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Influence Activation Function in Approximate Periodic Functions Using Neural Networks
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The aim of this paper is to design fast neural networks to approximate periodic functions, that is, design a fully connected networks contains links between all nodes in adjacent layers which can speed up the approximation times, reduce approximation failures, and increase possibility of obtaining the globally optimal approximation. We training suggested network by Levenberg-Marquardt training algorithm then speeding suggested networks by choosing most activation function (transfer function) which having a very fast convergence rate for reasonable size networks.             In all algorithms, the gradient of the performance function (energy function) is used to determine how to adjust the weights such that the performance function is minimized, where the back propagation algorithm has been used to increase the speed of training.

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Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
Smart Flow Steering Agent for End-to-End Delay Improvement in Software-Defined Networks
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To ensure fault tolerance and distributed management, distributed protocols are employed as one of the major architectural concepts underlying the Internet. However, inefficiency, instability and fragility could be potentially overcome with the help of the novel networking architecture called software-defined networking (SDN). The main property of this architecture is the separation of the control and data planes. To reduce congestion and thus improve latency and throughput, there must be homogeneous distribution of the traffic load over the different network paths. This paper presents a smart flow steering agent (SFSA) for data flow routing based on current network conditions. To enhance throughput and minimize latency, the SFSA distrib

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Publication Date
Wed Mar 01 2017
Journal Name
Ieee Transactions On Cognitive Communications And Networking
Multi-Channel Distributed DSA Networks: Connectivity and Rendezvous Tradeoffs
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In this paper, we investigate and characterize the effects of multi-channel and rendezvous protocols on the connectivity of dynamic spectrum access networks using percolation theory. In particular, we focus on the scenario where the secondary nodes have plenty of vacant channels to choose from a phenomenon which we define as channel abundance. To cope with the existence of multi-channel, we use two types of rendezvous protocols: naive ones which do not guarantee a common channel and advanced ones which do. We show that, with more channel abundance, even with the use of either type of rendezvous protocols, it becomes difficult for two nodes to agree on a common channel, thereby, potentially remaining invisible to each other. We model this in

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Publication Date
Mon Nov 01 2021
Journal Name
Indonesian Journal Of Electrical Engineering And Computer Science
Proposed emerged and enhanced routing protocols for wireless networks
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The problem motivation of this work deals with how to control the network overhead and reduce the network latency that may cause many unwanted loops resulting from using standard routing. This work proposes three different wireless routing protocols which they are originally using some advantages for famous wireless ad-hoc routing protocols such as dynamic source routing (DSR), optimized link state routing (OLSR), destination sequenced distance vector (DSDV) and zone routing protocol (ZRP). The first proposed routing protocol is presented an enhanced destination sequenced distance vector (E-DSDV) routing protocol, while the second proposed routing protocol is designed based on using the advantages of DSDV and ZRP and we named it as

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Publication Date
Mon Jun 19 2023
Journal Name
Journal Of Engineering
Linguistic Fuzzy Trust Model over Oscillating Wireless Sensor Networks
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Simulation  of  the  Linguistic  Fuzzy Trust  Model  (LFTM)  over  oscillating  Wireless  Sensor Networks (WSNs) where the goodness of the servers belonging to them could change along the time is presented in this paper, and the comparison between the outcomes achieved with LFTM model over oscillating WSNs with the outcomes obtained by applying the model over static WSNs where the servers maintaining always the same goodness, in terms of the selection percentage of trustworthy servers (the accuracy of the model) and the average path length are also presented here. Also in this paper the comparison between the LFTM and the Bio-inspired Trust and Reputation Model for Wireless Sensor Network

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Publication Date
Sun Oct 30 2022
Journal Name
Iraqi Journal Of Science
Gravity Model for Flow Migration Within Wireless Communication Networks
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     This paper investigated an Iraqi dataset from Korek Telecom Company as Call Detail Recorded (CDRs) for six months falling between Sep. 2020-Feb. 2021. This data covers 18 governorates, and it falls within the period of COVID-19. The Gravity algorithm was applied into two levels of abstraction in deriving the results as the macroscopic and mesoscopic levels respectively. The goal of this study was to reveal the strength and weakness of people migration in-between the Iraqi cities. Thus, it has been clear that the relationship between each city with the others is based on   and  of mobile people. However, the COVID-19 effects on the people’s migration needed to be explored. Whereas the main function of the gravity model is to

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Publication Date
Sat May 19 2012
Journal Name
Wireless Personal Communications
Stable-Aware Evolutionary Routing Protocol for Wireless Sensor Networks
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Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
Compression-based Data Reduction Technique for IoT Sensor Networks
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Energy savings are very common in IoT sensor networks because IoT sensor nodes operate with their own limited battery. The data transmission in the IoT sensor nodes is very costly and consume much of the energy while the energy usage for data processing is considerably lower. There are several energy-saving strategies and principles, mainly dedicated to reducing the transmission of data. Therefore, with minimizing data transfers in IoT sensor networks, can conserve a considerable amount of energy. In this research, a Compression-Based Data Reduction (CBDR) technique was suggested which works in the level of IoT sensor nodes. The CBDR includes two stages of compression, a lossy SAX Quantization stage which reduces the dynamic range of the

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Publication Date
Wed Nov 24 2021
Journal Name
2021 Ieee Asia Pacific Conference On Postgraduate Research In Microelectronics And Electronics (primeasia)
Review of 3D Networks-On-Chip Simulators and Plugins
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A comprehensive review focuses on 3D network-on-chip (NoC) simulators and plugins while paying attention to the 2D simulators as the baseline is presented. Discussions include the programming languages, installation configuration, platforms and operating systems for the respective simulators. In addition, the simulator’s properties and plugins for design metrics evaluations are addressed. This review is intended for the early career researchers starting in 3D NoC, offering selection guidelines on the right tools for the targeted NoC architecture, design, and requirements.

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Publication Date
Sat Jun 27 2020
Journal Name
Iraqi Journal Of Science
A Class of Harmonic Univalent Functions Defined by Differential Operator and the Generalization
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In this paper, a new class of harmonic univalent functions was defined by the differential operator. We obtained some geometric properties, such as the coefficient estimates, convex combination, extreme points, and convolution (Hadamard product), which are required

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Publication Date
Sun Jul 30 2023
Journal Name
Iraqi Journal Of Science
Generalization of Gamma and Beta Functions with Certain Properties and Statistical Application
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     This work is devoted to define new generalized gamma and beta functions involving the recently suggested seven-parameter Mittag-Leffler function, followed by a review of all related special cases. In addition, necessary investigations are affirmed for the new generalized beta function, including, Mellin transform, differential formulas, integral representations, and essential summation relations. Furthermore, crucial statistical application has been realized for the new generalized beta function.  

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