Iraqi EFL Students’ Attitudes towards Online Learning

Maysaa Rashid Abdul-Majeed Al-Rubaye

Details

Publication Date

Sat Dec 30 2023

Journal Name

Nasaq Journal

Volume

40

Issue Number

2

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13

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Iraqi EFL Students’ Attitudes towards Online Learning

Online Learning

Attitudes

Investigating

EFL Students

Online Instruction

Maysaa Rashid Abdul-Majeed Al-Rubaye

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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

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 S. H.

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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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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

...Show More Authors

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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(13)

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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(15)

(8)

Publication Date

Mon Oct 13 2025

Journal Name

Mesopotamian Journal Of Cybersecurity

Improvement of the Face Recognition Systems Security Against Morph Attacks using the Developed Siamese Neural Network

Face Recognition Systems (FRS)

Siamese Neural Network (SNN)

Faster Region-based Convolutional Neural Networks (R-CNN)

Local Binary Pattern-Convolutional Neural Network (LBP-CNN)

Deep Learning

Morphing Attacks

Sura

Thair

Nada

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Face Recognition Systems (FRS) are increasingly targeted by morphing attacks, where facial features of multiple individuals are blended into a synthetic image to deceive biometric verification. This paper proposes an enhanced Siamese Neural Network (SNN)-based system for robust morph detection. The methodology involves four stages. First, a dataset of real and morphed images is generated using StyleGAN, producing high-quality facial images. Second, facial regions are extracted using Faster Region-based Convolutional Neural Networks (R-CNN) to isolate relevant features and eliminate background noise. Third, a Local Binary Pattern-Convolutional Neural Network (LBP-CNN) is used to build a baseline FRS and assess its susceptibility to d

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Publication Date

Thu Jan 01 2026

Journal Name

Innovations In Sustainable Sports Infrastructure For Eco-friendly Design And Operations

Redacting The Performance of 110m Hurdle Runners Through the Analysis of Physical and Functional Abilities

Hurdling

Sprinting

Performance

Speed

Strength Further studies could increase sample size and employ machine learning to improve the model by including more biomechanical and psychological predictors

increasing the accuracy and applicability of performance predictions in sprint hurdling.

Thaer

Zina

Fatima

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The performance in the 110-meter hurdles at the sprint hurdles event is determined by several physical and physiological qualities. Nonetheless, relatively little attention has been paid to the predictability of such factors in determining race performance. This study seeks to fill this gap by establishing the most critical physical and physiological characteristics affecting elite hurdlers’ performance and creating a statistical model that predicts race times from the identified measurable characteristics. The study utilized a descriptive research design in-volving six elite male hurdlers, all of whom completed a battery of standardized physical and functional tests to assess their explosive lower-body strength, agility, reaction

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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