haytham elmousalami - Academia.edu (original) (raw)

Papers by haytham elmousalami

Research paper thumbnail of Artificial Intelligence for Drilling Lost Circulation: A Systematic Literature Review

Geoenergy science and engineering, Apr 1, 2024

Research paper thumbnail of Green artificial intelligence for cost-duration variance prediction (CDVP) for irrigation canals rehabilitation projects

Expert systems with applications, Sep 1, 2024

Research paper thumbnail of Assessment Framework for BIM-Digital Twin Readiness in the Construction Industry

Buildings, Jan 17, 2024

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Research paper thumbnail of Automated lost circulation severity classification and mitigation system using explainable Bayesian optimized ensemble learning algorithms

Journal of petroleum exploration and production technology, Jul 11, 2024

Research paper thumbnail of Cost-Duration Variance Prediction (CDVP) System Using Explainable Bayesian Optimized Ensemble Machine Learning

This research introduces an integrated system for a novel management concept called automated cos... more This research introduces an integrated system for a novel management concept called automated cost-duration variance prediction (CDVP) system which allows to the project managers to look ahead at project budget, duration, and its variances at the project completion. To obtain the optimal results of each algorithm, the proposed system conducted Bayesian optimization for each developed algorithm. As a result, Extremely Randomized Trees (ET) was the optimal model for duration prediction at 2.53% and 0.961 for Mean Absolute Percentage Error (MAPE) and adjusted determination coefficient (R*2), respectively. AdaBoost Regressor (ABR) was the best model for duration variance prediction with 8.970 MAPE, and 0.949 R*2. On the other hand, the optimal model for project cost was a Light Gradient Boosting Machine (LGBM) with 2.98% and 0.931 for MAPE and R*2, respectively. Extremely Randomized Tree (ET) was the best model for cost variance prediction with 4.151 MAPE, and 0.962 R*2. SHapley Additiv...

Research paper thumbnail of Comparison of Artificial Intelligence Techniques for Project Conceptual Cost Prediction

arXiv (Cornell University), Aug 8, 2019

Developing a reliable parametric cost model at the conceptual stage of the project is crucial for... more Developing a reliable parametric cost model at the conceptual stage of the project is crucial for projects managers and decision makers. Existing methods, such as probabilistic and statistical algorithms have been developed for project cost prediction. However, these methods are unable to produce accurate results for conceptual cost prediction due to small and unstable data samples. Artificial intelligence (AI) and machine learning (ML) algorithms include numerous models and algorithms for supervised regression applications. Therefore, a comparison analysis for AI models is required to guide practitioners to the appropriate model. The study focuses on investigating twenty artificial intelligence (AI) techniques which are conducted for cost modeling such as fuzzy logic (FL) model, artificial neural networks (ANNs), multiple regression analysis (MRA), case-based reasoning (CBR), hybrid models, and ensemble methods such as scalable boosting trees (XGBoost). Field canals improvement projects (FCIPs) are used as an actual case study to analyze the performance of the applied ML models. Out of 20 AI techniques, the results showed that the most accurate and suitable method is XGBoost with 9.091% and 0.929 based on Mean Absolute Percentage Error (MAPE) and adjusted R 2. Nonlinear adaptability, handling missing values and outliers, model interpretation and uncertainty have been discussed for the twenty developed AI models.

Research paper thumbnail of A novel conceptual cost estimation decision-making model for field canal improvement projects

International Journal of Construction Management

Research paper thumbnail of Stacking Deep Learning for Early COVID-19 Vision Diagnosis

Studies in Big Data, 2020

early and accurate COVID-19 diagnosis prediction plays a crucial role for helping radiologists an... more early and accurate COVID-19 diagnosis prediction plays a crucial role for helping radiologists and health care workers to take reliable corrective actions for classify patients and detecting the COVID 19 confirmed cases. Prediction and classification accuracy are critical for COVID-19 diagnosis application. Current practices for COVID-19 images classification are mostly built upon convolutional neural network (CNNs) where CNN is a single algorithm. On the other hand, ensemble machine learning models produce higher accuracy than a single machine leaning. Therefore, this study conducts stacking deep learning methodology to produce the highest results of COVID-19 classification. The stacked ensemble deep learning model accuracy has produced 98.6% test accuracy. Accordingly, the stacked ensemble deep learning model produced superior performance than any single model. Accordingly, ensemble machine learning evolves as a future trend due to its high scalability, stability, and prediction accuracy.

