Employee Attrition Prediction based on Grey Wolf Optimization and Deep Neural Networks

Despite the constructive application of promising technologies such as Neural Networks, their potential for predicting human resource management outcomes still needs to be explored. Therefore, the primary aim of this paper is to utilize neural networks and meta-heuristic technologies to predict employee attrition, thereby enhancing prediction model performance. The conventional Grey Wolf optimization optimization (GWO) has gained substantial attention notice because of its attributes of robust convergence, minimal parameters, and simple implementaton. However, it encounter problems with slow convergence rates and susceptibility to local optima in practical optimization scenarios. To address these problems, this paper introduces an enhanced Grey Wolf Optimization algorithm incorporating the utilization of Cauchy-Gaussian mutation, which contributes to enhancing diversity within the leader wolf population and enhances the algorithm's global search capabilities. Additionally, this work preserves exceptional grey wolf individuals through a greedy selection of 2 mechanisms to ensure accelerated convergence. Moreover, an enhanced exploration strategy is suggested to expand the optimization possibilities of the algorithm and improve its convergence speed. The results shows that the proposed model achieved the accuarcy of 97.85%, precision of  98.45%, recall of 98.14%, and f1-score of 97.11%. Nevertheless, this paper extends its scope beyond merely predicting employee attrition probability and activities to enhance the precision of such predictions by constructing an improved model employing a Deep Neural Network (DNN).

From Big Data to Deep Data to support people analytics for employee attrition prediction

In the era of data science and big data analytics, people analytics help organizations and their human resources (HR) managers to reduce attrition by changing the way of attracting and retaining talent. In this context, employee attrition presents a critical problem and a big risk for organizations as it affects not only their productivity but also their planning continuity. In this context, the salient contributions of this research are as follows. Firstly, we propose a people analytics approach to predict employee attrition that shifts from a big data to a deep data context by focusing on data quality instead of its quantity. In fact, this deep data-driven approach is based on a mixed method to construct a relevant employee attrition model in order to identify key employee features influencing his/her attrition. In this method, we started thinking ‘big’ by collecting most of the common features from the literature (an exploratory research) then we tried thinking ‘deep’ by filtering and selecting the most important features using survey and feature selection algorithms (a quantitative method). Secondly, this attrition prediction approach is based on machine, deep and ensemble learning models and is experimented on a large-sized and a medium-sized simulated human resources datasets and then a real small-sized dataset from a total of 450 responses. Our approach achieves higher accuracy (0.96, 0.98 and 0.99 respectively) for the three datasets when compared previous solutions. Finally, while rewards and payments are generally considered as the most important keys to retention, our findings indicate that ‘business travel’, which is less common in the literature, is the leading motivator for employees and must be considered within HR policies to retention.publishedVersio

A Comparison of Machine Learning Approaches for Predicting Employee Attrition

Employee attrition is a major problem that causes many companies to incur in significant costs to find and hire new personnel. The use of machine learning and artificial intelligence methods to predict the likelihood of resignation of an employee, and the quitting causes, can provide HR departments with a valuable decision support system and, as a result, prevent a large waste of time and resources. In this paper, we propose a preliminary exploratory analysis of the application of machine learning methodologies for employee attrition prediction. We compared several classification models with the goal of finding the one that not only performs best, but is also well interpretable, in order to provide companies with the possibility of improving those aspects that have been shown to produce the quitting of their employees. Among the proposed methods, Logistic Regression performs the best, with an accuracy of 88% and an AUC-ROC of 85%

ANALYZING EMPLOYEE ATTRITION USING DECISION TREE ALGORITHMS

Employee turnover is a serious concern in knowledge based organizations. When employees leave an organization, theycarry with them invaluable tacit knowledge which is often the source of competitive advantage for the business. In order foran organization to continually have a higher competitive advantage over its competition, it should make it a duty to minimizeemployee attrition. This study identifies employee related attributes that contribute to the prediction of employees’ attritionin organizations. Three hundred and nine (309) complete records of employees of one of the Higher Institutions in Nigeriawho worked in and left the institution between 1978 and 2006 were used for the study. The demographic and job relatedrecords of the employee were the main data which were used to classify the employee into some predefined attrition classes.Waikato Environment for Knowledge Analysis (WEKA) and See5 for Windows were used to generate decision tree modelsand rule-sets. The results of the decision tree models and rule-sets generated were then used for developing a a predictivemodel that was used to predict new cases of employee attrition. A framework for a software tool that can implement therules generated in this study was also proposed.Keywords: Employee Attrition, Decision Tree Analysis, Data Minin

Early Prediction of Employee Turnover Using Machine Learning Algorithms

Employee turnover is a serious challenge for organizations and companies. Thus, the prediction of employee turnover is a vital issue in all organizations and companies. The present work proposes prediction models for predicting the turnover intentions of workers during the recruitment process. The proposed models are based on k-nearest neighbors (KNN) and random forests (RF) machine learning algorithms. The models use the dataset of employee turnover created by IBM. The used dataset includes the most essential features, which are considered during the recruitment process of the employee and may lead to turnover. These features are salary, age, distance from home, marital status, and gender. The KNN-based model exhibited better performance in terms of accuracy, precision, F-score, specificity (SP), and false-positive rate (FPR) in comparison to the RF-based model. The models predict the average probability percentage of turnover intentions of the workers. Therefore, the models can be used to aid the human resource managers to make precautionary decisions; whether the candidate employee is likely to stay or leave the job, depending on the given relevant information about the candidate employee

