Details

Publication Date

Sat Dec 30 2023

Journal Name

Nasaq Journal

Volume

40

Issue Number

2

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1

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Iraqi EFL Students’ Attitudes towards Online Learning

Online Learning

Attitudes

Investigating

EFL Students

Online Instruction

Maysaa Rashid Abdul-Majeed

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Online learning is not a new concept in education, but it has been used extensively since the Covid-19 pandemic and is still in use now. Every student in the world has gone through this learning process from the primary to the college levels, with both teachers and students conducting instruction online (at home). The goal of the current study is to investigate college students’ attitudes towards online learning. To accomplish the goal of the current study, a questionnaire is developed and adjusted before being administered to a sample of 155 students. Additionally, validity and reliability are attained. Some conclusions, recommendations, and suggestions are offered in the end.

Publication Date

Sat Jun 01 2024

Journal Name

Journal Of Engineering

Intelligent Dust Monitoring System Based on IoT

IoT (Internet of Things)

Dust prediction

WSN (Wireless Sensor Network)

Deep Learning (DL)

Long Short-Term Memory (LSTM)

Muna Hadi

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Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system

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Publication Date

Wed Nov 27 2024

Journal Name

Frontiers In Education

The impact of using artificial intelligence techniques in improving the quality of educational services/case study at the University of Baghdad

artificial intelligence

AI on curricula

AI on decision making

AI on distance learning

AI on training

quality of educational services

Farhan,

Sadiq,

Mustafa Hussein Zwayyer

Arnout,

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The utilization of artificial intelligence techniques has garnered significant interest in recent research due to their pivotal role in enhancing the quality of educational offerings. This study investigated the impact of employing artificial intelligence techniques on improving the quality of educational services, as perceived by students enrolled in the College of Pharmacy at the University of Baghdad. The study sample comprised 379 male and female students. A descriptive-analytical approach was used, with a questionnaire as the primary tool for data collection. The findings indicated that the application of artificial intelligence methods was highly effective, and the educational services provided to students were of exceptional quality.

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Publication Date

Wed May 04 2022

Journal Name

Int. J. Nonlinear Anal. Appl.

Knee Meniscus Segmentation and Tear Detection Based On Magnitic Resonacis Images: A Review of Literature

Magnetic Resonance Imaging (M.R.I.)

Convolutional Neural Network (C.N.N.)

Deep learning (D.L.)

Musculoskeletal (M.S.K.)

Knee

Meniscus

Tear

Area Under the Curve (A.U.C.)

Mohammed

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The meniscus has a crucial function in human anatomy, and Magnetic Resonance Imaging (M.R.I.) plays an essential role in meniscus assessment. It is difficult to identify cartilage lesions using typical image processing approaches because the M.R.I. data is so diverse. An M.R.I. data sequence comprises numerous images, and the attributes area we are searching for may differ from each image in the series. Therefore, feature extraction gets more complicated, hence specifically, traditional image processing becomes very complex. In traditional image processing, a human tells a computer what should be there, but a deep learning (D.L.) algorithm extracts the features of what is already there automatically. The surface changes become valuable when

Publication Date

Mon Nov 11 2019

Journal Name

Spe

Modeling Rate of Penetration using Artificial Intelligent System and Multiple Regression Analysis

neural network

strength

machine learning

ROP prediction

Upstream Oil & Gas

prediction

ANN model

input parameter

multiple regression analysis

artificial neural network

Farqad

Hassan

Ethar

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AbstractOver the years, the prediction of penetration rate (ROP) has played a key rule for drilling engineers due it is effect on the optimization of various parameters that related to substantial cost saving. Many researchers have continually worked to optimize penetration rate. A major issue with most published studies is that there is no simple model currently available to guarantee the ROP prediction.The main objective of this study is to further improve ROP prediction using two predictive methods, multiple regression analysis (MRA) and artificial neural networks (ANNs). A field case in SE Iraq was conducted to predict the ROP from a large number of parame ... Show More

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(6)

Publication Date

Mon Dec 10 2018

Journal Name

Day 1 Mon, December 10, 2018

Wellbore Trajectory Optimization Using Rate of Penetration and Wellbore Stability Analysis

reservoir geomechanics

log analysis

machine learning

wellbore integrity

Artificial Intelligence

Wellbore Trajectory Optimization

neural network

wellbore stability analysis

Wellbore Design

wellbore trajectory

Ahmed K.