Research paper thumbnail of Stacking Deep Learning for Early COVID-19 Vision Diagnosis

Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach, 2020

early and accurate COVID-19 diagnosis prediction plays a crucial role for helping radiologists an... more early and accurate COVID-19 diagnosis prediction plays a crucial role for helping radiologists and health care workers to take reliable corrective actions for classify patients and detecting the COVID 19 confirmed cases. Prediction and classification accuracy are critical for COVID-19 diagnosis application. Current practices for COVID-19 images classification are mostly built upon convolutional neural network (CNNs) where CNN is a single algorithm. On the other hand, ensemble machine learning models produce higher accuracy than a single machine leaning. Therefore, this study conducts stacking deep learning methodology to produce the highest results of COVID-19 classification. The stacked ensemble deep learning model accuracy has produced 98.6% test accuracy. Accordingly, the stacked ensemble deep learning model produced superior performance than any single model. Accordingly, ensemble machine learning evolves as a future trend due to its high scalability, stability, and prediction a...

Research paper thumbnail of The Truth About 5G and COVID-19: Basics, Analysis, and Opportunities

Digital Transformation and Emerging Technologies for Fighting COVID-19 Pandemic: Innovative Approaches, 2021

5G is a paradigm shift for data transfer and wireless communication technology where 5G involves ... more 5G is a paradigm shift for data transfer and wireless communication technology where 5G involves massive bandwidths based on high carrier frequencies Unlike 4G, 5G is highly integrative to produce a seamless user experience and universal high-rate coverage The key role of 5G is increasing data capacity, improving data rate transfer, providing better service quality, and decreasing latency Recently, COVID-19 is declared as an international epidemic More than 4 5 million confirmed cases and +308,000 death cases have been recorded around more than 209 countries on 16th May 2020 There are several insane theories about 5G technology and human health Therefore, people are burning valuable 5G infrastructure down out of fear for their health People think that 5G towers are weakening the immune system and causing the global COVID-19 pandemic This chapter reviews the data transmission revolution from 1G to 5G technology and discusses the impact of 5G technology on human health, pandemic, and business perspectives © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG

Research paper thumbnail of Closure to “Artificial Intelligence and Parametric Construction Cost Estimate Modeling: State-of-the-Art Review” by Haytham H. Elmousalami

Journal of Construction Engineering and Management, 2021

Research paper thumbnail of Intelligent methodology for project conceptual cost prediction

Research paper thumbnail of Evaluation of Cost Drivers for Field Canals Improvement Projects

Water Resources Management, 2017

Field Canals Improvement Project (FCIP) aims to conserve fresh water. Several methods are existed... more Field Canals Improvement Project (FCIP) aims to conserve fresh water. Several methods are existed to predict project preliminary cost. However, identification of model inputs remains a challenging task during model development. This study utilizes two procedures, Traditional Delphi Method (TDM) and Fuzzy Delphi Method (FDM), which are used for collecting and initially ranking cost drivers. According to the second approach, Fuzzy Analytic Hierarchy Process (FAHP) is used for finally ranking the screened parameters by FDM. Delphi rounds and Likert scale are conducted to determine the most important factors from the viewpoint of consultant engineers and contractors. Regression analysis and R square is used as a comparison criteria between traditional technique and fuzzy techniques. The study suggests using Fuzzy theory and Delphi method with Analytic Hierarchy Process in order to identify cost drivers efficiently. Paper contribution is proposing a methodology to evaluate cost drivers of FCIP using qualitative data such as experts’ opinions.

