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A hybrid approach to evaluate employee performance using MCDA and artificial neural networks
Malik Haddad
*
,
David A. Sanders
*
Corresponding author for this work
Northeastern University
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INIS
performance
100%
neural networks
100%
hybrids
100%
employees
100%
evaluation
62%
testing
37%
accuracy
25%
learning
12%
applications
12%
tools
12%
Computer Science
Multiple-Criteria Decision Analysis
100%
Neural Network
100%
Hybrid Approach
100%
Artificial Neural Network
100%
Evaluation Criterion
50%
Performance Evaluation
25%
Transfer Learning
25%
Social Sciences
Multiple-Criteria Decision Analysis
100%
Neural Network
100%
Evaluation Method
50%
Performance Appraisal
50%
Performance Evaluation
25%
Learning Transfer
25%
Chemical Engineering
Decision Theory
100%
Neural Network
100%
Psychology
Neural Network
100%
Learning Transfer
25%