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.
Biodiversity is one of the important biological factors in determining water quality and maintaining the
ecological balance. In this study, there are 223 species of phytoplankton were identified, and they are as
follows: 88 species of Bacillariophyta and were at 44%,70 species of Chlorophyta and they were at 29 %, 39
species of Cyanophyta and they were at 16 %, 12 species of Euglenozoa and they were at 4 %, four species of
Miozoa and they were at 3 %, and, Phylum Charophyta and Ochrophyta were only eight and two species,
respectively and both of them were at 2%. The common phytoplankton recorded in the sites studied
include Nitzschia palea, Scenedesmus quadricauda, Oscillatoria princeps, and Peridinium
Permanent deformation in asphalt concrete pavements is pervasive distress [1], influenced by various factors such as environmental conditions, traffic loading, and mixture properties. A meticulous investigation into these factors has been conducted, yielding a robust dataset from uniaxial repeated load tests on 108 asphalt concrete samples. Each sample underwent systematic evaluation under varied test temperatures, loading conditions, and mixture properties, ensuring the data’s comprehensiveness and reliability. The materials used, sourced locally, were selected to enhance the study ʼs relevance to pavement constructions in hot climate areas, considering different asphalt cement grades and con- tents to understand material variability ef
... Show MoreAbstract
Black paint laser peening (bPLP) technique is currently applied for many engineering materials , especially for aluminum alloys due to high improvement in fatigue life and strength . Constant and variable bending fatigue tests have been performed at RT and stress ratio R= -1 . The results of the present work observed that the significance of the surface work hardening which generated high negative residual stresses in bPLP specimens .The fatigue life improvement factor (FLIF) for bPLP constant fatigue behavior was from 2.543 to 3.3 compared to untreated fatigue and the increase in fatigue strength at 107 cycle was 21% . The bPLP cumulative fatigue life behav
... Show MoreBackground: Sprite coding is a very effective technique for clarifying the background video object. The sprite generation is an open issue because of the foreground objects which prevent the precision of camera motion estimation and blurs the created sprite. Objective: In this paper, a quick and basic static method for sprite area detection in video data is presented. Two statistical methods are applied; the mean and standard deviation of every pixel (over all group of video frame) to determine whether the pixel is a piece of the selected static sprite range or not. A binary map array is built for demonstrating the allocated sprite (as 1) while the non-sprite (as 0) pixels valued. Likewise, holes and gaps filling strategy was utilized to re
... Show MoreThis paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance
... Show MoreAbstract
The problem of missing data represents a major obstacle before researchers in the process of data analysis in different fields since , this problem is a recurrent one in all fields of study including social , medical , astronomical and clinical experiments .
The presence of such a problem within the data to be studied may influence negatively on the analysis and it may lead to misleading conclusions , together with the fact that these conclusions that result from a great bias caused by that problem in spite of the efficiency of wavelet methods but they are also affected by the missing of data , in addition to the impact of the problem of miss of accuracy estimation
... Show More