Research paper thumbnail of Day Level Forecasting for Coronavirus Disease (COVID-19) Spread: Analysis, Modeling and Recommendations

arXiv: Populations and Evolution, 2020

In mid of March 2020, Coronaviruses such as COVID-19 is declared as an international epidemic. Mo... more In mid of March 2020, Coronaviruses such as COVID-19 is declared as an international epidemic. More than 125000 confirmed cases and 4,607 death cases have been recorded around more than 118 countries. Unfortunately, a coronavirus vaccine is expected to take at least 18 months if it works at all. Moreover, COVID -19 epidemics can mutate into a more aggressive form. Day level information about the COVID -19 spread is crucial to measure the behavior of this new virus globally. Therefore, this study presents a comparison of day level forecasting models on COVID-19 affected cases using time series models and mathematical formulation. The forecasting models and data strongly suggest that the number of coronavirus cases grows exponentially in countries that do not mandate quarantines, restrictions on travel and public gatherings, and closing of schools, universities, and workplaces (Social Distancing).

Research paper thumbnail of Prediction of Construction Cost for Field Canals Improvement Projects in Egypt

ArXiv, 2019

Field canals improvement projects (FCIPs) are one of the ambitious projects constructed to save f... more Field canals improvement projects (FCIPs) are one of the ambitious projects constructed to save fresh water. To finance this project, Conceptual cost models are important to accurately predict preliminary costs at the early stages of the project. The first step is to develop a conceptual cost model to identify key cost drivers affecting the project. Therefore, input variables selection remains an important part of model development, as the poor variables selection can decrease model precision. The study discovered the most important drivers of FCIPs based on a qualitative approach and a quantitative approach. Subsequently, the study has developed a parametric cost model based on machine learning methods such as regression methods, artificial neural networks, fuzzy model and case-based reasoning.

Research paper thumbnail of Data on Field Canals Improvement Projects for Cost Prediction Using Artificial Intelligence

Data in Brief

Field Canals Improvement Projects is an important sustainable project to save fresh water in our ... more Field Canals Improvement Projects is an important sustainable project to save fresh water in our world. Machine learning and artificial intelligence (AI) needs sufficient dataset size to model and predict the cost and duration of Field Canals Improvement Projects. Therefore, this data paper presents dataset includes the key parameters of such project to be used for analyzing and modelling project cost and duration. The data were acquired based on questionnaire survey and collecting historical cases of Field Canals Improvement Projects. The data consists of the following features: area served, total length of PVC pipe line, number of irrigation values, construction year, geographical zone, cost of FCIP, and duration of FCIP construction. The data can be applied to compare and evaluate the performance of machine learning algorithms for predicting cost and duration.

Research paper thumbnail of Sustainable Climate Change Policies Driven by Global CO2 Reduction During COVID-19

The Global Environmental Effects During and Beyond COVID-19

Research paper thumbnail of Automatic X-ray COVID-19 Lung Image Classification System based on Multi-Level Thresholding and Support Vector Machine

The early detection of SARS-CoV-2, the causative agent of (COVID-19) is now a critical task for t... more The early detection of SARS-CoV-2, the causative agent of (COVID-19) is now a critical task for the clinical practitioners. The COVID-19 spread is announced as pandemic outbreak between people worldwide by WHO since 11/ March/ 2020. In this consequence, it is top critical priority to become aware of the infected people so that prevention procedures can be processed to minimize the COVID-19 spread and to begin early medical health care of those infected persons. In this paper, the deep studying based totally methodology is usually recommended for the detection of COVID-19 infected patients using X-ray images. The help vector gadget classifies the corona affected X-ray images from others through usage of the deep features. The technique is useful for the clinical practitioners for early detection of COVID-19 infected patients. The suggested system of multi-level thresholding plus SVM presented high accuracy in classification of the infected lung with Covid-19. All images were of the same size and stored in JPEG format with 512 * 512 pixels. The average sensitivity, specificity, and accuracy of the lung classification using the proposed model results were 95.76%, 99.7%, and 97.48%, respectively.

