A comparison between CNN and combined CNN-LSTM for chest X-ray based COVID-19 detection

COVID-19 detection through radiological examination is favoured since it is fast and produces more accurate results than the laboratory approach. However, when it has infected many people and put a strain on the healthcare system, the need for fast, automatic COVID-19 detection in patients has become critical. This study proposes to detect COVID-19 from chest X-ray (CXR) images with a machine learning approach. The main contributions of this paper are to compare two powerful deep learning models, i.e., convolutional neural networks (CNN) and the combination of CNN and Long Short-Term Memory (LSTM). In the combination model, CNN is recommended for feature extraction, and COVID-19 is classified using the features of LSTM. The dataset used in this study amounted to 4,095 CXR images, consisting of 1,400 images of normal conditions, 1,350 images of COVID-19, and 1,345 images of pneumonia. Both CNN and CNN-LSTM were executed in a similar experimental setup and evaluated using a confusion matrix. The experiment results provide evidence that the CNN-LTSM is better than the CNN deep learning model, with an overall accuracy of about 98.78%. Furthermore, it has a precision and recall of 99% and 98%, respectively. These findings will be valuable in the fast and accurate detection of COVID-19. © 2023 by the authors;licensee Growing Science, Canada.

Time series prediction of novel coronavirus COVID-19 data in west Java using Gaussian processes and least median squared linear regression

In 2019, the COVID-19 epidemic swept throughout the globe. The virus was first identified in Wuhan, China. By the time several months had gone by, this virus had spread to numerous locations throughout the world. Consequently, this virus has become a worldwide pandemic. Multiple efforts have been made to limit the transmission of this virus. A possible course of action is to lock down the territory. Unfortunately, this strategy wrecked the economy, worsening the terrible situation. The world health organization (WHO) would breathe a sigh of relief if there were to be no new cases. However, the government should explore employing data from the future in addition to the data it already has. Prediction of time series may be utilized for this purpose. This study indicated that the Gaussian processes method outperformed the least median squared linear regression method (LMSLR). Applying a Pearson VII-based global kernel produces MAE and RMSE values of 23.12 and 53.43, respectively.(c) 2023 by the authors;licensee Growing Science, Canada.

Time series prediction of novel coronavirus COVID-19 data in west Java using Gaussian processes and least median squared linear regression

In 2019, the COVID-19 epidemic swept throughout the globe. The virus was first identified in Wuhan, China. By the time several months had gone by, this virus had spread to numerous locations throughout the world. Consequently, this virus has become a worldwide pandemic. Multiple efforts have been made to limit the transmission of this virus. A possible course of action is to lock down the territory. Unfortunately, this strategy wrecked the economy, worsening the terrible situation. The world health organization (WHO) would breathe a sigh of relief if there were to be no new cases. However, the government should explore employing data from the future in addition to the data it already has. Prediction of time series may be utilized for this purpose. This study indicated that the Gaussian processes method outperformed the least median squared linear regression method (LMSLR). Applying a Pearson VII-based global kernel produces MAE and RMSE values of 23.12 and 53.43, respectively. © 2023 by the authors;licensee Growing Science, Canada.

Analysis of Government Policy Sentiment Regarding Vacation during the COVID-19 Pandemic Using the Bidirectional Encoder Representation from Transformers (BERT)

To address the COVID-19 situation in Indonesia, the Indonesian government has adopted a number of policies. One of them is a vacation-related policy. Government measures with regard to this vacation policy have produced a wide range of viewpoints in society, which have been extensively shared on social media, including YouTube. However, there has not been any computerized system developed to date that can assess people's social media reactions. Therefore, this paper provides a sentiment analysis application to this government policy by employing a bidirectional encoder representation from transformers (BERT) approach. The study method began with data collecting, data labeling, data preprocessing, BERT model training, and model evaluation. This study created a new dataset for this topic. The data were collected from the comments section of YouTube, and were categorized into three categories: positive, neutral, and negative. This research yielded an F-score of 84.33%. Another contribution from this study regards the methodology for processing sentiment analysis in Indonesian. In addition, the model was created as an application using the Python programming language and the Flask framework. The government can learn the extent to which the public accepts the policies that have been implemented by utilizing this research. © 2023 by the authors.

Sentiment Analysis on Telemedicine App Reviews using XGBoost Classifier

In recent years, companies have widely used sentiment analysis with machine learning classification algorithms to help business decision-making. Sentiment analysis helps evaluate customer opinions on a product in goods or services. Companies need this opinion or sentiment to improve the performance, quality of their products, and customer satisfaction. Machine learning algorithms widely used for sentiment analysis are Naive Bayes Classifier, Maximum Entropy, Decision Tree, and Support Vector Machine. In this study, we propose an approach of sentiment analysis using a very popular method, Extreme Gradient Boosting or XGBoost. XGBoost combines weak learners into an ensemble classifier to build a strong learner. This study will focus on the reviews data of the most popular telemedicine application in Indonesia, Halodoc. This study aims to examine the people's sentiment towards telemedicine applications in Indonesia, especially during the COVID-19 pandemic. We also showed a fishbone diagram to analyze the most factors the users complained about. The data we have are imbalanced;however, XGBoost can perform well with 96.24% accuracy without performing techniques for imbalanced data. © 2021 IEEE.

