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Data Classification using Quantum Neural Network
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In this paper, integrated quantum neural network (QNN), which is a class of feedforward

neural networks (FFNN’s), is performed through emerging quantum computing (QC) with artificial neural network(ANN) classifier. It is used in data classification technique, and here iris flower data is used as a classification signals. For this purpose independent component analysis (ICA) is used as a feature extraction technique after normalization of these signals, the architecture of (QNN’s) has inherently built in fuzzy, hidden units of these networks (QNN’s) to develop quantized representations of sample information provided by the training data set in various graded levels of certainty. Experimental results presented here show that (QNN’s) are capable of recognizing structures in data, a property that conventional (FFNN’s) with sigmoidal hidden units lack. In addition, (QNN) gave a kind of fast and realistic results compared with the (FFNN). Simulation results indicate that QNN is superior (with total accuracy of 97.778%) than ANN (with total accuracy of 93.334%).

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Publication Date
Sat Oct 28 2023
Journal Name
Baghdad Science Journal
Quantum Theory of Atom in Molecules Investigation Trinuclear Ruthenium: DFT Approach
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The topological indices of the "[(µ3-2, 5-dioxyocyclohexylidene)-bis ((2-hydrido)-nonacarbonyltriruthenium]” were studied within the quantum theory of atoms in the molecule (QTAIM), clusters are
analyzed using the density functional theory (DFT). The estimated topological variables accord with prior
descriptions of comparable transition metal complexes. The Quantum Theory of Atom, in molecules
investigation of the bridging core component, Ru3H2, revealed critical binding points (chemical bonding)
between Ru (1) and Ru (2) and Ru (3). Consequently, delocalization index for this non-bonding interaction
was calculated in the core of Ru3H2, the interaction is of the (5centre–5electron) class.

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Publication Date
Sat Jun 03 2023
Journal Name
Journal Of Electronic Materials
Thiophosgene Detection by Ag-Decorated AlN Nanotube: A Mechanical Quantum Survey
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The density functional B3LYP is used to investigate the effect of decorating the silver (Ag) atom on the sensing capability of an AlN nanotube (AlN-NT) in detecting thiophosgene (TP). There is a weak interaction between the pristine AlN-NT and TP with the sensing response (SR) of approximately 9.4. Decoration of the Ag atom into the structure of AlN-NT causes the adsorption energy of TP to decrease from − 6.2 to − 22.5 kcal/mol. Also, the corresponding SR increases significantly to 100.5. Moreover, the recovery time when TP is desorbed from the surface of the Ag-decorated AlN-NT (Ag@AlN-NT) is short, i.e., 24.9 s. The results show that Ag@AlN-NT can selectively detect TP among other gases, such as N2, O2, CO2, CO, and H2O.

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Publication Date
Sat Jul 01 2017
Journal Name
Journal Of Nano Research
Electroluminescence Devices from Quantum Dots with TPD Polymer White Light Generation
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Quantum dots of CdSe, CdS and ZnS QDs were prepared by chemical reaction and used to fabricate organic quantum dot hybrid junction device. QD-LEDs were fabricated using ITO/TPD: PMMA/CdSe/Al, ITO/TPD: PMMA/CdS/Al and ITO/TPD: PMMA/ZnS/Al QDs devices which synthesized by phase segregation method. The hybrid white light emitting devices consists, of two-layers deposited successively on the ITO glass substrate; the first layer was of N, N’-bis (3-methylphenyl)-N, N’-bis (phenyl) benzidine (TPD) polymer mixed with polymethyl methacrylate (PMMA) polymers in ratio 1:1, while the second layer was 0.5wt% from each type of the (CdSe, CdS and ZnS) QDs for each device.The optical properties of QDs were characterized by UV-Vis. and photolum

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Publication Date
Sun Jan 13 2019
Journal Name
Iraqi Journal Of Physics
Quantum mechanical study of electronic properties of zigzag nanotubes (9,0) (SWCNTs)
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Quantum calculations on the most stable structure were carried
out for calculating the electronic properties, energies and the charge
density at the Carbon and Hydrogen atoms by Semi-empirical
method (PM3) of zigzag carbon nano tube CNT (9,0) (SWCNTs), at
the equilibrium geometry depending on the pictures of Zigzag
CNT(9,0) which was found to has D3d symmetry point group by
applying for (Gaussian 2003) program. In this work the results
include calculation the relation for axial bonds length, which are the
vertical C-C bonds (annular bonds) in the rings and bonds length
which are in the outer ring that called the circumferential bonds. Also
include a different kind of vibration modes like breathing, puckering

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Publication Date
Wed Jan 02 2019
Journal Name
Pramana
The description of quantum dielectric function for insulators over Bethe surface
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Publication Date
Sun Nov 26 2017
Journal Name
Journal Of Engineering
Compression Index and Compression Ratio Prediction by Artificial Neural Networks
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Information about soil consolidation is essential in geotechnical design. Because of the time and expense involved in performing consolidation tests, equations are required to estimate compression index from soil index properties. Although many empirical equations concerning soil properties have been proposed, such equations may not be appropriate for local situations. The aim of this study is to investigate the consolidation and physical properties of the cohesive soil. Artificial Neural Network (ANN) has been adapted in this investigation to predict the compression index and compression ratio using basic index properties. One hundred and ninety five consolidation results for soils tested at different construction sites

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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
Fri Sep 30 2011
Journal Name
Iraqi Journal Of Science
Using the band ratio classification method to detect the regions that need to remove sedimentation in Tigris River
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LandSat Satellite ETM+ image have been analyzed to detect the different depths of regions inside the Tigris river in order to detect the regions that need to remove sedimentation in Baghdad in Iraq Country. The scene consisted of six bands (without the thermal band), It was captured in March ٢٠٠١. The variance in depth is determined by applying the rationing technique on the bands ٣ and ٥. GIS ٩. ١ program is used to apply the rationing technique and determined the results.

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Publication Date
Sat Dec 30 2023
Journal Name
Nasaq
Quantum computers are opening new horizons for an advanced and changing future for the world
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This study addresses quantum computers as one of the most significant contemporary technological transformations that promise to reshape the future of global computing. It aims to clarify the conceptual foundations of quantum computing and to identify the fundamental differences between quantum and classical computers in terms of processing mechanisms, computational speed, and the ability to solve highly complex problems. The study focuses on key concepts such as the qubit, superposition, and entanglement, highlighting their role in enabling computational capabilities that exceed the limits of classical computing. It also discusses the future applications of quantum computers in areas such as cryptography, artificial intelligence, big data

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Publication Date
Fri Aug 12 2022
Journal Name
Future Internet
Improved DDoS Detection Utilizing Deep Neural Networks and Feedforward Neural Networks as Autoencoder
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Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr

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