Analysis of Covid-19 using machine learning techniques

Covid-19 is caused by a newly detected coronavirus (SARS-CoV-2). It is a respiratory infection that usually spreads from individual to individual through sneezing or coughing. The disease, which was first detected in the province of Wuhan, China, had effected more than one continent and was declared as a pandemic by the World Health Organization (WHO). The pandemic has affected health, social, economic, and psychological segments of life for billions of people. Though vaccines have been developed and are made available, we are still prone to the virus, which is similar to any other flu. This chapter presents an analysis of the symptoms of the disease and identifies significant symptoms that impact the cause of the illness. Machine learning techniques like multiple regression, support vector machine (SVM), Decision Tree, Random Forest, and Logistic Regression are applied to understand the evaluation with respect to the measures like coefficient of determination, and mean-squared error. Hypothesis testing is used to determine whether at least one of the features is useful in the diagnosis of the disease. Further feature selection process is used to identify the most significant symptoms that will cause the virus. Different visualization methods are used to figure the substantial reasoning from the model's prediction and perform analysis on the results obtained. © 2023 Elsevier Inc. All rights reserved.

Used Car Price Prediction in Surabaya Using Random Forest Regressor Algorithms

The use of private vehicles during the Covid-19 pandemic has increased because private vehicles, especially cars, are considered as the safest mode of transportation to maintain distance and prevent transmission of the Covid-19 virus. Based on data from two different Indonesian secondary car market place, a comparison of a price sample of Car X in the city of Surabaya with the specifications for the 2015 to 2018 car years with car milage under 1000 kilometers, the used cars have a variety of prices hence a used car price prediction system is needed so that people can find out the average price of used cars sold in the market. In this study the author will use the Random Forest Regressor as a machine learning algorithm to predict the price of a used car with a dataset from the AtapData website. The reason for choosing the Random Forest Regressor is because the algorithm has the power to handle large amounts of data with high dimensions with categorical and numerical data types. The evaluation method used in this study is the Root Mean Absolute Error which produces a value of 0.55612 for validation data and 0.56638 for testing data, while the evaluation proceed with Mean Absolute Error produces a value of 0.45208 for validation data and 0.47576 for testing data. © 2022 IEEE.

POST-PANDEMIC ANALYSIS OF HOUSE PRICE PREDICTION IN SURABAYA: A MACHINE LEARNING APPROACH

The COVID-19 outbreak caused a slowdown in the Indonesian economy, as it did in many other impacted nations. Consequently, the housing market in Indonesia, along with other industries, deteriorated. Other post-pandemic issues displace the property industry's priorities in Indonesia. Determining a fair property price is a problem occurring because of the economic slowdown. Property sellers expected their property selling prices to be the same before the pandemic or even increase, but property agents hoped the properties would be selling fast, creating a sense of distrust between the seller and the property agents. This work aims to develop a machine learning-based prediction model for real estate agents to use in determining property prices, with the expectation that the resulting predictions will be more accurate and supported by the data, increasing seller and buyer confidence. Following the suggestion from previous studies, several supervised algorithms such as Linear Regression, Decision Tree, and Random Forest were used to develop the model. Training data were collected from five property agents in Surabaya and as well as web scraping from the online home sales portals. Findings from the study show that Random Forest performs best in predicting with the highest coefficient of determination and lowest error. Using evaluation measures such as Mean Absolute Percent Error (MAPE), the error was calculated to be 23%, which is acceptable for prediction. © 2022 Science Press. All rights reserved.

Students' performance-prediction-Model based on Physical and Physiological Constraints

The consequences of the Covid-19 pandemic changed the education system and the lifestyle of all students in Jordan. To reduce the infection rate among students, the education institutes in Jordan decided to adopt online learning as an alternative to face-to-face education. The fast shift to online education raises a potent concern regarding its efficiency. For instance, many students in Jordan cannot afford digital tools and do not have an internet connection. Furthermore, the psychological impact of enforcing online learning is not fully recognized. This study presents two regression models based on Multilayer Perceptron (MLP) neural network and Random Forest (RF) regressor to analyze and predict students' performance in Jordan before and during the lockdown and under physical and psychological constraints. In this study, the Dataset of Jordanian University Students' Psychological Health Impacted by Using E-learning Tools during COVID-19 (JUSPH) is divided into four subsets based on their chronological timeline (Before/After Covid-19), physical and psychological states. Besides, the four subsets are pre-processed using a Simple Imputer (SI), label encoder, and on-hot encoding to impute the missing value and handle the categorical data, respectively. Then, the features are selected by using the Low Variance (LV) filter. Afterward, MLP and RF regressor is used to predict the future students' performance under online education in the following semester. Results showed that the proposed MLP models achieved the best accuracy score of 99.94% on the Before Covid-19 physical Subset, while the RF model achieved the best accuracy score of 85.58% on the After Covid-19 Psychological subset. © 2022 IEEE.

Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations' COVID-19 Pandemic.

