Usage the lazy learning meta-heuristic technique for predicting entrepreneurial marketing in the insurance industry
Due to the increasing importance of marketing, entrepreneurship and the role of organizational structure in their application, the purpose of this research is to predict entrepreneurial marketing using an organizational structure in the insurance industry. For this purpose, for marketing, seven indi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Ayandegan Institute of Higher Education, Iran
2021-11-01
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| Series: | Journal of Applied Research on Industrial Engineering |
| Subjects: | |
| Online Access: | http://www.journal-aprie.com/article_138885_d5c08844624183a919c0f29ce9abf911.pdf |
| _version_ | 1811339420010283008 |
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| author | Mohammad Javad Taghipourian Elham Fazeli Veisari Syed Mahmod Norashrafodin Mohammad Verij Kazemi |
| author_facet | Mohammad Javad Taghipourian Elham Fazeli Veisari Syed Mahmod Norashrafodin Mohammad Verij Kazemi |
| author_sort | Mohammad Javad Taghipourian |
| collection | DOAJ |
| description | Due to the increasing importance of marketing, entrepreneurship and the role of organizational structure in their application, the purpose of this research is to predict entrepreneurial marketing using an organizational structure in the insurance industry. For this purpose, for marketing, seven indicators and for organizational structure, three indicators are defined, then prediction of entrepreneurial marketing indicators has been done by organizational structure indicators using lazy learning algorithm. In the proposed method, after predicting each data by K vector from its closest neighbor, the algorithm database is enriched for better prediction of future data. The proposed algorithm is simulated and compared in five different modes by MATLAB software, also, three insurance (Iran, Karafarin and Parsiyan) companies are selected in Mazandaran province. In total, the statistical population in this study is 588 cases. The results of simulation indicate the proper accuracy of entrepreneurial marketing forecasting based on validation parameters MSE and NRMSD. In this research, Lazy Learning method can predict future without modeling the problem with previous information processing. |
| first_indexed | 2024-04-13T18:26:20Z |
| format | Article |
| id | doaj.art-27e1966cca37492d99a54795e8bd9563 |
| institution | Directory Open Access Journal |
| issn | 2538-5100 2676-6167 |
| language | English |
| last_indexed | 2024-04-13T18:26:20Z |
| publishDate | 2021-11-01 |
| publisher | Ayandegan Institute of Higher Education, Iran |
| record_format | Article |
| series | Journal of Applied Research on Industrial Engineering |
| spelling | doaj.art-27e1966cca37492d99a54795e8bd95632022-12-22T02:35:14ZengAyandegan Institute of Higher Education, IranJournal of Applied Research on Industrial Engineering2538-51002676-61672021-11-018Special Issue11310.22105/jarie.2021.277767.1277138885Usage the lazy learning meta-heuristic technique for predicting entrepreneurial marketing in the insurance industryMohammad Javad Taghipourian0Elham Fazeli Veisari1Syed Mahmod Norashrafodin2Mohammad Verij Kazemi3Department of Management, Chalous Branch, Islamic Azad University, Chalous, Iran.Department of Management, Tonekabon Branch, Islamic Azad University, Tonekabon, Iran.Foundation of the Oppressed of the Islamic Revolution of Iran, Tehran, Iran.West Mazandaran Electric Power Distribution Company, Nowshahr, Iran.Due to the increasing importance of marketing, entrepreneurship and the role of organizational structure in their application, the purpose of this research is to predict entrepreneurial marketing using an organizational structure in the insurance industry. For this purpose, for marketing, seven indicators and for organizational structure, three indicators are defined, then prediction of entrepreneurial marketing indicators has been done by organizational structure indicators using lazy learning algorithm. In the proposed method, after predicting each data by K vector from its closest neighbor, the algorithm database is enriched for better prediction of future data. The proposed algorithm is simulated and compared in five different modes by MATLAB software, also, three insurance (Iran, Karafarin and Parsiyan) companies are selected in Mazandaran province. In total, the statistical population in this study is 588 cases. The results of simulation indicate the proper accuracy of entrepreneurial marketing forecasting based on validation parameters MSE and NRMSD. In this research, Lazy Learning method can predict future without modeling the problem with previous information processing.http://www.journal-aprie.com/article_138885_d5c08844624183a919c0f29ce9abf911.pdfforecastentrepreneurial marketingorganizational structurekvnn algorithm |
| spellingShingle | Mohammad Javad Taghipourian Elham Fazeli Veisari Syed Mahmod Norashrafodin Mohammad Verij Kazemi Usage the lazy learning meta-heuristic technique for predicting entrepreneurial marketing in the insurance industry Journal of Applied Research on Industrial Engineering forecast entrepreneurial marketing organizational structure kvnn algorithm |
| title | Usage the lazy learning meta-heuristic technique for predicting entrepreneurial marketing in the insurance industry |
| title_full | Usage the lazy learning meta-heuristic technique for predicting entrepreneurial marketing in the insurance industry |
| title_fullStr | Usage the lazy learning meta-heuristic technique for predicting entrepreneurial marketing in the insurance industry |
| title_full_unstemmed | Usage the lazy learning meta-heuristic technique for predicting entrepreneurial marketing in the insurance industry |
| title_short | Usage the lazy learning meta-heuristic technique for predicting entrepreneurial marketing in the insurance industry |
| title_sort | usage the lazy learning meta heuristic technique for predicting entrepreneurial marketing in the insurance industry |
| topic | forecast entrepreneurial marketing organizational structure kvnn algorithm |
| url | http://www.journal-aprie.com/article_138885_d5c08844624183a919c0f29ce9abf911.pdf |
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