Sürdürülebilir Kalkınma Amaçları
Yayınlar
- Book ChapterYalnız künye2019Utilization of SVM, LSSVM and GP for predicting the medical waste generation
Jagan, J.; Dalkiliç., Yildirim; Samui, Pijush
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.
RDL Kalıcı Kaydıİncele - Book ChapterYalnız künye2019Utilization of SVM, LSSVM and GP for predicting the medical waste generation
Jagan, J.; Dalkiliç, Yildirim; Samui, Pijush
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.
RDL Kalıcı Kaydıİncele - Book ChapterAçık Erişim2016Reliability analysis of slope using MPMR, GRNN and GPR
Subburaman, Dhivya; Jagan, J.; Dalkiliç, Yildirim; Samui, Pijush
First Order Second Moment Method (FOSM) is generally for determination of reliability of slope. This article adopts Minimax Probability Machine Regression (MPMR), Generalized Regression Neural Network (GRNN) and Gaussian Process Regression (GPR) for reliability analysis of slope by using FOSM. In this study, an example of soil slope is given regarding how the proposed GPR-based FOSM, MPMR-based FOSM and GRNN-based FOSM analysis can be carried out. GPR, GRNN and MPMR have been used as regression techniques. A comparative study has been carried out between the developed GPR, MPMR and GRNN models. The results show that MPMR gives better performance than the other models.
RDL Kalıcı Kaydıİncele - Book ChapterYalnız künye2016Determination of bearing capacity of shallow foundation using soft computing
Jagan, J.; Chowdhury, Swaptik; Goyal, Pratik; Samui, Pijush; Dalkiliç, Yildirim
The ultimate bearing capacity is an important criterion for the successful implementation of any geotechnical projects. This chapter studies the feasibility of employing Gaussian process regression (GPR), Extreme learning machine (ELM) and Minimax probability machine regression (MPMR) for prediction of ultimate bearing capacity of shallow foundation based on cohesionless soils. The developed models have been compared on the basis of coefficient of relation (R) values (GPR= 0.9625, ELM= 0.938, MPMR= 0.9625). The results show that MPMR is more efficient tool but the models of GPR and ELM also gives satisfactory results.
RDL Kalıcı Kaydıİncele - Book ChapterYalnız künye2016Utilization of SVM, LSSVM and GP for predicting the medical waste generation
Jagan, J.; Dalkilç, Yildirim; Samui, Pijush
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.
RDL Kalıcı Kaydıİncele - Book ChapterYalnız künye2016Determination of bearing capacity of shallow foundation using soft computing
Jagan, J.; Goyal, Pratik; Chowdhury, Swaptik; Samui, Pijush; Dalkiliç, Yildirim
The ultimate bearing capacity is an important criterion for the successful implementation of any geotechnical projects. This chapter studies the feasibility of employing Gaussian process regression (GPR), Extreme learning machine (ELM) and Minimax probability machine regression (MPMR) for prediction of ultimate bearing capacity of shallow foundation based on cohesionless soils. The developed models have been compared on the basis of coefficient of relation (R) values (GPR= 0.9625, ELM= 0.938, MPMR= 0.9625). The results show that MPMR is more efficient tool but the models of GPR and ELM also gives satisfactory results.
RDL Kalıcı Kaydıİncele - Book ChapterYalnız künye2015Reliability analysis of slope using MPMR, GRNN and GPR
Subburaman, Dhivya; Jagan, J.; Dalkiliç, Yildirim; Samui, Pijush
First Order Second Moment Method (FOSM) is generally for determination of reliability of slope. This article adopts Minimax Probability Machine Regression (MPMR), Generalized Regression Neural Network (GRNN) and Gaussian Process Regression (GPR) for reliability analysis of slope by using FOSM. In this study, an example of soil slope is given regarding how the proposed GPR-based FOSM, MPMR-based FOSM and GRNN-based FOSM analysis can be carried out. GPR, GRNN and MPMR have been used as regression techniques. A comparative study has been carried out between the developed GPR, MPMR and GRNN models. The results show that MPMR gives better performance than the other models.
RDL Kalıcı Kaydıİncele - Book ChapterAçık Erişim2015Minimax probability machine: A new tool for modeling seismic liquefaction data
Samui, Pijush; Dalkiliç, Yildirim Huseyin; Rajadurai, Hariharan; Jagan, J.
Liquefaction in soil is one of the other major problems in geotechnical earthquake engineering. This chapter adopts Minimax Probability Machine (MPM) for prediction of seismic liquefaction potential of soil based on Shear Wave Velocity (Vs) data. MPM has been used as a classification technique. Two models (MODEL I and MODEL II) have been adopted. In MODEL I, input variables are Cyclic Stress Ratio (CSR), and Vs MODEL II uses Peck Ground Acceleration (PGA) and Vs as input variables. The developed MPM has been compared with the Artificial Neural Network (ANN) and Support Vector Machine (SVM) models. The developed MPM is a robust tool for determination of liquefaction susceptibility of soil.
RDL Kalıcı Kaydıİncele - Book ChapterYalnız künye2015Site characterization using GP, MARS and GPR
Samui, Pijush; Dalkiliç, Yildirim; Jagan, J.
This article examines the capability of Genetic Programming (GP), Multivariate Adaptive Regression Spline (MARS) and Gaussian Process Regression (GPR) for developing site characterization model of Bangalore (India) based on corrected Standard Penetration Test (SPT) value (Nc). GP, MARS and GPR have been used as regression techniques. GP is developed based on genetic algorithm. MARS does not assume any functional relationship between input and output variables. GPR is a probabilistic, non-parametric model. In GPR, different kinds of prior knowledge can be applied. In three dimensional analysis, the function.
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