Hooman KHosravi

   The current research was carried out with the aim of analyzing the challenges of developing electronic businesses in the context of domestic social networks. The research is practical in terms of purpose and has used the mixed research method. The community in the qualitative and quantitative part of the research includes managers and practitioners of electronic businesses active in Iranian social networks, which were conducted using the snowball sampling method in the qualitative part of 18 in-depth semi-structured interviews. And in the quantitative section, 50 samples of experts in this field with the most expertise were selected. The validity and reliability of the questionnaire was verified by experts in the qualitative section by selecting a suitable sample and reviewing the participants, and in the quantitative section, the questionnaire was confirmed by experts, and the reliability was also measured and confirmed through Cronbach's alpha (0.71%). In the qualitative section, the codes were checked and extracted using the content analysis method. The findings indicate the extraction of 7 main categories and 20 sub-categories. The main categories are; Challenges related to users are infrastructural and technical, laws, Iranian social networks, management, human and economic weakness. is The findings of the qualitative part were prioritized with the help of the Friedman test method, and according to the analysis, based on the results obtained from the Friedman ranking test of 79 factors under investigation, the factors; Absence of regulatory laws with a rating (09/64), causing disturbance to the contacts of social networks with a rating (13/63), lack of public acceptance of these networks with a rating (06/62), poor technical infrastructure with a rating (62/05), the audience's lack of security trust in domestic social networks with a rating (61/87), the multitude of rated social networks (61/31) and the low knowledge of experts in the field of social networks with a rating (67/60) were introduced as having the highest priority according to the respondents and the factors; The difficulty of user entering Iranian networks with a rating (17/49), the lack of familiarity of activists with these networks with a rating (12/25), information theft with a rating (25/27), the challenge of being more expensive and unstable Iranian server with a rating (25/29), failure to update these networks on time with a rating (25/33); Inaccuracy in choosing the media with a rating of (25/41) and weakness in the consequences of mistakes in networks with a rating of (25/78) had the lowest priority according to the respondents.

  This research has investigated and implemented a combined model of fuzzy logic and decision tree to diagnose heart disease using medical data of 920 patients (UCI database). After reviewing and preparing the data, the information of 304 patients were selected as complete data, and their quantification and normalization process was performed with the Min-Max method. The local database of the deputy scene hospital was also used to complete the model. The decision tree model was extracted with an accuracy of 78.30% and the area under the ROC chart equal to 0.798, but the classification error was reported as 22.36%. According to the decision tree, the fuzzy inference system was implemented with 9 inputs and 12 fuzzy rules. Then, the combined decision tree-fuzzy inference system model was implemented with 70 hospital data and evaluated with 35 new data. The results showed that the model was able to achieve an overall accuracy of 91.43% in data classification. The sensitivity of the model was 89.47% and 93.75% for the diagnosis of healthy people and heart patients, respectively. Comparison with other methods showed that the proposed model has a higher accuracy between 1 and 12% than the support vector machine, artificial neural network and k-nearest neighbor methods. The results of this research showed that the use of local data tailored to the specific characteristics of each region can improve the accuracy of machine learning models in medical applications. The combined decision tree-fuzzy model with high accuracy and simplicity in implementation was introduced as an effective tool for diagnosing heart diseases.

   راه هاي اساسي در راستاي تبديل نظريه هاي تدويني به عمل، ارائه و پيشنهاد راه حل هايي مناسب در هر پژوهش صورت گرفته است. اين كار مي تواند تا حد زيادي دستيابي به موفقيت در آينده را براساس نتايج به دست آمده از پژوهش را تضمين كند. اين مهم نشان مي دهد كه ارائه نتايج علمي و صحيح در قالب پيشنهادهايي مناسب تا چه اندازه در يك پژوهش داراي اهميت است. در واقع نتيجه گيري هايي كه بر اساس تحليل هاي صحيح شكل گرفته باشد، مي تواند شرايط مناسب تحقق موضوع و هدف پژوهش را ايجاد و مهيا كند. در ادامه به هر يك از سؤالات پژوهش با توجه به يافته­ها و نتايج به دست آمده پاسخ داده شده است.