Rabia Emhamed Al Mamlook
Verified email at wmich.edu
Department of Industrial and Manufacturing Engineering
Westerns Michigan University
Ph.D. graduate Industrial engineering &Engineering Management, and data scientist with strong skills in Statistics, and algorithms to big data sets related industries and health care engineering. Skilled in machine learning, data models, data mining, statistics process quality control, pre-processing, and visualization data to solve challenging business and industrial problems. Demonstrates experience with programming languages (e.g., R, SAS, Minitab, and Python).
EDUCATION
Ph.D. Industrial Engineering and Engineering Management from WMU, USA.
Master’s in Applied Statistics & Biostatistics from WMU, USA.
Master’s in Engineering Management from the University of Tripoli, Libya.
RESEARCH INTERESTS
Smart Manufacturing, Machine learning, Data Mining and Healthcare Management ,Data models, Statistics Process Quality Control, and Deep Learning
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
M.M. Samy, Rabia Emhamed Almamlook, Heba I. Elkhouly, and Shimaa Barakat
Sustainable Cities and Society, ISSN: 22106707, Published: September 2022
Elsevier BV
Odey Alshboul, Mohammad A. Alzubaidi, Rabia Emhamed Al Mamlook, Ghassan Almasabha, Ali Saeed Almuflih, and Ali Shehadeh
Sustainability (Switzerland), eISSN: 20711050, Published: May-2 2022
MDPI AG
Sustainable construction projects are essential for economic and societal thriving in modern communities. However, infrastructural construction is usually accompanied by delays in project delivery, which impact sustainability. Such delays adversely affect project time, cost, quality, safety objective functions, and associated Liquidated Damages (LDs). LDs are monetary charges to recompense the owner for additional expenses sustained if the project was not delivered on time due to delays caused by the contractor. This paper proposes modified regression modeling using machine learning (ML) techniques to develop solutions to the problem of predicting LDs for construction projects. The novel modeling methodology presented here is based on six years of data collection from many construction projects across the United States. It represents an innovative use of Multiple Linear Regression (MLR) models hybridized with machine learning (ML). The proposed methodology is evaluated using real datasets, where the developed model is designed to outperform the state-of-the-art LD forecast accuracy. Herein, seven modified regression-based models showed high accuracy in predicting the LDs. Nevertheless, those models’ forecasting ability was limited, so another second-order prediction model is proposed to provide better LD estimations. Independent variables were categorized based on their influence on the estimated LDs. The Total Bid Amount variable had the highest impact, while the Funding Indicator variable had a minimal impact. LD prediction was negatively correlated with all change-order-related variables and Total Adjustment Days, which suggests that those variables introduce extreme uncertainties due to their complex nature. The developed prediction models help decision-makers make better LDs predictions, which is essential for construction project sustainability.
Ali Shehadeh, Odey Alshboul, Rabia Emhamed Al Mamlook, and Ola Hamedat
Automation in Construction, ISSN: 09265805, Volume: 129, Published: September 2021
Elsevier BV
Irfan Ullah, Kai Liu, Toshiyuki Yamamoto, Rabia Emhamed Al Mamlook, and Arshad Jamal
Energy and Environment, ISSN: 0958305X, eISSN: 20484070, Published: 2021
SAGE Publications
The rapid growth of transportation sector and related emissions are attracting the attention of policymakers to ensure environmental sustainability. Therefore, the deriving factors of transport emissions are extremely important to comprehend. The role of electric vehicles is imperative amid rising transport emissions. Electric vehicles pave the way towards a low-carbon economy and sustainable environment. Successful deployment of electric vehicles relies heavily on energy consumption models that can predict energy consumption efficiently and reliably. Improving electric vehicles’ energy consumption efficiency will significantly help to alleviate driver anxiety and provide an essential framework for operation, planning, and management of the charging infrastructure. To tackle the challenge of electric vehicles’ energy consumption prediction, this study aims to employ advanced machine learning models, extreme gradient boosting, and light gradient boosting machine to compare with traditional machine learning models, multiple linear regression, and artificial neural network. Electric vehicles energy consumption data in the analysis were collected in Aichi Prefecture, Japan. To evaluate the performance of the prediction models, three evaluation metrics were used; coefficient of determination ( R2), root mean square error, and mean absolute error. The prediction outcome exhibits that the extreme gradient boosting and light gradient boosting machine provided better and robust results compared to multiple linear regression and artificial neural network. The models based on extreme gradient boosting and light gradient boosting machine yielded higher values of R2, lower mean absolute error, and root mean square error values have proven to be more accurate. However, the results demonstrated that the light gradient boosting machine is outperformed the extreme gradient boosting model. A detailed feature important analysis was carried out to demonstrate the impact and relative influence of different input variables on electric vehicles energy consumption prediction. The results imply that an advanced machine learning model can enhance the prediction performance of electric vehicles energy consumption.
