The aim of this study is to use style programming goal and technical programming goal fuzzy to study assessing need annual accurately and correctly depending on the data and information about the quantity the actual use of medicines and medical supplies in all hospitals and health institutions during a certain period where they were taking the company public for the marketing of medicines and medical supplies sample for research. Programming model was built goal to this problem, which included (15) variable decision, (19) constraint and two objectives:
1 - rational exchange of budget allocated for medicines and supplies.
2 - ensure that the needs of patients of medicines and supplies needed to improve
Research Summary :
Praise be to God, Lord of the Worlds, and prayers and peace be upon the Master of the Messengers, his family and all his companions, then after:
This is brief research that contained between its two covers one of the jurisprudence rules derived from Islamic Sharia that guarantees the right of others, in case of forcing to do the prohibited act, and it is a restriction of the rule: “Necessities allow prohibitions” and “Hardship brings facilitation” and support for the rule: “Necessities are valued.” It is an origin in alleviating the taxpayer definitely , and the study has briefly shown some of the jurisprudential appli
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... Show MoreThe 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
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