Applied multivariate statistical analysis
Applied multivariate statistical analysis
Data quality: management and technology
Data quality: management and technology
C4.5: programs for machine learning
C4.5: programs for machine learning
Attribute selection for modelling
Future Generation Computer Systems - Special double issue on data mining
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Machine Learning
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expert Systems with Applications: An International Journal
Modeling word perception using the Elman network
Neurocomputing
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Skin attribute tests, especially for women, have become critical in the development of daily cosmetics in recent years. However, clinical skin attribute testing is often costly and time consuming. In this paper, a novel prediction approach based on questionnaires using recurrent neural network models is proposed for participants' skin attribute prediction. The prediction engine, which is the most important part of this novel approach, is composed of three prediction models. Each of these models is a neural network allocated to predict different skin attributes: Tone, Spots, and Hydration. We also provide a detailed analysis and solution about the preprocessing of data, the selection of key features, and the evaluation of results. Our prediction system is much faster and more cost effective than traditional clinical skin attribute tests. The system performs very well, and the prediction results show good precision, especially for Tone.