The present research work was carried out to develop the prediction models for blended ring spun yarn evenness and tensile parameters using artificial neural networks (ANNs) and multiple linear regression (MLR). Polyester/cotton blend ratio, twist multiplier, back roller hardness and break draft ratio were used as input parameters to predict yarn evenness in terms of CVm% and yarn tensile properties in terms of tenacity and elongation. Feed forward neural networks with Bayesian regularisation support were successfully trained and tested using the available experimental data. The coefficients of determination of ANN and regression models indicate that there is a strong correlation between the measured and predicted yarn characteristics with an acceptable mean absolute error values. The comparative analysis of two modelling techniques shows that the ANNs perform better than the MLR models. The relative importance of input variables was determined using rank analysis through input saliency test on optimised ANN models and standardised coefficients of regression models. These models are suitable for yarn manufacturers and can be used within the investigated knowledge domain.
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apply (multiple) linearregression (MLR; Cook and Kairiukstis, 1992 ). However, while linear transfer functions usually fit data well, there are some concerns about predictions for data points close to the edge of observed data and points out of the calibration data. Linear models assume that the dependency between tree-ring parameters and climate changes linearly from the most favourable to the most unfavourable conditions. This assumption is contradictory to the well-accepted concept of ecological amplitudes ( Braak and Gremmen, 1987 ; Fritts, 1976 ), and trees
Maximal oxygen uptake (VO2max) is one of the most distinguished parameters in endurance sports and plays an important role, for instance, in predicting endurance performance. Different models have been used to estimate VO2max or performance based on VO2max. These models can use linear or nonlinear approaches for modeling endurance performance. The aim of this study was to estimate VO2max in healthy adults based on the Queens College Step Test (QCST) as well as the Shuttle Run Test (SRT) and to use these values for linear and nonlinear models in order to predict the performance in a maximal 1000 m run (i.e. the speed in an incremental 4×1000 m Field Test (FT)). 53 female subjects participated in these three tests (QCST, SRT, FT). Maximal oxygen uptake values from QCST and SRT were used as (a) predictor variables in a multiple linear regression (MLR) model and as (b) input variables in a multilayer perceptron (MLP) after scaling in preprocessing. Model output was speed [km·h−1] in a maximal 1000 m run. Maximal oxygen uptake values estimated from QCST (40.8 ± 3.5 ml·kg−1·min−1) and SRT (46.7 ± 4.5 ml·kg−1·min−1) were significantly correlated (r = 0.38, p < 0.01) and maximal mean speed in the FT was 12.8 ± 1.6 km·h−1. Root mean squared error (RMSE) of the cross validated MLR model was 0.89 km·h−1 while it was 0.95 km·h−1 for MLP. Results showed that the accuracy of the applied MLP was comparable to the MLR, but did not outperform the linear approach.
Prediction of greenhouse gas (GHG) emissions is important to minimise their negative impact on climate change and global warming. In this article, we propose new models based on data mining and supervised machine learning algorithms (regression and classification) for predicting GHG emissions arising from passenger and freight road transport in Canada. Four models are investigated, namely, artificial neural network multilayer perceptron, multiple linear regression, multinomial logistic regression and decision tree models. From the results, it was found that artificial neural network multilayer perceptron model showed better predictive performance over other models. Ensemble technique (Bagging & Boosting) was applied on the developed multilayer perceptron model, which significantly improved the model’s predictive performance.
In the article, an attempt was made to compile a dataset which was devoid of outliers, on the example of Cracovian apartment market. Robust estimation was the tool which was used, but only its two methods were considered: Baarda’s and Huber’s. Huber’s method belongs to the so-called active methods which means that it allows to eliminate gross errors during the estimation of the parameters of multiple linear regression where a unit price is called a dependent variable or forecasted one. Baarda’s method is a passive method which is based on statistical tests and allows, after determining the parameters of a multiple linear regression model, to indicate the observations which may be burdened with gross errors. Thus both mentioned algorithms differ from each other substantially. In this publication, Baarda’s and Huber’s methods were compared in the context of their effectiveness for the analyzed dataset, and as tools of preparing the data for further analysis. The results showed that Baarda’s method is more appropriate for the analyzed dataset than Huber’s algorithm, but it does not mean that the active method is worse.
The aim at this paper is to propose an econometric model for analyzing economic performance in the furniture industry in Romania, conducted on a sample of 293 firms. The net profit was considered as a dependent variable and the turnover, expenses with employees, value added, current liabilities and inventories as independent variables. Five hypotheses were proposed, tested and validated by using multiple linear regression. The most significant results show that there is a positive significant relationship between net profit and value added and a negative significant relationship between net profit and expenses with employees. Since the model has been validated statistically, we consider that it can provide useful predictions in terms of economic performance analysis in the furniture industry.
Economic and financial analysis is a method of knowing the mechanism of formation and modification of the economic phenomena by their decomposition into the component elements and by identifying the factors of influence. The object of decomposition by elements or factors may be a result (structural analysis), or a change in the result from a basis of comparison (causal analysis).
In the present paper I propose an analysis of the investments according to the number of passengers and the consumption of energy on national transport modes in Romania within a period of 15 years, respectively between 2000 and 2015. For this purpose the data that will be used was published by the National Institute of Statistics, namely three indicators: investments in transport infrastructure, the weight of each mode in passenger transport and the consumption of energy by modes of transport. Energy consumption by modes of transport is the final energy consumption of transport activity by modes of transport, expressed in tones oil equivalent (toe). The quantities of energy used in international maritime and air transport and pipeline transport are not included. The main types of fuels used are the main fuels covered by petroleum products, electricity and small amounts of gas and biofuels.
The weight of each mode in passenger transport is defined as the percentage share of each mode of transport in total domestic passenger transport. The modes of transport considered are: a) cars, b) buses and coaches, and c) trains (metro and trams and light metro are excluded). Domestic passenger transport includes data referring only to national transport, irrespective of the nationality of the transport vehicle. The weight is calculated from the passenger-km indicator (pkm), defined as the transport of a passenger over one kilometer.
The investments in the transport infrastructure represent the construction works carried out for the development of the transport infrastructure.
In order to carry out the statistical analysis of the investments in the transport infrastructure, the number of passengers and the energy consumption in transport modes in Romania, multiple linear regression models and time series analysis will be used.