Determination of Rate of Medical Waste Generation Using RVM, MARS and MPMR

2016 ◽  
pp. 1-18
Author(s):  
Jagan J. ◽  
Pijush Samui ◽  
Barnali Dixon
Keyword(s):  
Bayesian Learning ◽  
Piecewise Linear ◽  
Regression Function ◽  
Relevance Vector Machine ◽  
Medical Waste ◽  
Waste Generation ◽  
Learning Framework ◽  
Adaptive Regression ◽  
Regression Techniques

The prediction of medical waste generation is an important task in hospital waste management. This article uses Relevance Vector Machine (RVM), Multivariate Adaptive Regression Spline (MARS) and Minimax Probability Machine Regression (MPMR) for prediction of rate of medical waste generation. Type of hospital, Capacity and Bed Occupancy has been used as inputs of RVM, MARS and MPMR. RVM is a probabilistic bayesian learning framework. MARS builds flexible model by using piecewise linear regressions. MPMR maximizes the minimum probability that future predicted outputs of the regression model will be within some bound of the true regression function. MARS, RVM and MPMR have been used as regression techniques. The results show that the developed RVM, MPMR and MARS give excellent models for determination of rate of medical waste generation.

Determination of Rate of Medical Waste Generation Using RVM, MARS and MPMR

2020 ◽  
pp. 436-453
Author(s):  
J. Jagan ◽  
Pijush Samui ◽  
Barnali Dixon
Keyword(s):  
Bayesian Learning ◽  
Piecewise Linear ◽  
Regression Function ◽  
Relevance Vector Machine ◽  
Medical Waste ◽  
Waste Generation ◽  
Learning Framework ◽  
Adaptive Regression ◽  
Regression Techniques

The prediction of medical waste generation is an important task in hospital waste management. This article uses Relevance Vector Machine (RVM), Multivariate Adaptive Regression Spline (MARS) and Minimax Probability Machine Regression (MPMR) for prediction of rate of medical waste generation. Type of hospital, Capacity and Bed Occupancy has been used as inputs of RVM, MARS and MPMR. RVM is a probabilistic bayesian learning framework. MARS builds flexible model by using piecewise linear regressions. MPMR maximizes the minimum probability that future predicted outputs of the regression model will be within some bound of the true regression function. MARS, RVM and MPMR have been used as regression techniques. The results show that the developed RVM, MPMR and MARS give excellent models for determination of rate of medical waste generation.

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.

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.

scholarly journals Relevance Vector Machines for Enhanced BER Probability in DMT-Based Systems

2010 ◽  
Vol 2010 ◽  
pp. 1-8 ◽  
Author(s):  
Ashraf A. Tahat ◽  
Nikolaos P. Galatsanos
Keyword(s):  
Channel Estimation ◽  
Bayesian Learning ◽  
Linear Models ◽  
Estimation Method ◽  
Relevance Vector Machine ◽  
Sparse Bayesian Learning ◽  
Discrete Multitone ◽  
Learning Framework ◽  
Channel Estimate ◽  
Sparse Solutions

A new channel estimation method for discrete multitone (DMT) communication system based on sparse Bayesian learning relevance vector machine (RVM) method is presented. The Bayesian frame work is used to obtain sparse solutions for regression tasks with linear models. By exploiting a probabilistic Bayesian learning framework, sparse Bayesian learning provides accurate models for estimation and consequently equalization. We consider frequency domain equalization (FEQ) using the proposed channel estimate at both the transmitter (preequalization) and receiver (postequalization) and compare the resulting bit error rate (BER) performance curves for both approaches and various channel estimation techniques. Simulation results show that the proposed RVM-based method is superior to the traditional least squares technique.

Development and Validation of a Green Analytical Method for the Determination of Norfloxacin in Raw Material by FTIR Spectrophotometry

2016 ◽  
Vol 99 (6) ◽  
pp. 1533-1536 ◽  
Author(s):  
Jéssica Sayuri Hisano Natori ◽  
Eliane Gandolpho Tótoli ◽  
Hérida Regina Nunes Salgado
Keyword(s):  
Urinary Tract ◽  
Organic Solvents ◽  
Analytical Method ◽  
Environmentally Friendly ◽  
Raw Material ◽  
Waste Generation ◽  
Ftir Spectrophotometry ◽  
Tract Infections ◽  
Development And Validation

Abstract Norfloxacin is a broad-spectrum antimicrobial agent, widely used in humans and animals for the treatment of urinary tract infections. It is a second-generation fluoroquinolone. Several analytical methods to analyze norfloxacin have been described in the literature. However, most of them are complex and require the use of large amounts of organic solvents. This paper describes the development and validation of a green analytical method for the determination of norfloxacin in raw material by FTIR spectrophotometry. This method does not require the use of organic solvents, minimizing waste generation in the process and its environmental impacts. The development of methods that promote the reduction, prevention, or elimination of waste generation has become highly attractive to the pharmaceutical industry because of the growing demand from civil society and government authorities for environmentally friendly products and services. The FTIR spectrophotometry method was validated according to International Conference on Harmonization guidelines, showing adequate linearity (r = 0.9936), precision, accuracy, and robustness. This validated method can be used as an environmentally friendly alternative for the quantification of norfloxacin in raw material in QC routine analysis.

