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Efficient Eye Recognition for Secure Systems using Convolutional Neural Network
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Publication Date
Sun Jan 01 2023
Journal Name
Computers, Materials & Continua
An Efficient Method for Heat Recovery Process and燭emperature燨ptimization
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Publication Date
Sat Apr 01 2023
Journal Name
Baghdad Science Journal
Iraqi porcelanite Rocks for Efficient Removal of Safranin Dye from Aqueous Solution
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This research includes a study of the ability of Iraqi porcelanite rocks powder to remove the basic Safranine dye from its aqueous process by adsorption. The experiments were carried out at 298Kelvin in order to determine the effect of the starting concentration for Safranin dye, mixing time, pH, and the effect of ionic Strength. The good conditions were perfect for safranine dye adsorption was performed when0.0200g from that adsorbed particles and the removal max percentage  was found  be 96.86%  at 9 mg/L , 20 minutes adsorption time and at PH=8 and in 298 K. The isothermal equilibrum stoichiometric adsorption confirmed, the process data were examined by Langmuir, Freundlich and Temkin adsorption equations at different temperatures

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Publication Date
Sat Apr 01 2023
Journal Name
Bulletin Of Electrical Engineering And Informatics
Accurate license plate recognition system for different styles of Iraqi license plates
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Automatic license plate recognition (ALPR) used for many applications especially in security applications, including border control. However, more accurate and language-independent techniques are still needed. This work provides a new approach to identifying Arabic license plates in different formats, colors, and even including English characters. Numbers, characters, and layouts with either 1-line or 2-line layouts are presented. For the test, we intend to use Iraqi license plates as there is a wide range of license plate styles written in Arabic, Kurdish, and English/Arabic languages, each different in style and color. This variety makes it difficult for recent traditional license plate recognition systems and algorithms to recogn

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Publication Date
Wed Sep 01 2021
Journal Name
Expert Systems With Applications
A long short-term recurrent spatial-temporal fusion for myoelectric pattern recognition
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Publication Date
Mon Oct 30 2023
Journal Name
Traitement Du Signal
A Comprehensive Review on Machine Learning Approaches for Enhancing Human Speech Recognition
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Publication Date
Wed Nov 02 2016
Journal Name
Australian Journal Of Basic And Applied Sciences
Full synchronization of 2$\times$ 2 optocouplers network using LEDs
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The synchronization of a complex network with optoelectronic feedback has been introduced theoretically, with use of 2×2 oscillators network; each oscillator considered is an optocoupler (LED coupled with photo-detector). Fixing the bias current (δ) and increasing the feedback strength (Ԑ) of each oscillator, the dynamical sequence like chaotic and periodic mixed mode oscillations has been observed. Synchronization of unidirectionally coupled of light emitting diodes network has been featured when coupling strength equal to 1.7×10-4. The transition between non-synchronization and synchronization states by means of the spatio-temporal distribution has been investigated.

Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Human Pose Estimation Algorithm Using Optimized Symmetric Spatial Transformation Network
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Human posture estimation is a crucial topic in the computer vision field and has become a hotspot for research in many human behaviors related work. Human pose estimation can be understood as the human key point recognition and connection problem. The paper presents an optimized symmetric spatial transformation network designed to connect with single-person pose estimation network to propose high-quality human target frames from inaccurate human bounding boxes, and introduces parametric pose non-maximal suppression to eliminate redundant pose estimation, and applies an elimination rule to eliminate similar pose to obtain unique human pose estimation results. The exploratory outcomes demonstrate the way that the proposed technique can pre

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Publication Date
Fri Jan 01 2021
Journal Name
International Journal Of Agricultural And Statistical Sciences
DYNAMIC MODELING FOR DISCRETE SURVIVAL DATA BY USING ARTIFICIAL NEURAL NETWORKS AND ITERATIVELY WEIGHTED KALMAN FILTER SMOOTHING WITH COMPARISON
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Survival analysis is widely applied in data describing for the life time of item until the occurrence of an event of interest such as death or another event of understudy . The purpose of this paper is to use the dynamic approach in the deep learning neural network method, where in this method a dynamic neural network that suits the nature of discrete survival data and time varying effect. This neural network is based on the Levenberg-Marquardt (L-M) algorithm in training, and the method is called Proposed Dynamic Artificial Neural Network (PDANN). Then a comparison was made with another method that depends entirely on the Bayes methodology is called Maximum A Posterior (MAP) method. This method was carried out using numerical algorithms re

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Publication Date
Fri Jul 21 2023
Journal Name
Journal Of Engineering
Liquid-Liquid Extraction of Metal Ions Using Aqueous Biphasic Systems
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An investigation was conducted for the study of extraction of metal ions using aqueous biphasic systems. The extraction of iron, zinc and copper from aqueous sulphate media at different kinds of extractants SCN− , Cl- and I- , different values of pH of the feed solution, phase ratio, concentration of metals, concentration of extractant, concentration of polymer, and concentration of salt was investigated. Atomic absorption spectrophotometer was used to measure the concentration of iron, zinc and copper in the aqueous phase throughout the experiments. The results of the extraction experiments showed the use of SCN− as extractant, pH=2.5, phase ratio=1.5, concentration of metals 1g/l, concentration of extractant 0.06 %, concentration o

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Publication Date
Sun Dec 02 2012
Journal Name
Baghdad Science Journal
Stability of Back Propagation Training Algorithm for Neural Networks
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In this paper, we derive and prove the stability bounds of the momentum coefficient µ and the learning rate ? of the back propagation updating rule in Artificial Neural Networks .The theoretical upper bound of learning rate ? is derived and its practical approximation is obtained

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