ملخص البحث باللغة الإنجليزية

The concept of the phenomenon of nuns: an analytical ideological study

Dr. Samia bint Yassin Al-Badri

Department of Islamic Doctrine & Contemporary Ideologies

College of Shariʿah & Islamic Studies

Qassim University

The study of concepts is one of the main pillars of doctrinal studies, in order to understand the formation of the concept, and to understand its contexts in religious sources, in order to be systematically criticized; So, this research came with the title:

The concept of the phenomenon of nuns, an analytical doctrinal study

The study concluded with resu

There are many questions that have been presented and asked in order to know who the Lady Sharifa bint Al-Imam (peace be upon her) is? And then adopting the opinions that confirm or deny the existence of Mrs. Sharifa or the place of her death, and how did it happen? As well as the question, where is the shrine of Sharifa, daughter of Imam Hassan? Who are the daughters of Imam Hassan? Did she have a grave previously, and when was the trace of this tomb known

The study aims to clarify the truth of hearing the dead, and then the statement of the legitimate judgment of the indoctrination, by reference to the evidence contained in that regard, and try to combine and reconcile those evidence, and the study finds that the most correct in the matter of hearing is to say hearing the dead in the will of God and how Almighty teaches , As up to that indoctrination of the dead is permissible, so as to combine evidence.

احكام التركز الاقتصادي للمشاريع دراسة مقارنة

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Semantic segmentation is an exciting research topic in medical image analysis because it aims to detect objects in medical images. In recent years, approaches based on deep learning have shown a more reliable performance than traditional approaches in medical image segmentation. The U-Net network is one of the most successful end-to-end convolutional neural networks (CNNs) presented for medical image segmentation. This paper proposes a multiscale Residual Dilated convolution neural network (MSRD-UNet) based on U-Net. MSRD-UNet replaced the traditional convolution block with a novel deeper block that fuses multi-layer features using dilated and residual convolution. In addition, the squeeze and execution attention mechanism (SE) and the s

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