Usama

Mortadha

Salih

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Drilling deviated wells is a frequently used approach in the oil and gas industry to increase the productivity of wells in reservoirs with a small thickness. Drilling these wells has been a challenge due to the low rate of penetration (ROP) and severe wellbore instability issues. The objective of this research is to reach a better drilling performance by reducing drilling time and increasing wellbore stability.

In this work, the first step was to develop a model that predicts the ROP for deviated wells by applying Artificial Neural Networks (ANNs). In the modeling, azimuth (AZI) and inclination (INC) of the wellbore trajectory, controllable drilling parameters, unconfined compressive strength (UCS), formation

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Publication Date

Mon Dec 10 2018

Journal Name

Day 1 Mon, December 10, 2018

Wellbore Trajectory Optimization Using Rate of Penetration and Wellbore Stability Analysis

reservoir geomechanics

log analysis

machine learning

wellbore integrity

Artificial Intelligence

Wellbore Trajectory Optimization

neural network

wellbore stability analysis

Wellbore Design

wellbore trajectory

Ahmed K.

Usama

Mortadha

Salih

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Drilling deviated wells is a frequently used approach in the oil and gas industry to increase the productivity of wells in reservoirs with a small thickness. Drilling these wells has been a challenge due to the low rate of penetration (ROP) and severe wellbore instability issues. The objective of this research is to reach a better drilling performance by reducing drilling time and increasing wellbore stability.

In this work, the first step was to develop a model that predicts the ROP for deviated wells by applying Artificial Neural Networks (ANNs). In the modeling, azimuth (AZI) and inclination (INC) of the wellbore trajectory, controllable drilling parameters, unconfined compressive strength (UCS), formation

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(12)

Publication Date

Mon Apr 24 2017

Journal Name

Spe Kingdom Of Saudi Arabia Annual Technical Symposium And Exhibition

Optimization of Infill Drilling in Whicher Range Field in Australia

machine learning

reservoir simulation

asset and portfolio management

cumulative production

transient drainage area

production rate

reservoir model

infill well

tight gas reservoir

Modeling & Simulation

Omar

Aswin R.

Md Mofazzal

Akim H.

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AbstractNow that most of the conventional reservoirs are being depleted at a rapid pace, the focus is on unconventional reservoirs like tight gas reservoirs. Due to the heterogeneous nature and low permeability of unconventional reservoirs, they require a huge number of wells to hit all the isolated hydrocarbon zones. Infill drilling is one of the most common and effective methods of increasing the recovery, by reducing the well spacing and increasing the sweep efficiency. However, the problem with drilling such a large number of wells is the determination of the optimum location for each well that ensures minimum interference between wells, and accelerates the recovery from the field. Detail ... Show More

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Publication Date

Sat Oct 01 2016

Journal Name

Journal Of Theoretical And Applied Information Technology

Factors affecting global virtual teams’ performance in software projects

Today the trend is to perform software development work via distributed geographical area among team

individual or even as an organization. However

due to the global market and international presence of many companies

there is a need to implement a global virtual teams. The global virtual team members are gradually engaged in globalized business environments across space

time and organizational boundaries via information and communication technologies. A global virtual team relies on communication

collaboration

and information exchange are the most important criteria in global virtual teams’ operations. The purpose of this paper is to answer two research questions. The first research question is to identify the factors affecting global virtual teams’ performance. A systematic literature review was conducted to answer the first research question. The second research question is on what the rank of the factor affecting the global virtual teams’ performance according to their level of effect on global virtual teams’ performance. Online survey was conducted within 103 developers and IT managers from eight IT companies to answer the second research question. The Statistical Package for Social Science (SPSS 22) was used to analyze the collected data. In this study

we investigated factors that affect global virtual teams’ performance

factors considered include cultural differences

language problems

time-zone differences

team size

technical problems

lack of trust

lack of sufficient training

and ICT problems. Also the findings indicated that lack of sufficient training is the highest level of effect on global virtual teams’ performance. On the other hand

Ali Yahya Gheni

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