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Introdution: Ischemic heart disease is one of the most common diseases, which has led to high mortality rates all over the world. This disease is caused by narrowing or blockage of coronary arteries, which are the provider of blood to the heart. Identifying the people susceptible to this disease and bringing changes in their lifestyles has been said to reduce the related mortality rates and increase the patient's longevity. Methods: Yazd people Health Study (YaHS) was conducted on a random sample of 10,000 people living in the city of Yazd, Iran in the years 2014-15 for a general health and disease survey. These data were first balanced by bootstrapping technique due to their unbalanced nature. Next, classification methods were used in the training phase. Various classifiers, such as artificial neural network, rule inducer, regression, and AdaBoost were used in order to evaluate the proposed method with two scenarios. Results: The results showed that the screening of the people susceptible to ischemic heart disease had the most significant effect on increasing the sensitivity of the discovery classifier of CN2 subgroup through using balanced data by bootstrapping method followed by their analysis for the purpose of producing a sample of the patients. This classifier proved to have the potential for detecting 83.6% of the people susceptible to this disease. Conclusion: Therefore, it can be concluded that data mining methods are effective in screening for susceptible people with ischemic heart disease. This method can be compared with other traditional screening methods in that it is more cost-effective and faster.

The enterprise resource planning system is business process management software used to integrate the existing organizational information and used for the concentrated control and management of all facets of the operations. To successfully implement the enterprise resource planning systems, it seems necessary to identify and pay attention to the effective factors of its implementation. This study aims to identify and rank these factors by using fuzzy Delphi and fuzzy analytic hierarchy process. Based on the experts' opinions, the effective factors in terms of the experts' opinions are identified in the first step. These factors are then ranked based on the opinions of the experts of the tile and ceramic industry in Yazd. Eight main factors are identified and their priorities are the users,

Agent oriented software engineering (AOSE) is an emerging field in computer science and proposes some systematic ideas for multi agent systems analysis, implementation and maintenance. Despite the various methodologies introduced in the agent-oriented software engineering, the main challenges are defects in different aspects of methodologies. According to the defects resulted from weaknesses in agent oriented methodologies in different aspects, a combinatory solution named ARA using, ASPECS, ROADMAP and AOR has been proposed. The three methodologies were analyzed in a comprehensive analytical framework according to concepts and Perceptions, modeling language, process and pragmatism. According to time and resource limitations, sample methodologies for evaluation and in titration were selected. This selection was based on the use of methodologies' and their combination ability. The evaluation show that, the ROADMAP methodology supports stages of agent-oriented systems' analysis and the design stage is not complete because it doesn't model all semi agents. On the other hand, since AOR and ASPECS methodologies support the design stage and inter agent interactions, a mixed methodology has been proposed and is a combination of analysis stage of ROADMAP methodology and design stage of AOR and ASPECS methodologies. Furthermore, to increase the performance of proposed methodology of actor models, service model, capability and programming were also added to this proposed methodology. To describe its difference phases, it was used in a case study too. Results of this project can pave the way to introduce future agentoriented methodologies.