Random forest algorithm

Stock trend forecasting is a one of the main factors in choosing the best investment, hence prediction and comparison of different firms’ stock trend is one method for improving investment process. Stockholders need information for forecasting firm’s stock trend in order to make decision about firms’ stock trading. In this study stock trend, forecasting performs by data mining algorithm. It should mention that this research has two hypotheses. It aimed at being practical and it is correlation methodology. The research performed in deductive reasoning. Hypotheses analyzed based on collected data from 180 firms listed in Tehran stock exchange during 2009-2015. Results indicated that algorithms are able to forecast negative stock return. However, random forest algorithm is more powerful than decision tree algorithm. In addition, stock return from last three years and selling growth are the main variables of negative stock return forecasting.

The Coronavirus disease (Covid-19) is one of the infectious and contagious ones called 2019-nCoV acute respiratory disease. Its outbreak was first reported on December 31, 2019, in the Chinese city of Wuhan that quickly spread throughout the country within a few weeks and spread to several other countries, including Italy, the United States, and Germany, within a month. This disease was officially reported in Iran on February 19, 2020. It is important to detect and analyze high risk zones and establish regulations according to the data and the analyses of Geographic Information System (GIS) in epidemiological situations. Meanwhile, the GIS, with its location nature, can be effective in preventing the breakdown of Covid-19 by displaying and analyzing the dangerous zones where people infected with the disease. In fact, recognizing regions based on the risk of getting the disease can influence social restriction policies and urban movement rules in order to prepare daily and weekly plans in different urban regions. In this applied and analytical research, high and low risk zones of Tehran have been identified by using the random forest algorithm which is used for both classification and regression. The algorithm builds decision trees on data samples and then predicts data from each of them, and finally chooses the best solution. In this research, 7 effective criteria have been used in the level of risk of regions toward Covid-19 virus, which is: subway paths and bus for rapid transits, hospitals, administrative and commercial complexes, passageways, population densities and urban traffic. After providing the map of high-risk zones of Covid-19, the Receiver Operating Characteristic curve (ROC) has been used for evaluation. The area under the curve (AUC) obtained from ROC shows an accuracy of 98.8%, which means the high accuracy of this algorithm in predicting high and low zones toward getting the Covid-19 disease.

In the past, deciding over granting loans to bank customers in Iran would be made traditionally and based on personal judgments over the risk of repayment. However, increase in demands on banking facilities by economic enterprises and families on the one side, and increased as well as extended commercial competitions among banks and financial and credit institutions in the country for reduction of facility repayment risk on the other side, have caused application of novel methods such as some statistical ones in this context. Now to predict the risk of negligence in banking facility repayment and classification of the candidates, bankers use their customers’ credit ranking. Time efficiency, cost effectiveness, avoidance from personal judgments, and further accuracy in examining the candidates who apply for various funds are of its salient merits of this new combined method. Various statistical methods including biased analysis, logistic regression, non-parametric parallelism, and also some others such as neural networks have been employed for credit ranking. In this research, given the random forest metaheuristic algorithm-based smart pattern of real bank customers’ credit risk (case study: Bank Tejarat) was presented. According to the value of skewness, the data could be stated to have a normal distribution. Based on the observed results, the lowest mean was related to the variable of type of facility and its maximum value, to the amount of facility.

Since 2010, deep learning has been further developed, and the concept of multi-modality has penetrated into all walks of life. However, it has not been fully researched and applied in college English teaching, so this study modeled and practiced the multimodal teaching method of college English under the deep learning mode and its application. The definitions of modality and medium are first introduced, and then the definition of multimodality in this study is clarified. Then the classification of multimodal transport is expounded. The random forest algorithm is chosen as the main algorithm of this research, and a dynamic multimodal model is established. After that, there was a collaboration with a university and sophomore students were selected for practice. After processing and analyzing the collected data, it was found that in the data sample of 268 students, the number of students who did not study independently accounted for 24%, which indicates that most college students lack interest in learning English. Preliminary tests were also conducted on students' English proficiency throughout the year, and the results showed that the students' English proficiency was at a pass level and the overall English proficiency was weak. Reassessment of students' English proficiency showed that the actual teaching effect of each English proficiency was greater than 85%, and the effectiveness of English teaching in the selected universities was significantly improved. The average score improved by 8 points, indicating that multimodal teaching is scientifically effective After a semester of multimodal teaching, the English teaching effectiveness of the university selected in this article has significantly improved. The research results indicate that the development of deep computer learning has introduced multimodal concepts into the teaching field, which is very suitable for assisting language learning based on its own advantages. After a semester of multimodal teaching, the English teaching effectiveness of the university selected in this article has significantly improved. The research results indicate that the development of deep computer learning has introduced multimodal concepts into the teaching field, which is very suitable for assisting language learning based on its own advantages.