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.

Improved prediction of new COVID-19 cases using a simple vector autoregressive model: evidence from seven New York state counties.

With the rapid spread of COVID-19, there is an urgent need for a framework to accurately predict COVID-19 transmission. Recent epidemiological studies have found that a prominent feature of COVID-19 is its ability to be transmitted before symptoms occur, which is generally not the case for seasonal influenza and severe acute respiratory syndrome. Several COVID-19 predictive epidemiological models have been proposed; however, they share a common drawback - they are unable to capture the unique asymptomatic nature of COVID-19 transmission. Here, we propose vector autoregression (VAR) as an epidemiological county-level prediction model that captures this unique aspect of COVID-19 transmission by introducing newly infected cases in other counties as lagged explanatory variables. Using the number of new COVID-19 cases in seven New York State counties, we predicted new COVID-19 cases in the counties over the next 4 weeks. We then compared our prediction results with those of 11 other state-of-the-art prediction models proposed by leading research institutes and academic groups. The results showed that VAR prediction is superior to other epidemiological prediction models in terms of the root mean square error of prediction. Thus, we strongly recommend the simple VAR model as a framework to accurately predict COVID-19 transmission.

Validation of the Chinese version of the COVID-19 Phobia Scale among Chinese college students.

Background and objective: Different languages and versions of the COVID-19 Phobia Scale (C19P-S) have been developed and tested in several countries. Chinese college students are a large vulnerable group and are susceptible to psychological problems during the COVID-19 pandemic. However, no studies had yet examined the reliability and validity of the C19P-S in China among college students group. This study aims to evaluate the COVID-19-related phobia of Chinese college students and examine the reliability and validity of this scale. Methods: A total of 1689 Chinese college students participated in this study from April 27 to May 7, 2022. They finished the online questionnaire including demographic information and C19P-S. Cronbach's alpha and split-half reliability were used to examine the internal consistency of the scale. Confirmatory factor analysis was further used to examine the scale's construct validity. Convergence validity was also confirmed. Results: This scale in Chinese had high reliability and validity. The Cronbach's alpha and split-half reliability of the total scale were 0.960 and 0.935, respectively. The construct validity-related indicators of the total scale met the standards (RMSEA = 0.064, IFI = 0.907, TLI = 0.906, and CFI = 0.907). Regarding the subscales, the composite reliability (CR) and average variance extracted (AVE) also met the cutoff values (CR > 0.7 and AVE >0.5). Comparison between gender groups showed that total and subscale scores between male and female students differed significantly. Conclusion: The Chinese version of the C19P-S was appropriate for evaluating phobic symptoms among Chinese college students. Therefore, this tool could be used to evaluate the mental health of college students in the future.

Emotional fear of COVID-19, but not physiological expressions of fear, explains variability in COVID-19's impact on individuals' lives.

The COVID-19 pandemic has had a profound and robust impact on individuals' lives and has particularly negatively affected individuals' experiences with fear of catching COVID-19. To measure this fear, researchers created the unidimensional Fear of COVID-19 Scale (FCV-19S). However, some exploratory factor analysis studies suggested the presence of two factors, which are 1) emotional fear and 2) physiological expressions of fear. In the current exploratory study, we aimed to confirm this factor structure using confirmatory factor analysis and to examine how these two new factors of the FCV-19S explain variability in the impacts of COVID-19 on nine life domains (i.e., finances, loved ones, job, safety, school, mental health, physical health, social activities, and quality of life). Participants were undergraduate students (n = 224) from a Midwestern University (White: 60.7%; Male: 48.0%) who participated in the study for course credit. The results revealed that the two-factor model had an excellent fit for the FCV-19S, both subscales had excellent psychometric properties, and the emotional fear subscale significantly explained variability in all nine life domains (7% to 54%). However, the physiological fear subscale only significantly explained variability in the physical health domain along with emotional fear (28%). The findings suggested that emotional fear of COVID-19 may explain more variability in the impact of COVID-19 across life domains, while physiological fear may only explain the effects of COVID-19 on physical health. We further discussed implications, limitations, and future directions.

Chinese pregnant women's knowledge, attitude, and practice of self-protection against coronavirus disease 2019 during the post-pandemic period: A structural equation modeling-based survey.

