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

2020 ◽  
pp. 808-829
Author(s):  
J. Jagan ◽  
Yıldırım Dalkiliç ◽  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
System Modeling ◽  
Medical Waste ◽  
Least Square ◽  
Support Vector ◽  
Waste Generation ◽  
Nonlinear System Modeling ◽  
Hospital Capacity ◽  
Regression Techniques ◽  
Rate Type

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.

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

2016 ◽  
pp. 224-251 ◽  
Author(s):  
J. Jagan ◽  
Yıldırım Dalkiliç ◽  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
System Modeling ◽  
Medical Waste ◽  
Least Square ◽  
Support Vector ◽  
Waste Generation ◽  
Nonlinear System Modeling ◽  
Hospital Capacity ◽  
Regression Techniques ◽  
Rate Type

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.

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

2020 ◽  
pp. 990-1012
Author(s):  
J. Jagan ◽  
Yıldırım Dalkiliç ◽  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
System Modeling ◽  
Medical Waste ◽  
Least Square ◽  
Support Vector ◽  
Waste Generation ◽  
Nonlinear System Modeling ◽  
Hospital Capacity ◽  
Regression Techniques ◽  
Rate Type

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.

Civil Aeroengine Fault Diagnosis Based on Fuzzy Least Square Support Vector Machine

2011 ◽  
Vol 130-134 ◽  
pp. 2047-2050 ◽  
Author(s):  
Hong Chun Qu ◽  
Xie Bin Ding
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Coefficient Matrix ◽  
Least Square ◽  
Support Vector ◽  
Influence Coefficient ◽  
Structural Risk ◽  
Better Than

SVM(Support Vector Machine) is a new artificial intelligence methodolgy, basing on structural risk mininization principle, which has better generalization than the traditional machine learning and SVM shows powerfulability in learning with limited samples. To solve the problem of lack of engine fault samples, FLS-SVM theory, an improved SVM, which is a method is applied. 10 common engine faults are trained and recognized in the paper.The simulated datas are generated from PW4000-94 engine influence coefficient matrix at cruise, and the results show that the diagnostic accuracy of FLS-SVM is better than LS-SVM.

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