In this paper, we will provide a proposed method to estimate missing values for the Explanatory variables for Non-Parametric Multiple Regression Model and compare it with the Imputation Arithmetic mean Method, The basis of the idea of this method was based on how to employ the causal relationship between the variables in finding an efficient estimate of the missing value, we rely on the use of the Kernel estimate by Nadaraya – Watson Estimator , and on Least Squared Cross Validation (LSCV) to estimate the Bandwidth, and we use the simulation study to compare between the two methods.

This paper proposed a new  method to study functional non-parametric regression data analysis with conditional expectation in the case that the covariates  are functional and the Principal Component Analysis was utilized to de-correlate the multivariate response variables. It  utilized the formula of the Nadaraya Watson estimator (K-Nearest Neighbour (KNN)) for prediction with different types of the semi-metrics, (which are based on Second Derivative and Functional Principal Component Analysis (FPCA))  for measureing the closeness between curves.  Root Mean Square Errors is used for the  implementation of this model which is then compared to the independent response method. R program is used for analysing data. Then, when  the cov

Metallic solid radioactive waste class low level - short lived Radioactive Waste

(LL-SL RW) is the main type of radioactive waste generated from decommissioning operations. Transport, storage and disposal regulations require for gamma emitting radioactive waste (mainly by ¹³⁷Csisotope), that the dose rate in the proximity of the container should stand below a certain threshold. Also, the conditioning technique (using cementation technique) based on certain matrix with specific ratios should be able to attenuate the gamma radiation activity to the minimum level or to acceptable dosage rate at distance of 1m from the container. In this paper ,in absence of suitable labs for waste package assessment ,hypothetical method&n

In this paper we present a new method for solving fully fuzzy multi-objective linear programming problems and find the fuzzy optimal solution of it. Numerical examples are provided to illustrate the method.

Histidine was determined via measurement of total luminescence (i:e creation of chemiluminescence and insitu irradiation of released light to an acceptor fluorophore molecule to  initiate fluorescence from fluorescien molecule in flat – spiral micro cell designed for this measurement . A detailed description of robust linear equation for the range of 0.002 – 0.05 mol.L-1 for a sample size of 70 µL with a correlation coefficient of 0.9879 and a coefficient of determination of 97.59% while for a quadratic model of the same concentration range was 0.9881 correlation coefficient and 97.63% coefficient of determination. Analysis of variance was conducted for both kinds of models . It indicated that their was no significa

We propose a new method for detecting the abnormality in cerebral tissues present within Magnetic Resonance Images (MRI). Present classifier is comprised of cerebral tissue extraction, image division into angular and distance span vectors, acquirement of four features for each portion and classification to ascertain the abnormality location. The threshold value and region of interest are discerned using operator input and Otsu algorithm. Novel brain slices image division is introduced via angular and distance span vectors of sizes 24˚ with 15 pixels. Rotation invariance of the angular span vector is determined. An automatic image categorization into normal and abnormal brain tissues is performed using Support Vector Machine (SVM). St

Background/Objectives: The purpose of this study was to classify Alzheimer’s disease (AD) patients from Normal Control (NC) patients using Magnetic Resonance Imaging (MRI). Methods/Statistical analysis: The performance evolution is carried out for 346 MR images from Alzheimer's Neuroimaging Initiative (ADNI) dataset. The classifier Deep Belief Network (DBN) is used for the function of classification. The network is trained using a sample training set, and the weights produced are then used to check the system's recognition capability. Findings: As a result, this paper presented a novel method of automated classification system for AD determination. The suggested method offers good performance of the experiments carried out show that the