With the rapid development of smart devices, people's lives have become easier, especially for visually disabled or special-needs people. The new achievements in the fields of machine learning and deep learning let people identify and recognise the surrounding environment. In this study, the efficiency and high performance of deep learning architecture are used to build an image classification system in both indoor and outdoor environments. The proposed methodology starts with collecting two datasets (indoor and outdoor) from different separate datasets. In the second step, the collected dataset is split into training, validation, and test sets. The pre-trained GoogleNet and MobileNet-V2 models are trained using the indoor and outdoor se

The research aims to recognize the impact of the training program based on integrating future thinking skills and classroom interaction patterns for mathematics teachers and providing their students with creative solution skills. To achieve the goal of the research, the following hypothesis was formulated: There is no statistically significant difference at the level (0.05) between the mean scores of students of mathematics teachers whose teachers trained according to the proposed training program (the experimental group) and whose teachers were not trained according to the proposed training program (the control group) in Pre-post creative solution skills test. Research sample is consisted of (31) teachers and schools were distribut

The current research discusses the topic of the formal data within the methodological framework through defining the research problem, limits and objectives and defining the most important terms mentioned in this research. The theoretical framework in the first section addressed (the concept of the Bauhaus school, the philosophy of the Bauhaus school and the logical bases of this school). The second section dealt with (the most important elements and structural bases of the Bauhaus school) which are considered the most important formal data of this school and their implications on the fabrics and costumes design. The research came up with the most important indicators resulting from the theoretical framework.
Chapter three defined the

Traumatic spinal cord injury is a serious neurological disorder. Patients experience a plethora of symptoms that can be attributed to the nerve fiber tracts that are compromised. This includes limb weakness, sensory impairment, and truncal instability, as well as a variety of autonomic abnormalities. This article will discuss how machine learning classification can be used to characterize the initial impairment and subsequent recovery of electromyography signals in an non-human primate model of traumatic spinal cord injury. The ultimate objective is to identify potential treatments for traumatic spinal cord injury. This work focuses specifically on finding a suitable classifier that differentiates between two distinct experimental

Discriminant analysis is a technique used to distinguish and classification an individual to a group among a number of  groups based on a linear combination of a set of relevant variables know discriminant function. In this research  discriminant analysis used to analysis data from repeated measurements design. We  will  deal  with the problem of  discrimination  and  classification in the case of  two  groups by assuming the Compound Symmetry covariance structure  under  the  assumption  of  normality for  univariate  repeated measures data.

Acute appendicitis is the most common surgical abdominal emergency. Its clinical diagnosis remains a challenge to surgeons, so different imaging options were introduced to improve diagnostic accuracy. Among these imaging modality choices, diagnostic medical sonography (DMS) is a simple, easily available, and cost effective clinical tool. The purpose of this study was to assess the accuracy of DMS, in the diagnosis of acute appendicitis compared to the histopathology report, as a gold standard. Between May 2015 and May 2016, 215 patients with suspected appendicitis were examined with DMS. The DMS findings were recorded as positive and negative for acute appendicitis and compared with the histopathological results, as a gold standard

In recent years, data centre (DC) networks have improved their rapid exchanging abilities. Software-defined networking (SDN) is presented to alternate the impression of conventional networks by segregating the control plane from the SDN data plane. The SDN presented overcomes the limitations of traditional DC networks caused by the rapidly incrementing amounts of apps, websites, data storage needs, etc. Software-defined networking data centres (SDN-DC), based on the open-flow (OF) protocol, are used to achieve superior behaviour for executing traffic load-balancing (LB) jobs. The LB function divides the traffic-flow demands between the end devices to avoid links congestion. In short, SDN is proposed to manage more operative configur

The aim of this study is to estimate the parameters and reliability function for kumaraswamy distribution of this two positive parameter  (a,b > 0), which is a continuous probability that has many characterstics with the beta distribution with extra advantages.

The shape of the function for this distribution and the most important characterstics are explained and estimated the two parameter (a,b) and the reliability function for this distribution by using the maximum likelihood method (MLE) and Bayes methods. simulation experiments are conducts to explain the behaviour of the estimation methods for different sizes depending on the mean squared error criterion the results show that the Bayes is bet