درخت حوزه‌های تخصصی

An industrial city tourism that manages and sells various products and experiences for people who have a wide range of motivations, tendencies and cultural perspectives  and  engage in a dialectical interaction with the host community. The old and ancient texture of cities due to their existence and the presence of historical valuable elements, proper communication position, the main market of the city and its economic heart, and etc. have a unique value and place in the spatial and functional structure of the city. Historical textures with enduring cultural values are one of the most important fields of development for endogenous development. These textures have characteristics and properties such as: large number of buildings, spaces and memorabilia of the old texture so that old town is distinguished from the periphery and immediate parts of the city and surrounding areas. Among the different regions of the country, the area of the Holy Shrine of Abdulazim in Tehran is of the potential tourist areas due to its historical dating and the abundance of attractions, as well as the diversity in its texture. Therefore, the main goal of this research is development of tourism in the historical texture of the shrine of Hazrat Abdul Azim. Descriptive-analytical method was used in this applied-survey study. The analytical method of SWOT was used to answer research questions and to provide a strategy for tourism development. For this purpose, a list of strengths and weaknesses, as well as opportunities and threats was prepared adjusting the strategic factors in order to extract SWOT matrix and provide the approaches derived from these analyzes as solving strategies regarding strengthen of the range. In addition, it can be concluded with a systematic view over the weaknesses and strengths as well as the existing opportunities and threats, that there are more strengths and opportunities available in this field, which must be exploited using the strengths and existing opportunities. The results obtained from the evaluation of the factors affecting the tourism development of the historical texture of the Hazrat Abdul Azim Shrine using Analytic Hierarchy Process (AHP) model showed that the importance of the AHP technique, 6 main criteria and 23 sub-criteria based on the calculation of the relative and final weights among the evaluated criteria, historical and cultural attractions have the highest significance with coefficient (0.199), respectively.

The ability to predict the stock market and analyze market trends is invaluable to researchers and anyone interested in investing. However, this task is a challenging problem due to a large number of parameters and unpredictable noise that may affect the stock price. To overcome this issue, researchers have employed numerous approaches such as Moving Average (MA), Support Vector Machine (SVM), and Neural Networks. With technological advances, deep learning methods have become popular in processing time-series data. In this paper, we compare two recently introduced deep learning models, namely a Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), in forecasting daily movements of the Standard & Poor (S&P 500) index using the daily closing price of this index from 14/5/1991 to 14/5/2021. Results show that both models are effective and accurate in stock market prediction. In this case study, the mean squared error (MSE) and mean absolute error (MAE) for the GRU model are slightly lower than the LSTM model; hence, GRU outperformed the LSTM model despite its simpler structure. The results of this study are applicable in various instances where it is challenging to identify patterns among large volumes of unstructured data, such as medical data analysis, text mining, and financial time series modeling.