Various natural disasters such as floods, fires, earthquakes, etc. have affected human life. Detection and classification of large and small earthquakes caused by natural or abnormal events have been always important to Earth scientist. One of the most important research challenges in this field is the lack of an effective method for identifying and categorizing various types of seismic events at less important and important levels. Based on latest achievements of Data Mining international institutions such as Rexer-KDnugget-Gartner and also newest authentic articles, SVM, KNN, C4.5, MLP are from most important and popular and leading classifiers in data world.Therefor in present study, a boost learning system consisting support vector machine algorithms with linear regression, MLP Neural Network ، C4.5 decision tree and KNN near neighborhood have been utilized in a combined form to detect and categorize micro seismic events. In general, the steps involved in the proposed method are: 1) performing artificial seismic tests, 2) data gathering and analysis, 3) conducting preprocessing and separating training and testing samples, 4) generating relevant models with training samples and detecting and categorizing test samples and 5) extracting a cluster with the maximum candidate using boost learning. After simulations, it was observed that the accuracy of proposed boost method to the best answer was about 6.1% higher compare to other methods and the error rate was 0.082% of recalling. Accuracy of detection and classification to the best answer were also improved compare to other methods up to 2.31% and 6.34%, respectively.

Natural events such as floods, fires, tsunamis, earthquakes and others have nowadays caused serious damage to human beings and nature. The precise detection of these natural events and especially the earthquake has nowadays become the focus of many computer and geoscientific researchers. Computer science and machine learning algorithms have revolutionized early detection and prediction of these events. Hence, a fuzzy method has been initially used in this article to enhance the authenticity of data based on application of effective variables and then combination of neural network algorithms of the MLP perceptron and radial network of RBF in form of a collective learning system in order to more accurately identify seismic events on a small scale. It was observed after simulating the proposed method that the proposed method has significantly improved based on actual error and root-mean-square error (RMSE) criteria compared to basic methods.

Introduction: The 2019 novel coronavirus (COVID-19) is very contagious, and can be transmitted to other people by droplet, aerosol, sneezing, infected surface, and cough. There is no vaccine or effective treatment at this time. Therefore, the prevention of COVID-19 and the rapid diagnosis of infected patients is crucial. Method: We searched all relevant literature published up to February 28, 2020, from Embase, Scopus, PubMed, Web of Science, and the Cochrane library to collect the studies that reported clinical and laboratory characteristics of COVID-19 infected patients. The study quality was assessed with the Critical Appraisal Checklist. Depending on the heterogeneity test, we used either random or fixed-effect models to analyze the appropriateness of the pooled results. Result: Twenty studies were included in the meta-analysis, including a total of 52,251 patients with confirmed COVID-19 infection. 69.5% (95% CI 54.5-81, p < 0.001) of patients had a history of recent travel to Wuhan, contact with people from Wuhan, or lived in Wuhan. The most common symptoms among COVID-19 infected patients were fever 85.6 % (95% CI 73 -93, p < 0.001), and cough 63 % (95% CI 55.5-70, p < 0.001), respectively. The laboratory analysis showed that thrombocytosis was present in 91% (95% CI 81-98, p < 0.001) CRP was elevated in 81% (95% CI 65-91, p < 0.001), and lymphopenia in 62.5% of cases (95% CI 42-79, p < 0.001). The most common radiographic signs were bilateral involvement in 76.8% (95% CI 62.5-87, p < 0.001) and consolidation in 75.5% (95% CI 50.5-91, p < 0.001) of patients. Most patients (85.4%) were hospitalized, 20.6% of patients were admitted to the ICU in critical condition, and the mortality rate was 5.6%. Conclusions: Fever and cough are the most common symptoms of COVID-19 infection in the literature published to date. Thombocytosis, lymphopenia, and increased CRP were common lab findings although most patients included in the overall analysis did not have laboratory values reported. The most common radiographic sign was bilateral involvement in and consolidation. Among Chinese patients with COVID-19, rates of hospitalization, critical condition, and hospitalization were high in this study, but these findings may be biased by reporting only confirmed cases.