Abstract

were determine the problem of current research to answer the  question on the consciousness of the Ministry of Agriculture to adopt Staffing  strategy and identify the shortcomings  as the independent variable, represented by the three dimensions (recruitment , selection, placement) and its impact on high performance, as  dependent   variable is described  in  four dimensions (leadership, strategy, structure and processes, culture), in this research were  Used  analytical descriptive  style . This research aims to identify the correlation and impact  of Staffing strategy at high-performance in Ministry of Agriculture , To clarify the relationship between

Through research and research in the creative composition in the poetry of Zafer-Alhaddad it turned out to be superior in its systems, especially that he chose a linguistic and verbal texture accompanied by a cultural dictionary full of creativity and beauty helped this culture arrangement reflects the poetically wonderful and beautiful custom led to the harmony of colors and bodies painted by the poet brush the creative artist evaluates the metaphorical and metaphorical relations that would explain the poets idea, most of which were inspired by the wonderful spirit of nature, its beautiful landscapes and bright colors, especially that we do not forget the beautiful colors and the aesthetic touch that created a fascinating musical intera

Objective: To identify the effect of the cube model on visual-spatial intelligence and learning the skill of spikinging in volleyball for female students, The researchers used the experimental method by designing two equivalent groups with pre- and post-measurements. Research methodology: The main research sample of (30) female students was selected from the research community represented by second-stage students in the College of Physical Education and Sports Sciences - University of Baghdad for the academic year (2024-2025). The sample was divided equally into two control and experimental groups. The researchers conducted the sample homogenization process and the equivalence process between the two groups in the variables of visua

Imitation learning is an effective method for training an autonomous agent to accomplish a task by imitating expert behaviors in their demonstrations. However, traditional imitation learning methods require a large number of expert demonstrations in order to learn a complex behavior. Such a disadvantage has limited the potential of imitation learning in complex tasks where the expert demonstrations are not sufficient. In order to address the problem, we propose a Generative Adversarial Network-based model which is designed to learn optimal policies using only a single demonstration. The proposed model is evaluated on two simulated tasks in comparison with other methods. The results show that our proposed model is capable of completing co

Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet

Patients infected with the COVID-19 virus develop severe pneumonia, which typically results in death. Radiological data show that the disease involves interstitial lung involvement, lung opacities, bilateral ground-glass opacities, and patchy opacities. This study aimed to improve COVID-19 diagnosis via radiological chest X-ray (CXR) image analysis, making a substantial contribution to the development of a mobile application that efficiently identifies COVID-19, saving medical professionals time and resources. It also allows for timely preventative interventions by using more than 18000 CXR lung images and the MobileNetV2 convolutional neural network (CNN) architecture. The MobileNetV2 deep-learning model performances were evaluated