The problem of the current research as the Iraqi Drilling Company suffers from the many challenges facing its work as a result of the current conditions in Iraq and the subsequent changes in the policies of the State Which left an impact on its activities and objectives, which created pressure on the importance of an organizational power capable of diagnosing the core competencies and development and maintain them to contribute to the raising and success in the performance of the company. The researchers adopted the method of analytical research and adopted the questionnaire as a main tool in the collection of research data and information . It was distributed to a community of (140) individuals from the low mana

The research aims to build a training program to develop the willpower in the kindergarten children. In order to achieve the objective of the research, the two researchers have developed a Training Program according to the following steps:

- Determining the general objective of the Training Program.

- Determining the behavioral objectives of the Training Program.

- Determining the content of the Training Program.

- Implementing the content of the Training Program sessions.

- Evaluating the Training Program.

The training program consisted of (15) sessions, each session included a set of parts (title, general objective, methods, time and place to implement the sessions, behavioral goals, tool

Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a

In present work the effort has been put in finding the most suitable color model for the application of information hiding in color images. We test the most commonly used color models; RGB, YIQ, YUV, YCbCr1 and YCbCr2. The same procedures of embedding, detection and evaluation were applied to find which color model is most appropriate for information hiding. The new in this work, we take into consideration the value of errors that generated during transformations among color models. The results show YUV and YIQ color models are the best for information hiding in color images.