| 1 | Title of the Article | Employees Attrition Detection using PSONN |
| 2 | Author's name | Yogesh: M.Tech. Scholar, Department of ME Sagar Institute of Research and Technology Excellence, Bhopal, India (e.mail:yogeshyaduvanshi081@gmail.com) |
| 3 | Author's name | Sudhir Shrivastava |
| 4 | Subject | Electronics and Communication Engineering |
| 5 | Keyword(s) | Raw materials, Inventories, Inventory Management, Artificial Intelligence |
| 6 | Abstract | Raw materials, intermediate goods and finished goods are termed as inventories while considering it as portion of business’s assets which can be considered as prepared or are prepared for sale. One of the suitable solutions is to design optimal inventory model. Major concern of industry is to design suitable inventory model. Some of the existing inventory management research works are discussed in literature. But this field is still a big area of interest. Many research works uses artificial intelligence models for inventory management. One amongst the area for inventory management is worker behavior in a company. So, employees are taken into account to be as an inventory that contributes in growth of an organization. Employee Attrition may be a big issue for the organizations specially once trained, technical and key staff leave for a far better chance from the organization. This leads to loss to interchange a trained worker. Therefore, we use the present and past worker data to analyze attrition behavior of employees. |
| 7 | Publisher | Innovative Research Publication |
| 8 | Journal Name; vol., no. | International Journal of Innovative Research in Computer Science & Technology (IJIRCST); Volume-7 Issue-5 |
| 9 | Publication Date | September 2019 |
| 10 | Type | Peer-reviewed Article |
| 11 | Format | |
| 12 | Uniform Resource Identifier | https://ijircst.org/view_abstract.php?title=Employees-Attrition-Detection-using-PSONN&year=2019&vol=7&primary=QVJULTM2NQ== |
| 13 | Digital Object Identifier(DOI) | 10.21276/ijircst.2019.7.5.2 https://doi.org/10.21276/ijircst.2019.7.5.2 |
| 14 | Language | English |
| 15 | Page No | 139-142 |