Research paper thumbnail of Drilling stuck pipe classification and mitigation in the Gulf of Suez oil fields using artificial intelligence

Journal of Petroleum Exploration and Production Technology

Developing a reliable classification model for drilling pipe stuck is crucial for decision-makers... more Developing a reliable classification model for drilling pipe stuck is crucial for decision-makers in the petroleum drilling rig. Artificial intelligence (AI) includes several machine learning (ML) algorithms that are used for efficient predictive analytics, optimization, and decision making. Therefore, a comparison analysis for ML models is required to guide practitioners for the appropriate predictive model. Twelve ML techniques are used for drilling pipe stuck such as artificial neural networks, logistic regression, and ensemble methods such as scalable boosting trees and random forest. The drilling cases of the Gulf of Suez wells are collected as an actual dataset for analyzing the ML performance. The key contribution of the study is to automate pipe stuck classification using ML algorithms and mitigate the pipe stuck cases using the genetic algorithm optimization. Out of 12 AI techniques, the results presented that the most reliable algorithm was extremely randomized trees (extr...

Research paper thumbnail of Subword Attentive Model for Arabic Sentiment Analysis

ACM Transactions on Asian and Low-Resource Language Information Processing

Research paper thumbnail of Artificial Intelligence for Drilling Lost Circulation: A Systematic Literature Review

Geoenergy science and engineering, Apr 1, 2024

Research paper thumbnail of Green artificial intelligence for cost-duration variance prediction (CDVP) for irrigation canals rehabilitation projects

Expert systems with applications, Sep 1, 2024

Research paper thumbnail of Assessment Framework for BIM-Digital Twin Readiness in the Construction Industry

Buildings, Jan 17, 2024

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Research paper thumbnail of Automated lost circulation severity classification and mitigation system using explainable Bayesian optimized ensemble learning algorithms

Journal of petroleum exploration and production technology, Jul 11, 2024

Research paper thumbnail of Cost-Duration Variance Prediction (CDVP) System Using Explainable Bayesian Optimized Ensemble Machine Learning

This research introduces an integrated system for a novel management concept called automated cos... more This research introduces an integrated system for a novel management concept called automated cost-duration variance prediction (CDVP) system which allows to the project managers to look ahead at project budget, duration, and its variances at the project completion. To obtain the optimal results of each algorithm, the proposed system conducted Bayesian optimization for each developed algorithm. As a result, Extremely Randomized Trees (ET) was the optimal model for duration prediction at 2.53% and 0.961 for Mean Absolute Percentage Error (MAPE) and adjusted determination coefficient (R*2), respectively. AdaBoost Regressor (ABR) was the best model for duration variance prediction with 8.970 MAPE, and 0.949 R*2. On the other hand, the optimal model for project cost was a Light Gradient Boosting Machine (LGBM) with 2.98% and 0.931 for MAPE and R*2, respectively. Extremely Randomized Tree (ET) was the best model for cost variance prediction with 4.151 MAPE, and 0.962 R*2. SHapley Additiv...

Research paper thumbnail of Comparison of Artificial Intelligence Techniques for Project Conceptual Cost Prediction

arXiv (Cornell University), Aug 8, 2019

Developing a reliable parametric cost model at the conceptual stage of the project is crucial for... more Developing a reliable parametric cost model at the conceptual stage of the project is crucial for projects managers and decision makers. Existing methods, such as probabilistic and statistical algorithms have been developed for project cost prediction. However, these methods are unable to produce accurate results for conceptual cost prediction due to small and unstable data samples. Artificial intelligence (AI) and machine learning (ML) algorithms include numerous models and algorithms for supervised regression applications. Therefore, a comparison analysis for AI models is required to guide practitioners to the appropriate model. The study focuses on investigating twenty artificial intelligence (AI) techniques which are conducted for cost modeling such as fuzzy logic (FL) model, artificial neural networks (ANNs), multiple regression analysis (MRA), case-based reasoning (CBR), hybrid models, and ensemble methods such as scalable boosting trees (XGBoost). Field canals improvement projects (FCIPs) are used as an actual case study to analyze the performance of the applied ML models. Out of 20 AI techniques, the results showed that the most accurate and suitable method is XGBoost with 9.091% and 0.929 based on Mean Absolute Percentage Error (MAPE) and adjusted R 2. Nonlinear adaptability, handling missing values and outliers, model interpretation and uncertainty have been discussed for the twenty developed AI models.

