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Digital Intelligence for University Students Using Artificial Intelligence Techniques
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The research problem arose from the researchers’ sense of the importance of Digital Intelligence (DI), as it is a basic requirement to help students engage in the digital world and be disciplined in using technology and digital techniques, as students’ ideas are sufficiently susceptible to influence at this stage in light of modern technology. The research aims to determine the level of DI among university students using Artificial Intelligence (AI) techniques. To verify this, the researchers built a measure of DI. The measure in its final form consisted of (24) items distributed among (8) main skills, and the validity and reliability of the tool were confirmed. It was applied to a sample of 139 male and female students who were chosen in a random stratified manner from students at the University of Baghdad, College of Education for Pure Sciences/Ibn Al-Haitham, Department of Computer. The proposed AI model utilized three artificial intelligence techniques: Decision Tree (DT), Random Forest (RF), and Gradient Boosting Machine (GBM). The classification accuracy using DT was 92.85 and using GMB was 95.23. The RF technique was applied to find the essential features, and the Pearson correlation was used to find the correlation between the features. The findings indicated that students indeed possess digital intelligence, underscoring the potential for tailored interventions to enhance their digital skills and competencies. This research not only sheds light on the current DI landscape among university students but also paves the way for targeted educational initiatives to foster digital literacy and proficiency in the academic setting.

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Publication Date
Wed Jul 01 2015
Journal Name
Magnetic Resonance Imaging
Alpha shape theory for 3D visualization and volumetric measurement of brain tumor progression using magnetic resonance images
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Alpha shape theory for 3D visualization and volumetric measurement of brain tumor progression using magnetic resonance images

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Publication Date
Fri Jan 24 2020
Journal Name
Petroleum And Coal
Evaluation of Geomechanical Properties for Tight Reservoir Using Uniaxial Compressive Test, Ultrasonic Test, and Well Logs Data
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Tight reservoirs have attracted the interest of the oil industry in recent years according to its significant impact on the global oil product. Several challenges are present when producing from these reservoirs due to its low to extra low permeability and very narrow pore throat radius. Development strategy selection for these reservoirs such as horizontal well placement, hydraulic fracture design, well completion, and smart production program, wellbore stability all need accurate characterizations of geomechanical parameters for these reservoirs. Geomechanical properties, including uniaxial compressive strength (UCS), static Young’s modulus (Es), and Poisson’s ratio (υs), were measured experimentally using both static and dynamic met

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Publication Date
Fri Oct 30 2020
Journal Name
Journal Of Economics And Administrative Sciences
Comparison of some of reliability and Hazard estimation methods for Rayleigh logarithmic distribution using simulation with application
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The question of estimation took a great interest in some engineering, statistical applications, various applied, human sciences, the methods provided by it helped to identify and accurately the many random processes.

In this paper, methods were used through which the reliability function, risk function, and estimation of the distribution parameters were used, and the methods are (Moment Method, Maximum Likelihood Method), where an experimental study was conducted using a simulation method for the purpose of comparing the methods to show which of these methods are competent in practical application This is based on the observations generated from the Rayleigh logarithmic distribution (RL) with sample sizes

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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
Sat Aug 04 2012
Journal Name
University Of Thi-qar Journal
Prediction of Ultimate Soil Bearing Capacity for Shallow Strip Foundation on Sandy Soils by Using (ANN) Techniqu
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Bearing capacity of soil is an important factor in designing shallow foundations. It is directly related to foundation dimensions and consequently its performance. The calculations for obtaining the bearing capacity of a soil needs many varying parameters, for example soil type, depth of foundation, unit weight of soil, etc. which makes these calculation very variable–parameter dependent. This paper presents the results of comparison between the theoretical equation stated by Terzaghi and the Artificial Neural Networks (ANN) technique to estimate the ultimate bearing capacity of the strip shallow footing on sandy soils. The results show a very good agreement between the theoretical solution and the ANN technique. Results revealed that us

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Publication Date
Sat Aug 01 2020
Journal Name
Iop Conference Series: Materials Science And Engineering
Using BIM for Optimizing the Upgrading Cost to Convert the Traditional Buildings to Sustainable Buildings in Iraq
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Abstract<p>A high percentage of existing buildings in Iraq are traditional buildings, yet there is approximately no such green building in Baghdad or other governorates. Most of these buildings require urgent upgrading to increase their performance (operationally, economically, and environmentally), also the building owners looking for identifying and implementing many of the green building measures to reduce the operational and maintenance costs of their buildings. The decision-makers need to support the possibility of achieving sustainable measures of existing building rating systems such as LEED or BREEAM, and that would require an optimization model. The goal of this study is to maximize the</p> ... Show More
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Publication Date
Sun Dec 01 2019
Journal Name
Journal Of Engineering Science And Technology (jestec)
Water Quality Assessment And Total Dissolved Solids Prediction For Tigris River In Baghdad City Using Mathematical Models
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Total dissolved solids are at the top of the parameters list of water quality that requires investigations for planning and management, especially for irrigation and drinking purposes. If the quality of water is sufficiently predictable, then appropriate management is possible. In the current study, Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models were used as indicators of water quality and for the prediction of Total Dissolved Solids (TDS) along the Tigris River, in Baghdad city. To build these models five water parameters were selected from the intakes of four water treatment plants on the Tigris River, for the period between 2013 and 2017. The selected water parameters were Total Dissolved Solids (TDS

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Publication Date
Thu Aug 01 2019
Journal Name
Ieee Photonics Journal
Di-Iron Trioxide Hydrate-Multi-Walled Carbon Nanotube Nanocomposite for Arsenite Detection Using Surface Plasmon Resonance Technique
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Publication Date
Sun Jun 01 2025
Journal Name
Al-khwarizmi Engineering Journal
Improving Barcode Vision Scanning Process using a Drone-based Tracking PID Controller for Warehouse in Industry 4.0
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Drones play a vital role in the fundamental aspects of Industry 4.0 by converting conventional warehouses into intelligent ones, particularly in the realm of barcode scanning. Various potential issues frequently arise during barcode scanning by drones, specifically when the drone camera has difficulty obtaining distinct images due to certain factors, such as distance, capturing the image whilst flying, noise in the environment and different barcode dimensions. In addressing these challenges, this study proposes an approach that combines a proportional–integral–derivative (PID) controller with image processing techniques. The PID controller is responsible for continuously monitoring the camera’s input, detecting the difference

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Publication Date
Wed Apr 01 2009
Journal Name
International Journal Of Applied Environmental Sciences
An expert System for Predicting the Effects of Noise Pollution on Grass Trimming Task Using Fuzzy Modeling
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Grass trimming operation is widely done in Malaysia for the purpose of maintaining highways. Large number of operators engaged in this work encounters high level of noise generated by back pack type grass trimmer used for this purpose. High level of noise exposure gives different kinds of ill effect on human operators. Exact nature of deteriorated work performance is not known. For predicting the work efficiency deterioration, fuzzy tool has been used in present research. It has been established that a fuzzy computing system will help in identification and analysis of fuzzy models fuzzy system offers a convenient way of representing the relationships between the inputs and outputs of a system in the form of IF-THEN rules. The paper presents

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