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Intelligence framework dust forecasting using regression algorithms models
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<span>Dust is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. In the recent decade, climate change in Iraq, typified by increased droughts and deserts, has generated numerous environmental issues. This study forecasts dust in five central Iraqi districts using machine learning and five regression algorithm supervised learning system framework. It was assessed using an Iraqi meteorological organization and seismology (IMOS) dataset. Simulation results show that the gradient boosting regressor (GBR) has a mean square error of 8.345 and a total accuracy ratio of 91.65%. Moreover, the results show that the decision tree (DT), where the mean square error is 8.965, comes in second place with a gross ratio of 91%. Furthermore, Bayesian ridge (BR), linear regressor (LR), and stochastic gradient descent (SGD), with mean square error and with accuracy ratios of 84.365%, 84.363%, and 79%. As a result, the performance precision of these regression models yields. The interaction framework was designed to be a straightforward tool for working with this paradigm. This model is a valuable tool for establishing strategies to counter the swiftness of climate change in the area under study.</span>

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Publication Date
Tue Aug 15 2023
Journal Name
Al-academy
The effectiveness of artificial intelligence in contemporary digital graphic design
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In our world, technological development has become inherent in all walks of life and is characterized by its speed in performance and uses. This development required the emergence of new technologies that represent a future revolution for a fourth industrial revolution in various fields, which contributed to finding many alternatives and innovative technical solutions that shortened time and space in terms of making Machines are smarter, more accurate, and faster in accomplishing the tasks intended for them, and we find the emergence of what is called artificial intelligence (artificial intelligence), which is the technology of the future, which is one of the most important outputs of the fourth industrial revolution, and artificial inte

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Publication Date
Fri Feb 08 2019
Journal Name
Journal Of The College Of Education For Women
The role of kindergarten teachers in developing children's emotional intelligence
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The study aimed to get acquainted with kindergarten teachers in the development of
emotional intelligence in children, To achieve this a study too, which consisted of 40 items,
within four areas was condncted: (managing emotions, emotional knowledge, empathy, social
networking) The study tool was applied to the sample amounting (200) teachers of the
kindergarten teachers in the province of Jerash and after analyzing the results statistically
using arithmetic averages standard deviations and variance analysis quartet the following
results were reached :
- presence of statistically significant differences at the level of (α =0,05) is attributable to the
impact of the educational level in the areas of empathy and so

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Publication Date
Fri Feb 01 2019
Journal Name
Journal Of The College Of Education For Women
The Intelligence of the Child in Relation to some Variables
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The Intelligence of the Child in Relation to some Variables

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Publication Date
Mon Jan 01 2024
Journal Name
International Journal Of Mathematics And Computer Science
Artificial Intelligence Techniques to Identify Individuals through Palm Image Recognition
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Artificial intelligence (AI) is entering many fields of life nowadays. One of these fields is biometric authentication. Palm print recognition is considered a fundamental aspect of biometric identification systems due to the inherent stability, reliability, and uniqueness of palm print features, coupled with their non-invasive nature. In this paper, we develop an approach to identify individuals from palm print image recognition using Orange software in which a hybrid of AI methods: Deep Learning (DL) and traditional Machine Learning (ML) methods are used to enhance the overall performance metrics. The system comprises of three stages: pre-processing, feature extraction, and feature classification or matching. The SqueezeNet deep le

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Publication Date
Fri Jul 01 2016
Journal Name
Journal Of Economics And Administrative Sciences
Entrepreneurial performance in accordance with the competitive intelligence and strategic
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 The research aims to study the entrepreneurial performance of the banks, according to the intelligence of competitive and strategic as the entrepreneurial performance is the one who does not stand the benefits of excellence in accomplished when just achieving the bank's objectives planned, but exceed it down to creativity in accomplishing these goals in a manner leads to making a entrepreneurial bank in the markets and the focus the eyes of competitors and the banks and other Following his example.

Was chosen the subject of research and strategic intelligence and competitive because of its impact on the strategic success of the banking sector, the fact is the entrepreneurial in the Iraqi banking mar

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Publication Date
Tue Jun 30 2020
Journal Name
Journal Of Economics And Administrative Sciences
Using ARIMA models to forecast the volume of cargo handled in Iraqi ports An applied study in the general company of Iraqi ports
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Time series is an important statistical method adopted in the analysis of phenomena, practices, and events in all areas during specific time periods and predict future values ​​contribute to give a rough estimate of the status of the study, so the study aimed to adopt the ARIMA models to forecast the volume of cargo handled and achieved in four ports (Umm Qasr Port, Khor Al Zubair Port, Abu Flus Port, and Maqal Port(, Monthly data on the volume of cargo handled for the years (2006-2018) were collected (156) observations. The study found that the most efficient model is ARIMA (1,1,1).

The volume of go

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Publication Date
Fri Jan 01 2021
Journal Name
Artificial Intelligence For Covid-19
An Efficient Mixture of Deep and Machine Learning Models for COVID-19 and Tuberculosis Detection Using X-Ray Images in Resource Limited Settings
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Publication Date
Tue Sep 09 2014
Journal Name
Iosr Journal Of Mathematics (iosr-jm)
An Efficient Shrinkage Estimator for the Parameters of Simple Linear Regression Model
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Publication Date
Wed Jun 01 2016
Journal Name
Journal Of Economics And Administrative Sciences
Proposed method to estimate missing values in Non - Parametric multiple regression model
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In this paper, we will provide a proposed method to estimate missing values for the Explanatory variables for Non-Parametric Multiple Regression Model and compare it with the Imputation Arithmetic mean Method, The basis of the idea of this method was based on how to employ the causal relationship between the variables in finding an efficient estimate of the missing value, we rely on the use of the Kernel estimate by Nadaraya – Watson Estimator , and on Least Squared Cross Validation (LSCV) to estimate the Bandwidth, and we use the simulation study to compare between the two methods.

 

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Publication Date
Sun Jan 14 2018
Journal Name
Journal Of Engineering
Regression Modeling of EDM Process for AISI D2 Tool Steel with RSM
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In this paper, Response Surface Method (RSM) is utilized to carry out an investigation of the impact of input parameters: electrode type (E.T.) [Gr, Cu and CuW], pulse duration of current (Ip), pulse duration on time (Ton), and pulse duration off time (Toff) on the surface finish in EDM operation. To approximate and concentrate the suggested second- order regression model is generally accepted for Surface Roughness Ra, a Central Composite Design (CCD) is utilized for evaluating the model constant coefficients of the input parameters on Surface Roughness (Ra). Examinations were performed on AISI D2 tool steel. The important coefficients are gotten by achieving successfully an Analysis of V

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