Browsing by Author "AlRawi, Layth Nabeel"

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Comparative Analysis of Machine Learning Techniques using Customer Feedback Reviews of Oil and Gas Companies
(2020) AlRawi, Layth Nabeel; Ashour Ashour, Osama Ibraheem
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.
Investigating the factors that impact e-learning systems in oil and gas industry
(2022) AlRawi, Layth Nabeel; AlBella, Abdullah Hashim; Ashour, Osama Ibraheem
The use of digital media such as audio and video as a teaching media helps to achieve teaching goals better than following traditional teaching. It improves the engagement with the lecture and helps to deliver the education by lower cost and shorter time. Many companies use electronic Learning (e-learning) as an effective way to improve the knowledge, skills, and performance of their employees. For that, it became essential to investigate the factors that affect the usage of these systems. Many researches have performed on the factors affecting e-learning systems in different sectors, but studies into e-learning systems for Oil and Gas industry is limited. In this study, the Oil and Gas sector is targeted since we have found the use of distance training and learning is projected to increase in this industry especially with current unprecedented circumstances and the lockdown that are associated with Coronavirus 2019 Disease (COVID-19). Human factors that influence e-learning systems for Oil and Gas companies are targeted and investigated. An investigation had conducted in order to gain insights into human factors. Questionnaires were collected from 76 employees from the field. The findings show that learner's education, system interface, computer literacy, organization support, and teaching methods were identified as factors affecting e-learning systems. This research will play an essential role in helping oil and gas companies to develop and improve the use of their e-learning systems.