Research paper thumbnail of A novel conceptual cost estimation decision-making model for field canal improvement projects

International Journal of Construction Management

Research paper thumbnail of Stacking Deep Learning for Early COVID-19 Vision Diagnosis

Studies in Big Data, 2020

early and accurate COVID-19 diagnosis prediction plays a crucial role for helping radiologists an... more early and accurate COVID-19 diagnosis prediction plays a crucial role for helping radiologists and health care workers to take reliable corrective actions for classify patients and detecting the COVID 19 confirmed cases. Prediction and classification accuracy are critical for COVID-19 diagnosis application. Current practices for COVID-19 images classification are mostly built upon convolutional neural network (CNNs) where CNN is a single algorithm. On the other hand, ensemble machine learning models produce higher accuracy than a single machine leaning. Therefore, this study conducts stacking deep learning methodology to produce the highest results of COVID-19 classification. The stacked ensemble deep learning model accuracy has produced 98.6% test accuracy. Accordingly, the stacked ensemble deep learning model produced superior performance than any single model. Accordingly, ensemble machine learning evolves as a future trend due to its high scalability, stability, and prediction accuracy.

Research paper thumbnail of Stacking Deep Learning for Early COVID-19 Vision Diagnosis

Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach, 2020

early and accurate COVID-19 diagnosis prediction plays a crucial role for helping radiologists an... more early and accurate COVID-19 diagnosis prediction plays a crucial role for helping radiologists and health care workers to take reliable corrective actions for classify patients and detecting the COVID 19 confirmed cases. Prediction and classification accuracy are critical for COVID-19 diagnosis application. Current practices for COVID-19 images classification are mostly built upon convolutional neural network (CNNs) where CNN is a single algorithm. On the other hand, ensemble machine learning models produce higher accuracy than a single machine leaning. Therefore, this study conducts stacking deep learning methodology to produce the highest results of COVID-19 classification. The stacked ensemble deep learning model accuracy has produced 98.6% test accuracy. Accordingly, the stacked ensemble deep learning model produced superior performance than any single model. Accordingly, ensemble machine learning evolves as a future trend due to its high scalability, stability, and prediction a...

Research paper thumbnail of The Truth About 5G and COVID-19: Basics, Analysis, and Opportunities

Digital Transformation and Emerging Technologies for Fighting COVID-19 Pandemic: Innovative Approaches, 2021

5G is a paradigm shift for data transfer and wireless communication technology where 5G involves ... more 5G is a paradigm shift for data transfer and wireless communication technology where 5G involves massive bandwidths based on high carrier frequencies Unlike 4G, 5G is highly integrative to produce a seamless user experience and universal high-rate coverage The key role of 5G is increasing data capacity, improving data rate transfer, providing better service quality, and decreasing latency Recently, COVID-19 is declared as an international epidemic More than 4 5 million confirmed cases and +308,000 death cases have been recorded around more than 209 countries on 16th May 2020 There are several insane theories about 5G technology and human health Therefore, people are burning valuable 5G infrastructure down out of fear for their health People think that 5G towers are weakening the immune system and causing the global COVID-19 pandemic This chapter reviews the data transmission revolution from 1G to 5G technology and discusses the impact of 5G technology on human health, pandemic, and business perspectives © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG

Research paper thumbnail of Closure to “Artificial Intelligence and Parametric Construction Cost Estimate Modeling: State-of-the-Art Review” by Haytham H. Elmousalami

Journal of Construction Engineering and Management, 2021

Research paper thumbnail of Intelligent methodology for project conceptual cost prediction

