Variable selection is an essential and necessary task in the statistical modeling field. Several studies have triedto develop and standardize the process of variable selection, but it isdifficultto do so. The first question a researcher needs to ask himself/herself what are the most significant variables that should be used to describe a given dataset’s response. In thispaper, a new method for variable selection using Gibbs sampler techniqueshas beendeveloped.First, the model is defined, and the posterior distributions for all the parameters are derived.The new variable selection methodis tested usingfour simulation datasets. The new approachiscompared with some existingtechniques: Ordinary Least Squared (OLS), Least Absolute Shrinkage and Selection Operator (Lasso), and Tikhonov Regularization (Ridge). The simulation studiesshow that the performance of our method is better than the othersaccording to the error and the time complexity. Thesemethodsare applied to a real dataset, which is called Rock StrengthDataset.The new approach implemented using the Gibbs sampler is more powerful and effective than other approaches.All the statistical computations conducted for this paper are done using R version 4.0.3 on a single processor computer.
Quality control is an effective statistical tool in the field of controlling the productivity to monitor and confirm the manufactured products to the standard qualities and the certified criteria for some products and services and its main purpose is to cope with the production and industrial development in the business and competitive market. Quality control charts are used to monitor the qualitative properties of the production procedures in addition to detecting the abnormal deviations in the production procedure. The multivariate Kernel Density Estimator control charts method was used which is one of the nonparametric methods that doesn’t require any assumptions regarding the distribution o
... Show MoreA rapid, simple and sensitive spectrophotometric method for the determination of trace amounts of chromium (VI) was studied. The method is based on the reaction of chromium (VI) with promethazine forming a red colored species by chromium in hydrochloric acid medium and exhibits a maximum absorbance at 518 nm. A plot of absorbance with chromium (VI) gives a straight line indicating that Beer’s law has been obeyed over the range concentration of 0.05-4.0 µg/ml with a molar absorptivity of chromium(VI) 2.04  104 l.mol-1.cm-1 , Sandell’s sensitivity index of 0.0025 µg.cm-2 while the limit of detection (LOD) was found to be 0.0924 µg.ml
... Show MoreA rapid, simple and sensitive spectrophotometric method for the determination of trace amounts of chromium is studied. The method is based on the interaction of chromium with indigo carmine dye in acidic medium and the presence of oxalates as a catalyst for interaction, and after studying the absorption spectrum of the solution resulting observed decrease in the intensity of the absorption. As happened (Bleaching) for color dye, this palace and directly proportional to the chromium (VI) amount was measured intensity of the absorption versus solution was figurehead at a wavelength of 610 nm. A plot of absorbance with chromium (VI) concentration gives a straight line indicating that Beer’s law has been obeyed over the range of 0.5
... Show MoreRecurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning al
... Show MoreIntegrated reservoir rock typing in carbonate reservoirs is a significant step in reservoir modelling. The key purpose of this study is the identification of integrated rock types in the Sarvak Formation of an Iranian oilfield. In this study, electrofacies (EFAC) analysis of the Sarvak reservoir was done in detail to determine the reservoir quality and rock types of the Sarvak Formation in the studied field. The core data and conventional petrophysical logs were used for rock typing. Some petrophysical logs such as porosity, sonic, neutron, density, and Photo electric factor were applied as input data for electrofacies analysis. Multi-Resolution Graph-Based Clustering was used among six approaches, resulting in four electrofacies af
... Show MoreThe study aimed at the following:
Identify the differences in average scores core thinking skills kindergarten children by variable sex (male - female), and by variable age (5.6 - 5.11).
To achieve this researcher adopted a standardized test of core thinking skills for the kindergarten children, which was built and standardization by the researcher Meyada Asaad Mussa 2012 . applied test on a sample of (814) ) boys and girls who were randomly chosen form, from directorates of Baghdad Education Adoption of the proportional distribution.
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Face recognition is required in various applications, and major progress has been witnessed in this area. Many face recognition algorithms have been proposed thus far; however, achieving high recognition accuracy and low execution time remains a challenge. In this work, a new scheme for face recognition is presented using hybrid orthogonal polynomials to extract features. The embedded image kernel technique is used to decrease the complexity of feature extraction, then a support vector machine is adopted to classify these features. Moreover, a fast-overlapping block processing algorithm for feature extraction is used to reduce the computation time. Extensive evaluation of the proposed method was carried out on two different face ima
... Show MoreCryptography is a major concern in communication systems. IoE technology is a new trend of smart systems based on various constrained devices. Lightweight cryptographic algorithms are mainly solved the most security concern of constrained devices and IoE systems. On the other hand, most lightweight algorithms are suffering from the trade-off between complexity and performance. Moreover, the strength of the cryptosystems, including the speed of the algorithm and the complexity of the system against the cryptanalysis. A chaotic system is based on nonlinear dynamic equations that are sensitive to initial conditions and produce high randomness which is a good choice for cryptosystems. In this work, we proposed a new five-dimensional of a chaoti
... Show MoreColor image compression is a good way to encode digital images by decreasing the number of bits wanted to supply the image. The main objective is to reduce storage space, reduce transportation costs and maintain good quality. In current research work, a simple effective methodology is proposed for the purpose of compressing color art digital images and obtaining a low bit rate by compressing the matrix resulting from the scalar quantization process (reducing the number of bits from 24 to 8 bits) using displacement coding and then compressing the remainder using the Mabel ZF algorithm Welch LZW. The proposed methodology maintains the quality of the reconstructed image. Macroscopic and