کووید-19

Sentiment analysis, also called opinion mining, is one of the sub-areas of natural language processing that aims to classify texts according to the sentiments, beliefs and attitudes expressed in them. In the most current research, texts are divided into two "positive" and "negative" categories. However, there are also other categories such as good/bad" and agree/disagree, every one of which has its applications. The purpose of this paper is to analyze the opinions expressed by users on social media about the reopening of schools during the Covid-19 outbreak using supervised machine learning techniques, and to classify them into two "agree" and "disagree" categories. Users' opinions, in this paper, are in Persian. The lack of sufficient datasets and also the low accuracy of natural language processing tools are the most important problems of text processing in Persian. Due to the mentioned limitations, the use of supervised machine learning algorithms and also the extraction of effective features for training machine learning classifiers in Persian are facing a serious challenge. In this paper, first, a small dataset of the users' opinions about the reopening of schools was collected and manually labeled. Then, a combined method was used for data augmentation of the dataset. In the proposed method, first, Persian sentences were translated into English. Then nouns, verbs and adjectives of the English sentences were replaced with their synonyms. Next, the English sentences were translated into Persian again. The new sentence with the class label of the initial sentence was added to the training set. Thus, the size of the training set increased by 97 percent. After that, the efficiency of employing the common pre-processing steps and using common feature sets in sentiment analysis of the English texts for Persian were evaluated and the best of them were selected. Considering the low accuracy of the Persian natural language processing tools, it was tried to select those features that were less dependent on the tools. Finally, machine learning classification was used to determine agree/disagree class of the user opinions of the test sets. The results of the experiments indicated that by applying the proposed method for data augmentation and using selected features in this paper, 81 and 79 percent precision was obtained for the polarity classification of opinions using SVM and CNN algorithms, respectively.

Coronavirus disease 2019 or Covid-19, which is also called acute respiratory disease NCAV-2019 or commonly called corona, is a respiratory disease caused by acute respiratory syndrome coronavirus-2. Forecasting the number of new cases and deaths during todays can be a useful step in predicting the costs and facilities needed in the future. This study aims to model and predict new cases and deaths efficiently in the future. Nine popular forecasting techniques are tested on the data of Covid-19 in Yazd city as a case study. Using the evaluation criteria of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and the mean absolute percentage of error (MAPE) of the models are compared. According to the selected evaluation criteria, the results of the comprehensive analysis emphasize that the most efficient models are the ARIMA model for predicting the cumulative cases of hospitalization of Covid-19 and the Theta model for the cumulative cases of death. Also, the autoregressive neural network model has the worst performance among other models for both hospitalization and death cases.

Prediction with high reliability is very important in solving real-world problems, especially those that affect public health. The statistical properties of complex problems such as Covid-19 disease constantly change over time which makes modeling of such problems associated with high-level uncertainty. It has been proven that the type-2 fuzzy logic has the potential for modeling uncertainty to solve complex problems. In this research, for the first time, an intelligent method based on the capability of type-2 fuzzy logic was presented to manage uncertainty in predicting short-term and long-term time series in environmental crises such as the Covid-19 pandemic. The performance of the proposed model was evaluated using a real dataset collected from official sources. The results confirm the high efficiency of the proposed method on Covid-19 based on a ROC curve analysis. The obtained results showed an efficiency of 93.81% for short and 91.33% for long-term time series. This indicates the high efficiency and capability of the proposed model for managing uncertainty in predicting patterns of Covid-19 in comparison with similar methods. The proposed model can be useful to take strategic decisions and prevent the consequences of the Covid-19 epidemic in the short and long terms.