Research paper thumbnail of Evaluation of Cost Drivers for Field Canals Improvement Projects

Water Resources Management, 2017

Field Canals Improvement Project (FCIP) aims to conserve fresh water. Several methods are existed... more Field Canals Improvement Project (FCIP) aims to conserve fresh water. Several methods are existed to predict project preliminary cost. However, identification of model inputs remains a challenging task during model development. This study utilizes two procedures, Traditional Delphi Method (TDM) and Fuzzy Delphi Method (FDM), which are used for collecting and initially ranking cost drivers. According to the second approach, Fuzzy Analytic Hierarchy Process (FAHP) is used for finally ranking the screened parameters by FDM. Delphi rounds and Likert scale are conducted to determine the most important factors from the viewpoint of consultant engineers and contractors. Regression analysis and R square is used as a comparison criteria between traditional technique and fuzzy techniques. The study suggests using Fuzzy theory and Delphi method with Analytic Hierarchy Process in order to identify cost drivers efficiently. Paper contribution is proposing a methodology to evaluate cost drivers of FCIP using qualitative data such as experts’ opinions.

Research paper thumbnail of Day Level Forecasting for Coronavirus Disease (COVID-19) Spread: Analysis, Modeling and Recommendations

arXiv: Populations and Evolution, 2020

In mid of March 2020, Coronaviruses such as COVID-19 is declared as an international epidemic. Mo... more In mid of March 2020, Coronaviruses such as COVID-19 is declared as an international epidemic. More than 125000 confirmed cases and 4,607 death cases have been recorded around more than 118 countries. Unfortunately, a coronavirus vaccine is expected to take at least 18 months if it works at all. Moreover, COVID -19 epidemics can mutate into a more aggressive form. Day level information about the COVID -19 spread is crucial to measure the behavior of this new virus globally. Therefore, this study presents a comparison of day level forecasting models on COVID-19 affected cases using time series models and mathematical formulation. The forecasting models and data strongly suggest that the number of coronavirus cases grows exponentially in countries that do not mandate quarantines, restrictions on travel and public gatherings, and closing of schools, universities, and workplaces (Social Distancing).

Research paper thumbnail of Prediction of Construction Cost for Field Canals Improvement Projects in Egypt

ArXiv, 2019

Field canals improvement projects (FCIPs) are one of the ambitious projects constructed to save f... more Field canals improvement projects (FCIPs) are one of the ambitious projects constructed to save fresh water. To finance this project, Conceptual cost models are important to accurately predict preliminary costs at the early stages of the project. The first step is to develop a conceptual cost model to identify key cost drivers affecting the project. Therefore, input variables selection remains an important part of model development, as the poor variables selection can decrease model precision. The study discovered the most important drivers of FCIPs based on a qualitative approach and a quantitative approach. Subsequently, the study has developed a parametric cost model based on machine learning methods such as regression methods, artificial neural networks, fuzzy model and case-based reasoning.

Research paper thumbnail of Data on Field Canals Improvement Projects for Cost Prediction Using Artificial Intelligence

Data in Brief

Field Canals Improvement Projects is an important sustainable project to save fresh water in our ... more Field Canals Improvement Projects is an important sustainable project to save fresh water in our world. Machine learning and artificial intelligence (AI) needs sufficient dataset size to model and predict the cost and duration of Field Canals Improvement Projects. Therefore, this data paper presents dataset includes the key parameters of such project to be used for analyzing and modelling project cost and duration. The data were acquired based on questionnaire survey and collecting historical cases of Field Canals Improvement Projects. The data consists of the following features: area served, total length of PVC pipe line, number of irrigation values, construction year, geographical zone, cost of FCIP, and duration of FCIP construction. The data can be applied to compare and evaluate the performance of machine learning algorithms for predicting cost and duration.

