درخت حوزه‌های تخصصی

Basically, the progressing society without the presence and existence of specialized work resources with strong tendencies towards ethics and professional principles in the work environment will not be able to achieve its lofty goals and experience development and growth in various economic, political and cultural sectors. Based on this, the current research aims to provide a model of professionalism in the system Iran administration was done. The current research is a qualitative research method. Research in terms of the result of the component It is developmental-applied research that the statistical population of this research is 25 elite people identified in the University of Medical Sciences They were from Shiraz, who were interviewed who had at least 10 years of experience and had a master's degree and a doctorate and the snowball sampling continued until the opinions of these experts reached the point of theoretical saturation and direction Conducting research after designing the general questions and formulating the interview process, conducting in-depth interviews with experts became. The data collected in this research were combined using thematic analysis technique. To review the validity of the research findings was presented to the participants and the text of the theory was studied by them and their points of view were applied at the end of this research, it was studied and reviewed by the professors, and some points were made to modify or change the final theory became. Reliability of data by showing the path of researchers' decisions and also placing all the raw data of the analysis done, the codes, categories, the process of studying the primary goals and questions were given to the academic professors and the desired corrections applied. In order to analyze the data, schematic analysis and data base theory were used. In the presented model, and social accountability as causal factors, organizational conditions as background factors, requirements and necessity environmental factors as intervening factors. In addition, with strategies such as creating open doors for dialogue Knowledge and convergence of actions with professionalism can lead to consequences such as social, financial and behavioral functional results Clients and employees found hands.

This study aimed to evaluate the role of the banking system and, in particular, the monetary policies and provision of bank facilities, in recession and boom periods on employment in the labor market. In this research, the most important variables were employment, bank and financial development index, volume of facilities provided to the private sector, bank facilities rate, wage rate, workforce, capital stock, liquidity, degree of economic openness, inflation rate, direct foreign investment, government expenditures, and oil revenues. The research tool was the smooth transfer regression (STR) model applied to evaluate the relationship between the variables during 1989-2016. According to the results, there was a nonlinear correlation between banking variables and employment. In the section of the nonlinear model, it was observed that with a 2.87% increase in the inflation rate, the banking indexes (e.g., money market development, the volume of facilities granted, and liquidity) had a significant and different impact on employment. In this respect, it was found that the indicators of monetary policy and bank facilities had a weak effect on employment of the country, demonstrating the improper association between monetary policies and workforce supply and demand in the labor market of the country.

The application of machine learning technologies for cancer detection purposes are rising due to their ever-increasing accuracy. Melanoma is one of the most common types of skin cancer. Detection of melanoma in the early stages can significantly prevent illness and fetal death. The application of innovative machine learning technology is highly relevant and valuable due to medical practitioners' difficulty in early-stage diagnoses. This paper provides an open-source tutorial on the performance of an algorithm that helps to diagnose melanoma by extracting features from dermatoscopic images and their classification. First, we used a Dull-Razor preprocessing method to remove extra details such as hair. Next, histogram adjustments and lighting thresholds were used to increase the contrast and select lesion boundaries. After using a threshold, a binary-classified version of image was obtained, and the boundary of the lesion was determined. As a result, the features from skin tissue were extracted. Finally, a comparative study was conducted between three methods which are Artificial Neural Network (ANN), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). The results show that ANN could achieve better accuracy (83.5%). In order to mitigate the biases in existing studies, the source code of this research is available at hadi-naghavipour.com/ml to serve aspiring researchers for improvement, correction and learning and provide a guideline for technology manager practitioners.