Often there is no well drilling without problems. The solution lies in managing and evaluating these problems and developing strategies to manage and scale them. Non-productive time (NPT) is one of the main causes of delayed drilling operations. Many events or possibilities can lead to a halt in drilling operations or a marginal decrease in the advancement of drilling, this is called (NPT). Reducing NPT has an important impact on the total expenditure, time and cost are considered one of the most important success factors in the oil industry. In other words, steps must be taken to investigate and eliminate loss of time, that is, unproductive time in the drilling rig in order to save time and cost and reduce wasted time. The data of

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

Abstract
On 11/1/2008 amounts of snows fell on various sections of Iraq, one of
which is Baghdad. The analysis process of climatic maps proved that the
advance trough of Sudanese depression towards the city is the reason behind
the formation of this weather state supported by could trough in the (500) mb
pressure level. .
The research concluded that the phenomenon of global warming
witnessed by the world recently had a main role in the occurrence of this
phenomenon due to the raise in earth temperature as a result of evaporation
rate increase leading to an increase in water vapor and cloud formation with
high tops and low bases which form sows with high rates accumulating at high
and moderate latitudes le

The results of theoretical and experimental investigations carried out to study the effect of load and relative sliding speed on the abrasive wear behavior in drilling bit teeth surfaces of an insert tungsten carbide bit have been presented. Experimentally, an apparatus for abrasive wear tests conducted on the modified ASTM-G65 was modified and fabricated to facilitate loading and measurement of wear rate for the sand/ steel wheel abrasion test, which involves two cases of contact; first is at dry sand and second is under wet condition. These tests have been carried under varied operating parameters of normal load and sliding speed. A theoretical model based upon the Archard equation has been developed for predicting wear simulation by u

Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep lea

Visual analytics becomes an important approach for discovering patterns in big data. As visualization struggles from high dimensionality of data, issues like concept hierarchy on each dimension add more difficulty and make visualization a prohibitive task. Data cube offers multi-perspective aggregated views of large data sets and has important applications in business and many other areas. It has high dimensionality, concept hierarchy, vast number of cells, and comes with special exploration operations such as roll-up, drill-down, slicing and dicing. All these issues make data cubes very difficult to visually explore. Most existing approaches visualize a data cube in 2D space and require preprocessing steps. In this paper, we propose a visu