درخت حوزه‌های تخصصی

  Today, sustainable development of tourism as a strategy for the development of cities, especially remote cities is a relatively new idea that its importance has been realized by policymakers. This type of tourism can be considered as a source of income, creating jobs, and making positive changes in the income of cities. In tourism studies, there are also discussions about spiritual tourism. However, most scholars do not consider spiritual tourism as realtourism because it is like an umbrella that can include various types of tourism that leads to a transcendental sense and sacred values such as ‘Spiritual city tourism’.Then, using laboratory techniques of test and evaluation of fuzzy DEMATEL decision making, as one of the appropriate decision-making techniques that deal with systematic relations between variables, relations among factors influencing the development of Spiritual city tourism in Tehran are studied and modeled. The results of the fuzzy DEMATEL method showed that factors affecting the development of Spiritualcity tourism include Appropriate planning and management, Direct supervision of the government and institutions on those active in the field of tourism, Presence of experts fluent in live languages of the world beside tourism attractions, Attracting foreign investment for the development of Spiritual city tourism activities with contracts and special facilities, Marketing, advertising, and introducing tourism attractions in national and international areas, Culturalization of tourism development and promotion of tourist acceptance culture among city residents.

The quest for improving the software quality has given rise to various studies which focus on the enhancement of the quality of software through various processes. Code smells, which are indicators of the software quality have not been put to an extensive study for as to determine their role in the prediction of defects in the software. This study aims to investigate the role of code smells in prediction of non-faulty classes. We examine the Eclipse software with four versions (3.2, 3.3, 3.6, and 3.7) for metrics and smells. Further, different code smells, derived subjectively through iPlasma, are taken into conjugation and three efficient, but subjective models are developed to detect code smells on each of Random Forest, J48 and SVM machine learning algorithms. This model is then used to detect the absence of defects in the four Eclipse versions. The effect of balanced and unbalanced datasets is also examined for these four versions. The results suggest that the code smells can be a valuable feature in discriminating absence of defects in a software.