Due to the epidemic of the coronavirus (Covid-19) and its rapid spread around the world, the world has faced a huge crisis. To prevent the spread of the coronavirus, the World Health Organization (WHO) has introduced the use of masks and keeping social distance as the best preventive method. So, developing an automatic monitoring system for detection of facemask in some crowded places is essential. To do this, we propose a mask recognition system based on transfer learning and Inception v3 architecture. In the proposed method, two datasets are used simultaneously for training including: Simulated Mask Face Dataset (SMFD) and MaskedFace-Net (MFN).this paper tries to increase the accuracy of the proposed system by optimally setting hyper-parameters and accurately designing the fully connected layers. The main advantage of the proposed method is that in addition to masked and unmasked face, it can also detect cases of incorrect use of mask. Therefore, the proposed method classifies the input face images into three categories. Experimental results show the high accuracy and efficiency of the proposed method; so that, this method has achieved to accuracy of 99.47% and 99.33% in training and test data respectively.

Corona disease started in January 2020 in the wholesale fish market in Wuhan, China, and the World Health Organization declared it as a public disease and an international hazard, and in February 2020 named it Corona or Covid-19. Under this global pandemic, using artificial intelligence techniques especially convolutional neural networks based on deep learning for screening chest CT images are becoming more vital as before. The most studies in this field belong to the articles based on the deep learning methodologies using convolution neural networks. Already, obtained accuracies of detection and screening have benn reported in the article based on artificial intelligence and deep learning are more than 95 percent. The lack of comprehensive datasets of CT images with a large amount of samples is one of the most important problems in this field. Using hybrid architectures of convolutional neural networks have been increased the accuracy of these networks up to 99 percent.

The prevalence of coronavirus at the end of 2019, which continues to this day, has been charged in various respects around the world, some of which operate irreparably and others can compensate. compensate for the slow process. On the other hand, the global event known as the Corona crisis has painted a picture of the future of energy in the energy sector, it examines and analyzes the conditions and experiences of this crisis, resulting in faster and more alert emissions. Resist future changes in the global energy industry, sustainable development, and the economy. Therefore, this report has been approached by changing the energy change in the world, and finally looks at the state of energy during the Corona crisis and gives recommendations to face the picture. about the world's energy future. It is one of the most important agreements for the implementation of international agreements and treaties aimed at increasing the sale of more oil and natural gas, leading to increased government revenue and, ultimately, required capital gain. to invest in energy. On the other hand, reducing hidden energy subsidies could also reduce the dependence of some national industries on oil and gas and these subsidies in the areas of low carbon, research, and innovation to support growth. share of innovation, especially in transportation, power supply, heating, and navigation.

Under the global pandemic of COVID-19 over the last year, the use of image processing techniques and the artificial intelligence algorithms to analysis chest X-ray (CXR) images is becoming important. Determining the lung involvement and percentage development of COVID-19 is one of most important requirements for the hospitalization centers. The most studies in this field belong to the articles based on the deep learning methodologies using convolution neural networks, which are usually implemented to facilitate the screening process. Only a few number of studies are about the determining the percentage of lung involvement and development of coronavirus based on CXR images. The lack of comprehensive datasets of CT images with a large amount of samples is one of the most important issues in this field. Determining of lung infection in COVID-19 patients, based on different CXR images in different days, has its own challenges such as different image sizes, illumination density, radiation dose of X-ray and angle of radiation, which makes it impossible to the implement a simple differential filter on two different images. Using an optimization self-adaptive algorithm with differential and multi-objective approach can improve the performance accuracy with a corresponding reduction in computation time.

The new coronavirus (COVID-19) is caused by the acute respiratory syndrome 2. The disease was first diagnosed in December 2019 in Hubei Province, Wuhan City, China. On March 11, the World Health Organization declared the virus an epidemic. Until Mid-February, 2021, about 112 million people worldwide have been infected with the virus, of which about 2.500.000 people have lost their lives, according to the latest global statistics from the Worldometer database. The economic shock of the coronavirus outbreak has also led to negative economic impacts, including declining sales of many related businesses. In this paper, the effects of coronavirus on the Iranian economy are examined with a systems dynamics approach. First, the relationships of important variables affecting GDP in SPSS software are obtained and then the data is analyzed using Vensim software. In the following, by analyzing the sensitivity and providing recommendations, the impact of managerial insights to control the conditions of this new crisis will be examined. The results show that the correct use of the mask and the observance of social distance as well as air flow in spaces and avoidance of closed spaces have a significant effect on reducing the spread of this disease and reducing mortality.