Electronic News Sentiment Analysis Application to New Normal Policy during the Covid-19 Pandemic Using Fasttext and Machine Learning

The new phase in handling COVID-19 in Indonesia, called New Normal, gives various public perspectives regarding this policy. This study aims to analyze public sentiment towards the New Normal policy through an electronic news comment column. This study uses text data in the form of comments were collected from electronic news media sites, namely www.detik.com and www.kompas.com, and taken from the comments column on Instagram social media, namely the @detikcom account. Also, use FastText method to extract features by converting data into vector values and using three classification methods, Naive Bayes (NB), Support Vector Machine (SVM), and Multilayer Perceptron (MLP). This study conducted a hyperparameter test to obtain the most optimal model. Testing the hyperparameters from FastText produces an optimal model with dimensions of 250, window size 8, epoch 1.000, and a learning rate of 0,0025. Hyperparameter testing was also carried out on the SVM and MLP classifiers. Hyperparameter testing of the SVM and MLP classifiers produces the most optimal model with the SVM method using the RBF kernel, C of 1.000, gamma of 10. In contrast, the MLP method uses the relu activation function, hidden size layer (250,250), adam optimizer, alpha 0,0001, and adaptive learning rate. The classification model was evaluated using K-fold cross-validation to produce an average f1score. The result is for the NB method 72,25% f1score, for the SVM method 92,21% f1score, and for the MLP method 90,75% f1score. © 2021 IEEE.

Analysis of Prediction Data for the Third Wave of COVID-19 in Bogor Regency

The COVID-19 pandemic is far from over. The government has carried out several policies to suppress the development of COVID-19 is no exception in Bogor Regency. However, the public still has to be vigilant especially now we will face a year-end holiday that can certainly be a trigger for the third wave of COVID-19. Therefore, researchers aim to make predictions of the increase in positive cases, especially in the Bogor Regency area to help the government in making policies related to COVID-19. The algorithms used are Gaussian Process, Linear Regression, and Random Forest. Each Algorithm is used to predict the total number of COVID-19 cases for the next 21 days. Researchers approached the Time Series Forecasting model using datasets taken from the COVID-19 Information Center Coordinationn Center website. The results obtained in this study, the method that has the highest probability of accurate and appropriate data contained in the Gaussian Process method. Prediction data on the Linear Regression method has accurate results with actual data that occur with Root Mean Square Error 1202.6262. © 2021 IEEE.

COVID-19 Social Safety Nets Sentiment Analysis on Twitter Using Gated Recurrent Unit (GRU) Method

One of the Indonesian government's programs in dealing with Covid19 problems is the Social Safety Net program which is given to the community, especially Covid19 assistance which is given every month to the community. Based on the assistance provided by the government, many people expressed their opinions through Twitter social media. This study aims to analyze the sentiment on Twitter tweets regarding the Social Safety Net Program from March to December 2020. The data collected is 4061 tweets data. The data is classified into two classes, namely positive and negative. The classification algorithm used is Gated Recurrent Unit (GRU). Hyperparameter testing is carried out in order to produce an optimal model. In the optimal GRU hyperparameter, when there are 10 GRU units, the activation function is sigmoid, the optimizer used is Adam, the batch size is 128, with 10 epochs of iteration and 0.2 dropout size. The GRU model produces an f1score of 92.09%, a precision of 90.34%, and a recall of 93.90%. © 2021 IEEE.

Face Recognition Using Fisherface and Support Vector Machine Method

COVID-19 is declared as a pandemic by WHO and until now COVID-19 pandemic remains a problem in 2021. Many efforts have been made to reduce the spreading virus, one way to reduce its spread is by wearing a mask but most people often ignore it. Monitoring large groups of people becomes difficult by the government or the authorities. Face recognition, a biometric technology, is based on the identification of a face features of a person. This paper describes a face recognition using Fisherface and Support Vector Machine method to classify face mask dataset. Face recognition using Fisherface method is based on Principal Component Analysis (PCA) and Fisher's Linear Discriminant (FLD) method or also known as Linear Discriminant Analysis (LDA). The algorithm used in the process for feature extraction is Fisherface algorithm while classification using Support Vector Machine method. The results show that for face recognition on face mask dataset using cross validation with 10 fold, the average percentage accuracy is 99.76%. © 2021 IEEE.

COVID-19 Detection in Chest X-Rays Using Inception Resnet-v2

Screening for COVID-19 is a vital part of the triage process. The current COVID-19 gold standard, the RT-PCR test, is regarded to be costly and time consuming. Artificial intelligence can be utilized to identify COVID-19 in radiographic pictures to overcome the limitations of existing testing methods. This study describes how the Inception-ResNet-v2 architecture was used to categorize pictures into three categories using transfer learning (Normal, Viral Pneumonia, and COVID-19,). Despite only running for 29 epochs, the resultant model had an accuracy of 0.966. This demonstrates the utility of AI in the diagnosis of illnesses. © 2021 IEEE.

Comparison multi-layer perceptron and linear regression for time series prediction of novel coronavirus covid-19 data in West Java

Until now, the pandemic conditions of Covid-19 are still ravaging the world, even in Indonesia and West Java. Various attempts have been made to stop it. West Java implements Large Scale Social Restrictions, is known as Pembatasan Sosial Skala Besar (PSBB). However, over time, a discourse emerged to loosen PSBB. One of the World Health Organization's (WHO) requirements to loosen is the effective reproduction rate of Corona Virus cases below 1. Therefore, this study focuses on predicting the number of cases in West Java. The methods based on multi-layer perception (MLP) and linear regression (LR). The data were obtained from the C Covid -19 positive case from March to mid-August 2020 in West Java. The experiments show that MLP reaches optimal if it used 13 hidden layers with learning rate and momentum = 0.1. The MLP had a smaller error than LR. Both of them predict the number of cases in the next 30 days from August 14, 2020. The results show that West Java will still have an increase in the number of new cases of Covid -19. © 2021 Institute of Physics Publishing. All rights reserved.