Fuzzy Regression Model Based on Incentre Distance and Application to Employee Performance Evaluation

Guo, Yu; Yang, Shuxin; Ma, H.; Ge, Min

doi:10.1007/s40815-018-0536-x

Cited by 10 publications

(5 citation statements)

References 26 publications

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“…The TOPSIS-cloud modeling approach is characterized by simplicity and ease of use in solving interval-based decision information problems. Gong (2022) used the cloud similarity TOPSIS method for water resource management and allocation scheme problems to effectively solve the fuzzy uncertainty of indicator weights (Wei, 2016;Gong et al, 2018), and optimized the assessment of relay protection status under the combination of the cloud modeling method, grayscale correlation, and TOPSIS; In addition, the TOPSIS-cloud model validates the applicability of the metrics and models on construction safety, disaster risk assessment, and building engineering.…”

Section: Applied Research Of the Cloud Modelmentioning

confidence: 99%

Multi-Dimensional Cloud Model-Based Assessment and Its Application to the Risk of Supply Chain Financial Companies

Zhou,

Zhan,

Chen

2023

International Journal of Fuzzy System Applications

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The multi-dimensional cloud model is proposed as the expansion of the one-dimensional cloud model. The features of ambiguity and stochasticity in complex information situations are considered; thus, this optimized model can be utilized upon multiple value classifications and ordering via which the objects' attributes of physical and social can be reflected. Therefore, this promoted model is wildly used. This paper provides a knowledge graph by reviewing the theoretical research of the multi-dimensional cloud model and its related bibliographies, and Cite Space is applied here to give a visualization conclusion. In recent years, a multitude of theories and methods have emerged to address the challenges posed by fuzzy and stochastic uncertainty in various domains, such as image segmentation, data mining, prediction techniques, and comprehensive evaluation of multiple metrics and dimensions using uncertain linguistic variables.

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Section: Applied Research Of the Cloud Modelmentioning

confidence: 99%

Multi-Dimensional Cloud Model-Based Assessment and Its Application to the Risk of Supply Chain Financial Companies

Zhou,

Zhan,

Chen

2023

International Journal of Fuzzy System Applications

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“…Employees are believed to be the most important asset of an organization [ 22 ]. For this reason, the management must consider the factors that might hinder the workers’ performance [ 23 ]. Dynamic firms seek individuals who give their best when performing at work.…”

Section: Theory and Literaturementioning

confidence: 99%

Impact of Sleep Deprivation on Job Performance of Working Mothers: Mediating Effect of Workplace Deviance

Deng

Cherian

Kumari

et al. 2022

IJERPH

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The current study takes its philosophical roots from organizational behavior and psychology domains to investigate the impact of sleep deprivation on the job performance of mothers working in primary, secondary, and higher education institutions (HEIs) of Pakistan. It also examines the mediating role of workplace deviance in the relationship between sleep deprivation and the job performance of working mothers. The authors followed the non-probability convenience sampling technique to study the relationship between sleep deprivation, workplace deviance, and job performance. The structural analyses indicated that sleep deprivation has a significant negative impact on the job performance of working mothers and sleep-deprived individuals often tend to perform poorly at the workplace. Such workers are also more likely to engage in workplace deviant behaviors. Moreover, workplace deviance is also found to act as a mediating variable in the relationship between sleep deprivation and job performance. The present research bridges the literature gap on the rarely investigated factors, namely sleep deprivation and workplace deviance, and provide a detailed understanding of how these factors can influence the performance of working mothers, specifically in Pakistan.

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“…e proposed neural network method convergence does not depend on choosing a suitable initial point, since it is globally convergent. e computational burden can be greatly reduced using the proposed approach compared with some existing methods because it is not needed to calculate the inverse of a matrix (see, for example, [31,50]) in each iteration in order to obtain weighting coefficients. In order to evaluate the proposed model, three different distances and goodness-of-fit measures are used.…”

Section: Journal Of Mathematicsmentioning

confidence: 99%

“…However, in real-world applications, because of parameter uncertainty, human errors data are usually not exact but have uncertainty. As a result, the classical regression models experience some restriction, which need to be adjusted accordingly to deal with fuzzy data (see [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]). As in regression models in traditional statistical theory, multicollinearity is also an important issue in fuzzy regression models.…”

Section: Introductionmentioning

confidence: 99%

A Generalized Bridge Regression in Fuzzy Environment and Its Numerical Solution by a Capable Recurrent Neural Network

Karbasi

Rabiei

Nazemi

2020

Journal of Mathematics

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Bridge regression is a special family of penalized regressions using a penalty function ∑ A j γ with γ ≥ 1 that for γ = 1 and γ = 2 , it concludes lasso and ridge regression, respectively. In case where the output variable in the regression model was imprecise, we developed a bridge regression model in a fuzzy environment. We also exhibited penalized fuzzy estimates for this model when the input variables were crisp. So, we perform the presented optimization problem for the model that leads to a multiobjective program. Also, we try to determine the shrinkage parameter and the tuning parameter from the same optimization problem. In order to estimate fuzzy coefficients of the proposed model, we introduce a hybrid scheme based on recurrent neural networks. The suggested neural network model is constructed based on some concepts of convex optimization and stability theory which guarantees to find the approximate parameters of the proposed model. We use a simulation study to depict the performance of the proposed bridge technique in the presence of multicollinear data. Furthermore, real data analysis is used to show the performance of the proposed method. A comparison between the fuzzy bridge regression model and several other shrinkage models is made with three different well-known fuzzy criteria. In this study, we visualize the performance of the model by Taylor’s diagram and Bubble plot. Also, we examine the predictive ability of the model, thus, obtained by cross validation. The numerical results clearly showed higher accuracy of the proposed fuzzy bridge method compared to the other existing fuzzy regression models: fuzzy bridge regression model, multiobjective optimization, recurrent neural network, stability convergence, and goodness-of-fit measure.

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Fuzzy Regression Model Based on Incentre Distance and Application to Employee Performance Evaluation

Cited by 10 publications

References 26 publications

Multi-Dimensional Cloud Model-Based Assessment and Its Application to the Risk of Supply Chain Financial Companies

Multi-Dimensional Cloud Model-Based Assessment and Its Application to the Risk of Supply Chain Financial Companies

Impact of Sleep Deprivation on Job Performance of Working Mothers: Mediating Effect of Workplace Deviance

A Generalized Bridge Regression in Fuzzy Environment and Its Numerical Solution by a Capable Recurrent Neural Network

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