It is no secret to anyone the lofty classifications and wonderful investigations made by Muslim scholars in various eras, with which they removed the dust of ignorance from the nation, clarified the argument, and illuminated the path of education, especially in the legal sciences, which are the foundation of religion.

It is the life of hearts and the path of grammarians in this world and the hereafter.

Among those scientific classifications are the investigations they have written in the science of the principles of legislation, which have established the general evidence and the original rules to which practical legal rulings are referred. And as you know, it is the basis of Islamic jurisprudence, a means of knowing its

Borrowing in linguistics refers to the process whereby a group of speakers incorporates certain foreign linguistic components into their home language via a process known as linguistic borrowing. The process by which these foreign linguistic elements, known as loanwords, go through phonological, morphological, or semantic changes in order for them to fit the grammar of the recipient language is referred to as loanword adaptation. Loanwords go through these changes in order for them to become compatible with the grammar of the recipient language. One of the most divisive topics in loanword phonology is whether adaptations occur at the phonemic or phonetic levels, and current literature distinguishes three primary viewpoints: nativiza

General Background: Deep image matting is a fundamental task in computer vision, enabling precise foreground extraction from complex backgrounds, with applications in augmented reality, computer graphics, and video processing. Specific Background: Despite advancements in deep learning-based methods, preserving fine details such as hair and transparency remains a challenge. Knowledge Gap: Existing approaches struggle with accuracy and efficiency, necessitating novel techniques to enhance matting precision. Aims: This study integrates deep learning with fusion techniques to improve alpha matte estimation, proposing a lightweight U-Net model incorporating color-space fusion and preprocessing. Results: Experiments using the AdobeComposition-1k

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p

The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog

Deep Learning Techniques For Skull Stripping of Brain MR Images

The Purpose of this research is a comparison between two types of multivariate GARCH models BEKK and DVECH to forecast using financial time series which are the series of daily Iraqi dinar exchange rate with dollar, the global daily of Oil price with dollar and the global daily of gold price with dollar for the period from 01/01/2014 till 01/01/2016.The estimation, testing and forecasting process has been computed through the program RATS. Three time series have been transferred to the three asset returns to get the Stationarity, some tests were conducted including Ljung- Box, Multivariate Q and Multivariate ARCH to Returns Series and Residuals Series for both models with comparison between the estimation and for