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Object Tracking Using Adaptive Diffusion Flow Active Model
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Object tracking is one of the most important topics in the fields of image processing and computer vision. Object tracking is the process of finding interesting moving objects and following them from frame to frame. In this research, Active models–based object tracking algorithm is introduced. Active models are curves placed in an image domain and can evolve to segment the object of interest. Adaptive Diffusion Flow Active Model (ADFAM) is one the most famous types of Active Models. It overcomes the drawbacks of all previous versions of the Active Models specially the leakage problem, noise sensitivity, and long narrow hols or concavities. The ADFAM is well known for its very good capabilities in the segmentation process. In this research, it is adopted for segmentation and tracking purposes. The proposed object tracking algorithm is initiated by detecting the target moving object manually. Then, the ADFAM convergence of the current video frame is reused as an initial estimation for the next video frame and so on. The proposed algorithm is applied to several video sequences, different in terms of the nature of the object, the nature of the background, the speed of the object, object motion direction, and the inter-frame displacement. Experimental results show that the proposed algorithm performed very well and successfully tracked the target object in all different cases.

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Publication Date
Tue Mar 30 2021
Journal Name
Baghdad Science Journal
Solving Fractional Damped Burgers' Equation Approximately by Using The Sumudu Transform (ST) Method
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       In this work, the fractional damped Burger's equation (FDBE) formula    = 0,

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Publication Date
Fri Feb 01 2019
Journal Name
Journal Of Physics: Conference Series
Study the Electronic Properties of Boron Nitride Diamondiod Nanostructure using Ab-initio DFT
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Publication Date
Sun Mar 31 2013
Journal Name
Inventi Impact: Artificial Intelligence
SIMULATION OF IDENTIFICATION AND CONTROL OF SCARA ROBOT USING MODIFIED RECURRENT NEURAL NETWORKS
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This paper presents a modified training method for Recurrent Neural Networks. This method depends on the Non linear Auto Regressive (NARX) model with Modified Wavelet Function as activation function (MSLOG) in the hidden layer. The modified model is known as Modified Recurrent Neural (MRN). It is used for identification Forward dynamics of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot. This model is also used in the design of Direct Inverse Control (DIC). This method is compared with Recurrent Neural Networks that used Sigmoid activation function (RS) in the hidden layer and Recurrent Neural Networks with Wavelet activation function (RW). Simulation results shows that the MRN model is bett

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Publication Date
Sun Jan 01 2023
Journal Name
3rd International Conference On Smart Cities And Sustainable Planning
Towards sustainable tourism: EIA of natural environment in Sawa lake using RIAM method
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Publication Date
Wed Mar 01 2023
Journal Name
Iraqi Journal Of Physics
Geographical Distribution of Landfills in the Outskirts of Baghdad Governorate using the GIS
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The problem of solid waste from domestic, industrial, commercial and medical sources is one of the most important problems facing the local administration in all Iraqi cities. The danger of this problem increases with the rapid increase in the population, changing lifestyles, consumption patterns, limited land suitable for landfill, and high costs of collection and disposal. This research aims to solve these problems by determining the locations of current landfills located in the outskirts of Baghdad Governorate. The ArcGIS program was used, where the sites of the landfills were determined on the map and through the available data about the areas. it was concluded that the existing landfill sites do not meet environmental conditions and

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Publication Date
Thu Apr 30 2020
Journal Name
Journal Of Economics And Administrative Sciences
Comparison Between Tree regression (TR), and Negative binomial regression (NBR) by Using Simulation.
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            In this paper, the process of comparison between the tree regression model and the negative binomial regression. As these models included two types of statistical methods represented by the first type "non parameter statistic" which is the tree regression that aims to divide the data set into subgroups, and the second type is the "parameter statistic" of negative binomial regression, which is usually used when dealing with medical data, especially when dealing with large sample sizes. Comparison of these methods according to the average mean squares error (MSE) and using the simulation of the experiment and taking different sample

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Publication Date
Sun Jun 20 2021
Journal Name
Baghdad Science Journal
Performance Evaluation of Intrusion Detection System using Selected Features and Machine Learning Classifiers
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Some of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems.  Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic.  Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance.  In this study, two different sets of select

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Publication Date
Mon Jan 01 2024
Journal Name
2nd International Conference For Engineering Sciences And Information Technology (esit 2022): Esit2022 Conference Proceedings
Calculating land surface temperature of South Baghdad by the using Landsat 8 images
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Publication Date
Fri Jan 20 2023
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
A Review Article: Green Synthesis by using Different Plants to preparation Oxide Nanoparticles
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Green nanotechnology is a thrilling and rising place of technology and generation that bracesthe ideas of inexperienced chemistry with ability advantages for sustainability, protection, andthe general protection from the race human. The inexperienced chemistry method introduces aproper technique for the production, processing, and alertness of much less dangerous chemicalsubstances to lessen threats to human fitness and the environment. The technique calls for inintensity expertise of the uncooked materials, particularly in phrases in their creation intonanomaterials and the resultant bioactivities that pose very few dangerous outcomes for peopleand the environment. In the twenty-first century, nanotechnology has become a systematic

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Publication Date
Mon Apr 01 2019
Journal Name
Materials Chemistry And Physics
Investigation of silica polymorphs stratified in siliceous geode using FTIR and XRD methods
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