This paper deals with the process of evaluating the performance of agricultural activity in Iraq and in particular the agricultural initiative launched by Prime Minister Nuri al-Maliki, during the period from 2008 - 2011. Where it is possible to use criteria or indicators that are fabricated by a statement on the calendar and the results of which may be by comparing planned performance with actual performance or the evaluation of the actual performance of successive periods selected. With an emphasis on the agricultural initiative is subject to the evaluation process by implementing and destinations specialized loan funds its own projects as one of the agricultural lending. With the need to co

The research aims to identify Determination talent management dimensions of (attracting talent, developing talent, directing talent, performance management talent, retain talent) in the entrepreneurial performance of the organization dimensions of (advance planning, efficiency, effectiveness), so search occupies the extreme importance of being treated important and recent issue of the performance Entrepreneuria, management talent, aware of the importance of the subject and expected results of the company surveyed, was an analysis of data obtained through field visits in addition to the questionnaire and interviews, highlights were the results that have been reached to take the sample into account all the management requirements knack of att

The current research aims to analyze the role of participatory budgeting in improving performance, especially during crises such as the Covid-19 crisis. The research used the descriptive analytical method to reach the results by distributing 100 questionnaires to a number of employees in Iraqi joint stock companies and at multiple administrative levels. The research came to several important conclusions, the most important of which is that the bottom-up approach to budgeting produces more achievable budgets than the top-down approach, which is imposed on the company by senior management with much less employee participation. Additionally, there is a better information flow from the lower levels of the organization to the upper management

Text based-image clustering (TBIC) is an insufficient approach for clustering related web images. It is a challenging task to abstract the visual features of images with the support of textual information in a database. In content-based image clustering (CBIC), image data are clustered on the foundation of specific features like texture, colors, boundaries, shapes. In this paper, an effective CBIC) technique is presented, which uses texture and statistical features of the images. The statistical features or moments of colors (mean, skewness, standard deviation, kurtosis, and variance) are extracted from the images. These features are collected in a one dimension array, and then genetic algorithm (GA) is applied for image clustering.

Calcifying epithelial odontogenic tumour (CEOT) is a benign odontogenic neoplasm of epithelial origin that secretes an amyloid‐like protein tending towards calcification. This study aims to describe a case series from Iraq of one of the rarest odontogenic tumours.

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Background: Few updated retrospective histopathological-based studies in Iraq evaluate a comprehensive spectrum of oro-maxillofacial lesions. Also, there was a need for a systematic way of categorizing the diseases and reporting results in codes according to the WHO classification that helps occupational health professionals in the clinical-epidemiological approach.

Objectives: to establish an electronic archiving database according to the ICD-10 that encompasses oro-maxillofacial lesions in Sulaimani city for the last 12 years, then to study the prevalence trend and correlation with clinicopathological parameters.

Subjects and Methods:  A descri