Preferred Language
Articles
/
mRe9Zo4BVTCNdQwCbkZ3
Advanced Machine Learning application for Permeability Prediction for (M) Formation in an Iraqi Oil Field
...Show More Authors

Permeability estimation is a vital step in reservoir engineering due to its effect on reservoir's characterization, planning for perforations, and economic efficiency of the reservoirs. The core and well-logging data are the main sources of permeability measuring and calculating respectively. There are multiple methods to predict permeability such as classic, empirical, and geostatistical methods. In this research, two statistical approaches have been applied and compared for permeability prediction: Multiple Linear Regression and Random Forest, given the (M) reservoir interval in the (BH) Oil Field in the northern part of Iraq. The dataset was separated into two subsets: Training and Testing in order to cross-validate the accuracy and the performance of the algorithms. The random forest algorithm was the most accurate method leading to lowest Root Mean Square Prediction Error (RMSPE) and highest Adjusted R-Square than multiple linear regression algorithm for both training and testing subset respectively. Thus, random Forest algorithm is more trustable in permeability prediction in non-cored intervals and its distribution in the geological model.

Crossref
View Publication Preview PDF
Quick Preview PDF
Publication Date
Fri Sep 30 2016
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Using Different Surfactants to Increase Oil Recovery of Rumaila Field (Experimental Work)
...Show More Authors

Enhanced oil recovery is used in many mature oil reservoirs to increase the oil recovery factor. Surfactant flooding has recently gained interest again. To create micro emulsions at the interface between crude oil and water, surfactant flooding is the injection of surfactants (and co-surfactants) into the reservoir, thus achieving very low interfacial tension, which consequently assists mobilize the trapped oil.

In this study a flooding system, which has been  manufactured and described at high pressure. The flooding processes included oil, water and surfactants. 15 core holders has been prepared at first stage of the experiment and  filled with washed sand grains 80-500 mm and then packing the sand to obtain sand packs

... Show More
View Publication Preview PDF
Publication Date
Mon Jan 01 2024
Journal Name
Fifth International Conference On Applied Sciences: Icas2023
A modified Mobilenetv2 architecture for fire detection systems in open areas by deep learning
...Show More Authors

This research describes a new model inspired by Mobilenetv2 that was trained on a very diverse dataset. The goal is to enable fire detection in open areas to replace physical sensor-based fire detectors and reduce false alarms of fires, to achieve the lowest losses in open areas via deep learning. A diverse fire dataset was created that combines images and videos from several sources. In addition, another self-made data set was taken from the farms of the holy shrine of Al-Hussainiya in the city of Karbala. After that, the model was trained with the collected dataset. The test accuracy of the fire dataset that was trained with the new model reached 98.87%.

Scopus Crossref
Publication Date
Tue Jan 14 2025
Journal Name
South Eastern European Journal Of Public Health
Deep learning-based threat Intelligence system for IoT Network in Compliance With IEEE Standard
...Show More Authors

The continuous advancement in the use of the IoT has greatly transformed industries, though at the same time it has made the IoT network vulnerable to highly advanced cybercrimes. There are several limitations with traditional security measures for IoT; the protection of distributed and adaptive IoT systems requires new approaches. This research presents novel threat intelligence for IoT networks based on deep learning, which maintains compliance with IEEE standards. Interweaving artificial intelligence with standardization frameworks is the goal of the study and, thus, improves the identification, protection, and reduction of cyber threats impacting IoT environments. The study is systematic and begins by examining IoT-specific thre

... Show More
View Publication
Crossref
Publication Date
Thu Apr 18 2019
Journal Name
Al-kindy College Medical Journal
Seroprevalence of Immunoglobulin G (IgG) and Immunoglobulin M (IgM) and Risk Factors of Toxoplasmosis for A sample of Pregnant Women in Baghdad
...Show More Authors

Background: Toxoplasmosis is a very common infection caused by the obligate intracellular protozoan parasite. This parasite is called Toxoplasma gondii widely distributed around the world . Toxoplasma gondii can be vertically transmitted to the fetus during pregnancy and may cause wide range of clinical manifestations in the offspring.

