Machine Learning Techniques for Stress Prediction in Working Employees

Reddy, U. Srinivasulu; Thota, Aditya Vivek; Dharun, A

doi:10.1109/iccic.2018.8782395

Cited by 90 publications

(46 citation statements)

References 2 publications

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“…Today, machine learning is the fastest spreading field in computer science that encompasses varied areas as information security, manufacturing, marketing, transportation and health care [19][20][21][22][23]. Machine learning is a sub set of artificial intelligence that offers computers and computer systems with the capacity to learn and enhance separately from prior experiences without explicit human programming.…”

Section: Machine Learning Classificationmentioning

confidence: 99%

Machine learning with multistage classifiers for identification of of ectoparasite infected mud crab genus <i>Scylla</i>

et al. 2021

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Recently, the mud-crab farming can help the rural population economically. However, the existing parasite in the mud-crabs could interfere the long live of the mud-crabs. Unfortunately, the parasite has been identified to live in hundreds of mud-crabs, particularly it happened in Terengganu Coastal Water, Malaysia. This study investigates the initial identification of the parasite features based on their classes by using machine learning techniques. In this case, we employed five classifiers i.e logistic regression (LR), k-nearest neighbors (kNN), Gaussian Naive Bayes (GNB), support vector machine (SVM), and linear discriminant analysis (LDA). We compared these five classfiers to best performance of classification of the parasites. The classification process involving three stages. First, classify the parasites into two classes (normal and abnormal) regardless of their ventral types. Second, classified sexuality (female or male) and maturity (mature or immature). Finally, we compared the five classifiers to identify the species of the parasite. The experimental results showed that GNB and LDA are the most effective classifiers for carrying out the initial classification of the rhizocephalan parasite within the mud crab genus Scylla.

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Section: Machine Learning Classificationmentioning

confidence: 99%

Machine learning with multistage classifiers for identification of of ectoparasite infected mud crab genus <i>Scylla</i>

et al. 2021

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“…Though organizations do offer a nice workplace environment and different activities or workshops to relieve this stress, still the risk increases among the employees. Various machine learning techniques like Boosting and Decision trees were implemented by Reddy et al (2018) in their study, and have determined that data on family history of illness, gender and health benefits provided by employers plays an important role in evaluating this type of risks [15]. Ensemble method gave the highest degree of accuracy and precision compared to Random Forest.…”

Section: Literature Reviewmentioning

confidence: 99%

Churn Prediction of Employees Using Machine Learning Techniques

Bandyopadhyay

Jadhav

2021

Teh. glas. (Online)

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Employees are considered as the most valuable assets of any organization. Various policies have been introduced by the HR professionals to create a good working environment for them, but still, the rate of employees quitting the Technology Industry is quite high. Often the reason behind their early attrition could be due to company-related or personal issues, such as No satisfaction at the workplace, Fewer opportunities for learning, Undue Workload, Less Encouragement, and many others. This paper aims in discussing a structured way for predicting the churn rate of the employees by implementing various Classification techniques like SVM, Random Forest classifier, and Naives Bayes classifier. The performance of the classifiers was compared using metrics like Confusion Matrix, Recall, False Positive Rate, and Accuracy to determine the best model for the churn prediction. We found that among the models, the Random Forest classifier proved to be the best model for IT employee churn prediction. A Correlation Matrix was generated in the form of a heatmap to identify the important features that might impact the attrition rate.

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“…For instance, ML techniques have been widely applied to disease prognosis and prediction, such as predicting cancer susceptibility, recurrence and survival (e.g., Kourou, Exarchos, Exarchos, Karamouzis, & Fotiadis, 2015). Such predictive models can be transferred to an organizational setting where ML has been used to predict, for example, employee turnover (e.g., de Oliveira, Zylka, Gloor, & Joshi, 2019), return to work after sick leave (e.g., Na & Kim, 2019), physiological markers of stress (e.g., Bacciu, USING MACHINE LEARNING IN CAUSAL LEADERSHIP RESEARCH Colombo, Morelli, & Plans, 2018;Reddy, Thota, & Dharun, 2018), and employee performance (Kirimi, & Moturi, 2016). For leadership scholars who are interested in understanding relationships between leadership and follower, team, or organizational outcomes, the application of ML to data collected in the field represents an opportunity to examine exactly such relationships and build powerful leadership models.…”

Section: Introductionmentioning

confidence: 99%