Browsing by Author "Alrawi, L.N."

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Citation - Scopus: 1
Comparative Analysis of Machine Learning Techniques using Customer Feedback Reviews of Oil and Gas Companies
(Association for Computing Machinery, 2020) Alrawi, L.N.; Ashour, O.I.A.
Sentiment analysis is the process of computationally identifying and categorizing opinions from a piece of text to determine whether the writer's attitude towards a practical topic, products or services is positive, negative or neutral. In this study, Machine Learning techniques are used to perform sentiment analysis on Oil and Gas customer feedback data. We present a comparison of different classification algorithms used for opinion mining, including Support Vector Machine (SVM), Naïve Bayes (NB), Instance Based Learning (IB3), Random Forest (RF), Partial Decision trees (PART), and Logit Boost (LB). Many studies have been performed on sentiment analysis in different sectors, but research into Oil and Gas customer feedback has been limited. Therefore, we have targeted a pathless sector, namely the Petroleum sector, where companies express their opinions towards specific products or services. Waikato Environment for Knowledge Analysis (WEKA) is used for experimental results. The WEKA environment is open source software entailing a collection of machine learning algorithms to solve data mining problems. The main aim of this study is to evaluate the efficiency of the above mentioned classifiers in terms of Precision, Recall, F-Measure and Accuracy. The findings of the comparison analysis indicate that the Naïve-Bayes classifier gives the best Accuracy of all classifiers. A small dataset could be considered as a limitation to our study due to the difficulty of gaining more datasets at the time of the research. However, this research will play a vital role for researchers in making decisions about the algorithm that they are going to use to solve their data mining problems. © 2020 ACM.
Citation - Scopus: 5
Investigating end user errors in oil and gas critical control systems
(Association for Computing Machinery, 2020) Alrawi, L.N.; Pusatli, T.; 51704
System availability and efficiency are critical in the petroleum sector as any fault affecting those systems may negatively impact operations resources, such as money, human resources and time. Therefore, it has become important to investigate the reasons for such errors. In this study, human error has been targeted since a number of these errors is projected to increase in the sector. The factors that affect end user behavior are investigated in addition to an evaluation of the relation between system availability and human behavior. An investigation has been performed following the descriptive methodology in order to gain insights into human error factors. Questionnaires related to software/hardware errors and errors due to the end user were collected from 81 site workers. The findings indicate a potential relation between end user behavior and system availability. Training, experience, education, work shifts, system interface, usage of memory sticks and I/O devices were identified as factors affecting end user behavior, hence system availability and efficiency. © 2020 ACM.
Citation - Scopus: 0
Survey on Interface Usability Evaluation for Oil and Gas Critical Control Systems
(Institute of Electrical and Electronics Engineers Inc., 2021) Alrawi, L.N.; Ashour, O.I.; Zeain, A.
Usability is the key to develop and improve any system as it represents the direct contact point between users and machines. The use of the critical control system in the oil and gas industry is increasing. Due to the complexity of these systems, its interface usability should be assessed and developed periodically. In this research, the attributes that affect interface usability are identified. The usability of the Torque Turns System (TTS) is evaluated since the periods of downtime is projected to increase in the field. There are some works similar to our work however none of them had collected data directly from real operators from the field. An evaluation of the torque turn system interface usability is performed using questionnaire related to common interface usability attributes including accessibility, learnability, effectiveness, memorability, efficiency, safety, cognitive load, understandability, and satisfaction. The findings indicate a potential weakness in terms of understandability and accessibility © 2021 IEEE.