laleh asgari

Managing the process of attracting and transforming the status of faculty members of universities and higher education institutions is one of the important categories This article emphasizes that attracting expert and committed professors has a significant impact on university performance.. The purpose of the research is to analyze the performance of faculty members based on the inputs of the process of attracting and transforming the situation with the fuzzy data mining approach. Among the methods of performance analysis is data collection and analysis with data mining. The research method was using neural fuzzy data mining to analyze the performance of faculty members. This research deals with the design of neural fuzzy system to analyze the personality and performance of the faculty members recruited and converted. For this purpose, 16 personality data and 2 functional data of 1000 faculty members were recruited and transformed, firstly, they were collected, cleaned and reduced, and finally, the fuzzy neural network model was designed with machine learning methods, genetic algorithm and Ril algorithm. The implementation tool is MATLAB software. The research findings of designing a model with fuzzy neural network and Ril algorithm with the validity of high correlation coefficient and low MSE in evaluating the scientific and general competence of faculty members. From the results of the research, it is predicted that the faculty members have stable, influential, honest personality traits in general competence and extrovert, stable, influential, truthful in scientific competence to attract and transform the situation.

Conflict modeling and analysis has been done from the perspective of various Fields. One of the challenges of institutions is the lack of integration of information between them. The purpose of this research is to analyze the conflicts and develop a strategy for recruiting faculty members using drama theory. The present research is considered to be of the applied purpose type. It is qualitative in terms of data type. Because its findings pass through the clarity of understanding and interpretation of the researcher after interviewing experts and reviewing documents. The nature of the research is also exploratory. The current statistical population is information technology managers of organizations and university faculty members in the field of information technology. In research, interviews with experts, review of documents that have been used to collect information. Among the strategies for recruiting academic staff electronically, the implementation of an integrated recruitment system with the management of the Heiat Aali Jazb of Recruitment is one option, the implementation of the system of the Ministry of Science and the Ministry of Health alone, and the implementation of the systems of the ministers in the mentioned ministries separately under the supervision of the Heiat Aali Jazb. and Compiling operational features and routine reports are among other options. The finding of the research is to find the best strategy approved by all the stakeholders to implement the ministerial systems in the mentioned ministries individually under the supervision of the Heiat Aali Jazb and compiling operational features and routine reports.

Timing of curriculum planning for students and faculty could be done using diverse methods. The present research concerns with curriculum planning for professors considering the student's opinions. In doing so, the courses and the timing are determined based on the professor's common timetable, the professor's intensive courses timing and the class limitations. To achieve this goal, the genetic algorithm methodology was used in two steps. In the first stage, single-point cutting operator was used and in the second stage of the algorithm, a new intelligent operator called cyclic reverse list (RIL) was used provided that gold, silver and bronze time types were used for different courses. The advantages of this algorithm are using a new appropriate function (hot rolled), as well as new criteria and a new operator (RIL). Unlike conventional methods, in this method the appropriateness is considered in proportion with the whole population and we try to remove the impossible solutions. The optimal solution is chosen from among a multitude of provided responses. Therefore, it was found that we can reach the optimal solutions with regard to a better appropriateness.