In this paper modeling, analysis and prediction of novel epidemic of COVID-19 are concerned to identify effective spread parameters of it in Iran. For this purpose, the basic susceptible-infected-removed (SIR) model is used which has two parameters: the infection rate and remove rate. The occurrence of several maximum points in the Iranian data and the single peak of the SIR model makes it impossible to use a model with the same parameters for all times. For this reason, the Iranian data are divided into five time periods and then the parameters of each period are obtained. In addition to adapting to the behavior of disease-related data, these time periods are consistent with the realities of society, including the timing of government decisions and the changing patterns of individuals in society. Finally, an analysis based on the obtained parameters and the trend of disease spread in the continuation of this year is presented. Since the economic,social and health consequences of this virus are catastrophic, using the results of mathematical modeling to identify the factors affecting the spread of the disease can be a step towards future actions to control the disease.

In this paper, a model is proposed based on the different levels of social restrictions for the COVID-19.spread restraint in Iran. Also, a Genetic Algorithm (GA) identifies parameters of model using reported main data from the Iranian Ministry of Health and simulated data based on proposed model. Whereas Model Predictive Control (MPC) is a popular method which has been widely used in process control, after the discretization of model by a common method like Euler method, then we can consider the appropriate constraints and solve online optimization problem. In this paper, we have shown that the MPC controller able to flatten infected (symptomatic) individual curve and decrease its peak by applying the different levels of social restrictions. Numerical example and simulation results, based on main data, are given to illustrate the capability of this method.

This paper presents a survey on haptic technology, virtual reality, and artificial intelligence applications in medical training during the COVID-19 pandemic. Over the last few decades, there has been a great deal of interest in using new technologies to establish capable approaches for medical training purposes. These methods are intended to minimize surgerychr('39')s adverse effects, mostly when done by an inexperienced surgeon. Due to the worldchr('39')s unique situation during the pandemic, which causes several cities to be locked up, these methodologies are becoming more critical. They eliminate the physical contact requirement between medical personnel and fellows, which decreases the risk of being infected with the virus. This study aims to present new applications for haptic technology, virtual reality, artificial intelligence, and new fields where they can provide a viable solution in the COVID-19 pandemic or any other similar crises.

According to the global prevalence of coronavirus (COVID-19) pandemic, mathematical models can predict and control the dynamic behavior of the pandemic. Therefore, in this study, a comprehensive model is considered to examine the trend of COVID-19 based on Susceptible, Exposed, Infected (Symptomatic and Asymptomatic), and Recovered individuals. In the absence of a curative treatment or vaccination campaign, the group of "quarantined people" is added to the model. Then, a positivity analysis of states is examined, and the threshold criterion is determined. The equilibrium points (disease-free and endemic) are also calculated, and their stability is investigated using the Jacobin matrix. The quarantine rate is regulated as the only control input using the fuzzy sliding mode controller. The efficiency of the controller is also investigated in the presence of uncertainty in model parameters. Also, the impact of the infected community on other communities, considering the controller, will be examined. Finally, the performance and efficiency of the proposed controller are evaluated.