Employee Attrition System Prediction using Random Forest Classifier

Despite rising unemployment, most job coverage of the COVID-19 outbreak has concentrated on layoffs. Employees have been fired for reasons related to the epidemic, which has been a less prominent issue. COVID-19 is still doing damage to the country\u27s economy. Companies are in the midst of a recession, so they are beginning to fire off unproductive employees. Making critical decisions like laying off employees or cutting an employee\u27s compensation is a challenging undertaking that must be done with extreme attention and accuracy. Adding negligence would harm the employee\u27s career and the company\u27s image in the industry. In this paper, we have predicted employee attrition using Logistic Regression, Random Forest, and Decision Tree techniques. Random Forest Classifier has outperformed other algorithms in this work. After using different machine learning techniques, we can say that Random Forest gives the best performance with a recall of 70%, and also, we have found Precision, Accuracy, and F1- Score

On exploring the possibilities and the limits of AI for an interoperable and empowering industry 4.0

This paper aims to raise awareness on certain interoperability issues as we intend to shape industry 5.0 in order to enable a human-centric resilient society. We advocate that the need of sharing small and specific data will become more intensive as AI-based solutions will become more pervasive. Consequently, dataspaces should be carefully designed to address this need. We advance the conversation by presenting a case study from HR demonstrating how to predict the possibility of an employee experiencing attrition. Our experimental results show that we need more than 500 samples for developing a machine learning model to be sufficiently capable to generalize the problem. Consequently, our experimental results show the feasibility of the idea. However, in small and medium sized companies this approach cannot be implemented due to the limited number of samples. At the same time, we advocate that this obstacle may be overcome if multiple companies will join a shared dataspace, thus raising interoperability issues.</p

On exploring the possibilities and the limits of AI for an interoperable and empowering industry 4.0

This paper aims to raise awareness on certain interoperability issues as we intend to shape industry 5.0 in order to enable a human-centric resilient society. We advocate that the need of sharing small and specific data will become more intensive as AI-based solutions will become more pervasive. Consequently, dataspaces should be carefully designed to address this need. We advance the conversation by presenting a case study from HR demonstrating how to predict the possibility of an employee experiencing attrition. Our experimental results show that we need more than 500 samples for developing a machine learning model to be sufficiently capable to generalize the problem. Consequently, our experimental results show the feasibility of the idea. However, in small and medium sized companies this approach cannot be implemented due to the limited number of samples. At the same time, we advocate that this obstacle may be overcome if multiple companies will join a shared dataspace, thus raising interoperability issues.</p

A STUDY ON ATTRITION AND RETENTION STRATEGIES OF MICROFINANCE SECTOR IN BANGALORE

This article explores the critical issues of attrition and retention strategies within the context of Microfinance Sector, Title: A Study on Attrition and Retention Strategies in the Microfinance Sector.The microfinance sector plays a pivotal role in extending financial services to the underserved and economically vulnerable populations.This study aims to investigate the factors contributing to attrition and the strategies employed for retaining talent in the microfinance sector. The study employs a mixed-methods approach, which includes both quantitative and qualitative data gathering methodologies. Primary data is gathered through surveys, interviews, and focus group discussions with microfinance employees and managers from a diverse range of organizations across various geographies. Secondary data is also analysed from industry reports and existing literature. The findings reveal that attrition in the microfinance sector is affected by a complex interplay of factors, including low remuneration, limited career growth opportunities, job stress, and work-life balance issues. Furthermore, the study identifies several successful retention strategies implemented by microfinance institutions, such as training and development programs, performance incentives, employee engagement initiatives, and effective leadership. This study adds to the current body of information by offering insights into the unique issues that microfinance organisations encounterin retaining their workforce and the innovative strategies they have adopted to address these challenges. The outcomes of this study can be used by microfinance organizations, policymakers, and industry stakeholders to develop and implement more effective retention strategies, ultimately improving the sustainability and social impact of microfinance operations. In conclusion, understanding attrition and retention dynamics in the microfinance sector is crucial for fostering a stable and motivated workforce, which in turn can enhance the sector's capacity to serve its target clientele and drive financial inclusion

An Efficient Authentication Approach with Optimization Algorithms and Elliptical Curve Cryptography for Cloud Environment

The fast-emerging of cloud computing technology today has sufficiently benefited its wide range of users from individuals to large organizations. It carries an attractive characteristic by renting myriad virtual storages, computing resources and platform for users to manipulate their data or utilize the processing resources conveniently over Internet without the need to know the exact underlying infrastructure which is resided remotely at cloud servers. Security is very important for any kind of networks. As a main communication mode, the security mechanism for multicast is not only the measure to ensure secured communications, but also the precondition for other security services. Attacks are one of the biggest concerns for security professionals. Attackers usually gain access to a large number of computers by exploiting their vulnerabilities to set up attack armies. This paper presents a dual optimizer based key generation method for the improving the authentication with Elliptical Curve Cryptography (ECC) encryption algorithm. The optimal private and secret key for the encryption and decryption are obtained with the optimization techniques like Animal Migration Optimization (AMO), and Brain Storm Optimization (BSO) for strengthening the security in the Cloud Computing environment