In the paper, the authors investigated and predicted the future environmental circumstances of a COVID-19 to minimize its effects using artificial intelligence techniques. The experimental investigation of COVID-19 instances has been performed in ten countries, including India, the United States, Russia, Argentina, Brazil, Colombia, Italy, Turkey, Germany, and France using machine learning, deep learning, and time series models. The confirmed, deceased, and recovered datasets from January 22, 2020, to May 29, 2021, of Novel COVID-19 cases were considered from the Kaggle COVID dataset repository. The country-wise Exploratory Data Analysis visually represents the active, recovered, closed, and death cases from March 2020 to May 2021. The data are pre-processed and scaled using a MinMax scaler to extract and normalize the features to obtain an accurate prediction rate. The proposed methodology employs Random Forest Regressor, Decision Tree Regressor, K Nearest Regressor, Lasso Regression, Linear Regression, Bayesian Regression, Theilsen Regression, Kernel Ridge Regressor, RANSAC Regressor, XG Boost, Elastic Net Regressor, Facebook Prophet Model, Holt Model, Stacked Long Short-Term Memory, and Stacked Gated Recurrent Units to predict active COVID-19 confirmed, death, and recovered cases. Out of different machine learning, deep learning, and time series models, Random Forest Regressor, Facebook Prophet, and Stacked LSTM outperformed to predict the best results for COVID-19 instances with the lowest root-mean-square and highest R 2 score values.

COVID-19: the impact of the pandemic fear on IPO underpricing

Purpose The purpose of this study is to assess how the information disclosed in prospectuses impacted the initial public offering (IPO) underpricing at a time of high government interference amid the ongoing pandemic. Design/methodology/approach The design of this study has several tracks, namely, a macro-level track, which is represented by the government measures to halt the pandemic;a micro-level track, which is followed by textual analysis of IPO prospectuses;and, finally, a machine learning track, in which the authors use state-of-the-art tools to improve their linear regression model. Findings The authors found that strict government anti-COVID-19 measures indeed contribute to the reduction of the IPO underpricing. Interestingly, the mere fact of such measures taking place is enough to take effect on financial markets, regardless of the resulting efficiency of such measures. At the micro-level, the authors show that prospectus sentiments and their significance differ across prospectus sections. Using linear regression and machine learning models, the authors find robust evidence that such sections as "Risk factors", "Prospectus summary", "Financial Information" and "Business" play a crucial role in explaining the underpricing. Their effect is different, namely, it turns out that the more negative "Risk factors" and "Financial Information" sentiment, the higher the resulting underpricing. Conversely, the more positive "Prospectus summary" and "Business" sentiments appear, the lower the resulting underpricing is. In addition, we used machine learning methods. Consisting of more than 580 IPO prospectuses, the study sample required modern and powerful machine learning tools like Isolation Forest for pre-processing or Random Forest Regressor and Light Gradient Boosting Model for modelling purposes, which enabled the authors to gain better results compared to the classic linear regression model. Originality/value At the micro level, this study is not confined to 2020, but also embraces 2021, the year of the record number of IPOs held. Moreover, in this paper, these were prospectuses that served as a source of management sentiment. In addition, the authors used a tailor-made government stringency index. At the micro level, basing the study on behavioural finance hypotheses, the authors conducted both separate and holistic analysis of prospectuses to assess investors' reaction to different aspects of IPO companies as well as to the characteristics of the IPOs themselves. Lastly, the authors introduced a few innovations to the research methodology. Textual analysis was conducted on a corpus of prospectuses included in a study sample. However, the authors did not use pre-trained dictionaries, but instead opted for FLAIR, a modern open-source framework for natural language processing.

A Big Impact of Social Network Analysis and Machine Learning Algorithms for Predicting Facts of Covid-19 Pandemic

As we can see, covid-19 is becoming a global pandemic. At first, it was seen in India on 30 January, 2020. In such situation, there has been two important challenges before the government of India. The first is to fight the pandemic and second is to make awareness about it. Since analyses of social networks reveal technological advancement and show how World Health Organization’s post is beneficial for awareness and prevention from covid-19 as well as showing impact of algorithm and identifying ‘networkx’ technique. We are trying to show through this article, about the interaction of all public health organizations on Facebook portal and how it could be used by employing random forest machine learning algorithm on the datasets generated by these interactions. We collected data from World Health Organization’s dataset for predicting future forecast to know new cases in India by using Arima and Sarima model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Feature Selection and Validation of a Machine Learning-Based Lower Limb Risk Assessment Tool: A Feasibility Study.

Early and self-identification of locomotive degradation facilitates us with awareness and motivation to prevent further deterioration. We propose the usage of nine squat and four one-leg standing exercise features as input parameters to Machine Learning (ML) classifiers in order to perform lower limb skill assessment. The significance of this approach is that it does not demand manpower and infrastructure, unlike traditional methods. We base the output layer of the classifiers on the Short Test Battery Locomotive Syndrome (STBLS) test used to detect Locomotive Syndrome (LS) approved by the Japanese Orthopedic Association (JOA). We obtained three assessment scores by using this test, namely sit-stand, 2-stride, and Geriatric Locomotive Function Scale (GLFS-25). We tested two ML methods, namely an Artificial Neural Network (ANN) comprised of two hidden layers with six nodes per layer configured with Rectified-Linear-Unit (ReLU) activation function and a Random Forest (RF) regressor with number of estimators varied from 5 to 100. We could predict the stand-up and 2-stride scores of the STBLS test with correlation of 0.59 and 0.76 between the real and predicted data, respectively, by using the ANN. The best accuracies (R-squared values) obtained through the RF regressor were 0.86, 0.79, and 0.73 for stand-up, 2-stride, and GLFS-25 scores, respectively.