مدتی است شهرداری های ایران برای بازآفرینی نظام اداری و فرآیندهای کسب و کار خود، راهبرد واگذاری در حوزة فرآیندهای صدور مجوز های ساختمانی را در پیش گرفته اند. لیکن بسیاری از مدیران و سیاست گذاران ذی ربط برای تصمیم گیری در خصوص راهکار صحیح و اصولیِ واگذاری آن حوزه از فعالیت ها و دست یابی به آینده ای مطلوب، در سایه ابهامات و تردیدها هستند. امروزه فعالیت های آینده نگاری به عنوان روشی مناسب برای مقابله با پیچیدگی و عدم اطمینان در تصمیم گیری ها، مورد توجه قرار گرفته است؛ از این رو پژوهش حاضر متناسب با فرآیند آینده نگاری و با مشارکت گروهی از صاحب نظران و خِبرگان به شناسایی و بررسی راهکارهای بدیل برای واگذاری فرآیندهای صدور مجوزهای ساختمانی پرداخته تا بتواند راهکار برتر را مشخص نماید. بدین منظور با استفاده از روش تحلیل چند معیارة مبتنی بر کارت امتیازی متوازن، عملکرد آتی شهرداری ها در صورت به کارگیری هریک از آن راهکار ها بررسی شد. یافته ها مبنی بر این است که شهرداری ها در صورت بهره گیری از مدل توسعه یافتة هوشمندسازی و واگذاری فرآیندهای صدور مجوزهای ساختمانی با سیماک(سامانه یکپارچه مدیریت املاک) به عنوان راهکار برتر، مسیر لازم جهت نوسازی و تحول در مدیریت و نظام اداری و شکل گیری آینده ای مطلوب برای آن حوزه از فعالیت ها را طی خواهند نمود. تفاصيل المقالة

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. تفاصيل المقالة

AbstractLots of information about customers are stored in the databases of banks. These databases can be used to assess the credit risk. Feature selection is a well-known concept to reduce the dimension of such databases. In this paper, a multi-stage feature selection approach is proposed to reduce the dimension of database of an Iranian bank including 50 features. The first stage of this paper is devoted to removal of correlated features. The second stage of it is allocated to select the important features with genetic algorithm. The third stage is proposed to weight the variables using different filtering methods. The fourth stage selects feature through clustering algorithm. Finally, selected features are entered into the K-nearest neighbor (K-NN) and Decision Tree (DT) classification algorithms. The aim of the paper is to predict the likelihood of risk for each customer based on effective and optimum subset of features available from the customers. تفاصيل المقالة