Energy Based Logic Mining Analysis with Hopfield Neural Network for Recruitment Evaluation
An effective recruitment evaluation plays an important role in the success of companies, industries and institutions. In order to obtain insight on the relationship between factors contributing to systematic recruitment, the artificial neural network and logic mining approach can be adopted as a dat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/1/40 |
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| author | Siti Zulaikha Mohd Jamaludin Mohd Shareduwan Mohd Kasihmuddin Ahmad Izani Md Ismail Mohd. Asyraf Mansor Md Faisal Md Basir |
| author_facet | Siti Zulaikha Mohd Jamaludin Mohd Shareduwan Mohd Kasihmuddin Ahmad Izani Md Ismail Mohd. Asyraf Mansor Md Faisal Md Basir |
| author_sort | Siti Zulaikha Mohd Jamaludin |
| collection | DOAJ |
| description | An effective recruitment evaluation plays an important role in the success of companies, industries and institutions. In order to obtain insight on the relationship between factors contributing to systematic recruitment, the artificial neural network and logic mining approach can be adopted as a data extraction model. In this work, an energy based <i>k</i> satisfiability reverse analysis incorporating a Hopfield neural network is proposed to extract the relationship between the factors in an electronic (E) recruitment data set. The attributes of E recruitment data set are represented in the form of <i>k</i> satisfiability logical representation. We proposed the logical representation to 2-satisfiability and 3-satisfiability representation, which are regarded as a systematic logical representation. The E recruitment data set is obtained from an insurance agency in Malaysia, with the aim of extracting the relationship of dominant attributes that contribute to positive recruitment among the potential candidates. Thus, our approach is evaluated according to correctness, robustness and accuracy of the induced logic obtained, corresponding to the E recruitment data. According to the experimental simulations with different number of neurons, the findings indicated the effectiveness and robustness of energy based <i>k</i> satisfiability reverse analysis with Hopfield neural network in extracting the dominant attributes toward positive recruitment in the insurance agency in Malaysia. |
| first_indexed | 2024-03-10T13:41:03Z |
| format | Article |
| id | doaj.art-cff3289eea464c6d90ac1b506aa029cf |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T13:41:03Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-cff3289eea464c6d90ac1b506aa029cf2023-11-21T03:02:44ZengMDPI AGEntropy1099-43002020-12-012314010.3390/e23010040Energy Based Logic Mining Analysis with Hopfield Neural Network for Recruitment EvaluationSiti Zulaikha Mohd Jamaludin0Mohd Shareduwan Mohd Kasihmuddin1Ahmad Izani Md Ismail2Mohd. Asyraf Mansor3Md Faisal Md Basir4School of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800, MalaysiaSchool of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800, MalaysiaSchool of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800, MalaysiaSchool of Distance Education, Universiti Sains Malaysia, Penang 11800, MalaysiaDepartment of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, Bahru, Johor 81310, MalaysiaAn effective recruitment evaluation plays an important role in the success of companies, industries and institutions. In order to obtain insight on the relationship between factors contributing to systematic recruitment, the artificial neural network and logic mining approach can be adopted as a data extraction model. In this work, an energy based <i>k</i> satisfiability reverse analysis incorporating a Hopfield neural network is proposed to extract the relationship between the factors in an electronic (E) recruitment data set. The attributes of E recruitment data set are represented in the form of <i>k</i> satisfiability logical representation. We proposed the logical representation to 2-satisfiability and 3-satisfiability representation, which are regarded as a systematic logical representation. The E recruitment data set is obtained from an insurance agency in Malaysia, with the aim of extracting the relationship of dominant attributes that contribute to positive recruitment among the potential candidates. Thus, our approach is evaluated according to correctness, robustness and accuracy of the induced logic obtained, corresponding to the E recruitment data. According to the experimental simulations with different number of neurons, the findings indicated the effectiveness and robustness of energy based <i>k</i> satisfiability reverse analysis with Hopfield neural network in extracting the dominant attributes toward positive recruitment in the insurance agency in Malaysia.https://www.mdpi.com/1099-4300/23/1/40satisfiability representationHopfield neural networklogic miningrecruitment evaluationeconomic well-being |
| spellingShingle | Siti Zulaikha Mohd Jamaludin Mohd Shareduwan Mohd Kasihmuddin Ahmad Izani Md Ismail Mohd. Asyraf Mansor Md Faisal Md Basir Energy Based Logic Mining Analysis with Hopfield Neural Network for Recruitment Evaluation Entropy satisfiability representation Hopfield neural network logic mining recruitment evaluation economic well-being |
| title | Energy Based Logic Mining Analysis with Hopfield Neural Network for Recruitment Evaluation |
| title_full | Energy Based Logic Mining Analysis with Hopfield Neural Network for Recruitment Evaluation |
| title_fullStr | Energy Based Logic Mining Analysis with Hopfield Neural Network for Recruitment Evaluation |
| title_full_unstemmed | Energy Based Logic Mining Analysis with Hopfield Neural Network for Recruitment Evaluation |
| title_short | Energy Based Logic Mining Analysis with Hopfield Neural Network for Recruitment Evaluation |
| title_sort | energy based logic mining analysis with hopfield neural network for recruitment evaluation |
| topic | satisfiability representation Hopfield neural network logic mining recruitment evaluation economic well-being |
| url | https://www.mdpi.com/1099-4300/23/1/40 |
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