ان الغرض من هذا البحث هو المزج بين القيود الضبابية والاحتمالية. كما يهدف الى مناقشة اكثر حالات مشكلات البرمجة الضبابية شيوعا وهي عندما تكون المشكلة الضبابية تتبع دالة الانتماء مرة دالة الاتنماء المثلثية مرة اخرى، من خلال التطبيق العملي والتجريبي. فضلا عن توظيف البرمجة الخطية الضبابية في معالجة مشكلات تخطيط وجدولة الإنتاج لشركة العراق لصناعة الأثاث، وكذلك تم استخدام الطرائق الكمية للتنبؤ بالطلب واعتماده

The evolution in the field of Artificial Intelligent (AI) with its training algorithms make AI very important in different aspect of the life. The prediction problem of behavior of dynamical control system is one of the most important issue that the AI can be employed to solve it. In this paper, a Convolutional Multi-Spike Neural Network (CMSNN) is proposed as smart system to predict the response of nonlinear dynamical systems. The proposed structure mixed the advantages of Convolutional Neural Network (CNN) with Multi -Spike Neural Network (MSNN) to generate the smart structure. The CMSNN has the capability of training weights based on a proposed training algorithm. The simulation results demonstrated that the proposed

The current study was designed to compare some of the vital markers in the sera of diabetic and neuropathy patients via estimating Adipsin, Fasting blood Glucose(FBG), Glycated(HbA1c) hemoglobin, Homeostasis Model Assessment Index (Homa IR ), Cholesterol, High density lipoprotein (HDL), Triglycerides (T.G), Low-density, and lipoprotein (LDL), Very Low Density Lipoprotein (VLDL), in sera of Iraqi patients with diabetes and neuropathy. A total  of ninety  subjects  were divided into three groups: group I (30 diabetic with neuropathy males) and group II (30 diabetic males without neuropathy), and 30 healthy sujects were employed as control group. The results showed a significant decline in Adipsin levels (p>0.05) in neuropathy, T2DM g

The paper proposes a methodology for predicting packet flow at the data plane in smart SDN based on the intelligent controller of spike neural networks(SNN). This methodology is applied to predict the subsequent step of the packet flow, consequently reducing the overcrowding that might happen. The centralized controller acts as a reactive controller for managing the clustering head process in the Software Defined Network data layer in the proposed model. The simulation results show the capability of Spike Neural Network controller in SDN control layer to improve the (QoS) in the whole network in terms of minimizing the packet loss ratio and increased the buffer utilization ratio.

Abstract

Characterized by the Ordinary Least Squares (OLS) on Maximum Likelihood for the greatest possible way that the exact moments are known , which means that it can be found, while the other method they are unknown, but approximations to their biases correct to 0(n^-1) can be obtained by standard methods. In our research expressions for approximations to the biases of the ML estimators (the regression coefficients and scale parameter) for linear (type 1) Extreme Value Regression Model for Largest Values are presented by using the advanced approach depends on finding the first derivative, second and third.

This paper proposes a new structure of the hybrid neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Weight parameters of the hybrid neural structure with its serial-parallel configuration are adapted by using the Back propagation learning algorithm. The ability of the proposed hybrid neural structure for nonlinear system has achieved a fast learning with minimum number

The current research aims to identify the self-regulation of university students, as well as to identify the significance of the difference in self-regulation according to the variable of sex (male-female), specialization (scientific-human), and grade (first-fourth). To achieve the research objectives, the two researchers developed a scale of (28) items about self-regulation According to the theory of (Pandora, 1991). The scale was administered to (500) students from the first and fourth stages of Al -Mustansiriyah University who were selected based on the random stratification method for the 2020/2021 academic year. The results showed that university students have a good level of self-regulation. There are no significant differences in