This study aimed to investigate the Chinese pregnant women's levels of knowledge, attitude, and practice (KAP) of self-protection against coronavirus disease 2019 (COVID-19) during the post-pandemic period, to aid the development of targeted health education. An online questionnaire was conducted for 2156 Chinese pregnant women from October 1, 2021, to December 31, 2021, to collect socio-demographic and KAP information. Structural equation modeling (SEM) was used to determine self-protection-related factors. The mean age of the participants was 30 ± 4.1 years. SEM indicated that pregnant women's level of knowledge can directly and indirectly affect the practice of self-protection (r = 0.23) through their belief, with a correlation coefficient of 0.56 and 0.46 between knowledge and belief and belief and practice, respectively. The "basic protection" and "hospital visits after infection" exerted the greatest impact on knowledge formation, with correlation coefficients of 0.85 and 0.89, respectively. Attitude had a direct effect on practice with a correlation coefficient of 0.46. "Awareness of prevention and control" and "family and social support" had the greatest impact on belief formation, with correlation coefficients of 0.77 and 0.73, respectively. Pregnant Chinese women were generally familiar with COVID-19 knowledge, and their levels of knowledge and beliefs particularly affect the practice of self-protection. Health education aimed at improving pregnant women's knowledge and belief toward self-protection against COVID-19 may be an effective way to guide them toward positive practices and promote their health and that of their babies.

Artificial Neural Network Modeling on PM10, PM2.5, and NO2 Concentrations between Two Megacities without a Lockdown in Korea, for the COVID-19 Pandemic Period of 2020.

The mutual relationship among daily averaged PM10, PM2.5, and NO2 concentrations in two megacities (Seoul and Busan) connected by the busiest highway in Korea was investigated using an artificial neural network model (ANN)-sigmoid function, for a novel coronavirus (COVID-19) pandemic period from 1 January to 31 December 2020. Daily and weekly mean concentrations of NO2 in 2020 under neither locked down cities, nor limitation of the activities of vehicles and people by the Korean Government have decreased by about 15%, and 12% in Seoul, and Busan cities, than the ones in 2019, respectively. PM 10 (PM2.5) concentration has also decreased by 15% (10%), and 12% (10%) in Seoul, and Busan, with a similar decline of NO2, causing an improvement in air quality in each city. Multilayer perception (MLP), which has a back-propagation training algorithm for a feed-forward artificial neural network technique with a sigmoid activation function was adopted to predict daily averaged PM10, PM2.5, and NO2 concentrations in two cities with their interplay. Root mean square error (RMSE) with the coefficient of determination (R2) evaluates the performance of the model between the predicted and measured values of daily mean PM10, PM2.5, and NO2, in Seoul were 2.251 with 0.882 (1.909 with 0.896; 1.913 with 0.892), 0.717 with 0.925 (0.955 with 0.930; 0.955 with 0.922), and 3.502 with 0.729 (2.808 with 0.746; 3.481 with 0.734), in 2 (5; 7) nodes in a single hidden layer. Similarly, they in Busan were 2.155 with 0.853 (1.519 with 0.896; 1.649 with 0.869), 0.692 with 0.914 (0.891 with 0.910; 1.211 with 0.883), and 2.747 with 0.667 (2.277 with 0.669; 2.137 with 0.689), respectively. The closeness of the predicted values to the observed ones shows a very high Pearson r correlation coefficient of over 0.932, except for 0.818 of NO2 in Busan. Modeling performance using IBM SPSS-v27 software on daily averaged PM10, PM2.5, and NO2 concentrations in each city were compared by scatter plots and their daily distributions between predicted and observed values.

Transfer Function Model for COVID-19 Deaths in USA Using Case Counts as Input Series.

This paper presents a transfer function time series forecast model for COVID-19 deaths using reported COVID-19 case positivity counts as the input series. We have used deaths and case counts data reported by the Center for Disease Control for the USA from July 24 to December 31, 2021. To demonstrate the effectiveness of the proposed transfer function methodology, we have compared some summary results of forecast errors of the fitted transfer function model to those of an adequate autoregressive integrated moving average model and observed that the transfer function model achieved better forecast results than the autoregressive integrated moving average model. Additionally, separate autoregressive integrated moving average models for COVID-19 cases and deaths are also reported.

An Estimation of COVID-19 Confirmed, Death and Recovered Cases Across India Using Novel Ridge Regularization and Comparing with Support Vector Machine

COVID-19 (CoronaVirus disease) is caused by coronavirus which leads to mild to moderate symptoms like cough,sneezing. It causes severe acute respiratory syndrome. India has the third highest number of COVID-19 confirmed cases in the world. The COVID-19 analysis was about the estimation of confirmed, death and recovered cases across India. The aim of the study was to introduce the Novel Ridge Regularization model for effective prediction of COVID-19 cases, therefore by reducing the overfitting of data. In this study two groups were used for classification namely Ridge regularization with sample size of 110 and SVM (Support Vector Machine) technique with sample size of 110, similarly the dataset size of 65896 was used for this experiment. Based on the experiment it was observed that the ridge regularization has got Least RMSE values than the SVM model with significance p=0.032. Ridge Regularization model provides a better approach for analyzing COVID-19 cases than SVM model. © The Electrochemical Society

Applicability evaluation of a demand-controlled ventilation system in livestock.

