LSTM. © 2022 IEEE.

Residential Load Forecasting based on Deep Neural Network

This paper presents residential load forecasting using multivariate multi-step Deep Neural Networks (DNN) such as LSTM, CNN, Stacked LSTM, and Hybrid CNN-LSTM. A preliminary Exploratory Data Analysis (EDA) is conducted, and the decision variables are identified. An elbowing method is used to determine the number of clusters. Data is categorized based on weekdays, weekends, vacations, and Covid-Lockdown. Dimensionality-reduction using principal component analysis (PCA) is conducted. Seasonality-based clustering is found to improve the DNN model prediction accuracy further. A comparative analysis employs error metrics such as RMSE, MSE, MAPE, and MAE. The multivariate LSTM model with feedback is found to be the best fit model with the better performance indices. © 2023 IEEE.

Utilizing CNN-LSTM techniques for the enhancement of medical systems

COVID-19 is one of the most chronic and serious infections of recent years due to its worldwide spread. Determining who was genuinely affected when the disease spreads more widely is challenging. More than 60% of affected individuals report having a dry cough. In many recent studies, diagnostic models were developed using coughing and other breathing sounds. With the development of technology, body sounds are now collected using digital techniques for respiratory and cardiovascular tests. Early research on identifying COVID-19 utilizing speech and diagnosing signs yielded encouraging findings. The gathering of extensive, multi-group, airborne acoustical sound data is used in the developed framework to conduct an efficient assessment to test for COVID-19. An effective classification model is created to assess COVID-19 utilizing deep learning methods. The MIT-Covid-19 dataset is used as the input, and the Weiner filter is used for pre-processing. Following feature extraction done by Mel-frequency cepstral coefficients, the classification is performed using the CNN-LSTM approach. The study compared the performance of the developed framework with other techniques such as CNN, GRU, and LSTM. Study results revealed that CNN-LSTM outperformed other existing approaches by 97.7%. © 2023 Faculty of Engineering, Alexandria University

A Hybrid CNN-LSTM Model with Word-Emoji Embedding for Improving the Twitter Sentiment Analysis on Indonesia's PPKM Policy

The policy of limiting community mobilization is implemented to reduce the daily rate of COVID-19. However, a high-accuracy sentiment analysis model can determine public sentiment toward such policies. Our research aims to improve the accuracy of the LSTM model on sentiment analysis of the Jakarta community towards PPKM using Indonesian language Tweets with emoji embedding. The first stage is modeling using the hybrid CNN-LSTM model. It is a combination between CNN and LSTM. The CNN model cites word embedding and emoji embedding features that reflect the dependence on temporary short-term sentiment. At the same time, LSTM builds long-term sentiment relationships between words and emojis. Next, the model evaluation uses Accuracy, Loss, the receiver operating curve (ROC), the precision and recall curve, and the area under curve (AUC) value to see the performance of the designed model. Based on the results of the tests, we conclude that the CNN-LSTM Hybrid Model performs better with the words+emoji dataset. The ROC AUC is 0.966, while the precision-recall curve AUC is 0.957. © 2022 IEEE.

Depression screening using hybrid neural network.

Depression is a common cause of increased suicides worldwide, and studies have shown that the number of patients suffering from major depressive disorder (MDD) increased several-fold during the COVID-19 pandemic, highlighting the importance of disease detection and depression management, while increasing the need for effective diagnostic tools. In recent years, machine learning and deep learning methods based on electroencephalography (EEG) have achieved significant results in the field of automatic depression detection. However, most current studies have focused on a small number of EEG signal channels, and experimental data require special processing by professionals. In this study, 128 channels of EEG signals were simply filtered and 24-fold leave-one-out cross-validation experiments were performed using 2DCNN-LSTM classifier, support vector machine, K-nearest neighbor and decision tree. The current results show that the proposed 2DCNN-LSTM model has an average classification accuracy of 95.1% with an AUC of 0.98 for depression detection of 6-second participant EEG signals, and the model is much better than 72.05%, 79.7% and 79.49% for support vector machine, K nearest neighbor and decision tree. In addition, we found that the model achieved a 100% probability of correctly classifying the EEG signals of 300-second participants.

