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Use Dynamic Bayesian network to estimate the reliability of Adamia Water Network
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In this research, estimated the reliability of water system network in Baghdad was done. to assess its performance during a specific period. a fault tree through static and dynamic gates was belt and these gates represent logical relationships between the main events in the network and analyzed using dynamic Bayesian networks . As it has been applied Dynamic Bayesian networks estimate reliability by translating dynamic fault tree to Dynamic Bayesian networks and reliability of the system appreciated. As was the potential for the expense of each phase of the network for each gate . Because there are two parts to the Dynamic Bayesian networks and two part of gate (AND), which includes the three basic units of the network (A, B, C) and part of gate (WSP), which contains all of the tank (D) and alternative (S) and is a dynamic . then been integrated into the two segments together at gate Association previous two gates of a (OR), and estimate the probability of total failure of the network and through which found reliability. The result of the estimation was in put table shows the change of with time reliability.

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Publication Date
Thu Mar 01 2007
Journal Name
Journal Of Economics And Administrative Sciences
Alternative distribution to estimate the Dose – Response model in bioassay excrement
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 Alternative  distribution  to estimate the Dose – Response  model in bioassay  excrement

This research   concern to study five different distribution (Probit , Logistic, Arc sine , extreme value , One hit  ), to estimate  dose –response model by using m.l.e  and probit method This is done by determining different  weights in each  distribution in addition find all particular statistics for vital model . 

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Publication Date
Sun Jun 01 2008
Journal Name
2008 Ieee International Joint Conference On Neural Networks (ieee World Congress On Computational Intelligence)
Linear block code decoder using neural network
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Publication Date
Wed Jan 01 2020
Journal Name
Solid State Technology
Image Fusion Using A Convolutional Neural Network
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Image Fusion Using A Convolutional Neural Network

Publication Date
Sun Jun 01 2014
Journal Name
Baghdad Science Journal
A Proposed Agent System for Network Monitoring
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The traditional centralized network management approach presents severe efficiency and scalability limitations in large scale networks. The process of data collection and analysis typically involves huge transfers of management data to the manager which cause considerable network throughput and bottlenecks at the manager side. All these problems processed using the Agent technology as a solution to distribute the management functionality over the network elements. The proposed system consists of the server agent that is working together with clients agents to monitor the logging (off, on) of the clients computers and which user is working on it. file system watcher mechanism is used to indicate any change in files. The results were presente

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Publication Date
Fri Sep 30 2022
Journal Name
Iraqi Journal Of Science
Network Traffic Prediction Based on Boosting Learning
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Classification of network traffic is an important topic for network management, traffic routing, safe traffic discrimination, and better service delivery. Traffic examination is the entire process of examining traffic data, from intercepting traffic data to discovering patterns, relationships, misconfigurations, and anomalies in a network. Between them, traffic classification is a sub-domain of this field, the purpose of which is to classify network traffic into predefined classes such as usual or abnormal traffic and application type. Most Internet applications encrypt data during traffic, and classifying encrypted data during traffic is not possible with traditional methods. Statistical and intelligence methods can find and model traff

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Publication Date
Sun Jun 11 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Artificial Neural Network for TIFF Image Compression
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The main aim of image compression is to reduce the its size to be able for transforming and storage, therefore many methods appeared to compress the image, one of these methods is "Multilayer Perceptron ". Multilayer Perceptron (MLP) method which is artificial neural network based on the Back-Propagation algorithm for compressing the image. In case this algorithm depends upon the number of neurons in the hidden layer only the above mentioned will not be quite enough to reach the desired results, then we have to take into consideration the standards which the compression process depend on to get the best results. We have trained a group of TIFF images with the size of (256*256)  in our research, compressed them by using MLP for each

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Publication Date
Wed Sep 07 2022
Journal Name
2022 Iraqi International Conference On Communication And Information Technologies (iiccit)
Vehicular Ad-hoc Network (VANET) – A Review
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This paper explores VANET topics: architecture, characteristics, security, routing protocols, applications, simulators, and 5G integration. We update, edit, and summarize some of the published data as we analyze each notion. For ease of comprehension and clarity, we give part of the data as tables and figures. This survey also raises issues for potential future research topics, such as how to integrate VANET with a 5G cellular network and how to use trust mechanisms to enhance security, scalability, effectiveness, and other VANET features and services. In short, this review may aid academics and developers in choosing the key VANET characteristics for their objectives in a single document.

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Publication Date
Sat Aug 03 2024
Journal Name
Proceedings Of Ninth International Congress On Information And Communication Technology
Offline Signature Verification Based on Neural Network
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The investigation of signature validation is crucial to the field of personal authenticity. The biometrics-based system has been developed to support some information security features.Aperson’s signature, an essential biometric trait of a human being, can be used to verify their identification. In this study, a mechanism for automatically verifying signatures has been suggested. The offline properties of handwritten signatures are highlighted in this study which aims to verify the authenticity of handwritten signatures whether they are real or forged using computer-based machine learning techniques. The main goal of developing such systems is to verify people through the validity of their signatures. In this research, images of a group o

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Publication Date
Sun Jan 30 2022
Journal Name
Iraqi Journal Of Science
Arabic Keywords Extraction using Conventional Neural Network
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    Keywords provide the reader with a summary of the contents of the document and play a significant role in information retrieval systems, especially in search engine optimization and bibliographic databases. Furthermore keywords help to classify the document into the related topic. Keywords extraction included manual extracting depends on the content of the document or article and the judgment of its author. Manual extracting of keywords is costly, consumes effort and time, and error probability. In this research an automatic Arabic keywords extraction model based on deep learning algorithms is proposed. The model consists of three main steps: preprocessing, feature extraction and classification to classify the document

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Publication Date
Sat Jan 01 2011
Journal Name
Journal Of Engineering
FILTRATION MODELING USING ARTIFICIAL NEURAL NETWORK (ANN)
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In this research Artificial Neural Network (ANN) technique was applied to study the filtration process in water treatment. Eight models have been developed and tested using data from a pilot filtration plant, working under different process design criteria; influent turbidity, bed depth, grain size, filtration rate and running time (length of the filtration run), recording effluent turbidity and head losses. The ANN models were constructed for the prediction of different performance criteria in the filtration process: effluent turbidity, head losses and running time. The results indicate that it is quite possible to use artificial neural networks in predicting effluent turbidity, head losses and running time in the filtration process, wi

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