scholarly journals Determination of the Starting Point in Time Series for Trend Detection Based on Overlapping Trend

2016 ◽  
Vol 16 (6) ◽  
pp. 98-110
Author(s):  
Gao Xuedong ◽  
Gu Kan
Keyword(s):  
Time Series ◽  
Piecewise Linear ◽  
Negative Influence ◽  
Trend Detection ◽  
Computational Accuracy ◽  
Starting Point ◽  
Time Series Studies ◽  
Definition Of ◽  
Stage Characteristic

Abstract The traditional time series studies consider the time series as a whole while carrying on the trend detection; therefore not enough attention is paid to the stage characteristic. On the other hand, the piecewise linear fitting type methods for trend detection are lacking consideration of the possibility that the same node belongs to multiple trends. The above two methods are affected by the start position of the sequence. In this paper, the concept of overlapping trend is proposed, and the definition of milestone nodes is given on its base; these way not only the recognition of overlapping trend is realized, but also the negative influence of the starting point of sequence is effectively reduced. The experimental results show that the computational accuracy is not affected by the improved algorithm and the time cost is greatly reduced when dealing with the processing tasks on dynamic growing data sequence.

scholarly journals DIETARY FIBERS: ANALYSIS METHODS

2018 ◽  
Vol 5 (3) ◽  
pp. 174-179
Author(s):  
Karen Carvalho Ferreira ◽  
Juliana Aparecida Correia Bento ◽  
Lázaro Sátiro De Jesus ◽  
Priscila Zaczuk Bassinelo
Keyword(s):  
Dietary Fiber ◽  
Resistant Starch ◽  
Analysis Time ◽  
Dietary Fibers ◽  
Waste Generation ◽  
Analysis Methods ◽  
Aoac Official ◽  
Innovative Techniques

Dietary fiber (DF) intake is associated with a number of benefits and these effects depend not only on intake as well as its composition. The DF includes polysaccharides such as cellulose, hemicellulose, pectins, gums, oligosaccharides and lignin, and can be divided into soluble and insoluble. The concept of DF was expanded to include resistant starch, inulin and fructo-oligosaccharides. The determination of DF costly and time depends on methods that have been modified for this new concept. The AOAC Official methods of determining all components present in a DF, without specific methods for each component. Studies show innovative techniques to ensure a shorter analysis time, less waste generation by the use of reagents and more convenience in the analysis.

The DMT Power Line Channel Sparse Bayesian Regression Estimation Based on Communication System

2014 ◽  
Vol 960-961 ◽  
pp. 1308-1311
Author(s):  
Yi Pei Huang ◽  
Ya Jun Han ◽  
Bao Fan Chen
Keyword(s):  
Channel Estimation ◽  
Bayesian Learning ◽  
Estimation Method ◽  
Power Line ◽  
Previous Method ◽  
Bayesian Regression ◽  
Estimation Model ◽  
Power Line Communications ◽  
Learning Framework ◽  
Performance Curves

This paper introduces the power line communications channel estimation method based on sparse Bayesian regression, it is through the use of Bayesian learning framework that provides a sparse model in the presence of noise accurate channel estimation model. Improved channel estimation using the power line for the system to consider the frequency domain equalization (FREQ) transmitter and receiver, the bit error rate and comparing the two methods for generating various channel estimation techniques, and (BER) performance curves simulation the results show that the performance of the method is better than the previous method of least squares technique.

Time-series-based hybrid mathematical modelling method adapted to forecast automotive and medical waste generation: Case study of Lithuania

2018 ◽  
Vol 36 (5) ◽  
pp. 454-462 ◽  
Author(s):  
Aistė Karpušenkaitė ◽  
Tomas Ruzgas ◽  
Gintaras Denafas
Keyword(s):  
Time Series ◽  
Mathematical Modelling ◽  
Error Rate ◽  
Medical Waste ◽  
Waste Generation ◽  
Prediction Horizon ◽  
Modelling Method ◽  
Forecasting Method ◽  
Hybrid Forecasting

The aim of the study was to create a hybrid forecasting method that could produce higher accuracy forecasts than previously used ‘pure’ time series methods. Mentioned methods were already tested with total automotive waste, hazardous automotive waste, and total medical waste generation, but demonstrated at least a 6% error rate in different cases and efforts were made to decrease it even more. Newly developed hybrid models used a random start generation method to incorporate different time-series advantages and it helped to increase the accuracy of forecasts by 3%–4% in hazardous automotive waste and total medical waste generation cases; the new model did not increase the accuracy of total automotive waste generation forecasts. Developed models’ abilities to forecast short- and mid-term forecasts were tested using prediction horizon.

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