Odey Alshboul, Ali Shehadeh, Maha Al-Kasasbeh, Rabia Emhamed Al Mamlook, Neda Halalsheh, and Muna Alkasasbeh
Engineering, Construction and Architectural Management, ISSN: 09699988, Published: 2021
Emerald
PurposeHeavy equipment residual value forecasting is dynamic as it relies on the age, type, brand and model of the equipment, ranking condition, place of sale, operating hours and other macroeconomic gauges. The main objective of this study is to predict the residual value of the main types of heavy construction equipment. The residual value of heavy construction equipment is predicted via deep learning (DL) and machine learning (ML) approaches.Design/methodology/approachBased on deep and machine learning regression network integrated with data mining, random forest (RF), decision tree (DT), deep neural network (DNN) and linear regression (LR)-based modeling decision support models are developed. This research aims to forecast the residual value for different types of heavy construction equipment. A comprehensive investigation of publicly accessible auction data related to various types and categories of construction equipment was utilized to generate the model's training and testing datasets. In total, four performance metrics (i.e. the mean absolute error (MAE), mean squared error (MSE), the mean absolute percentage error (MAPE) and coefficient of determination (R2)) were used to measure and compare the developed algorithms' accuracy.FindingsThe developed algorithm's efficiency has been demonstrated by comparing the deep and machine learning predictions with real residual value. The accuracy of the results obtained by different proposed modeling techniques was comparable based on the performance evaluation metrics. DT shows the highest accuracy of 0.9111 versus RF with an accuracy of 0.8123, followed by DNN with an accuracy of 0.7755 and the linear regression with an accuracy of 0.5967.Originality/valueThe proposed novel model is designed as a supportive tool for construction project managers for equipment selling, purchasing, overhauling, repairing, disposing and replacing decisions.
Rabia Emhamed Al Mamlook, Shengfeng Chen, and Hanin Fawzi Bzizi
IEEE International Conference on Electro Information Technology, ISSN: 21540357, eISSN: 21540373, Volume: 2020-July, Pages: 98-104, Published: July 2020
IEEE
Pneumonia is one of the serious and life-threatening diseases that is caused by a bacterial or viral infection of the lungs and have the potential to result in severe consequences within a short period. Therefore, early diagnosis is a significant factor in terms of the successful treatment process. Thus, there is a need for an intelligent and automatic system that has the capability of diagnosing chest X-rays, and to simplify the Pneumonia detection process for experts and novices. This study aims to develop a model that will help with the classification of chest X-ray medical images into normal(healthy) vs. abnormal(sick). To achieve this, seven existing state-of-the-art Machine Learning techniques and well-known Convolutional Neural Network models have been used to increase efficiency and accuracy. In this study, we propose our Deep Learning for the classification task, which is trained with changed images, through multiple steps of pre-processing. Experimentally, it showed that the Deep Learning technique for the classification task performs the best, compared to the other seven Machine Learning techniques. In this study, we successfully classified chest infection in Chest X-ray Images using Deep Learning based on CNN with an overall accuracy of 98.46%. It achieved a more successful result in detecting Pneumonia cases.
