Mental health-related quality of life and work performance in adults with knee osteoarthritis

Cagnin, A.; Chionière, M.; Bureau, Nathalie J.; Durand, Madéleine; Polo, Laura de; Hagemeister, Nicola

doi:10.1016/j.joca.2018.02.519

Cited by 1 publication

(2 citation statements)

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“…This range meets the criteria of interpretability proposed in the model so that linguistic characteristics are plausible for interpretation. In the model proposed in this paper, consider bt=32 and =0.6 were used (previously defined by cross-validation in previous tests using the range of bt = [8,16,32,64], = [0.4, 0.5, 0.6, 0.7, 0.8]) and unineuron (defined by the cross-validation method with 10 k-fold. The best values in 50 replicates were chosen).…”

Section: Tests With Fuzzy Neural Networkmentioning

confidence: 99%

“…When the phenomenon occurs, a task, activity, or decision-making may not be performed, and as a result, the costs increase, or the overhead of work for other company employees may result in decreased quality or demotivation of people [2]. Recent research list characteristics that can cause absenteeism at work like Some sickness [3][4][5] as well as personal reasons [6], financial difficulties [7], lack of motivation [8], incoherent performance of managers [9] among other reasons [10,11]. Such factors combined can lead to many absences from an employee in their work period.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid approach of intelligent systems to help predict absenteeism at work in companies

et al. 2019

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In recent years, several surveys have been conducted on absenteeism and how this affects the routine of conducting productive operations in companies. Therefore, having criteria for predicting absenteeism at work can help managers in contingency actions reduce financial losses due to the absence of a worker in their workplace. The objective of this work is to apply the artificial intelligence concepts of a regularized fuzzy neural network, which combines the benefits of artificial neural networks with the fuzzy set theory to obtain more accurate results in predicting corporate absenteeism. The database called absenteeism at work, taken from the UCI Machine Learning Repository, which captured elements of a Brazilian company, was applied in a fuzzy neural network model that allows the calculation of the regressors, defining the estimate of the lack of hours of an employee. The results of the experiments prove that the intelligent model can help in the creation of a specialist system that assists in the prediction of absenteeism.

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Section: Tests With Fuzzy Neural Networkmentioning

confidence: 99%