درخت حوزه‌های تخصصی

Falls are a serious concern among the elderly people, causing severe physical pain to them and placing a strain on medical infrastructure. The global elderly population is expected to grow significantly in the coming years, as advances in healthcare allow lifespans to increase globally. This will bring more chances for falls to occur. With this in mind, there is a need for new research to be conducted on finding ways to reduce this problem. One area which shows promise is the use of Machine Learning to perform fall detection. Machine Learning is a rapidly growing field, and it has many applications in various fields such as finance, technology and medicine. When it comes to fall detection, Machine Learning systems are often able to detect falls much better and efficiently than a human can, given the same input data. The goal of this paper is to conduct a survey study on the main and most common machine learning algorithms implemented in the field of early fall detection for elderly people and the characteristics. The paper will discuss the different types of fall detection systems, algorithms, tools, datasets, applications, and challenges.  By conducting this research, a better understanding of the context, progress and trends in the field will be possible so that future research will have a guide to build upon.

The main purpose of this paper is providing a model to calculate the credit risk of Melli bank clients and implement it at Mellat Bank. Therefore, the present study uses a multi-layered neural network method. The statistical population of this research is all real and legal clients of Melli and Mellat banks. Sampling method used in this research is a simple random sampling method. Friedman test was used to calculate the required number of samples in a random sampling method from Cochran formula (1977) and Friedman test was used to rank the factors affecting the credit risk. Friedman test was also performed using data from a completed questionnaire of active experts at the Melli Bank. Based on the results obtained from Friedman test, five important factors in the credit risk of real clients of the Melli Bank of Iran, type of occupation, guarantee value, loan amount, having return checks, the balance average, and the value of the guarantee, the amount of the loan, the average of the balance, having returned checks and deferred loans are the most important factors affecting the credit risk of legal clients, which have been used as inputs in the neural network model. The results of credit risk prediction using the neural network showed that the designed model has a high ability to predict the credit risk of real and legal clients of the Melli bank, while it did not have this ability for the Mellat bank.

Stock prices in each industry are one of the major issues in the stock market. Given the increasing number of shareholders in the stock market and their attention to the price of different stocks in transactions, the prediction of the stock price trend has become significant. Many people use the share price movement process when com-paring different stocks while investing, and also want to predict this trend to see if the trend continues to increase or decrease over time. In this research, stock price prediction for 1170 years -company during 2011-2016 (a six-year period) of listed companies in stock exchange has been studied using the machine learning method (Chaid rule-based algorithm and Particle Swarm Optimization Algorithm). The results of the research show that there is a significant relationship between earnings per share, e / p ratio, company size, inventory turnover ratio, and stock returns with stock prices. Also, particle swarm optimization (pso) algorithm has a good ability to predict stock prices.

In recent decades, lifestyle has been one of the most important concepts in marketing, and management studies. Hence, this study aimed to identify the values and lifestyles of Iranian consumers. According to Cochran’s formula, 1580 people were selected as the statistical sample. Eighteen values were identified by examining the theoretical foundations and interviewing 77 Iranian citizens. The data collected by Likert-scale questionnaire were evaluated using R software, clustering method. Considering the importance of Iranian values, three clusters were identified: “normal lifestyle,” “phlegmatic lifestyle,” and “ideal lifestyle.” This study is one of the first attempts to create a general typology of Iranian consumers based on values and lifestyle and focusing on cultural, ideological and belief differences of Iranian consumers. Researchers are suggested to take advantage of the lifestyles identified in this research in order to identify and deeply understand the consumption behaviors specific to each style in different markets.

The article is devoted to the research process of digitalization of vegetable development management as an important component of food security of the Black Sea region. Vegetables are necessary for the normal functioning of the human body, provide it with the necessary vitamins and trace elements. one of the conditions of good health. The annual volume of vegetables in food kits for the main social and demographic groups of the population of Ukraine is calculated. The dynamics of vegetable production of vegetable crops in the Black Sea region of Ukraine is studied. In addition, information and communication (digital) technologies in the agar sector are considered. It is proved that the digitization of agribusiness significantly saves the amount of materials and other resources, and execution time and production are optimized. Due to the systematization and grouping of data, the costs of document circulation are reduced, the process of accumulation and use of information is stimulated, the production and economic indicators of agricultural enterprises are improved, which will help increase their competitiveness in the long run.