فهرست مطالب ali zare abarghouei

The research aims to identify players in the database of public and private banks using a meta-heuristic algorithm. This issue pertains to enhancing the human resources management system to ensure consistent stability in the bank's operations. In this database analysis process, Poisson distribution and artificial intelligence are utilized to analyze data with an exponential distribution. For this purpose, the VIS, CNSGA-II, NSGA-II, MISA, NNIA, and NRGA algorithms were implemented using MATLAB software. The VIS algorithm showed the best performance in most criteria. Algorithms CNSGA-II and MISA are both ranked second and exhibit similar performances. NSGA-II algorithm is ranked second. The NNIA algorithm performs the best, while the NRGA algorithm performs the worst. These analyses are conducted to assess the performance of algorithms based on various criteria. The results obtained from these analyses show that the VIS algorithm generally demonstrates the best performance. This means that VIS is known as an identification of players in the databases of public and private banks. In addition to the Variable in Neighborhood Search (VIS) algorithm, other algorithms like CNSGA-II and MISA are also closely ranked and share the second position in various criteria. These algorithms have similar functions and can make comparable enhancements in identifying players in the databases of public and private banks.

The current research was conducted to apply knowledge extraction in the classification of jobs to identify the key role players using a mixed method (qualitative and sufficient data). The application of expert systems or decision support systems based on organizational data is increasing in the selection and hiring of personnel. The data was derived from in-depth and semi-structured interviews with 17 subject experts in bank human resources, who were selected based on purposeful sampling.Data analysis was done based on the Strauss and Corbin model in the form of open, axial, and selective coding in the Atlas TI8 software. The results showed that the classification of jobs for the key role players in public and private banks includes causal conditions (requirement of talent substitution, human resource management developments, and organizational challenges), intervening conditions  (organizational limitations and fear and resistance), and contextual conditions (strengthens and drivers) strategies (developmental, supportive and creating) and short-term and long-term consequences are among the components of the job classification model for the key role players in public and private banks. Next, based on the database with the CART method, the data mining of job classification was done. Regarding the performance of the model, it showed variance values of 311.92 and a risk value of 288.19. The predictions in the model explained 28.9% of the differences observed in the variable "employment status of A employees' category".