In this paper, we investigate the prevalence rate of smoking in COVID-19 patients and examine whether there is a difference in the distribution of smokers between the two statistical populations of critically ill COVID-19 patients and the entire Iranian population or not. To do this, we first prepared a sample of 300 COVID-19 patients admitted to hospitals in Tehran and Rasht. Then, through the non-parametric statistical runs test, we show that the sample was randomly selected and it is possible to generalize the result of tests on the sample to the community of hospitalized COVID-19 patients. In continuation, we examined the hypothesis that the prevalence of smoking among COVID-19 patients admitted to hospitals is equal to the prevalence rate of smoking in the whole Iranian society. For this purpose, we used the non-parametric chi-square test and it was observed that this hypothesis is rejected. The data show that there is a significant difference in the prevalence of smoking between critically ill COVID-19 patients and the whole of Iranian society. Also, it can be concluded that, the prevalence rate of smoking among COVID-19 hospitalized patients is lower than this rate in the whole Iranian society. The above results show the need for serious research in this field and confirm that while avoiding any positive or negative tendency towards smoking, the causes and factors affecting this phenomenon should be investigated and drugs can be prepared and produced accordingly.

Accurate prediction and diagnosis of COVID-19 disease is very important for everyone, especially for medical professionals. On the other hand, the use of fuzzy systems in medicine is increasing rapidly. In this study, a fuzzy system was designed using the information of 375 patients suspected of having COVID-19 disease who referred to Imam Khomeini(Tehran), Alborz(Karaj) and Kowsar(Karaj) hospitals. For this purpose, 300 people were considered to extract the rules and 75 people were considered as test data. Information on 12 important parameters of COVID-19 disease including fever, cough,  headache, gastrointestinal symptoms,  skin rash,  sense of smell and taste, underlying disease, chest CT, blood oxygen level,  lethargy, age, family history and severity of COVID-19 disease received. The fuzzy expert system was designed with 29 rules after reviewing the rules and removing similar and contradictory rules by using their degree calculation. In this system, by integrating some factors, finally 8 input variables and one output variable were considered that was used by product inference engine, singleton fuzzifier and center average defuzzifier. It was observed that the designed fuzzy expert system provides very good results, so that it detects 93% of Covid-19 disease with high accuracy and also the sensitivity of the system is more than 95% and the specificity of the designed system is more than 87%.

The present study examined the level of knowledge, attitude, and practice of the people of Isfahan in the field of using electronic services during the corona outbreak.

The research was applied in terms of purpose and descriptive-correlational nature and was conducted on 384 people over 18 years old living in Isfahan. The tool was a researcher-made questionnaire consisting of 35 questions that were distributed among eligible people by snowball sampling method. . Data were analyzed by SPSS and Smart PLS software.
 

The knowledge, attitude, and practice of the studied individuals in the field of using electronic services during the corona outbreak are significantly different from the average and is higher than the average. This rate was 3.99 for knowledge, 4.11 for attitude and 3.75 for performance. There is a difference between women and men in the level of knowledge and attitude in the use of electronic services and it is more in men, but there is no significant difference between the performance of women and men in the use of electronic services. The level of knowledge and practice does not differ based on education. The attainment of people with Ph.D. education is stronger in the field of using electronic services. Also, there is a significant difference between the variables of knowledge, attitude and practice of Isfahan people in using electronic services based on job and there is more difference in faculty members. And are in the ages of 30 to 50 years old.

The results show that the knowledge, attitude and practice of the citizens of Isfahan in the use of electronic services are higher than average. The launch of various electronic service delivery systems in many organizations, organizations and commercial businesses and stores has enabled the citizens of Isfahan to receive various services provided by these organizations at home and through mobile or computer systems. During the Covid-19 pandemic, there is less need to be physically present in public places...

On February 2020, Iran reported its first confirmed cases of infections COVID-19 pandemic and since than its effects have been widely spread across in Iran and worldwide. The devastating consequences of the COVID-19 pandemic showed that science and technologies play an important role in ensuring national security and social stability is such crises and we must learn from this event to better prepare for similar cases. In this paper, we will review the developed technologies in Iran to deal with the COVID-19 pandemic. We tried to gather the information of the startup companies and their presented technologies across Iran. After validating the gathered information, the startup companies have been classified based on their fields and their geographical locations.