Support vector machines (SVMs) are supervised learning models that analyze data for classification or regression. For classification, SVM is widely used by selecting an optimal hyperplane that separates two classes. SVM has very good accuracy and extremally robust comparing with some other classification methods such as logistics linear regression, random forest, k-nearest neighbor and naïve model. However, working with large datasets can cause many problems such as time-consuming and inefficient results. In this paper, the SVM has been modified by using a stochastic Gradient descent process. The modified method, stochastic gradient descent SVM (SGD-SVM), checked by using two simulation datasets. Since the classification of different ca

The most famous phenomena in the media administration, is the transfer of power, from the decision-makers inside the administration, especially those who occupy administrative positions by law, to those who enjoy a natural influence based on knowledge, skill and competence, especially on the psychological, political and social sides.

The old way of dividing the administration into heads and hands is no longer accepted, and the separation of thinking and work based on a traditional format has become rather refused.

Also, a chapter like this one might become a constant routine, but it doesn’t keep pace with the development of field concepts and practices of administrations. The logic of modern administration emphasizes th

 compensation those affected by technological development risks within the scope of medical works.       

The unprecedented technological development the world witnesses nowadays has been providing brilliant medical service to the human being including examination, diagnosis and the treatment or follow up.

However, such works hide behind potential risks threatening people's lives and such risks my be discovered within the limits of now – how and technical knowledge prevailing the time of rendering the medical service. Also this the question is raised on how to keep up between the safety of the patients and such risks are being unknown by the provider and questioning them contradic

إن التطور التقني غير المسبوق الذي يشهده العالم في الآونة الأخيرة ، قدم للإنسان أعمالاً طبية جليلة من فحص أو تشخيص أو معالجة أو متابعة .إلا إن هذه الأعمال تحمل بذات الوقت في طياتها أخطاراً كامنة تهدد حياة الناس وقد لا يكون من الممكن اكتشافها في حدود المعرفة العلمية والفنية السائدة وقت تقديم الخدمة الطبية وهذا ما يثير التساؤل عن كيفية التوفيق بين سلامة المتضررين ( المرضى) وبين كون هذه المخاطر غير معلومة أو معروف

As the process of  estimate for model and variable selection significant is a crucial process in the semi-parametric modeling At the beginning of the modeling process often At there are many explanatory variables to Avoid the loss of any explanatory elements may be important as a result , the selection of significant variables become necessary , so the process of variable selection is not intended to simplifying  model complexity explanation , and also predicting. In this research was to use some of the semi-parametric methods (LASSO-MAVE , MAVE and The proposal method (Adaptive LASSO-MAVE) for variable selection and estimate semi-parametric single index model (SSIM) at the same time .

   Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct

بعد مرور عام واكثر على ظهور فيروس كورونا والعالم يواجه تسونامي تلك الجائحة التي تكاد تعصف بالوجود البشري برمته لذا اخذت البشرية على عاتقها مواصلة الجهود والابحاث للوصول الى علاج طبي يتم من خلاله مواجهة هذا الفيروس او التقليل من آثاره التي تهدد الحياة البشرية بالموت المباشر دون الاصابة، مما نتج عن تلك الجهود مجموعة من اللقاحات التي تبنتها الشركات العالمية المسؤولة الا ان الضرورة القصوى التي نتجت عنها تلك الل

The emergence of the computer and the expansion of the use of the Internet in all areas of life The various negative effects and risks arising from this expansion will appear, as the greater the reliance On these technologies in development, the greater the risks associated with protecting information, and with increasing Global reliance on information and communication technology, especially with regard to the governmental aspect Countries are vulnerable to attacks through cyberspace, as it has become and public facilities, it has also increased This space is vulnerable to violations by network intruders, whether they are countries, organizations or individuals. Hence, it has become necessary to research the legality of using this type

The Machine learning methods, which are one of the most important branches of promising artificial intelligence, have great importance in all sciences such as engineering, medical, and also recently involved widely in statistical sciences and its various branches, including analysis of survival, as it can be considered a new branch used to estimate the survival and was parallel with parametric, nonparametric and semi-parametric methods that are widely used to estimate survival in statistical research. In this paper, the estimate of survival based on medical images of patients with breast cancer who receive their treatment in Iraqi hospitals was discussed. Three algorithms for feature extraction were explained: The first principal compone

         The issue of penalized regression model has received considerable critical attention to variable selection. It plays an essential role in dealing with high dimensional data. Arctangent denoted by the Atan penalty has been used in both estimation and variable selection as an efficient method recently. However, the Atan penalty is very sensitive to outliers in response to variables or heavy-tailed error distribution. While the least absolute deviation is a good method to get robustness in regression estimation. The specific objective of this research is to propose a robust Atan estimator from combining these two ideas at once. Simulation experiments and real data applications show that the p