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Design of New Hybrid Neural Structure for Modeling and Controlling Nonlinear Systems
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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 of epoch, minimum number of neurons in the hybrid network, high accuracy in the output without oscillation response as well as useful model for a one step ahead prediction controller for the nonlinear CSTR system that is used in the MATLAB simulation.

 

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Publication Date
Tue Feb 01 2022
Journal Name
Baghdad Science Journal
Numerical Solution for Linear State Space Systems using Haar Wavelets Method
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In this research, Haar wavelets method has been utilized to approximate a numerical solution for Linear state space systems. The solution technique is used Haar wavelet functions and Haar wavelet operational matrix with the operation to transform the state space system into a system of linear algebraic equations which can be resolved by MATLAB over an interval from 0 to . The exactness of the state variables can be enhanced by increasing the Haar wavelet resolution. The method has been applied for different examples and the simulation results have been illustrated in graphics and compared with the exact solution.

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Publication Date
Mon Dec 01 2008
Journal Name
Journal Of Economics And Administrative Sciences
Neural Networks as a Discriminant Purposes
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Discriminant between groups is one of the common procedures because of its ability to analyze many practical phenomena, and there are several methods can be used for this purpose, such as linear and quadratic discriminant functions. recently, neural networks is used as a tool to distinguish between groups.

In this paper the simulation is used to compare neural networks and classical method for classify observations to group that is belong to, in case of some variables that don’t follow the normal distribution. we use the proportion of number of misclassification observations to the all observations as a criterion of comparison.  

 

 

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Publication Date
Tue Jun 23 2020
Journal Name
Baghdad Science Journal
Anomaly Detection Approach Based on Deep Neural Network and Dropout
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   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

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Publication Date
Thu May 24 2018
Journal Name
Aip Conference Proceedings
The study effect of weight fraction on thermal and electrical conductivity for unsaturated polyester composite alone and hybrid
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In this research prepared two composite materials , the first prepared from unsaturated polyester resin (UP) , which is a matrix , and aluminum oxide (Al2O3) , and the second prepared from unsaturated polyester resin and aluminum oxide and copper oxide (CuO) , the two composites materials (Alone and Hybrid) of percentage weight (5,10,15)% . All samples were prepared by hand layup process, and study the electrical and thermal conductivity. The results showed decrease electrical conductivity from (10 - 2.39) ×10-15 for (Up+ Al2O3) and from (10 - 2.06)×10-15 for (Up+ Al2O3+ CuO) .But increase thermal conductivity from( 0.17 - 0.505) for (Up+ Al2O3) and from (0.17 - 0.489) for (Up+ Al2O3+ CuO).

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Publication Date
Tue Dec 01 2020
Journal Name
Iraqi Journal Of Physics
Coupled channels calculations of fusion reactions for 46Ti+64Ni, 40Ca+194Pt and 40Ar+148Sm systems
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In this work, the fusion cross section ,  fusion barrier distribution  and the probability of fusion  have been investigated by coupled channel method  for the systems 46Ti+64Ni, 40Ca+194Pt and 40Ar+148Sm with semi-classical and quantum mechanical approach using SCF and CCFULL Fortran codes respectively. The results for these calculations are compared with available experimental data. The results show that the quantum calculations agree better with experimental data, especially bellow the Coulomb barrier, for the studied systems while above this barrier, the two codes reproduce the data.

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Publication Date
Sun Jun 21 2020
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Nanotechnology-Based Topical Drug Delivery Systems for Management of Dandruff and Seborrheic Dermatitis: An overview
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Dandruff and seborrheic dermatitis (SD) are common skin disorders affecting the scalp and extending to other body sites in the case of SD. They are associated with pruritus and scaling, causing an esthetical disturbance in the population affected.   Treatment of such conditions involves using a variety of drugs for long terms, thus optimizing drug formulation is essential to improve therapeutic efficacy and patient compliance. Conventional topical formulations like shampoos and creams have been widely used but their use is associated with disadvantages. To overcome such effects, novel topical nanotechnology-based formulations are currently under investigation. In the following article, we highlight recently published formulatio

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Publication Date
Mon Oct 09 2023
Journal Name
2023 Ieee 34th International Symposium On Software Reliability Engineering Workshops (issrew)
Semantics-Based, Automated Preparation of Exploratory Data Analysis for Complex Systems
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Publication Date
Wed Nov 17 2021
Journal Name
International Journal Of Knowledge Management
Assessing the Success of the Perceived Usefulness for Knowledge Management Systems
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Recently, Knowledge Management Systems (KMS) consider one of the major fields of study in educational institutions, caused by the necessity to identify their knowledge value and success. Hence, based on the updated DeLone and McLean’s Information Systems Success Model (DMISSM), this study set out to assess the success of the Perceived Usefulness of Knowledge Management Systems (PUKMS) in Iraqi universities. To achieve this objective, the quantitative method is selected as the research design. In total, 421 university administration staff members from 13 Iraqi private universities were conducted. This study highlights a number of significant results depending on structural equation modeling which confirms that system, information, and s

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Publication Date
Sun May 01 2022
Journal Name
Journal Of Engineering
Performance Analysis of different Machine Learning Models for Intrusion Detection Systems
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In recent years, the world witnessed a rapid growth in attacks on the internet which resulted in deficiencies in networks performances. The growth was in both quantity and versatility of the attacks. To cope with this, new detection techniques are required especially the ones that use Artificial Intelligence techniques such as machine learning based intrusion detection and prevention systems. Many machine learning models are used to deal with intrusion detection and each has its own pros and cons and this is where this paper falls in, performance analysis of different Machine Learning Models for Intrusion Detection Systems based on supervised machine learning algorithms. Using Python Scikit-Learn library KNN, Support Ve

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Publication Date
Wed Jun 01 2022
Journal Name
Bulletin Of Electrical Engineering And Informatics
Proposed model for data protection in information systems of government institutions
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Information systems and data exchange between government institutions are growing rapidly around the world, and with it, the threats to information within government departments are growing. In recent years, research into the development and construction of secure information systems in government institutions seems to be very effective. Based on information system principles, this study proposes a model for providing and evaluating security for all of the departments of government institutions. The requirements of any information system begin with the organization's surroundings and objectives. Most prior techniques did not take into account the organizational component on which the information system runs, despite the relevance of

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