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Energy Based Logic Mining Analysis with Hopfield Neural Network for Recruitment Evaluation.

Siti Zulaikha Mohd Jamaludin¹, Mohd Shareduwan Mohd Kasihmuddin¹, Ahmad Izani Md Ismail¹

¹School of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia.

Entropy (Basel, Switzerland)

|January 5, 2021

Summary

This study introduces an energy-based k-satisfiability reverse analysis with a Hopfield neural network to identify key recruitment factors. The method effectively extracts dominant attributes for positive recruitment in an insurance agency.

Keywords:

Hopfield neural network economic well-being logic mining recruitment evaluation satisfiability representation

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Area of Science:

Artificial Intelligence
Data Mining
Computational Logic

Background:

Effective recruitment evaluation is crucial for organizational success.
Understanding factors driving systematic recruitment requires advanced data analysis.
Electronic (E) recruitment data offers a rich source for identifying recruitment drivers.

Purpose of the Study:

To propose an energy-based k-satisfiability reverse analysis model using a Hopfield neural network.
To extract relationships between factors within an E-recruitment dataset.
To identify dominant attributes contributing to positive recruitment outcomes.

Main Methods:

Representing E-recruitment data attributes using k-satisfiability logical representations (2-SAT and 3-SAT).
Employing an energy-based k-satisfiability reverse analysis incorporating a Hopfield neural network.
Evaluating the model's correctness, robustness, and accuracy on an insurance agency's E-recruitment data from Malaysia.

Main Results:

The proposed model successfully extracted relationships between recruitment factors.
Experimental simulations demonstrated the effectiveness of the approach.
The energy-based k-satisfiability reverse analysis with Hopfield neural network proved robust in identifying dominant recruitment attributes.

Conclusions:

The developed approach is effective for extracting dominant attributes related to positive recruitment.
The Hopfield neural network-based method provides a robust framework for analyzing E-recruitment data.
This technique offers valuable insights for improving recruitment strategies in the insurance sector.