BSpace

The British University in Dubai (BUiD) Digital Repository

Welcome to BSpace, the online institutional repository of the British University in Dubai. BSpace provides access to the Dissertations, Thesis, Research projects, Faculty publications and archives of BUiD.

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Now showing 1 - 7 of 7

Recent Submissions

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Failures Root Cause Analysis for Sewer Pipeline Network System
(The British University in Dubai (BUiD), 2023-06) JAROOR, SAADI; Professor Alaa A-Ameer
Wastewater System is one of the most critical infrastructure systems. It is known that there are several types of Wastewater, and this dissertation is concerned with the Sewer type of Wastewater in particular. In addition, there are several systems of Sewer Wastewater as the dissertation is centered around Sewer Wastewater Pipeline Systems. Furthermore, there are two types of Sewer Wastewater Pipeline Systems: the gravity and pressure systems, and both systems will be discussed in this dissertation. The Sewer Wastewater Pipeline System was chosen for several reasons, the most important of which is finding Sewer Wastewater Pipeline System failures that affect the Sewer Wastewater as a whole System. In addition, to the danger effects of Sewer Wastewater Pipeline System failures. These effects may reach the residents of the area where the Sewer Wastewater Pipeline System failures occur. Furthermore, there are very harmful environmental effects. This dissertation aims to study the Sewer Pipelines system failures in an infrastructure organization in one of the Middle East countries. The period of data collected from 2014-2022. For studying and analyzing this topic, some techniques will be used Failure Rate, Pareto Chart, FMEA, and Fishbone Diagram. The research findings are summarized as the failure rate of the Gravity Pipeline system is higher than the Pressure Pipeline system. Also, the most frequent system failures are Pipeline broken, joint dislocation, connection broken, and damage. In addition, the most important consequences of the sewage pipeline system effects are the health, environmental impact, and road collapse in some failure cases. Furthermore, the most critical root cause is Third Party, Water Hammer, and Groundwater movement.
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The impact of leadership on employees’ performance while working remotely, a case study of DEWA in Dubai
(The British University in Dubai (BUiD), 2023-12) ALFARDAN, AMEER JASSIM ZAINAB; Dr Gul Ahmed
The current investigation sets out to discover the impact of varying leadership approaches on staff performance at DEWA amidst the “COVID-19” pandemic, a time marked by an upsurge in “remote work”. We focused our attention on four distinctive leadership styles: “transformational”, “transactional”, “autocratic”, and “laissez-faire”. Moreover, we considered the potential moderating effects of “intrinsic motivation” and the equilibrium between work and personal life. Our findings reveal that “transformational leadership” played a substantial role in boosting “employee performance” and promoting a healthy “work-life balance”. “Transactional leadership” displayed a marginal influence on “employee performance”, while autocratic and “laissez-faire leadership” styles demonstrated no noteworthy effects. A fascinating finding was the positive moderating role of “intrinsic motivation”, which intensified the correlation between “transformational leadership” and “employee performance”. The implications of this study underscore the need to embrace “transformational leadership” practices at DEWA in order to cultivate a more efficient “remote working” atmosphere in the post-COVID landscape. Furthermore, it highlights the importance of fostering “intrinsic motivation” within the workforce to achieve improved performance results. Future research endeavors are encouraged to delve into other leadership styles and potential moderating factors within similar contexts of “remote work”.
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Exploring Nursing Education Stakeholders’ Perceptions of Students with Disabilities Inclusion in Nursing Education Programs in the United Arab Emirates: Issues and Challenges
(The British University in Dubai (BUiD), 2023-01) DARWISH, AMANI ABDUL RAZZAK; Professor Emaan Gaad
The purpose of the study was to explore nursing education stakeholders’ perceptions about inclusion of students with disabilities in nursing education programs in the UAE and the barriers and facilitators to their inclusion. A sequential exploratory mixed-methods design was used to conduct this study. Data was collected using unstructured interviews, -semi-structured interviews, and questionnaires. Thematic analysis of the interviews with 7 nursing education stakeholders revealed the following barriers to the inclusion of nursing students with disabilities: 1) Nature of Disability 2) Knowledge of Nursing Faculty 3) Attitudes 4) Communication 5) Resources 6) Nursing Program Requirements 7) Admission and Support Policies 8) Disability Outreach Activities. Semi-structured interviews with 14 nursing education stakeholders showed eight themes related to the facilitators of the inclusion of nursing students with disabilities in nursing education programs: 1) Disability Laws and Policies 2) Disability Awareness 3) Establish Early Detection of Cases 4) Education and Training Programs 5) Creative Access 6) Attitudes 7) Communication to Meet Needs 8) Collaboration with Potential Employers. 