Several correlations have been proposed for bubble point pressure, however, the correlations could not predict bubble point pressure accurately over the wide range of operating conditions. This study presents Artificial Neural Network (ANN) model for predicting the bubble point pressure especially for oil fields in Iraq. The most affecting parameters were used as the input layer to the network. Those were reservoir temperature, oil gravity, solution gas-oil ratio and gas relative density. The model was developed using 104 real data points collected from Iraqi reservoirs. The data was divided into two groups: the first was used to train the ANN model, and the second was used to test the model to evaluate their accuracy and trend stability

Computer vision seeks to mimic the human visual system and plays an essential role in artificial intelligence. It is based on different signal reprocessing techniques; therefore, developing efficient techniques becomes essential to achieving fast and reliable processing. Various signal preprocessing operations have been used for computer vision, including smoothing techniques, signal analyzing, resizing, sharpening, and enhancement, to reduce reluctant falsifications, segmentation, and image feature improvement. For example, to reduce the noise in a disturbed signal, smoothing kernels can be effectively used. This is achievedby convolving the distributed signal with smoothing kernels. In addition, orthogonal moments (OMs) are a cruc

Summary of the research : Our research tagged (Arabic language in the media between warning and development) attempts to follow the most prominent phenomena that accompanied the evolution of the use of Arabic language in the media with the development of these means and spread technically globally, and how divided researchers and linguists and intellectuals Arabs into two teams, each demanding what contradicts the other, in the matter The use of the Arabic language in the media, and the arguments of each team in the need to deal with the media as one of the pillars of the nation culturally, historically and civilized, in order to enhance its position and maintain unity, continued the research highlighted the positions of hard-lin

Biaxial hollow slab is a reinforced concrete slab system with a grid of internal spherical voids included to reduce the self-weight. This paper presents an experimental study of behavior of one-way prestressed concrete bubbled slabs. Twelve full-scale one-way concrete slabs of (3000mm) length with rectangular cross-sectional area of (460mm) width and (150mm) depth. Different parameters like type of specimen (solid or bubbled slabs), type of reinforcement (normal or prestress), range of PPR and diameter of plastic spheres (100 or 120mm) are considered. Due to the using of prestressing force in bubbled slabs (with ratio of plastic sphere diameter D to slab thickness H, D/H=0.67), the specimens showed an increase in ultimat

The research is conducted on target of investigating the role of growth strategy via diversification in value maximization of a firm in terms of controversies literatures had witnessed. Using a descriptive approach for analyzing and verifying the harmony of variables of research and their conceptualized logic , it could be reached to many conclusions agreed in their essence upon that the related diversification has the major role in value maximization of a firm and the wealth of its owners .                       

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob

Abstract

       Although the subject of biofuels industry is linked directly to the energy sector, but has links and numerous indirect effects, in particular effects on the environment and agriculture, this study (opportunities and challenges of biofuels industry and impact on the development of the agricultural sector in developing countries) a modest step to identify the industry in detail and identify the types of products and raw materials entering, then define or limit the positive and negative impacts of this industry in General and for specific products In particular, detailed, and then flip all those effects on the agricultural sector in developing countries can benef