The distribution of agricultural and livestock products has been limited owing to the recent rapid population growth and the COVID-19 pandemic; this has led to an increase in the demand for food security. The livestock industry is interested in increasing the growth performance of livestock that has resulted in the need for a mechanical ventilation system that can create a comfortable indoor environment. In this study, the applicability of demand-controlled ventilation (DCV) to energy-efficient mechanical ventilation control in a pigsty was analyzed. To this end, an indoor temperature and CO2 concentration prediction model was developed, and the indoor environment and energy consumption behavior based on the application of DCV control were analyzed. As a result, when DCV control was applied, the energy consumption was smaller than that of the existing control method; however, when it was controlled in an hourly time step, the increase in indoor temperature was large, and several sections exceeded the maximum temperature. In addition, when it was controlled in 15-min time steps, the increase in indoor temperature and energy consumption decreased; however, it was not energy efficient on days with high-outdoor temperature and pig heat.

A novel extended Gumbel Type II model with statistical inference and Covid-19 applications

Statistical models play an important role in data analysis, and statisticians are constantly looking for new or relatively new statistical models to fit data sets across a wide range of fields. In this study, we used a new alpha power transformation and the Gumbel Type -II distribution to suggest an unique statistical model. The study contains a simulation analysis to determine the parameters’ efficiency. Two real-life data sets were utilized to demonstrate the use of novel alpha power Gumbel Type II (NAPGT-II) distribution. NAPGT-II distribution yields a better fit than Weibull, new alpha power exponential, exponentiated Gumbel Type-II, Gumbel Type-II and exponentiated generalized Gumbel Type-II distribution, as evidenced by the data.

Modeling and forecasting the total number of cases and deaths due to pandemic.

The COVID-19 pandemic has appeared as the predominant disease of the 21st century at the end of 2019 and was a drastic start with thousands of casualties and the COVID-19 victims in 2020. Due to the drastic effect, COVID-19 scientists are trying to work on pandemic diseases and Governments are interested in the development of methodologies that will minimize the losses and speed up the process of cure by providing vaccines and treatment for such pandemics. The development of a new vaccine for any pandemic requires long in vitro and in vivo trials to use. Thus the strategies require understanding how the pandemic is spreading in terms of affected cases and casualties occurring from this disease, here we developed a forecasting model that can predict the no of cases and deaths due to pandemic and that can help the researcher, government, and other stakeholders to devise their strategies so that the damages can be minimized. This model can also be used for the judicial distribution of resources as it provides the estimates of the number of casualties and number of deaths with high accuracy, Government and policymakers on the basis of forecasted value can plan in a better way. The model efficiency is discussed on the basis of the available dataset of John Hopkins University repository in the period when the disease was first reported in the six countries till the mid of May 2020, the model was developed on the basis of this data, and then it is tested by forecasting the no of deaths and cases for next 7 days, where the proposed strategy provided excellent forecasting. The forecast models are developed for six countries including Pakistan, India, Afghanistan, Iran, Italy, and China using polynomial regression of degrees 3-5. But the models are analyzed up to the 6th-degree and the suitable models are selected based on higher adjusted R-square (R2 ) and lower root-mean-square error and the mean absolute percentage error (MAPE). The values of R2 are greater than 99% for all countries other than China whereas for China this R2 was 97%. The high values of R2 and Low value of MAPE statistics increase the validity of proposed models to forecast the total no cases and total no of deaths in all countries. Iran, Italy, and Afghanistan also show a mild decreasing trend but the number of cases is far higher than the decrease percentage. Although India is expected to have a consistent result, more or less it depicts some other biasing factors which should be figured out in separate research.

Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods.

The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered in real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. A several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organisations working against COVID-19 and determining their long-term plans.

Relationships among changes in walking and sedentary behaviors, individual attributes, changes in work situation, and anxiety during the COVID-19 pandemic in Japan.

