In this paper, preliminary test Shrinkage estimator have been considered for estimating the shape parameter α of pareto distribution when the scale parameter equal to the smallest loss and when a prior estimate α0 of α is available as initial value from the past experiences or from quaintance cases. The proposed estimator is shown to have a smaller mean squared error in a region around α0 when comparison with usual and existing estimators.

This paper interest to estimation the unknown parameters for generalized Rayleigh distribution model based on censored samples of singly type one . In this paper the probability density function for generalized Rayleigh is defined with its properties . The maximum likelihood estimator method is used to derive the point estimation for all unknown parameters based on iterative method , as Newton – Raphson method , then derive confidence interval estimation which based on Fisher information matrix . Finally , testing whether the current model ( GRD ) fits to a set of real data , then compute the survival function and hazard function for this real data.

Autonomous motion planning is important area of robotics research. This type of planning relieves human operator from tedious job of motion planning. This reduces the possibility of human error and increase efficiency of whole process.

This research presents a new algorithm to plan path for autonomous mobile robot based on image processing techniques by using wireless camera that provides the desired image for the unknown environment . The proposed algorithm is applied on this image to obtain a optimal path for the robot. It is based on the observation and analysis of the obstacles that lying in the straight path between the start and the goal point by detecting these obstacles, analyzing and studying their shapes, positions and

Abstract

The  common types of movement disorders are ; dystonia which is a syndrome  of  repetitive muscle contractions. While , Huntington disease is autosomal dominant progressive neurodegenerative disorder, which is characterized by involuntary movements (“chorea”).

Tetrabenazine therapy has been shown to effectively control this movements compared with placebo.

Design the proper dosing approach for patients treated with tetrabenazine with genotype polymorphisms and their hepatic effect on patients.

A prospective case controlled study was carried on 50 patients whom    divided into 2 groups :first group involved 25 patients who had cho

Infectious diseases pose a global challenge, necessitating an exploration of novel methodologies for diagnostics and treatments. Since the onset of the most recent pandemic, COVID-19, which was initially identified as a worldwide health crisis, numerous countries experienced profound disruptions in their healthcare systems. To combat the spread of the COVID-19 pandemic, governments across the globe have mobilized significant efforts and resources to develop treatments and vaccines. Researchers have put forth a multitude of approaches for COVID-19 detection, treatment protocols, and vaccine development, including groundbreaking mRNA technology, among others.

This matter represents not only a scientific endeavor but also an essenti

Semantic segmentation is an exciting research topic in medical image analysis because it aims to detect objects in medical images. In recent years, approaches based on deep learning have shown a more reliable performance than traditional approaches in medical image segmentation. The U-Net network is one of the most successful end-to-end convolutional neural networks (CNNs) presented for medical image segmentation. This paper proposes a multiscale Residual Dilated convolution neural network (MSRD-UNet) based on U-Net. MSRD-UNet replaced the traditional convolution block with a novel deeper block that fuses multi-layer features using dilated and residual convolution. In addition, the squeeze and execution attention mechanism (SE) and the s

<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

In this paper we define and study new concepts of fibrewise topological spaces over B namely, fibrewise near topological spaces over B. Also, we introduce the concepts of fibrewise near closed and near open topological spaces over B; Furthermore we state and prove several Propositions concerning with these concepts.

Due to the impact of industrial companies on the ransformations and developments witnessed by the world in general nd Iraq in particular, especially in the industrial and economic Jonrnal spects, the liberalization of internal and external trade, and the idoption of the open market policy in Iraq, which led to the intensification of competition between industrial companies and heir products and services, and the increase and diversity of the Onumber of alternatives Available to the public on the one hand, and he diversity and change of their desires and needs on the other hand, which was reflected in the reality of those companies and heir products or services, especially in light of the great challenges facing companies and the deteriorat

Heart disease is a significant and impactful health condition that ranks as the leading cause of death in many countries. In order to aid physicians in diagnosing cardiovascular diseases, clinical datasets are available for reference. However, with the rise of big data and medical datasets, it has become increasingly challenging for medical practitioners to accurately predict heart disease due to the abundance of unrelated and redundant features that hinder computational complexity and accuracy. As such, this study aims to identify the most discriminative features within high-dimensional datasets while minimizing complexity and improving accuracy through an Extra Tree feature selection based technique. The work study assesses the efficac