AN EFFICIENT HUMAN RESOURCE MANAGEMENT SYSTEM MODEL USING WEB-BASED HYBRID TECHNIQUE

The proliferation of international business activities drives organizations to expand their operations into new areas and propels human resource management (HRM) to ensure hiring and retaining competent personnel. Consequently, firms have been struggling to place qualified people in relevant roles and provide adequate training. This study utilized information technology to solve these challenges using a web-based system to interconnect the processes, receive the data from the job applicants via a web-based interface, and connect them with suitable employment. Firstly, the proposed model presented a hybrid technique of Convolutional Neural Network (CNN) with Long Short Term Memory (LSTM) Cloud Web-based Human Resource Management System (CLWHRMS) by recognizing distinct features and forecasts the candidate’s potential under various classification tasks. For this, the study used a set of various software tools for web pages and database designing, including for the alteration of images. The hybrid model was executed using real-time data of 250 resumes, which were collected through an online database to validate the overall performance of the developed web-based system in terms of its accuracy, sensitivity, and specificity. Though the specificity was the same with all the techniques, the results illustrated CNN-LSTM technique was 91% accurate and 90% sensitive compared to the traditional methods. This CNN-LSTM model automatically estimates the suitability of a job candidate and projects his/her workability contributing to Saudi Arabian firms to ease and enhance their recruitment process.