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Multi-Sites Multi-Variables Forecasting Model for Hydrological Data using Genetic Algorithm Modeling
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A two time step stochastic multi-variables multi-sites hydrological data forecasting model was developed and verified using a case study. The philosophy of this model is to use the cross-variables correlations, cross-sites correlations and the two steps time lag correlations simultaneously, for estimating the parameters of the model which then are modified using the mutation process of the genetic algorithm optimization model. The objective function that to be minimized is the Akiake test value. The case study is of four variables and three sites. The variables are the monthly air temperature, humidity, precipitation, and evaporation; the sites are Sulaimania, Chwarta, and Penjwin, which are located north Iraq. The model performance was checked by comparing it's results with the results of six forecasting models developed for the same data by Al-Suhili and khanbilvardi, 2014.The check of the performance of the new developed model was made for three forecasted series for each variable, using the Akaike test which indicates that the developed model is more successful, since it gave the minimum (AIC) values for (91.67 %) of the forecasted series. This indicates that the developed model had improved the forecasting performance. For the rest of cases (8.33%), other models gave the lowest AIC value, however it is slightly lower than that given by the developed model. Moreover the t-test for monthly means comparison between the models indicates that the developed model has the highest percent of succeed (100%).

 

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Publication Date
Wed Mar 23 2022
Journal Name
World Economics & Finance Bulletin (wefb)
ANALYSIS OF THE CAUSALITY RELATIONSHIP BETWEEN SPENDING ON HIGHER EDUCATION AND SOME ECONOMIC VARIABLES IN IRAQ FOR THE PERIOD 1990-2020
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Higher education is important because it creates and develops human capital and provides qualified human cadres, which requires restructuring government spending so that an increase in funding allocated to education is brought about. During the period 1990-2020, government spending was weak on educational institutions in Iraq, which led to a decline in The role of these institutions in the economic development of the country. The highest percentage of spending on higher education of GDP was 0.47% in 2007 and the lowest was 0.01% in 2005. The number of public universities reached 35, and the number of private universities and colleges reached 64 universities and private colleges in 2020. This was accompanied by an increase in the number of s

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Publication Date
Sat Feb 09 2019
Journal Name
Journal Of The College Of Education For Women
The counseling needs for classroom teachers specialization in Jerash University from their point of view and its relationship with certain variables
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This study aimed at identifying the counseling needs for classroom teachers specialization in
Jerash university. The sample of the study consisted (112) students. To achieve this goal a
scale with (39) items was built by the researcher, this scale has four domains they are as
follows; family, social, psychological and academic domain. The results of this study showed
that counseling needs obtain the heighest average. As well as, the results of the study revealed
that there were statistical differences in favor of gender and teaching year variable. Finally the
study stated a list of recommendations.

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Publication Date
Sun Mar 01 2015
Journal Name
Computer Systems Science & Engineering
Parameters' fine tuning of differential evolution algorithm
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Most heuristic search method's performances are dependent on parameter choices. These parameter settings govern how new candidate solutions are generated and then applied by the algorithm. They essentially play a key role in determining the quality of the solution obtained and the efficiency of the search. Their fine-tuning techniques are still an on-going research area. Differential Evolution (DE) algorithm is a very powerful optimization method and has become popular in many fields. Based on the prolonged research work on DE, it is now arguably one of the most outstanding stochastic optimization algorithms for real-parameter optimization. One reason for its popularity is its widely appreciated property of having only a small number of par

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Scopus (7)
Scopus
Publication Date
Sun Nov 01 2020
Journal Name
Iop Conference Series: Materials Science And Engineering
Developed JPEG Algorithm Applied in Image Compression
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Abstract<p>JPEG is most popular image compression and encoding, this technique is widely used in many applications (images, videos and 3D animations). Meanwhile, researchers are very interested to develop this massive technique to compress images at higher compression ratios with keeping image quality as much as possible. For this reason in this paper we introduce a developed JPEG based on fast DCT and removed most of zeros and keeps their positions in a transformed block. Additionally, arithmetic coding applied rather than Huffman coding. The results showed up, the proposed developed JPEG algorithm has better image quality than traditional JPEG techniques.</p>
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Scopus (8)
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Publication Date
Tue Aug 23 2022
Journal Name
Int. J. Nonlinear Anal. Appl.
Face mask detection based on algorithm YOLOv5s
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Determining the face of wearing a mask from not wearing a mask from visual data such as video and still, images have been a fascinating research topic in recent decades due to the spread of the Corona pandemic, which has changed the features of the entire world and forced people to wear a mask as a way to prevent the pandemic that has calmed the entire world, and it has played an important role. Intelligent development based on artificial intelligence and computers has a very important role in the issue of safety from the pandemic, as the Topic of face recognition and identifying people who wear the mask or not in the introduction and deep education was the most prominent in this topic. Using deep learning techniques and the YOLO (”You on