Automatic COVID-19 prediction using explainable machine learning techniques

The coronavirus is considered this century's most disruptive catastrophe and global concern. This disease has prompted extreme social, psychological and economic impacts affecting millions of people around the globe. COVID-19 is transmitted from one infected person's body to another through respiratory droplets. This virus proliferates when people breathe in air-contaminated space with droplets and microscopic airborne particles. This research aims to analyze automatic COVID-19 detection using machine learning techniques to build an intelligent web application. The dataset has been preprocessed by dropping null values, feature engineering, and synthetic oversampling (SMOTE) techniques. Next, we trained and evaluated different classifiers, i.e., logistic regression, random forest, decision tree, k-nearest neighbor, support vector machine (SVM), ensemble models (adaptive boosting and extreme gradient boosting) and deep learning (artificial neural network, convolutional neural network and long short-term memory) techniques. Explainable AI with the LIME framework has been applied to interpret the prediction results. The hybrid CNN-LSTM algorithm with the SMOTE approach performed better than the other models on the employed open-source dataset obtained from the Israeli Ministry of Health website, with 96.34% accuracy and a 0.98 F1 score. Finally, this model was chosen to deploy the proposed prediction system to a website, where users may acquire an instantaneous COVID-19 prognosis based on their symptoms. © 2023 The Authors

Automatic COVID-19 Prediction Using Explainable Machine Learning Techniques

The coronavirus is considered this century's most disruptive catastrophe and global concern. This disease has prompted extreme social, psychological and economic impacts affecting millions of people around the globe. COVID-19 is transmitted from one infected person's body to another through respiratory droplets. This virus proliferates when people breathe in air-contaminated space with droplets and microscopic airborne particles. This research aims to analyze automatic COVID-19 detection using machine learning techniques to build an intelligent web application. The dataset has been preprocessed by dropping null values, feature engineering, and synthetic oversampling (SMOTE) techniques. Next, we trained and evaluated different classifiers, i.e., logistic regression, random forest, decision tree, k-nearest neighbor, support vector machine (SVM), ensemble models (adaptive boosting and extreme gradient boosting) and deep learning (artificial neural network, convolutional neural network and long short-term memory) techniques. Explainable AI with the LIME framework has been applied to interpret the prediction results. The hybrid CNN-LSTM algorithm with the SMOTE approach performed better than the other models on the employed open-source dataset obtained from the Israeli Ministry of Health website, with 96.34% accuracy and a 0.98 F1 score. Finally, this model was chosen to deploy the proposed prediction system to a website, where users may acquire an instantaneous COVID-19 prognosis based on their symptoms.

SENTIMENT ANALYSIS OF COVID-19 VACCINE WITH DEEP LEARNING

After the emergence of the Covid-19 virus, pharmaceutical companies began making vaccines against this virus. Peoples' reactions towards vaccines varies between acceptance and rejection. Information about these reactions can be found in social media which has become the largest and best source of users' opinions on a specific topic nowadays. One of the most important social media through which this data can be collected is Twitter. It is important to analyze people's opinions about these vaccines to find out the percentage of supporters and opponents of vaccines. Sentiments analysis can be used to analyze people's opinions. In this paper, we proposed a hybrid deep learning model to analyze user sentiment towards the COVID-19 vaccine. The contributions of our work are to adopt an efficient-designed model by combines Convolutional Neural Network (CNN), which has the capability to extract features, and Long Short-Term Memory (LSTM), which can monitor and study long-term dependencies between words. And provide the proposed network topology setting that contributed in producing high performance in sentiment analysis of the COVID-19 vaccine tweets. Extensive experiments have been conducted on a data set of 13,190 tweets. The results proved that the proposed model with the proposed topology setting outperformed the other machine learning models. © 2022 Little Lion Scientific.

