A NEURAL NETWORK APPROACH FOR INTELLIGENT COMPETENT BASED LEARNING PATH PREDICTION IN HUMAN RESOURCE MANAGEMENT SYSTEM

Abstract

With the advancement of technology, data has been exponentially increasing. For this purpose, there is a need to develop such expert systems which may have the capability to deal with the variety ofthe complex problems. This research proposes an expert system that assists employees in order to recognize the pattern of employee performance throughout his/her tenure. It further helps predicting learning path for such employees. This may also help in automation ofthe complete HR process and reduction ofworkload ofHR department within the organization. In the literature related to neural networks, error correction learning algorithm is one of the algorithms that may automate human resource management system for helping organizations in order to predict employees’ performances. This predication can reduce time, provide accurate information, improvement in planning and program developments, remove language biasness and improve employees’ retentions. These advantages can directly improve an organizational culture. This culture may provide a transparency to employees for their performance evaluations and also reduce communication gap between management and employees. This culture may affect an overall progress of an organizational success. Hence, this research will evaluates the performance of an error correction learning algorithm in a human resource system. For the said purpose data set of 1470 employees have been taken from Kaggle. For this purpose, sigmoid function is used to select 123 employees for the particular criteria. This research concludes that 90% accuracy has been achieved through the use of error learning algorithm in a human resource management system. This research may facilitate management in order to identify top performers of any organization in more transparent way