Studies from many countries, including Japan, have reported decreased physical activity during the coronavirus disease 2019 (COVID-19) pandemic. However, the individual attributes as related to changes in physical activity during the pandemic in Japan have been scarcely investigated. The present study explored the relationships among individual attributes including demographic, socioeconomic, and geographic characteristics, work situation changes, perception of anxiety, and changes in walking and sedentary behaviors, during the pandemic in Japan. To obtain data indicating individual circumstances during the first wave of the pandemic in Japan, we conducted a nationwide online survey from May 19 to May 23, 2020 (n = 1,200). To observe changes in walking behavior objectively and retrospectively, we collected data on the number of daily steps as measured by the iPhone's Health application. Path analysis was employed to examine relationships between individual attributes and changes in walking and sedentary behaviors. Decreased physical activity, especially, decreased walking behavior among younger individuals and those living in highest-density neighborhoods were identified. There was increased sedentary behavior among females. Moreover, individuals with higher socioeconomic status (SES) tended to become inactive due to work-from-home/standby-at-home and individuals with lower SES tended to become inactive due to decreased amount of work. Decreased walking behavior and increased sedentary behavior were associated with a perception of strong anxiety related to the pandemic. Our findings would be helpful in considering measures to counteract health risks during the pandemic by taking into account individual backgrounds.

The association between problematic internet use, psychological distress, and sleep problems during COVID-19.

The implementation of mandatory stay-at-home and isolation policies during the COVID-19 pandemic has resulted in people relying more on smartphone use to obtain the latest developments regarding the pandemic, interact with people, and for entertainment. Unfortunately, as people spend more time participating in Internet activities, they are more likely to encounter problematic internet use (PIU) issues. The main purpose of this study was to examine the association between two kinds of PIU [problematic smartphone use (PSU) and problematic social media use (PSMU)], psychological distress, and sleep problems. In addition, the moderating effect of sleep problems was examined. A total of 11014 school teachers completed the online survey. The participants were divided into two (high and low sleep problem) groups, according to the severity of their sleep problems, for comparison. The research conducted a comparison between the degree of PIU and psychological distress, and then provided correction for the two groups separately. The results indicated that the high sleep problem group exhibited significantly greater psychological distress [mean (SD) = 12.94 (11.29)] than the low sleep problem group [(mean (SD) = 3.42 (6.57)]. Both PSU and PSMU were positively correlated with psychological distress in the two groups. The moderating effect of sleep problems was supported and PSMU was more harmful to psychological distress in the high sleep problem group, while the effect of PSU on psychological distress was not significantly different between the two groups.

Prediction of the number of COVID-19 confirmed cases based on K-means-LSTM.

COVID-19 is a pandemic disease that began to rapidly spread in the US, with the first case detected on January 19, 2020, in Washington State. March 9, 2020, and then quickly increased with total cases of 25,739 as of April 20, 2020. Although most people with coronavirus 81%, according to the U.S. Centers for Disease Control and Prevention (CDC), will have little to mild symptoms, others may rely on a ventilator to breathe or not at all. SEIR models have broad applicability in predicting the outcome of the population with a variety of diseases. However, many researchers use these models without validating the necessary hypotheses. Far too many researchers often "overfit" the data by using too many predictor variables and small sample sizes to create models. Models thus developed are unlikely to stand validity check on a separate group of population and regions. The researcher remains unaware that overfitting has occurred, without attempting such validation. In the paper, we present a combination algorithm that combines similar days features selection based on the region using Xgboost, K-Means, and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., K-Means-LSTM) for short-term COVID-19 cases forecasting in Louisana state USA. The weighted k-means algorithm based on extreme gradient boosting is used to evaluate the similarity between the forecasts and past days. The results show that the method with K-Means-LSTM has a higher accuracy with an RMSE of 601.20 whereas the SEIR model with an RMSE of 3615.83.

Learning it the hard way - how enjoying life and positive appraisal buffer the negative effects of stressors on mental health in the COVID-19 pandemic.

BACKGROUND: Higher levels of stress and negative emotions such as anxiety and depression have been reported since the beginning of the COVID-19 pandemic, but it remains less clear how positive emotions, such as hedonic capacity, may be affected. Further, during lockdowns, the ability to learn new pleasurable activities (hedonic learning) may be particularly relevant. Here, we investigated if state hedonia and/or hedonic learning mediated the relationship between COVID-19 stress and mental health. Moreover, we explored whether positive appraisal style (PAS), a major resilience factor, influenced these relationships. METHODS: Using a cross-sectional design, 5000 German-speaking participants filled out online questionnaires targeting stressors, mental health, state hedonia, hedonic learning, and PAS between April 9 and May 15, 2020. After confirming the factor structure of our constructs, we applied latent structural equation modeling to test mediation as well as moderated mediation models. RESULTS: Stress showed a positive association with mental health symptoms, which was buffered by both state hedonia and hedonic learning. While higher stress was related to lower state hedonia, participants reported more hedonic learning with greater stressor load. The latter effect was greater for individuals with high PAS. LIMITATIONS: The present results should be replicated in longitudinal designs with representative samples to confirm the directionality and generalizability of effects. CONCLUSIONS: Both state hedonia and hedonic learning buffered the effect of stress on mental health in an early phase of the COVID-19 pandemic. Learning new rewarding activities in combination with a PAS may be especially relevant for maintaining mental health during lockdowns.