Depression Detection on COVID 19 Tweets Using Chimp Optimization Algorithm

The Covid-19 outbreak has an unprecedented effects on people's daily lives throughout the world, causing immense stress amongst individuals owing to enhanced psychological disorders like depression, stress, and anxiety. Researchers have used social media data to detect behaviour changes in individuals with depression, postpartum changes and stress detection since it reveals critical aspects of mental and emotional diseases. Considerable efforts have been made to examine the psychological health of people which have limited performance in accuracy and demand increased training time. To conquer such issues, this paper proposes an efficient depression detection framework named Improved Chimp Optimization Algorithm based Convolution Neural Network-Long Short Term Memory and Natural Language Processing for Covid-19 Twitter data. In the proposed method, the tweets are pre-processed, user's frequent tweet identification, and hash tag identification has been done. The processed tweets are then clustered through cluster head selection using Swap-Displacement-ReversionBull based Optimization Algorithm and cluster formation using the Bregman distance-based K-Means algorithm. Then, the psycholinguistic features are extracted from the clustered data and inputted to the Improved Chimp Optimization Algorithm-based-Convolution Neural Network-Long Short Term Memory network for depression classification. Preliminary results show that the proposed method provides greater performance with 97.7% efficiency and outperforms the existing methodologies.

Short-Term Traffic Forecasting using LSTM-based Deep Learning Models

Accurate short-term traffic volume forecasting has become a component with growing importance in traffic management in intelligent transportation systems (ITS). A significant amount of related works on short-term traffic forecasting has been proposed based on traditional learning approaches, and deep learning-based approaches have also made significant strides in recent years. In this paper, we explore several deep learning models that are based on long-short term memory (LSTM) networks to automatically extract inherent features of traffic volume data for forecasting. A simple LSTM model, LSTM encoder-decoder model, CNN-LSTM model and a Conv-LSTM model were designed and evaluated using a real-world traffic volume dataset for multiple prediction horizons. Finally, the experimental results are analyzed, and the Conv-LSTM model produced the best performance with a MAPE of 9.03% for the prediction horizon of 15 minutes. Also, the paper discusses the behavior of the models with the traffic volume anomalies due to the Covid-19 pandemic.

Coverage Prediction of Novel Coronavirus Vaccine Based on Deep Learning Time Series Analysis

Since the beginning of 2020, COVID-19 has swept the world, bringing many inconveniences and even threats to human life. Through medical scientists' constant study, the vaccine was finally developed earlier this year. According to mathematical and medical modelling, if novel coronavirus transmission is counted as three (i.e., one patient can infect three), 70% of the vaccinations will be required for protection to be substantially achieved. To tackle the issue of vaccine coverage prediction, this paper proposed three-time series analysis models, which can be utilized to analyze and predict the COVID-19 Vaccine coverage worldwide with the application of machine learning. For a long time, statistical methods have mostly solved time series prediction problems (AR, AM, ARMA, ARIMA). Mathematicians try to constantly refine these techniques to constrain stationary and non-stationary time series, but the results are often not very good. In this paper, we propose a method based on deep learning, using CNN-LSTM, VAE-LSTM, DeepAR, and other models to analyze and predict the data of vaccine coverage rate. The experimental results demonstrated that the RMSE of LSTM, CNN-LSTM, VAE-LSTM and DeepAR are 9.295522e+07, 1.028151e+07, 1.857031e+06 and 1.961001e+07 separately. © 2021 IEEE.

India perspective: CNN-LSTM hybrid deep learning model-based COVID-19 prediction and current status of medical resource availability.

The epidemic situation may cause severe social and economic impacts on a country. So, there is a need for a trustworthy prediction model that can offer better prediction results. The forecasting result will help in making the prevention policies and remedial action in time, and thus, we can reduce the overall social and economic impacts on the country. This article introduces a CNN-LSTM hybrid deep learning prediction model, which can correctly forecast the COVID-19 epidemic across India. The proposed model uses convolutional layers, to extract meaningful information and learn from a given time series dataset. It is also enriched with the LSTM layer's capability, which means it can identify long-term and short-term dependencies. The experimental evaluation has been performed to gauge the performance and suitability of our proposed model among the other well-established time series forecasting models. From the empirical analysis, it is also clear that the use of extra convolutional layers with the LSTM layer may increase the forecasting model's performance. Apart from this, the deep insides of the current situation of medical resource availability across India have been discussed.