[Luo05] proposes the Anticipation Breadth Annual (PLS)‚ a annual in which a adaptable bulge uses advice about its antecedent accompaniment to adumbrate its approaching state. After-effects appearance that PLS has lower aerial and lower breadth absurdity than GLS‚ SLS‚ and LEAP. Abundant of the accustomed breadth anticipation assay is focused on ambiguous breadth models breadth the geographic admeasurement is broken into regular-shape cells. These models are not adequate for assertive LBSs whose cold
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4.1.1 Factor Analysis of the WTCS A principal component factor analysis was conducted on the 25 items with varimax rotation. Because the Kaiser-Meyer-Olkin (KMO) measure verified that the whole WTC scale was .832 (>.8 is applicable for factor analysis according to Kaiser)‚ it gave me the confidence that the sample size of the research was adequate for factor analysis. The scree plot for WTC scale showed the inflexions that the scale is not unidimensional. Consequently‚ I retained the five factors
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The linkage between Environmental Management Systems implementation and Firms’ economic performance: an Empirical Analysis The summary version Author: K. Nishitani Student: Dang Tien Loc SUBMITTED TO UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM THE NETHERLAND PROGRAMME 1. Paper’s objective the purpose of this paper is to answer the question whether EMS implementation can improves a firm’s performance or not by using panel data from Japanese manufacturing firms during 1996-2007
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3. FMECA Methodology 3.1 Failure mode effect & criticality analysis FMEA is called as FMECA (failure mode‚ effects and criticality analysis) when it is used for criticality analysis. In general‚ FMECA is performed in two parts: (I) to identify the different failure modes and its effects by failure mode and effect analysis (FMEA); (ii) to Classify failure mode criticality analysis by probability of occurrence and its severity. (Bowles & Pelaez‚ 1995). FMEA is traditionally calculated by developing
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1. Chi-square Goodness-of-fit Tests Jake is trying to invest his money in stock market‚ is not sure that he could earn a profit or lose his money when he invests to an AT&T company’s stock or a stock market index‚ Dow Jones Industry Average. So he called his friend who works at financial consulting company and heard that the monthly positive and negative investment returns on AT&T and Dow Jones Industry Average were historically almost the same. However the economic situation recently has been getting
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Implementation of Regression Testing of Test Case Prioritization Abstract: In this paper‚ we described the regression testing of test case prioritization. Regression Testing is a significant and precious movement of the software preservation lifecycle. In that studies‚ various regression test variety and prioritization methods are available depends upon the coverage‚ specification‚ past history & risk. To categorize the cruel mistakes and get better the rate of fault detection‚ test case prioritization
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L={116*(Y/Yn) for (Y/Yn)>0.008856 903.3*(Y/Yn); Otherwise a = 500*(f(X/Xn –f(Y-Yn)) b = 200*(f(X/Xn –f(Z-Zn)) where Xn ‚Yn and Zn are the tristimulus values of the reference white. A median filter is applied on the L band in order to preserve edges and to reduce noise . A Contrast Limited Adaptive Histogram Equalization (CLAHE) technique is used. To enhance the contrast and the separability between exudates and the background ‚ a Contrast Limited Adaptive
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squared elements in the GLCM and it is by default one for constant image. E=∑_(i‚j)▒(i‚j)^2 (3.8) 3.1.2.1.2 Tamura Texture Feature According to quantitative analysis one of the first descriptions given by the Tamura [69] proposed six textural properties and gave descriptions common over all texture patterns in Broadtz’s photographic images. These are six different texture features given by Tamura Coarseness
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Residual Analysis The concepts behind residual analysis for a multiple linear regression model are similar to those for a simple linear regression model. However‚ they are much more important for the multiple linear regression models because of the lack of good graphical representations of the data set and the fitted model. In simple linear regression a plot of the response variable against the input variable showing the data points and the fitted regression line provides a good graphical summary
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Regression Analysis: IBI versus Area The regression equation is IBI = 52.9 + 0.460 Area Predictor Coef SE Coef T P Constant 52.923 4.484 11.80 0.000 Area 0.4602 0.1347 3.42 0.001 S = 16.5346 R-Sq = 19.9% R-Sq(adj) = 18.2% Analysis of Variance Source DF SS MS F P Regression 1 3189.3 3189.3 11.67 0.001 Residual Error 47 12849.5 273.4 Total 48 16038.8 Unusual Observations Obs Area
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