Abstract

The research study about the empowerment as an independent variable, in which details include (training and improvement, incentives, information sharing, trust, and delegation), has also focused on the performance of the service organization as a dependent variable in all dimensions which include (improve work efficiency, building the core competencies, focus on the beneficiary of the service, increasing the feeling of satisfaction of the employees, and the organizational support commitment). The research has been based on the opinions of a chosen sample of 75 service officers of the Ministry of Interior who work at the General Directorate of Traffic. The research problem has been identified by t

Ferritin is a key organizer of protected deregulation, particularly below risky hyperferritinemia, by straight immune-suppressive and pro-inflammatory things. , We conclude that there is a significant association between levels of ferritin and the harshness of COVID-19. In this paper we introduce a semi- parametric method for prediction by making a combination between NN and regression models. So, two methodologies are adopted, Neural Network (NN) and regression model in design the model; the data were collected from مستشفى دار التمريض الخاص for period 11/7/2021- 23/7/2021, we have 100 person, With COVID 12 Female & 38 Male out of 50, while 26 Female & 24 Male non COVID out of 50. The input variables of the NN m

<span>Dust is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. In the recent decade, climate change in Iraq, typified by increased droughts and deserts, has generated numerous environmental issues. This study forecasts dust in five central Iraqi districts using machine learning and five regression algorithm supervised learning system framework. It was assessed using an Iraqi meteorological organization and seismology (IMOS) dataset. Simulation results show that the gradient boosting regressor (GBR) has a mean square error of 8.345 and a total accuracy ratio of 91.65%. Moreover, the results show that the decision tree (DT), where the mean square error is 8.965, c

    This research aims to review the importance of estimating the nonparametric regression function using so-called Canonical Kernel which depends on re-scale the smoothing parameter, which has a large and important role in Kernel  and give the sound amount of smoothing .

We has been shown the importance of this method through the application of these concepts on real data refer to international exchange rates to the U.S. dollar against the Japanese yen for the period from January 2007 to March 2010. The results demonstrated preference the nonparametric estimator with Gaussian on the other nonparametric and parametric regression estima

In this research, the methods of Kernel estimator (nonparametric density estimator) were relied upon in estimating the two-response logistic regression, where the comparison was used between the method of Nadaraya-Watson and the method of Local Scoring algorithm, and optimal Smoothing parameter λ was estimated by the methods of Cross-validation and generalized Cross-validation, bandwidth optimal λ has a clear effect in the estimation process. It also has a key role in smoothing the curve as it approaches the real curve, and the goal of using the Kernel estimator is to modify the observations so that we can obtain estimators with characteristics close to the properties of real parameters, and based on medical data for patients with chro

    This article aims to explore the importance of estimating the a semiparametric regression function ,where we suggest a new estimator beside the other combined estimators and then we make a comparison among them by using simulation technique . Through the simulation results we find  that the suggest estimator is the best with the first and second models ,wherealse for the third model we find Burman and Chaudhuri (B&C) is best.

The purpose of this article is to improve and minimize noise from the signal by studying wavelet transforms and showing how to use the most effective ones for processing and analysis. As both the Discrete Wavelet Transformation method was used, we will outline some transformation techniques along with the methodology for applying them to remove noise from the signal. Proceeds based on the threshold value and the threshold functions Lifting Transformation, Wavelet Transformation, and Packet Discrete Wavelet Transformation. Using AMSE, A comparison was made between them , and the best was selected. When the aforementioned techniques were applied to actual data that was represented by each of the prices, it became evident that the lift

The pancreatic ductal adenocarcinoma (PDAC), which represents over 90% of pancreatic cancer cases,
has the highest proliferative and metastatic rate in comparison to other pancreatic cancer compartments. This
study is designed to determine whether small nucleolar RNA, H/ACA box 64 (snoRNA64) is associated with
pancreatic cancer initiation and progression. Gene expression data from the Gene Expression Omnibus (GEO)
repository have shown that snoRNA64 expression is reduced in primary and metastatic pancreatic cancer as
compared to normal tissues based on statistical analysis of the in Silico analysis. Using qPCR techniques,
pancreatic cancer cell lines include PK-1, PK-8, PK-4, and Mia PaCa-2 with differ

Collaborative learning in class‐based teaching presents a challenge for a tutor to ensure every group and individual student has the best learning experience. We present Group Tagging, a web application that supports reflection on collaborative, group‐based classroom activities. Group Tagging provides students with an opportunity to record important moments within the class‐based group work and enables reflection on and promotion of professional skills such as communication, collaboration and critical thinking. After class, students use the tagged clips to create short videos showcasing their group work activities, which can later be reviewed by the teacher. We report on a deployment of Group Tagging in an undergraduate Computing Scie