Abstract : This research is a field study of international cities that hosted various events of which (Sports, Exhibitions, Scientific, Cultural) and other events, these positive impacts on the city driven by such hosts were identified. The research goal was to support hosting events to improve the hostess city and to draw future plans for further developments and to invest event’s hosting to strengthen the city’s value according to a strategic vision that looks for the future the most important conclusions are Entering events hosting is a part of urban development strategies and The most important recommendations are coordination between hosting event activities and its facilities with the infrastructure structure of the host city, its

Image pattern classification is considered a significant step for image and video processing. Although various image pattern algorithms have been proposed so far that achieved adequate classification, achieving higher accuracy while reducing the computation time remains challenging to date. A robust image pattern classification method is essential to obtain the desired accuracy. This method can be accurately classify image blocks into plain, edge, and texture (PET) using an efficient feature extraction mechanism. Moreover, to date, most of the existing studies are focused on evaluating their methods based on specific orthogonal moments, which limits the understanding of their potential application to various Discrete Orthogonal Moments (DOM

تزخر ذاكرة بغداد ب سماء العديد من الشخصيا ث في الناشطة والبارزة والتردي
لهذا الاثر البارز والمثير في ميدان الابداع و

The research included the statement of some prophetic
guidelines for preserving food and drink from contamination that
causes diseases, which originate either viruses and microbes, or
insects and vermin spread in the air,

Background Solar irradiance is a nonlinear and intermittent function, which makes accurate forecasting of solar power generation a challenge. The high variability of meteorological conditions is not well represented by conventional atmospheric models, thus hampering forecasting skill and model robustness. In this work, an advanced hybridization of multi-population cuckoo search (HMPCS) algorithm with machine learning (ML) methods is developed to enhance the prediction performance of photovoltaic (PV) power forecasting with more reliability. Methods In this study, a hybrid modeling framework is proposed, called HMPCS–ML framework which captures the global search capacity of HMPCS and predictive power of sophisti

The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi

The research aimed to measure the reality of monetary policy and its role in neutralizing the impact of fluctuations in total domestic oil prices, through the most important monetary policy variable (money supply). An example of this is using a simple technique in the previous example, turning it into a straightforward user interface by (Judd and Kunee). After estimating the impact of the policy with the domestic gross domestic oil prices in Iraq, the effect of fluctuations in the domestic gross domestic oil prices in the simple regression model, while the morale of oil prices was not proven with a negative sign, while the morale of money supply and their impact on the increase of the domestic was proven in the multiple regressio