The research discusses the need to find the innovative structures and methodologies for developing Human Capital (HC) in Iraqi Universities. One of the most important of these structures is Communities of Practice (CoPs) which contributes to develop HC by using learning, teaching and training through the conversion speed of knowledge and creativity into practice. This research has been used the comparative approach through employing the methodology of Data Envelopment Analysis (DEA) by using (Excel 2010 - Solver) as a field evidence to prove the role of CoPs in developing HC. In light of the given information, a researcher adopted on an archived preliminary data about (23) colleges at Mosul University as a deliberate sample for t

Discussed the research variables are important, privatization options and strategic analysis of the external environment, and that the purpose of the research is the trade-off between privatization options and choose the most appropriate alternative in proportion to the external environment, the research aims to determine the privatization the most appropriate option for companies and public contracting, showing the importance of the study provide the privatization of public companies as a strategy can all its way public sector organizations from the transfer of work practices or private sector organizations and mechanisms to it as contributing to improving the level of skills Develop the current and future level of performance,

Abstract

This study aimed to identify the business risks using the approach of the client strategy analysis in order to improve the efficiency and effectiveness of the audit process. A study of business risks and their impact on the efficiency and effectiveness of the audit process has been performed to establish a cognitive framework of the main objective of this study, in which the descriptive analytical method has been adopted. A survey questionnaire has been developed and distributed to the targeted group of audit firms which have profession license from the Auditors Association in the Gaza Strip (63 offices). A hundred questionnaires have been distributed to the study sample of which, a total of 84 where answered and

The purpose of this work is to study the classification and construction of (k,3)-arcs in the projective plane PG(2,7). We found that there are two (5,3)-arcs, four (6,3)-arcs, six (7,3)arcs, six (8,3)-arcs, seven (9,3)-arcs, six (10,3)-arcs and six (11,3)-arcs. All of these arcs are incomplete. The number of distinct (12,3)-arcs are six, two of them are complete. There are four distinct (13,3)-arcs, two of them are complete and one (14,3)-arc which is incomplete. There exists one complete (15,3)-arc.

This abstract focuses on the significance of wireless body area networks (WBANs) as a cutting-edge and self-governing technology, which has garnered substantial attention from researchers. The central challenge faced by WBANs revolves around upholding quality of service (QoS) within rapidly evolving sectors like healthcare. The intricate task of managing diverse traffic types with limited resources further compounds this challenge. Particularly in medical WBANs, the prioritization of vital data is crucial to ensure prompt delivery of critical information. Given the stringent requirements of these systems, any data loss or delays are untenable, necessitating the implementation of intelligent algorithms. These algorithms play a pivota

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

In every country in the world, there are a number of amputees who have been exposed to some accidents that led to the loss of their upper limbs. The aim of this study is to suggest a system for real-time classification of five classes of shoulder girdle motions for high-level upper limb amputees using a pattern recognition system. In the suggested system, the wavelet transform was utilized for feature extraction, and the extreme learning machine was used as a classifier. The system was tested on four intact-limbed subjects and one amputee, with eight channels involving five electromyography channels and three-axis accelerometer sensor. The study shows that the suggested pattern recognition system has the ability to classify the sho

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod