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Recognizing Different Foot Deformities Using FSR Sensors by Static Classification of Neural Networks
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Sensing insole systems are a promising technology for various applications in healthcare and sports. They can provide valuable information about the foot pressure distribution and gait patterns of different individuals. However, designing and implementing such systems poses several challenges, such as sensor selection, calibration, data processing, and interpretation. This paper proposes a sensing insole system that uses force-sensitive resistors (FSRs) to measure the pressure exerted by the foot on different regions of the insole. This system classifies four types of foot deformities: normal, flat, over-pronation, and excessive supination. The classification stage uses the differential values of pressure points as input for a feedforward neural network (FNN) model. Data acquisition involved 60 subjects diagnosed with the studied cases. The implementation of FNN achieved an accuracy of 96.6% using 50% of the dataset as training data and 92.8% using only 30% training data. The comparison with related work shows good impact of using the differential values of pressure points as input for neural networks compared with raw data.

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Publication Date
Tue May 01 2018
Journal Name
Journal Of Engineering
Prediction of Municipal Solid Waste Generation Models Using Artificial Neural Network in Baghdad city, Iraq
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The importance of Baghdad city as the capital of Iraq and the center of the attention of delegations because of its long history is essential to preserve its environment. This is achieved through the integrated management of municipal solid waste since this is only possible by knowing the quantities produced by the population on a daily basis. This study focused to predicate the amount of municipal solid waste generated in Karkh and Rusafa separately, in addition to the quantity produced in Baghdad, using IBM SPSS 23 software. Results that showed the average generation rates of domestic solid waste in Rusafa side was higher than that of Al-Karkh side because Rusafa side has higher population density than Al-Karkh side. T

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Publication Date
Wed Feb 01 2017
Journal Name
Journal Of Economics And Administrative Sciences
The added value of the knowledge assets using a scale (VAIC) in a sample of the industrial sector companies
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The sustainable competitive advantage for organizations is one of the requirements for value creation, which centered on the possession of scarce resources that achieve maximum flows to invest in intellectual capital, if what has been interest in them, measured and employed the way properly and style, so I figured the need for new technologies to enable organizations to measure the intellectual and physical assets and to assess its performance accordingly, so it sheds search light on the measurement of the added value of existing knowledge using the standard value-added factor is the intellectual (value added intellectual coefficient) (VAIC) and to develop a set of assumptions about the extent of the difference between the sample

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Publication Date
Wed Mar 25 2020
Journal Name
2nd International Conference On Materials Engineering & Science (iconmeas 2019)
Foundation relative stiffness effects in sand under static loading
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In the geotechnical engineering applications, precise understandings are yet to be established on the effects of a foundation stiffness on its bearing capacity and settlement. The modern foundation construction uses the new available construction materials that totally change the relative stiffness of the footing structures-soil interactions such as waste material and landfill area of more residential purposes. Conventional bearing capacity equations were dealt with common rigid footing and thus cannot be used for reduced foundation rigidity. Therefore, this study investigates the effects of foundation relative stiffness on its load-displacement behaviour and the soil deformation field using compression test of a strip smooth footings on su

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Publication Date
Sun Apr 01 2018
Journal Name
Journal Of Engineering/
Water quality assessment and total dissolved solids prediction using artificial neural network in Al-Hawizeh marsh south of Iraq
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The Iraqi marshes are considered the most extensive wetland ecosystem in the Middle East and are located in the middle and lower basin of the Tigris and Euphrates Rivers which create a wetlands network and comprise some shallow freshwater lakes that seasonally swamped floodplains. Al-Hawizeh marsh is a major marsh located east of Tigris River south of Iraq. This study aims to assess water quality through water quality index (WQI) and predict Total Dissolved Solids (TDS) concentrations in Al-Hawizeh marsh based on artificial neural network (ANN). Results showed that the WQI was more than 300 for years 2013 and 2014 (Water is unsuitable for drinking) and decreased within the range 200-300 in years 2015 and 2016 (Very poor water). The

