An Enhanced Deep Neural Network for Predicting Workplace Absenteeism
Organizations can grow, succeed, and sustain if their employees are committed. The main assets of an organization are those employees who are giving it a required number of hours per month, in other words, those employees who are punctual towards their attendance. Absenteeism from work is a multibil...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/5843932 |
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| author | Syed Atif Ali Shah Irfan Uddin Furqan Aziz Shafiq Ahmad Mahmoud Ahmad Al-Khasawneh Mohamed Sharaf |
| author_facet | Syed Atif Ali Shah Irfan Uddin Furqan Aziz Shafiq Ahmad Mahmoud Ahmad Al-Khasawneh Mohamed Sharaf |
| author_sort | Syed Atif Ali Shah |
| collection | DOAJ |
| description | Organizations can grow, succeed, and sustain if their employees are committed. The main assets of an organization are those employees who are giving it a required number of hours per month, in other words, those employees who are punctual towards their attendance. Absenteeism from work is a multibillion-dollar problem, and it costs money and decreases revenue. At the time of hiring an employee, organizations do not have an objective mechanism to predict whether an employee will be punctual towards attendance or will be habitually absent. For some organizations, it can be very difficult to deal with those employees who are not punctual, as firing may be either not possible or it may have a huge cost to the organization. In this paper, we propose Neural Networks and Deep Learning algorithms that can predict the behavior of employees towards punctuality at workplace. The efficacy of the proposed method is tested with traditional machine learning techniques, and the results indicate 90.6% performance in Deep Neural Network as compared to 73.3% performance in a single-layer Neural Network and 82% performance in Decision Tree, SVM, and Random Forest. The proposed model will provide a useful mechanism to organizations that are interested to know the behavior of employees at the time of hiring and can reduce the cost of paying to inefficient or habitually absent employees. This paper is a first study of its kind to analyze the patterns of absenteeism in employees using deep learning algorithms and helps the organization to further improve the quality of life of employees and hence reduce absenteeism. |
| format | Article |
| id | doaj-art-9514853c83a54572a58efceaf15f2274 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-9514853c83a54572a58efceaf15f22742025-02-03T01:01:32ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/58439325843932An Enhanced Deep Neural Network for Predicting Workplace AbsenteeismSyed Atif Ali Shah0Irfan Uddin1Furqan Aziz2Shafiq Ahmad3Mahmoud Ahmad Al-Khasawneh4Mohamed Sharaf5Faculty of Engineering and Information Technology, Northern University, Nowshehra, PakistanInstitute of Computing, Kohat University of Science and Technology, Kohat, PakistanCenter for Excellence in IT, IMSciences, Peshawar, PakistanKing Saud University, College of Engineering, Department of Industrial Engineering, Riyadh 11421, Saudi ArabiaFaculty of Computer & Information Technology, Al-Madinah International University, Kuala Lumpur, MalaysiaKing Saud University, College of Engineering, Department of Industrial Engineering, Riyadh 11421, Saudi ArabiaOrganizations can grow, succeed, and sustain if their employees are committed. The main assets of an organization are those employees who are giving it a required number of hours per month, in other words, those employees who are punctual towards their attendance. Absenteeism from work is a multibillion-dollar problem, and it costs money and decreases revenue. At the time of hiring an employee, organizations do not have an objective mechanism to predict whether an employee will be punctual towards attendance or will be habitually absent. For some organizations, it can be very difficult to deal with those employees who are not punctual, as firing may be either not possible or it may have a huge cost to the organization. In this paper, we propose Neural Networks and Deep Learning algorithms that can predict the behavior of employees towards punctuality at workplace. The efficacy of the proposed method is tested with traditional machine learning techniques, and the results indicate 90.6% performance in Deep Neural Network as compared to 73.3% performance in a single-layer Neural Network and 82% performance in Decision Tree, SVM, and Random Forest. The proposed model will provide a useful mechanism to organizations that are interested to know the behavior of employees at the time of hiring and can reduce the cost of paying to inefficient or habitually absent employees. This paper is a first study of its kind to analyze the patterns of absenteeism in employees using deep learning algorithms and helps the organization to further improve the quality of life of employees and hence reduce absenteeism.http://dx.doi.org/10.1155/2020/5843932 |
| spellingShingle | Syed Atif Ali Shah Irfan Uddin Furqan Aziz Shafiq Ahmad Mahmoud Ahmad Al-Khasawneh Mohamed Sharaf An Enhanced Deep Neural Network for Predicting Workplace Absenteeism Complexity |
| title | An Enhanced Deep Neural Network for Predicting Workplace Absenteeism |
| title_full | An Enhanced Deep Neural Network for Predicting Workplace Absenteeism |
| title_fullStr | An Enhanced Deep Neural Network for Predicting Workplace Absenteeism |
| title_full_unstemmed | An Enhanced Deep Neural Network for Predicting Workplace Absenteeism |
| title_short | An Enhanced Deep Neural Network for Predicting Workplace Absenteeism |
| title_sort | enhanced deep neural network for predicting workplace absenteeism |
| url | http://dx.doi.org/10.1155/2020/5843932 |
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