This research aims to study the methods of reduction of dimensions that overcome the problem curse of dimensionality when traditional methods fail to provide a good estimation of the parameters So this problem must be dealt with directly . Two methods were used to solve the problem of high dimensional data, The first method is the non-classical method Slice inverse regression ( SIR ) method and the proposed weight standard Sir (WSIR) method and principal components (PCA) which is the general method used in reducing dimensions,    (SIR ) and (PCA) is based on the work of linear combinations of a subset of the original explanatory variables, which may suffer from the problem of heterogeneity and the problem of linear

This article examines the usage features of the phraseological units in Russian political discourse and the peculiarities of the phraseological units’ usage by politicians in their speeches.  The main focus is on the expressive and pragmatic functions of using phraseological units in political texts due to their evaluative and expressive nature.  The present article also examines the structural and stylistic changes of the phraseological units in political discourse.  Moreover, the work provides examples from the speeches of the Minister of Foreign Affairs of the Russian Federation S.V.  Lavrov.  As a result, the author comes to the conclusion that phraseological units make it possible to establish a contact be

The aim of the research is to demonstrate the concept, characteristics, efficiency and adequacy of infrastructure in Khor Al-Zubair, as it represents an important benchmark for judging the development of the city. The researchers thus aim to measure the efficiency, adequacy and personal impression of each service in Khor Al-Zubair city and its residential neighborhoods. To meet this objective, the researchers used the descriptive and analytical approach that involves randomly distributing a questionnaire with specific questions to the neighbors of the city. The study has reached a set of results and recommendations that contribute to addressing the problems to the sector of infrastructure services in the city, such as: the per capita sha

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

Image compression is a suitable technique to reduce the storage space of an image, increase the area of storage in the device, and speed up the transmission process. In this paper, a new idea for image compression is proposed to improve the performance of the Absolute Moment Block Truncation Coding (AMBTC) method depending on Weber's law condition to distinguish uniform blocks (i.e., low and constant details blocks) from non-uniform blocks in original images. Then, all elements in the bitmap of each uniform block are represented by zero. After that, the lossless method, which is Run Length method, is used for compressing the bits more, which represent the bitmap of these uniform blocks. Via this simple idea, the result is improving