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Publication Date
Mon Oct 01 2018
Journal Name
Journal Of Engineering
Water Quality Assessment and Total Dissolved Solids Prediction using Artificial Neural Network in Al-Hawizeh Marsh South of Iraq
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The Iraqi marshes are considered the most extensive wetland ecosystem in the Middle East and are located in the middle and lower basin of the Tigris and Euphrates Rivers which create a wetlands network and comprise some shallow freshwater lakes that seasonally swamped floodplains. Al-Hawizeh marsh is a major marsh located east of Tigris River south of Iraq. This study aims to assess water quality through water quality index (WQI) and predict Total Dissolved Solids (TDS) concentrations in Al-Hawizeh marsh based on artificial neural network (ANN). Results showed that the WQI was more than 300 for years 2013 and 2014 (Water is unsuitable for drinking) and decreased within the range 200-300 in years 2015 and 2016 (Very poor water). The develope

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Publication Date
Sat Jan 01 2011
Journal Name
Journal: Ibn Al-haitham Journal For Pure And Applied Sciences
A Study the effect the direction of the distribution of lighting to improve Images in different lighting by using technique adaptive histogram equalization
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Publication Date
Sun Apr 28 2024
Journal Name
Iraqi Journal Of Agricultural Sciences
COMPARISON OF TWO TYPES OF SENSORS AND THEIR EFFECT ON SPRAY QUALITY PEAR TREES
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This study was aimed to reduce the amount of the sprayed solution lost during trees spraying.  At the same time, the concentration of the sprayed solution on the target (tree or bush) must be ensured and to find the best combination of treatments. Two factors controls the spraying process: (i) spraying speed (1.2 km/h, 2.4 km/h, 3.6 km/h), and (ii) the type of sensor. The test results showed a significant loss reduction percentage. It reached (6.05%, 5.39% and 2.05%) at the speed (1.2 km/h, 2.4 km/h, 3.6 km/h), respectively. It was noticed that when the speed becomes higher the loss becomes less accordingly. The interaction between the 3.6 km/h speed and the type of Ultrasonic sensor led to a decrease in the percentage of the spray

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Publication Date
Wed Aug 17 2022
Journal Name
Aip Conference Proceedings
The effect of using Gaussian, Kurtosis and LogCosh as kernels in ICA on the satellite classification accuracy
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This study focusses on the effect of using ICA transform on the classification accuracy of satellite images using the maximum likelihood classifier. The study area represents an agricultural area north of the capital Baghdad - Iraq, as it was captured by the Landsat 8 satellite on 12 January 2021, where the bands of the OLI sensor were used. A field visit was made to a variety of classes that represent the landcover of the study area and the geographical location of these classes was recorded. Gaussian, Kurtosis, and LogCosh kernels were used to perform the ICA transform of the OLI Landsat 8 image. Different training sets were made for each of the ICA and Landsat 8 images separately that used in the classification phase, and used to calcula

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Publication Date
Fri Dec 01 2023
Journal Name
Al-khwarizmi Engineering Journal
Development of an ANN Model for RGB Color Classification using the Dataset Extracted from a Fabricated Colorimeter
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Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de

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Publication Date
Tue Jun 01 2021
Journal Name
Al-khwarizmi Engineering Journal
The The Application of Virtual Reality to (Mechatronics Engineering) by Creating an Articulated Robotic Work Cell Using EON Reality V9.22.24.24477
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Virtual reality, VR, offers many benefits to technical education, including the delivery of information through multiple active channels, the addressing of different learning styles, and experiential-based learning. This paper presents work performed by the authors to apply VR to engineering education, in three broad project areas: virtual robotic learning, virtual mechatronics laboratory, and a virtual manufacturing platform. The first area provides guided exploration of domains otherwise inaccessible, such as the robotic cell components, robotic kinematics and work envelope.  The second promotes mechatronics learning and guidance for new mechatronics engineers when dealing with robots in a safe and interactive manner. And the thir

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