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      The present study was designed to estimate the active ingredients in the aqueous extract of fennel Foeniculum vulgare L. fruits and test the effects of different concentrations of the extract on serum estrogen and prolactin levels in female rats.  The work was conducted to prepare the aqueous extract in the laboratory, while the secondary active substances in the extract were estimated using High-Performance Liquid Chromatography (HPLC) technology. The experiments were conducted in the animal house of the College of Science, Tikrit university,on a total of 12 adult albino virgin female rats divided into four groups, each having three rats.The aqueous extract of the fruit plant was administrate

Researchers employ behavior based malware detection models that depend on API tracking and analyzing features to identify suspected PE applications. Those malware behavior models become more efficient than the signature based malware detection systems for detecting unknown malwares. This is because a simple polymorphic or metamorphic malware can defeat signature based detection systems easily. The growing number of computer malwares and the detection of malware have been the concern for security researchers for a large period of time. The use of logic formulae to model the malware behaviors is one of the most encouraging recent developments in malware research, which provides alternatives to classic virus detection methods. To address the l

In this paper, new brain tumour detection method is discovered whereby the normal slices are disassembled from the abnormal ones. Three main phases are deployed including the extraction of the cerebral tissue, the detection of abnormal block and the mechanism of fine-tuning and finally the detection of abnormal slice according to the detected abnormal blocks. Through experimental tests, progress made by the suggested means is assessed and verified. As a result, in terms of qualitative assessment, it is found that the performance of proposed method is satisfactory and may contribute to the development of reliable MRI brain tumour diagnosis and treatments.

Recently, the phenomenon of the spread of fake news or misinformation in most fields has taken on a wide resonance in societies. Combating this phenomenon and detecting misleading information manually is rather boring, takes a long time, and impractical. It is therefore necessary to rely on the fields of artificial intelligence to solve this problem. As such, this study aims to use deep learning techniques to detect Arabic fake news based on Arabic dataset called the AraNews dataset. This dataset contains news articles covering multiple fields such as politics, economy, culture, sports and others. A Hybrid Deep Neural Network has been proposed to improve accuracy. This network focuses on the properties of both the Text-Convolution Neural

يعد علم التدريب الرياضي الحديث عملية تربوية علمية مبنية على اسس صحيحة هدفها وصول اللاعبين الى التكامل في الاداء الفني وهذا يتم عن طريق التأثير المنظم والدقيق بواسطة استعمال التمارين البدنية التي تحدث تغيرات خاصة في عمل اعضاء واجهزة جسم الرياضي والتي بدورها تؤدي الى رفع كفاءة الاعضاء والاجهزة لتحقيق الانجازات الرياضية العالية ولقد استعملت الباحثتان اسلوب حديث من اساليب التدريب الرياضي من اجل تطوير تحم

The increase in the Iraqi population put pressure on urban cities as there were no new cities built since the 1980s due to the wars and the economic blockade imposed in 1991 and the deteriorating security situation after 2003, where the population in 2018 reached about forty million people. Iraq also suffered during the past decades from problems and challenges in many respects that affected the local environment, and the constructed buildings had a role in increasing these impacts, so the Ministry of Housing worked to issue the Iraqi Green Architecture Code in 2019 to reduce damage to the environment and use resources more efficiently. And because the constructed buildings were not constructed according to green standards, including Bas

The purpose of this paper is to model and forecast the white oil during the period (2012-2019) using volatility GARCH-class. After showing that squared returns of white oil have a significant long memory in the volatility, the return series based on fractional GARCH models are estimated and forecasted for the mean and volatility by quasi maximum likelihood QML as a traditional method. While the competition includes machine learning approaches using Support Vector Regression (SVR). Results showed that the best appropriate model among many other models to forecast the volatility, depending on the lowest value of Akaike information criterion and Schwartz information criterion, also the parameters must be significant. In addition, the residuals