Impact of Covid-19 pandemic on electricity demand in the UK based on multivariate time series forecasting with Bidirectional Long Short Term Memory.

Due to lockdown measures taken by the UK government during the Coronavirus disease 2019 pandemic, the national electricity demand profile presented a notably different performance. The Coronavirus disease 2019 crisis has provided a unique opportunity to investigate how such a landscape-scale lockdown can influence the national electricity system. However, the impacts of social and economic restrictions on daily electricity demands are still poorly understood. This paper investigated how the UK-wide electricity demand was influenced during the Coronavirus disease 2019 crisis based on multivariate time series forecasting with Bidirectional Long Short Term Memory, to comprehend its correlations with containment measures, weather conditions, and renewable energy supplies. A deep-learning-based predictive model was established for daily electricity demand time series forecasting, which was trained by multiple features, including the number of coronavirus tests (smoothed), wind speed, ambient temperature, biomass, solar & wind power supplies, and historical electricity demand. Besides, the effects of Coronavirus disease 2019 pandemic on the Net-Zero target of 2050 were also studied through an interlinked approach.

The physical and mental health of the medical staff in Wuhan Huoshenshan Hospital during COVID-19 epidemic: A Structural Equation Modeling approach.

INTRODUCTION: Early in the epidemic of coronavirus disease 2019, the Chinese government recruited a proportion of healthcare workers to support the designated hospital (Huoshenshan Hospital) in Wuhan, China. The majority of front-line medical staff suffered from adverse effects, but their real health status during COVID-19 epidemic was still unknown. The aim of the study was to explore the latent relationship of the physical and mental health of front-line medical staff during this special period. METHODS: A total of 115 military medical staff were recruited between February 17th and February 29th, 2020 and asked to complete questionnaires assessing socio-demographic and clinical characteristics, self-reported sleep status, fatigue, resilience and anxiety. RESULTS: 55 medical staff worked within Intensive Care and 60 worked in Non-intensive Care, the two groups were significantly different in reported general fatigue, physical fatigue and tenacity (P<0.05). Gender, duration working in Wuhan, current perceived stress level and health status were associated with significant differences in fatigue scores (P<0.05), the current perceived health status (P<0.05) and impacted on the resilience and anxiety of participants. The structural equation modeling analysis revealed resilience was negatively associated with fatigue (ß=-0.52, P<0.01) and anxiety (ß=-0.24, P<0.01), and fatigue had a direct association with the physical burden (ß=0.65, P<0.01); Fatigue mediated the relationship between resilience and anxiety (ß=-0.305, P=0.039) as well as resilience and physical burden (ß=-0.276, P=0.02). CONCLUSION: During an explosive pandemic situation, motivating the effect of protective resilience and taking tailored interventions against fatigue are promising ways to protect the physical and mental health of the front-line medical staff.

COVID-19 Mortality Rate Prediction for India Using Statistical Neural Network Models.

The primary aim of this study is to investigate suitable Statistical Neural Network (SNN) models and their hybrid version for COVID-19 mortality prediction in Indian populations and is to estimate the future COVID-19 death cases for India. SNN models such as Probabilistic Neural Network (PNN), Radial Basis Function Neural Network (RBFNN), and Generalized Regression Neural Network (GRNN) are applied to develop the COVID-19 Mortality Rate Prediction (MRP) model for India. For this purpose, we have used two datasets as D1 and D2. The performances of these models are evaluated using Root Mean Square Error (RMSE) and "R," a correlation value between actual and predicted value. To improve prediction accuracy, the new hybrid models have been constructed by combining SNN models and the Non-linear Autoregressive Neural Network (NAR-NN). This is to predict the future error of the SNN models, which adds to the predicted value of these models for getting better MRP value. The results showed that the PNN and RBFNN-based MRP model performed better than the other models for COVID-19 datasets D2 and D1, respectively.