An efficient human resource management system model using web-based hybrid technique


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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.

This project was supported by the Deanship of Scientific Research at Prince Sattam Bin Abdulaziz University, AlKharj, Saudi Arabia under the Specialized Research Grant program with Grant No-2021/02/18747.

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    • Figure 1. Global chart of the database design
    • Figure 2. Design of the matching scheme
    • Figure 3a. Vanilla LSTM network design
    • Figure 3b. Stacked LSTM network design
    • Figure 3c. Stacked LSTM network design
    • Figure 4. CNN-LSTM network architecture
    • Figure 5. Data flow diagram
    • Figure 6. Conceptual diagram of the CLWHRMS
    • Figure 7. System conceptual architecture
    • Figure 8. Overall system performance evaluation in terms of various parameters
    • Table 1. Relational database supported by the system
    • Table 2. Knowledge about applicant and job composed
    • Table 3. Overall proposed system validation
    • Conceptualization
      Sania Khan
    • Data curation
      Sania Khan
    • Investigation
      Sania Khan
    • Methodology
      Sania Khan
    • Project administration
      Sania Khan
    • Validation
      Sania Khan
    • Writing – original draft
      Sania Khan
    • Writing – review & editing
      Sania Khan