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A Comparative Study of Various Intelligent Optimization Algorithms Based on Path Planning and Neural Controller for Mobile Robot
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In this paper, a cognitive system based on a nonlinear neural controller and intelligent algorithm that will guide an autonomous mobile robot during continuous path-tracking and navigate over solid obstacles with avoidance was proposed. The goal of the proposed structure is to plan and track the reference path equation for the autonomous mobile robot in the mining environment to avoid the obstacles and reach to the target position by using intelligent optimization algorithms. Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) Algorithms are used to finding the solutions of the mobile robot navigation problems in the mine by searching the optimal paths and finding the reference path equation of the optimal path. As well as, PSO algorithm is used to find and tune on-line the neural control gains values of the nonlinear neural controller to obtain the best torques actions of the wheels for the mining autonomous mobile robot. Simulation results by matlab showed that the proposed cognitive system is more accurate in terms of planning reference path to avoid obstacles and online finding and tuning parameters of the controller which generated smoothness control action without saturation state for tracking the reference path equation as well as minimize the mobile robot tracking pose error to zero value.

 

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Publication Date
Sat Jan 01 2022
Journal Name
Computers, Materials & Continua
An Optimal Method for Supply Chain Logistics Management Based on Neural Network
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Publication Date
Mon Dec 01 2014
Journal Name
Advances In Engineering Software
System identification and control of robot manipulator based on fuzzy adaptive differential evolution algorithm
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Publication Date
Mon Mar 01 2010
Journal Name
Al-khwarizmi Engineering Journal
Bionics-Based Approach for Object Tracking to Implement in Robot Applications
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In this paper, an approach for object tracking that is inspired from human oculomotor system is proposed and verified experimentally. The developed approach divided into two phases, fast tracking or saccadic phase and smooth pursuit phase. In the first phase, the field of the view is segmented into four regions that are analogue to retinal periphery in the oculomotor system. When the object of interest is entering these regions, the developed vision system responds by changing the values of the pan and tilt angles to allow the object lies in the fovea area and then the second phase will activate. A fuzzy logic method is implemented in the saccadic phase as an intelligent decision maker to select the values of the pan and tilt angle based

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Publication Date
Wed Jan 01 2020
Journal Name
International Journal Of Computational Intelligence Systems
Evolutionary Feature Optimization for Plant Leaf Disease Detection by Deep Neural Networks
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Publication Date
Sat Jan 01 2022
Journal Name
Aip Conference Proceedings
Appraisal of intelligent notification system for smart university campus based internet of objects for social activities
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Publication Date
Sun Mar 01 2020
Journal Name
Baghdad Science Journal
Mobile-based Telemedicine Application using SVD and F-XoR Watermarking for Medical Images
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A medical- service platform is a mobile application through which patients are provided with doctor’s diagnoses based on information gleaned from medical images. The content of these diagnostic results must not be illegitimately altered during transmission and must be returned to the correct patient. In this paper, we present a solution to these problems using blind, reversible, and fragile watermarking based on authentication of the host image. In our proposed algorithm, the binary version of the Bose_Chaudhuri_Hocquengham (BCH) code for patient medical report (PMR) and binary patient medical image (PMI) after fuzzy exclusive or (F-XoR) are used to produce the patient's unique mark using secret sharing schema (SSS). The patient’s un

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Publication Date
Tue Jun 23 2020
Journal Name
Baghdad Science Journal
Anomaly Detection Approach Based on Deep Neural Network and Dropout
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   Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct

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Publication Date
Sun Jul 27 2025
Journal Name
Journal Of Baghdad College Of Dentistry
The effects of various beverages on the shear bond strength of light-cured orthodontic composite (An in vitro comparative study)
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Background: This study was conducted to assess the effects of various beverages on the shear bond strength of light-cured orthodontic composite used to bond stainless steel orthodontic brackets on human teeth and to determine the site of bonding failure of this material. Materials and Methods: Fifty extracted human premolars were selected and randomly divided into five equal groups each with 10 teeth according to the beverage type (Control, One Tiger, Milk, Green tea and Coffee). After bonding, the teeth were immersed in specific beverages for 5 minutes twice daily with equal intervening intervals then washed and stored in distilled water at 37º C for the reminder of the day. The process was carried out for 30 days. The samples were then

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Publication Date
Fri Jan 01 2016
Journal Name
Journal Of Engineering
Mobile Position Estimation using Artificial Neural Network in CDMA Cellular Systems
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Using the Neural network as a type of associative memory will be introduced in this paper through the problem of mobile position estimation where mobile estimate its location depending on the signal strength reach to it from several around base stations where the neural network can be implemented inside the mobile. Traditional methods of time of arrival (TOA) and received signal strength (RSS) are used and compared with two analytical methods, optimal positioning method and average positioning method. The data that are used for training are ideal since they can be obtained based on geometry of CDMA cell topology. The test of the two methods TOA and RSS take many cases through a nonlinear path that MS can move through tha

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Publication Date
Fri Jan 01 2016
Journal Name
Journal Of Engineering
Mobile position estimation using artificial neural network in CDMA cellular systems
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Using the Neural network as a type of associative memory will be introduced in this paper through the problem of mobile position estimation where mobile estimate its location depending on the signal strength reach to it from several around base stations where the neural network can be implemented inside the mobile. Traditional methods of time of arrival (TOA) and received signal strength (RSS) are used and compared with two analytical methods, optimal positioning method and average positioning method. The data that are used for training are ideal since they can be obtained based on geometry of CDMA cell topology. The test of the two methods TOA and RSS take many cases through a nonlinear path that MS can move through that region. The result

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