Rabia Emhamed Al Mamlook, Hanin Fawzi Bzizi, and Shengfeng Chen
IEEE International Conference on Electro Information Technology, ISSN: 21540357, eISSN: 21540373, Volume: 2020-July, Pages: 145-150, Published: July 2020
IEEE
Lung cancer is the leading cause of cancer death worldwide. Evidence-based medical, well-defined risk scoring systems is essential to identify patients with a poor prognosis. This study aimed to test the performance scores rate of patients who suffer from advanced lung cancer based on daily activities. Data from 228 patients were analyzed to create a scoring system. Survival analysis was performed using Cox's proportional hazards regression and the Kaplan-Meier method. Karnofsky Performance Status(KPS) and the Eastern Cooperative Oncology Group Performance Status Scale (ECOG PS) is used for measuring the performance of usual daily activities by physicians. The results of the study found that there are increased risks of death for patients with scores higher than zero. It also shows that the amount of calories consumed during meals is not a good indicator of survival for patients with advanced lung cancer. The KPS score is not a decent indicator of survival for advanced lung cancer patients since there are no significant increased risks of death for patients. However, since the ECOG score was a decent indicator of survival, it is refreshing that doctors can use that as a tool that helps them make more informed decisions about treatment plans when discussing prognosis with their patients. It will also help patients to develop more educated decisions about different treatment options.
Rabia Emhamed Al Mamlook, Tiba Zaki Abdulhameed, Raed Hasan, Hasnaa Imad Al-Shaikhli, Ihab Mohammed, and Shadha Tabatabai
IEEE International Conference on Electro Information Technology, ISSN: 21540357, eISSN: 21540373, Volume: 2020-July, Pages: 105-111, Published: July 2020
IEEE
Car crash can cause serious and severe injuries that impact people every day. Those injuries could be especially damaging for elderly drivers of age 60 or more. The goal of this research is to investigate the risk factors that contribute to crash injury severity among elderly drivers. This is accomplished by designing accurate machine learning based predictive models. Naïve Bayesian (NB), Decision Tree (DT), Logistic Regression (LR), Light-GBM, and Random Forest (RF) model are proposed. A set of influential factors are selected to build the five predictive models to classify the severity of injuries as severe injury or non-severe injury. Michigan traffic data of the elderly population is used in this paper. Data normalization and Synthetic Minority Oversampling Technique (SMOTE) as injury classes balancing technique are used in the pre-processing phase. Results show that the Light-GBM achieved the highest accuracy among the five tested models with 87%. According to the Light-GBM model, the three most important factors that impact the severity of injuries are the driver's age, traffic volume, and car's age.
Rabia Emhamed Al Mamlook, Abdulla Ali, Raed Abdullah Hasan, and Haider A. Mohamed Kazim
Proceedings of the IEEE National Aerospace Electronics Conference, NAECON, ISSN: 05473578, eISSN: 23792027, Volume: 2019-July, Pages: 630-634, Published: July 2019
IEEE
The aimed of this research is to evaluate and compare different approaches to modeling crash severity as well as investigating the effect of risk factors on the fatality outcomes of traffics crashes using machine learning-based driving simulation. We developed prediction models to identify risk factors of traffics crashes can be targeted to reduce accident. The Random Forest model demonstrated the best performance from among the six different techniques with accurate 82.6%.
Rabia Emhamed AlMamlook, Keneth Morgan Kwayu, Maha Reda Alkasisbeh, and Abdulbaset Ali Frefer
2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology, JEEIT 2019 - Proceedings, Pages: 272-276, Published: 16 May 2019
IEEE
Traffic accidents are among the most critical issues facing the world as they cause many deaths, injuries, and fatalities as well as economic losses every year. Accurate models to predict the traffic accident severity is a critical task for transportation systems. This investigation effort establishes models to select a set of influential factors and to build up a model for classifying the severity of injuries. These models are formulated by various machine learning techniques. Supervised machine learning algorithms, such as AdaBoost, Logistic Regression (LR), Naive Bayes (NB), and Random Forests (RF) are implemented on traffic accident data. SMOTE algorithm is used to handle data imbalance. The findings of this study indicate that the RF model can be a promising tool for predicting the injury severity of traffic accidents. RF algorithm has shown better performance with 75.5% accuracy than LR with 74.5%, NB with 73.1%, and AdaBoost with 74.5% accuracy.
Proceedings of the International Conference on Industrial Engineering and Operations Management, eISSN: 21698767, Volume: 2018, Issue: SEP, Pages: 274-286, Published: 2018
Proceedings of the International Conference on Industrial Engineering and Operations Management, eISSN: 21698767, Volume: 2018, Issue: SEP, Pages: 2602-2602, Published: 2018
Proceedings of the International Conference on Industrial Engineering and Operations Management, eISSN: 21698767, Volume: 2018, Issue: JUL, Pages: 65-73, Published: 2018