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jcoeduw-1361
Image classification with Deep Convolutional Neural Network Using Tensorflow and Transfer of Learning
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The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Convolutional Neural Network (CNN) has been chosen as a better option for the training process because it produces a high accuracy. The final accuracy has reached 91.18% in five different classes. The results are discussed in terms of the probability of accuracy for each class in the image classification in percentage. Cats class got 99.6 %, while houses class got 100 %.Other types of classes were with an average score of 90 % and above.

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Publication Date
Sat Dec 01 2018
Journal Name
Journal Of Economics And Administrative Sciences
(Estimation and Analysis of the Cobb-Duglas Production Function for the Rail Transport Sector in Iraq for the Period 1990-2016 using the ARDL Model)
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Abstract:

Since the railway transport sector is very important in many countries of the world, we have tried through this research to study the production function of this sector and to indicate the level of productivity under which it operates.

It was found through the estimation and analysis of the production function Kub - Duglas that the railway transport sector in Iraq suffers from a decline in the level of productivity, which was reflected in the deterioration of the level of services provided for the transport of passengers and goods. This led to the loss of the sector of importance in supporting the national economy and the reluctance of most passengers an

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Publication Date
Fri Apr 01 2022
Journal Name
Journal Of Engineering
Prediction of Shear Strength Parameters of Gypseous Soil using Artificial Neural Networks
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The shear strength of soil is one of the most important soil properties that should be identified before any foundation design. The presence of gypseous soil exacerbates foundation problems. In this research, an approach to forecasting shear strength parameters of gypseous soils based on basic soil properties was created using Artificial Neural Networks. Two models were built to forecast the cohesion and the angle of internal friction. Nine basic soil properties were used as inputs to both models for they were considered to have the most significant impact on soil shear strength, namely: depth, gypsum content, passing sieve no.200, liquid limit, plastic limit, plasticity index, water content, dry unit weight, and initial

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Publication Date
Sat Jun 01 2024
Journal Name
Civil And Environmental Engineering
Constructing a Corrected Stations Network Using Some Geomatic Techniques
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Abstract<p>The major objective of this study is to establish a network of Ground Control Points-GCPs which can use it as a reference for any engineering project. Total Station (type: Nikon Nivo 5.C), Optical Level and Garmin Navigator GPS were used to perform traversing. Traversing measurement was achieved by using nine points covered the selected area irregularly. Near Civil Engineering Department at Baghdad University Al-jadiriya, an attempt has been made to assess the accuracy of GPS by comparing the data obtained from the Total Station. The average error of this method is 3.326 m with the highest coefficient of determination (R<sup>2</sup>) is 0.077 m observed in Northing. While in</p> ... Show More
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Publication Date
Thu Aug 02 2012
Journal Name
International Journal Of Advanced Research In Computer Science
User Authentication based on Keystroke Dynamics Using Backpropagation Network
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Computer systems and networks are being used in almost every aspect of our daily life; as a result the security threats to computers and networks have also increased significantly. Traditionally, password-based user authentication is widely used to authenticate legitimate user in the current system0T but0T this method has many loop holes such as password sharing, shoulder surfing, brute force attack, dictionary attack, guessing, phishing and many more. The aim of this paper is to enhance the password authentication method by presenting a keystroke dynamics with back propagation neural network as a transparent layer of user authentication. Keystroke Dynamics is one of the famous and inexpensive behavioral biometric technologies, which identi

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Publication Date
Sat Jan 01 2022
Journal Name
Technologies And Materials For Renewable Energy, Environment And Sustainability: Tmrees21gr
Theoretical investigation of charge transfer at N3 sensitized molecule dye contact with TiO2 and ZnO semiconductor
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(3) (PDF) Theoretical investigation of charge transfer at N3 sensitized molecule dye contact with TiO2 and ZnO semiconductor. Available from: https://www.researchgate.net/publication/362773606_Theoretical_investigation_of_charge_transfer_at_N3_sensitized_molecule_dye_contact_with_TiO2_and_ZnO_semiconductor [accessed May 01 2023].

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Publication Date
Wed Feb 01 2023
Journal Name
Baghdad Science Journal
Spectrophotometric Method Combined with HPLC for Bisphenol F Determination in Plastic Bottled Water and Thermal Paper
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Based on the diazotization-coupling reaction, a new, simple, and sensitive spectrophotometric method for determining of a trace amount of (BPF) is presented in this paper. Diazotized metoclopramide reagent react with bisphenol F produces an orange azo-compound with a maximum absorbance at 461 nm in alkaline solution. The experimental parameters were optimized such as type of alkaline medium, concentration of NaOH, diazotized metoclopramide amount, order additions, reaction time, temperature, and effect of organic solvents to achieve the optimal performance for the proposed method. The absorbance increased linearly with increasing bisphenol F concentration in the range of 0.5-10 μg mL-1 under ideal conditions, with a correlati

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Publication Date
Tue Jan 01 2013
Journal Name
Thesis
User Authentication Based on Keystroke Dynamics Using Artificial Neural Networks
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Computer systems and networks are being used in almost every aspect of our daily life, the security threats to computers and networks have increased significantly. Usually, password-based user authentication is used to authenticate the legitimate user. However, this method has many gaps such as password sharing, brute force attack, dictionary attack and guessing. Keystroke dynamics is one of the famous and inexpensive behavioral biometric technologies, which authenticate a user based on the analysis of his/her typing rhythm. In this way, intrusion becomes more difficult because the password as well as the typing speed must match with the correct keystroke patterns. This thesis considers static keystroke dynamics as a transparent layer of t

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Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
Merge Operation Effect On Image Compression Using Fractal Technique
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Fractal image compression gives some desirable properties like fast decoding image, and very good rate-distortion curves, but suffers from a high encoding time. In fractal image compression a partitioning of the image into ranges is required. In this work, we introduced good partitioning process by means of merge approach, since some ranges are connected to the others. This paper presents a method to reduce the encoding time of this technique by reducing the number of range blocks based on the computing the statistical measures between them . Experimental results on standard images show that the proposed method yields minimize (decrease) the encoding time and remain the quality results passable visually.

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Publication Date
Thu Aug 01 2024
Journal Name
Iop Conference Series: Earth And Environmental Science
Collapse Pattern in Gypseous Soil using Particle Image Velocimetry
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Abstract<p>Gypseous soil is prevalent in arid and semi-arid areas, is from collapsible soil, which contains the mineral gypsum, and has variable properties, including moisture-induced volume changes and solubility. Construction on these soils necessitates meticulous assessment and unique designs due to the possibility of foundation damage from soil collapse. The stability and durability of structures situated on gypseous soils necessitate close collaboration with specialists and careful, methodical preparation. It had not been done to find the pattern of failure in the micromechanical behavior of gypseous sandy soil through particle image velocity (PIV) analysis. This adopted recently in geotech</p> ... Show More
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Publication Date
Sun Feb 10 2019
Journal Name
Journal Of The College Of Education For Women
Ciphered Text Hiding in an Image using RSA algorithm
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In this paper, a method for hiding cipher text in an image file is introduced . The
proposed method is to hide the cipher text message in the frequency domain of the image.
This method contained two phases: the first is embedding phase and the second is extraction
phase. In the embedding phase the image is transformed from time domain to frequency
domain using discrete wavelet decomposition technique (Haar). The text message encrypted
using RSA algorithm; then Least Significant Bit (LSB) algorithm used to hide secret message
in high frequency. The proposed method is tested in different images and showed success in
hiding information according to the Peak Signal to Noise Ratio (PSNR) measure of the the
original ima

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