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Publication Date
Tue Mar 30 2021
Journal Name
Baghdad Science Journal
Delivery Route Management based on Dijkstra Algorithm
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For businesses that provide delivery services, the efficiency of the delivery process in terms of punctuality is very important. In addition to increasing customer trust, efficient route management, and selection are required to reduce vehicle fuel costs and expedite delivery. Some small and medium businesses still use conventional methods to manage delivery routes. Decisions to manage delivery schedules and routes do not use any specific methods to expedite the delivery settlement process. This process is inefficient, takes a long time, increases costs and is prone to errors. Therefore, the Dijkstra algorithm has been used to improve the delivery management process. A delivery management system was developed to help managers and drivers

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Publication Date
Thu May 18 2023
Journal Name
Journal Of Engineering
A Modified Strength Pareto Evolutionary Algorithm 2 based Environmental /Economic Power Dispatch
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A Strength Pareto Evolutionary Algorithm 2 (SPEA 2) approach for solving the multi-objective Environmental / Economic Power Dispatch (EEPD) problem is presented in this paper. In the past fuel cost consumption minimization was the aim (a single objective function) of economic power dispatch problem. Since the clean air act amendments have been applied to reduce SO2 and NOX emissions from power plants, the utilities change their strategies in order to reduce pollution and atmospheric emission as well, adding emission minimization as other objective function made economic power dispatch (EPD) a multi-objective problem having conflicting objectives. SPEA2 is the improved version of SPEA with better fitness assignment, density estimation, an

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Publication Date
Fri Jan 01 2016
Journal Name
Journal Of Engineering
A Mathematical Model of a Thermally Activated Roof (TAR) Cooling System Using a Simplified RC-Thermal Model with Time Dependent Supply Water Temperature
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This paper presents a computer simulation model of a thermally activated roof (TAR) to cool a room using cool water from a wet cooling tower. Modeling was achieved using a simplified 1-D resistance-capacitance thermal network (RC model) for an infinite slab. Heat transfer from the cooling pipe network was treated as 2-D heat flow. Only a limited number of nodes were required to obtain reliable results. The use of 6th order RC-thermal model produced a set of ordinary differential equations that were solved using MATLAB - R2012a. The computer program was written to cover all possible initial conditions, material properties, TAR system geometry and hourly solar radiation. The cool water supply was considered time

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Publication Date
Thu Jan 30 2020
Journal Name
Journal Of Engineering
  Positional Accuracy Assessment for Updating Authoritative Geospatial Datasets Based on Open Source Data and Remotely Sensed Images
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OpenStreetMap (OSM) represents the most common example of online volunteered mapping applications. Most of these platforms are open source spatial data collected by non-experts volunteers using different data collection methods. OSM project aims to provide a free digital map for all the world. The heterogeneity in data collection methods made OSM project databases accuracy is unreliable and must be dealt with caution for any engineering application. This study aims to assess the horizontal positional accuracy of three spatial data sources are OSM road network database, high-resolution Satellite Image (SI), and high-resolution Aerial Photo (AP) of Baghdad city with respect to an analogue formal road network dataset obtain

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Publication Date
Mon Mar 31 2025
Journal Name
The Iraqi Geological Journal
Evaluation of Machine Learning Techniques for Missing Well Log Data in Buzurgan Oil Field: A Case Study
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The investigation of machine learning techniques for addressing missing well-log data has garnered considerable interest recently, especially as the oil and gas sector pursues novel approaches to improve data interpretation and reservoir characterization. Conversely, for wells that have been in operation for several years, conventional measurement techniques frequently encounter challenges related to availability, including the lack of well-log data, cost considerations, and precision issues. This study's objective is to enhance reservoir characterization by automating well-log creation using machine-learning techniques. Among the methods are multi-resolution graph-based clustering and the similarity threshold method. By using cutti

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