Welcome to the InfoSci Platform
Reference Hub2
Utilization of SVM, LSSVM and GP for Predicting the Medical Waste Generation

Utilization of SVM, LSSVM and GP for Predicting the Medical Waste Generation

J. Jagan, Yıldırım Dalkiliç, Pijush Samui
Copyright: © 2016 |Pages: 28
ISBN13: 9781522503026|ISBN10: 1522503021|EISBN13: 9781522503033
DOI: 10.4018/978-1-5225-0302-6.ch008
Cite Chapter Cite Chapter

MLA

Jagan, J., et al. "Utilization of SVM, LSSVM and GP for Predicting the Medical Waste Generation." Smart Cities as a Solution for Reducing Urban Waste and Pollution, edited by Goh Bee Hua, IGI Global, 2016, pp. 224-251. https://doi.org/10.4018/978-1-5225-0302-6.ch008

APA

Jagan, J., Dalkiliç, Y., & Samui, P. (2016). Utilization of SVM, LSSVM and GP for Predicting the Medical Waste Generation. In G. Hua (Ed.), Smart Cities as a Solution for Reducing Urban Waste and Pollution (pp. 224-251). IGI Global. https://doi.org/10.4018/978-1-5225-0302-6.ch008

Chicago

Jagan, J., Yıldırım Dalkiliç, and Pijush Samui. "Utilization of SVM, LSSVM and GP for Predicting the Medical Waste Generation." In Smart Cities as a Solution for Reducing Urban Waste and Pollution, edited by Goh Bee Hua, 224-251. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-5225-0302-6.ch008

Export Reference

Mendeley
Favorite

Abstract

The prediction of wastes generated in the hospital will help their management for several activities like storage, transport and disposing. This chapter adopts Support Vector Machine (SVM), Least Square Support Vector Machine (LSSVM) and Genetic Programming (GP) in order to estimate the rate of medical waste generation. In the event of predicting the rate, type of hospital, capacity and bed occupancy has been used as inputs of SVM, LSSVM and GP. SVM is based on statistical learning theory, which provides an elegant tool for nonlinear system modeling. LSSVM is the re-formulation to the general SVM. GP, a best part of evolutionary algorithm and also the specification of Genetic Algorithm (GA). These SVM, LSSVM and GP have been used as the regression techniques. The results show the performance of the developed SVM, LSSVM and GP models were elegant and outstanding.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.