Research paper thumbnail of Sustainable Climate Change Policies Driven by Global CO2 Reduction During COVID-19

The Global Environmental Effects During and Beyond COVID-19

Research paper thumbnail of Automatic X-ray COVID-19 Lung Image Classification System based on Multi-Level Thresholding and Support Vector Machine

The early detection of SARS-CoV-2, the causative agent of (COVID-19) is now a critical task for t... more The early detection of SARS-CoV-2, the causative agent of (COVID-19) is now a critical task for the clinical practitioners. The COVID-19 spread is announced as pandemic outbreak between people worldwide by WHO since 11/ March/ 2020. In this consequence, it is top critical priority to become aware of the infected people so that prevention procedures can be processed to minimize the COVID-19 spread and to begin early medical health care of those infected persons. In this paper, the deep studying based totally methodology is usually recommended for the detection of COVID-19 infected patients using X-ray images. The help vector gadget classifies the corona affected X-ray images from others through usage of the deep features. The technique is useful for the clinical practitioners for early detection of COVID-19 infected patients. The suggested system of multi-level thresholding plus SVM presented high accuracy in classification of the infected lung with Covid-19. All images were of the same size and stored in JPEG format with 512 * 512 pixels. The average sensitivity, specificity, and accuracy of the lung classification using the proposed model results were 95.76%, 99.7%, and 97.48%, respectively.

Research paper thumbnail of Drilling stuck pipe classification and mitigation in the Gulf of Suez oil fields using artificial intelligence

Journal of Petroleum Exploration and Production Technology

Developing a reliable classification model for drilling pipe stuck is crucial for decision-makers... more Developing a reliable classification model for drilling pipe stuck is crucial for decision-makers in the petroleum drilling rig. Artificial intelligence (AI) includes several machine learning (ML) algorithms that are used for efficient predictive analytics, optimization, and decision making. Therefore, a comparison analysis for ML models is required to guide practitioners for the appropriate predictive model. Twelve ML techniques are used for drilling pipe stuck such as artificial neural networks, logistic regression, and ensemble methods such as scalable boosting trees and random forest. The drilling cases of the Gulf of Suez wells are collected as an actual dataset for analyzing the ML performance. The key contribution of the study is to automate pipe stuck classification using ML algorithms and mitigate the pipe stuck cases using the genetic algorithm optimization. Out of 12 AI techniques, the results presented that the most reliable algorithm was extremely randomized trees (extr...

Research paper thumbnail of Subword Attentive Model for Arabic Sentiment Analysis

ACM Transactions on Asian and Low-Resource Language Information Processing

Research paper thumbnail of Haytham H. Elmousalami PREDICTION OF CONSTRUCTION COST FOR FIELD CANALS IMPROVEMENT PROJECTS IN EGYPT Submitted by

PREDICTION OF CONSTRUCTION COST FOR FIELD CANALS IMPROVEMENT PROJECTS IN EGYPT, 2018

Field canals improvement projects (FCIPs) are one of the ambitious projects constructed to save f... more Field canals improvement projects (FCIPs) are one of the ambitious projects constructed to save fresh water. To finance this project, Conceptual cost models are important to accurately predict preliminary costs at early stages of the project. The first step is to develop a conceptual cost model to identify key cost drivers affecting the project. Therefore, input variables selection remains an important part of model development, as the poor variables selection can decrease model precision. The study discovered the most important drivers of FCIPs based on a qualitative approach and a quantitative approach. Subsequently, the study has developed a parametric cost model based on machine learning methods such as regression methods, artificial neural networks, fuzzy model and case based reasoning.
There are several methods to achieve prediction for project preliminary cost. However, cost model inputs identification remains a challenging part during model development. Therefore, this study has conducted two procedures consisted of traditional Delphi method, Fuzzy Delphi Method (FDM) and the Fuzzy Analytic Hierarchy Process (FAHP) to determine these drivers. A Delphi rounds and Likert scale were used to determine the most important factors from viewpoints of consultant engineers and involved contractors. The study concluded that proposed approaches provided satisfying and consistent results. Finally, cost drivers of FCIPs were identified and can be used to develop a reliable conceptual cost model. On the other hand, the study has determined the key cost drivers for FCIPS based on quantitative data and statistical techniques. Factor …