Correlation Coefficients For Measurements Research Articles

The Correlation Coefficient of Hesitancy Fuzzy Graphs in Decision Making

The hesitancy fuzzy graphs (HFGs), an extension of fuzzy graphs, are useful tools for dealing with ambiguity and uncertainty in issues involving decision-making (DM). This research implements a correlation coefficient measure (CCM) to assess the strength of the association between HFGs in this article since CCMs have a high capacity to process and interpret data. The CCM that is proposed between the HFGs has better qualities than the existing ones. It lowers restrictions on the hesitant fuzzy elements’ length and may be used to establish whether the HFGs are connected negatively or favorably. Additionally, a CCM-based attribute DM approach is built into a hesitant fuzzy environment. This article suggests the use of weighted correlation coefficient measures (WCCMs) using the CCM concept to quantify the correlation between two HFGs. The decision-making problems of hesitancy fuzzy preference relations (HFPRs) are considered. This research proposes a new technique for assessing the relative weights of experts based on the uncertainty of HFPRs and the correlation coefficient degree of each HFPR. This paper determines the ranking order of all alternatives and the best one by using the CCMs between each option and the ideal choice. In the meantime, the appropriate example is given to demonstrate the viability of the new strategies.

Evaluation of acetabular anteversion after total hip arthroplasty

To investigate whether the anteversion angle of acetabular prosthesis can be evaluated on the anteroposterior X-ray film of common double hip joint. Total 32 patients(41 hips) after total hip arthroplasty were selected, including 18 males and 14 females, aged(66.2±4.1) years. All patients completed the positive X-ray film of both hips and plain CT scan of pelvis after operation. Acetabular anteversion was measured by plain CT scan of pelvis, and measured by Saka and other measurement formulas on X-ray film. The acetabular anteversion measured by X-ray film was(16.2±5.0)° and that measured by CT was (31.8±9.7)°(P=0.00). In addition, there was a significant linear correlation between X-ray film and CT(Pearson correlation coefficient (r=0.84, P=0.00). CT can accurately measure the acetabular anteversion, but it has obvious disadvantages, such as large radiation, high cost, phantom CT artifact and so on. Although Saka measurement formula can not directly obtain the accurate acetabular anteversion as CT measurement, it has a high correlation with the acetabular anteversion measured by CT. Therefore, the method proposed in this study can also preliminarily evaluate the acetabular anteversion.

Juxtatumoral perinephric fat analysis in clear cell renal cell carcinoma.

The purpose of the study was to evaluate the feasibility of using contrast-enhanced computed tomography (CECT)-based texture analysis (CTTA) metrics to differentiate between juxtatumoral perinephric fat (JPF) surrounding low-grade (ISUP 1-2) versus high-grade (ISUP 3-4) clear cell renal cell carcinoma (ccRCC). In this IRB-approved study, we retrospectively queried the surgical database between June 2009 and April 2016 and identified 83 patients with pathologically confirmed ccRCC (low grade: n = 54, mean age = 61.5 years, 18F/35M; high grade n = 30, mean age = 61.7 years, 8F/22M) who also had pre-operative multiphase CT acquisitions. CT images were transferred to a 3D workstation, and nephrographic phase JPF regions were manually segmented. Using an in-house developed Matlab program, a CTTA panel comprising of texture metrics extracted using six different methods, histogram, 2D- and 3D-Gray-level co-occurrence matrix (GLCM) and Gray-level difference matrix (GLDM), and 2D-Fast Fourier Transform (FFT) analyses, was applied to the segmented images to assess JPF textural heterogeneity in low- versus high-grade ccRCC. Univariate analysis and receiver-operator characteristics (ROC) analysis were used to assess interclass differences in texture metrics and their prediction accuracy, respectively. All methods except GLCM consistently revealed increased heterogeneity in the JPF surrounding high- versus low-grade ccRCC. FFT showed increased complexity index (p < 0.01). Histogram analysis showed increased kurtosis and positive skewness in (p < 0.03), and GLDM analysis showed decreased measure of correlation coefficient (MCC) (p < 0.04). Several of the GLCM metrics showed statistically significant (p < 0.04) textural differences between the two groups, but with no consistent trend. ROC analysis showed that MCC in GLCM analysis had an area under the curve of 0.75. Our study suggests that CTTA of ccRCC shows statistically significant textural differences in JPF surrounding high- versus low-grade ccRCC.

Impact of Foreign Investments on Indian Stock Market An Empirical Study

FDI and FII have become instruments of international economic integration, stimulation and a major catalyst to development. Fast growing economies like Singapore, China, and Korea etc have registered the incredible growth at onset of FDI. Promoting FDI to supplement the domestic capital, human skills and technology for accelerated domesticate economic growth has been the objective of the Government of India since the inception of LPG in 1991. Superior and sophisticated technology has helped India greatly. Industries have been marching ahead riding on the incoming superior and advanced technology. FDI not only influences the stock market by creating positive sentiments in the market participants, they also play a vital role in the employment creation in different sectors. Being a long-term investment FDI plays a major role in bringing the stability to the country‘s economy. The Foreign Institutional Investors (FIIs) have emerged as noteworthy players in the Indian stock market and their growing contribution adds as an important feature of the development of stock markets in India. FIIs are more volatile in nature and more profit driven investment. Hence, FDI is more desirable for any country, not to undermine the impact of FII. This paper attempts to develop a study to find the relationship and impact of FDI & FII on Indian stock market using the statistical measures-correlation coefficient and multi regression for 14 years data spanning from 2000 to 2014. Sensex and Nifty are considered as the representative of stock market. The analysis of the current study provides the evidence of strong positive correlation between FDI & Sensex and FDI & nifty. It also provides the evidence of moderate positive correlation between FII activity and effects on Indian Capital Market. Both FDI and FII determine the trend of Indian stock market.

A Combined Intensity and Gradient-Based Similarity Criterion for Interindividual SPECT Brain Scan Registration

An evaluation of a new similarity criterion for interindividual image registration is presented. The proposed criterion combines intensity and gradient information from the images to achieve amore robust and accurate registration. It builds on a combination of the normalised mutual information (NMI) cost function and a gradient-weighting function, calculated from gradient magnitude and relative gradient angle values from the images. An investigation was made to determine the best settings for the number of bins in the NMI joint histograms, subsampling, and smoothing of the images prior to the registration. The new method was compared with the NMI and correlation-coefficient (CC) criterions for interindividual SPECT image registration. Two different validation tests were performed, based on the displacement of voxels inside the brain relative to their estimated true positions after registration. The results show that the registration quality was improved when compared with the NMI and CC measures. The actual improvements, in one of the tests, were in the order of 30-40% for the mean voxel displacement error measured within 20 different SPECT images. A conclusion from the studies is that the new similarity measure significantly improves the registration quality, compared with the NMI and CC similarity measures.