A Ubiquitous Clinic Recommendation System Using the Modified Mixed-Binary Nonlinear Programming-Feedforward Neural Network Approach

Most of the existing ubiquitous clinic recommendation (UCR) systems adopt linear mechanisms to aggregate the attribute-level performances of a clinic to evaluate the overall performance. However, such linear mechanisms may not be able to explain the choices of all patients. To solve this problem, th...

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Vydané v:	Journal of theoretical and applied electronic commerce research Ročník 16; číslo 7; s. 3282 - 3298
Hlavní autori:	Lin, Yu-Cheng, Chen, Toly
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Curicó MDPI AG 01.12.2021
Predmet:	Artificial neural networks clinic Clinics Coronaviruses COVID-19 Decision making Dentists Electronic commerce feedforward neural network Methods mixed-binary nonlinear programming Neural networks Nonlinear programming Patient satisfaction Performance evaluation Questionnaires Recommender systems ubiquitous recommendation User needs Taiwan
ISSN:	0718-1876, 0718-1876
On-line prístup:	Získať plný text
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