درخت حوزه‌های تخصصی

Being aware of the waiting time for selling residential units is one of the important issues in the housing sector for the majority of people, especially investors. There are several factors affecting the waiting time for selling residential units. Determining the influential factors on the time period of selling real estates can lead to an informed decision making by real estate consultants, sellers as well as those seeking to buy real estates. Using a real estate database in Iran, the present paper proposes a two-module procedure. The first module deals with implementation of association rule mining. Using the well-known association rule mining techniques namely FP-Growth, several association rules have been extracted which indicate the effective factors on the waiting time for selling residential units. Generated association rules have been evaluated based on metrics such as support, confidence and lift and finally the best rules are selected. The main objective of the second module is to develop a fuzzy inference system which can determine the factors influencing the waiting time for selling residential units from historical data, so that the model can be used to estimate the time it to sell the property for a real estate agency. Several IF-THEN rules are extracted from this module. Extracted rules can be used by real estate agencies as well as buyers and sellers of residential units to make better decisions in their investments. In conclusion section, a number of suggestions for future studies are presented. For example, machine learning algorithms such as neural networks, decision trees, etc. can also be used to predict the duration of residential units’ sale. The main objective of the second module is to develop a fuzzy inference system which can learn about the factors that influence the waiting time for selling residential units from historical data, so that the model can be used to estimate the time it takes to sell the property for a real estate agency. Several IF-THEN rules are extracted from this module. Extracted rules can be used by real estate agencies as well as buyers and sellers of residential units to make better decisions in their investments.

INTRODUCTION: Helicopter Emergency Medical Services (HEMS) provide medical care to critically ill patients and the injured immediately and provide faster treatment, thereby reducing the length of hospitalization. This study aimed to identify the policy implications of HEMS in Isfahan province. METHODS: This qualitative study was conducted using the thematic analysis method. The study population included the managers, personnel, and flight crews of Isfahan HEMS. The participants were selected using the purposive sampling method, followed by the snowball method. Initially, 14 individuals from the HEMS personnel were interviewed using the interview guide form, and then, the opinions of 15 participants from the flight crews were collected through an open questionnaire. Maxqda10 software was used for data coding. FINDINGS: The research findings were classified into challenges, opportunities, and implications. In terms of challenges, 11 themes and 34 categories were identified in this study. HEMS opportunities were also divided into 8 themes and 11 categories. The policy implications provided by the participants were also categorized into 8 themes and 28 categories. CONCLUSION: Considering that one of the most important known challenges was the lack of specialized helicopters and medicopters, according to the results, it is suggested that the managers of Isfahan HEMS take measures to solve the existing challenges and develop plans to strengthen the capacities and utilize the opportunities.