درخت حوزه‌های تخصصی

For investors, predicting stock market changes has always been attractive and challenging because it helps them accurately identify profits and reduce potential risks. Deep learning-based models, as a subset of machine learning, receive attention in the field of price prediction through the improvement of traditional neural network models. In this paper, we propose a model for predicting stock prices of Tehran Stock Exchange companies using a long-short-term memory (LSTM) deep neural network. The model consists of two LSTM layers, one Dense layer, and two DropOut layers. In this study, using our studies and evaluations, the adjusted stock price with 12 technical index variables was taken as an input for the model. In assessing the model's predictive outcomes, we considered RMSE, MAE, and MAPE as criteria. According to the results, integrating technical indicators increases the model's accuracy in predicting the stock price, with the LSTM model outperforming the RNN model in this task.

INTRODUCTION: An earthquake is a major natural disaster with widespread and great effects on human life and nature. Taking care of the basic needs of earthquake victims is an essential step that should be addressed immediately. Moreover, the evaluation of various problems after disasters, especially earthquakes, is of crucial importance for the affected people. The management of humanitarian aid and public assistance among these affected people is considered one of the most important post-earthquake concerns. Therefore, the present study was conducted to evaluate the post-earthquake challenges and strengths of providing humanitarian aid services in Sisakht, Yasuj, in February 2019. METHODS: This qualitative case study was conducted from March 2019 to July 2020. The studied population consisted of 20 participants including medical staff (n=10), clergy (n=2), crisis managers (n=4), and military personnel (n=4). The required data were collected through observation and semi-structured interviews, which continued until data saturation. To evaluate the reliability of the data, the strategies recommended by Guba were used. The data were analyzed by conventional content analysis based on the method proposed by Graneheim and Lundman. FINDINGS: The advantages and strengths of this study included humanitarian cooperation and coordination, as well as the timely presence of relief and rescue teams and law enforcement agencies from the post-earthquake first hours, which had a great impact on reducing the suffering of people. The challenges in providing post-earthquake services included health, medical, management, geographical, and spatial concerns and issues related to the earthquake victims (relief goods distribution problem and inappropriate information dissemination as the main challenges). CONCLUSION: Effective and appropriate management policies regarding the management of providing post-earthquake services and elimination of the structural and managerial obstacles can improve the operation and management of humanitarian aid.