Objective: To determine seroprevalence Immunoglobulin G (IgG) and Immunoglobulin M  (IgM ) to toxoplasma gondii among pregnant women and to identify the risk factors.

Type of the study: A cross-sectional study.

Methods: A total of 110 blood samples of pregnant women were collected from

... Show More
View Publication Preview PDF
Crossref
Publication Date
Mon Sep 01 2014
Journal Name
Al-khwarizmi Engineering Journal
Heterogeneous Photocatalytic Degradation for Treatment of Oil from Wastewater
...Show More Authors

In the present study, advanced oxidation process / heterogeneous photocatalytic process (UV/TiO2/Fenton) system was investigated to the treatment of oily wastewater. The present study was conducted to evaluate the effect of hydrogen peroxide concentration H2O2, initial amount of the iron catalyst Fe+2, pH, temperature, amount of TiO2 and the concentration of oil in the wastewater.  The removal efficiency for the system UV/TiO2/Fenton at optimal conditions and dosage (H2O2 = 400mg/L, Fe+2 = 40mg/L, pH=5, temperature =30oC, TiO2=75mg/L) for 1000mg/L load was found to be 77%.

Aluminum foil cover around the re

... Show More
View Publication Preview PDF
Publication Date
Thu Dec 28 2017
Journal Name
Al-khwarizmi Engineering Journal
Obstacles Avoidance for Mobile Robot Using Enhanced Artificial Potential Field
...Show More Authors

In this paper, an enhanced artificial potential field (EAPF) planner is introduced. This planner is proposed to rapidly find online solutions for the mobile robot path planning problems, when the underlying environment contains obstacles with unknown locations and sizes. The classical artificial potential field represents both the repulsive force due to the detected obstacle and the attractive force due to the target. These forces can be considered as the primary directional indicator for the mobile robot. However, the classical artificial potential field has many drawbacks. So, we suggest two secondary forces which are called the midpoint

... Show More
View Publication Preview PDF
Publication Date
Fri Sep 01 2023
Journal Name
Journal Of Engineering
Iraqi Sentiment and Emotion Analysis Using Deep Learning
...Show More Authors

Analyzing sentiment and emotions in Arabic texts on social networking sites has gained wide interest from researchers. It has been an active research topic in recent years due to its importance in analyzing reviewers' opinions. The Iraqi dialect is one of the Arabic dialects used in social networking sites, characterized by its complexity and, therefore, the difficulty of analyzing sentiment. This work presents a hybrid deep learning model consisting of a Convolution Neural Network (CNN) and the Gated Recurrent Units (GRU) to analyze sentiment and emotions in Iraqi texts. Three Iraqi datasets (Iraqi Arab Emotions Data Set (IAEDS), Annotated Corpus of Mesopotamian-Iraqi Dialect (ACMID), and Iraqi Arabic Dataset (IAD)) col

... Show More
View Publication Preview PDF
Crossref (6)
Crossref
Publication Date
Sat Apr 30 2022
Journal Name
Revue D'intelligence Artificielle
Performance Evaluation of SDN DDoS Attack Detection and Mitigation Based Random Forest and K-Nearest Neighbors Machine Learning Algorithms
...Show More Authors

Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne

... Show More
View Publication
Scopus (19)
Crossref (10)
Scopus Crossref
Publication Date
Sun Dec 30 2001
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Analyzing and Optimizing Bits Performance in Three Iraqi Oil-Fields
...Show More Authors

View Publication Preview PDF
Publication Date
Tue Oct 01 2013
Journal Name
Journal Of Economics And Administrative Sciences
The impact of Innovation in offering the banking services in achieving competitive advantage for banks ( An applied study in the Iraqi private Banks )
...Show More Authors

     This research aims at recognizing the concept of Innovation in offering the banking services as well as the concept and dimensions of competitive advantage . And to identify and analyze the relationship ( correlation and impact ) between the concept of Innovation in  offering the banking services and the dimensions of the competitive advantage under discussion . The research includes all Iraqi private banks in Baghdad city only of the (20) banks . The researcher adopted , in this study , a random sample of the distribution of the questionnaire to members of the research sample are managers , customers and employees in these banks , and were distributed ( 115 ) form  questionnaire , ( 97 ) form w

... Show More
View Publication Preview PDF
Crossref