284 nursing education students and 29 nursing education faculty members from health science academic institutions in Abu Dhabi, Ajman, Al-Ain, and Al Dhafra in the UAE completed the questionnaires. The descriptive and inferential statistical analysis of the responses of nursing education faculty members and nursing education students was performed using SPSS software. The findings showed that the educators and students had concerns regarding inclusion of students with disabilities. However, implementing facilitators and overcoming barriers can enhance the accessibility of nursing students with disabilities to nursing education programs. The findings also showed significant differences in nursing students' and faculty's perceptions toward inclusion concerning their interactions with students with disabilities and having completed a course about individuals with disabilities. This research fills a knowledge gap related to disability inclusivity in nursing education in the UAE. Understanding the perceptions of nursing education stakeholders towards the inclusion of students with disabilities in nursing education may help in reducing negative attitudes and discriminatory practices and may assist in improving the general understanding of how to increase the participation of students with disabilities in nursing education and the access to care for underrepresented groups. Moreover, the findings of the will support nursing educators in meeting the needs of students with disabilities from legal, ethical and individualistic perspectives. Furthermore, findings will aid nursing education faculty members, nursing education administrators, disability services, and clinical practice partners to determine and provide reasonable accommodations that promote success of nursing students with disabilities in theory and clinical settings, Finally, findings will aid in the revision and subsequent development of policies and guidelines related to educating and supporting students with disabilities in nursing education programs in the UAE.
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Analysis of Synergy of Information Technology and Engineering Departments in a Telecommunication Company in United Arab Emirates and Middle East
(The British University in Dubai (BUiD), 2023-10) ALMUHIRI, RASHID; Professor Alaa Ameer
The following research is an investigation of the dynamic interplay between Information Technology (IT) and Engineering departments of a well-known telecommunication company from the United Arab Emirates and the Middle East. The main focus of this study is on increasing organizational efficiency, employee motivation, and cost-effectiveness, and thus the study delves into the consequences of integrating these departments. The objective of this study is to reveal the links between the synergy between IT and Engineering departments and important organizational factors, comprising of approval processes, employee motivation, and outdated interfaces costs. It aims to deliver actionable insights for telecommunication companies targeting optimization of their operations. Quantitative research approach was adopted for the study. The findings support that merging of the two departments significantly enhances efficiency of approval processes, streamlines workflows, reduces delays, and improves overall coordination. Positive link between departmental merger and heightened employee motivation is observed. It furthermore reveals mitigation of costs linked with outdated interfaces through proactive technology adoption. The study can help guide organizations to gain a competitive edge, optimize operations, and enhance customer satisfaction. This study informs a path that leads to organizational efficiency, competitiveness, and strategic integration.
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Sentiment Analysis of the Emirati Dialect text using Ensemble Stacking Deep Learning Models
(The British University in Dubai (BUiD), 2023-03) AL SHAMSI, ARWA AHMED; Professor Sherief Abdallah
The study of thoughts, feelings, judgments, values, attitudes, and emotions regarding goods, services, organizations, persons, tasks, occasions, titles, and their attributes is known as sentiment analysis and it involves a polarity classification task for recognizing positive, negative, or neutral text to quantify what individuals believe using textual qualitative data. The rise of social media platforms provided an excellent source for sentiment analysis data. People use these platforms for various reasons, ranging from sharing their opinions and thoughts to gaining knowledge. Twitter, Instagram, and Facebook are examples of social media platforms. As more users join social media platforms, the amount of data that is generated online continues to grow at an accelerating pace. Most of the previous research that studied sentiment analysis for the Arabic language focused on Modern Standard Arabic and Egyptian, Saudi, Algerian, Jordanian, Tunisian, and Levantine Dialects. However, to our knowledge, no study involved employing deep learning models to conduct sentiment analysis on the Emirati dialect texts. Dialects are the informal form of a language. Each country of the Arab world has its own Dialect, and each dialect may have several sub-dialects. The main objective of this study is to develop a deep learning model that outperforms the state-of-the-art for Sentiment Analysis of the Emirati Dialect. Toward this objective, I first conducted a systematic review to identify the research gaps in the existing literature and investigate the available constructed resources for Arabic dialects and the used approaches for sentiment analysis of Arabic dialects. The systematic review focused on empirical research on the subject of Sentiment Analysis of the Arabic Dialect that was released between January 2015 and January 2021. Through the analysis, I found, with the exception of a few articles that investigated Saudi, Levantine, Jordanian, Algerian, Tunisian, and Egyptian dialects, researchers rarely specified the dialect type in their papers; instead, it was mostly mentioned (MSA and Arabic Dialects). Emirati Dialect has not been explored for Sentiment Analysis purposes. The sizes of most datasets of previous research were between 10,000 and 50,000. Moreover, the Twitter platform was the most popular online platform for constructing Arabic datasets. Most of the studies evaluated basic Machine Learning approaches for Sentiment Analysis of Arabic Dialects. My research aims to fill these identified gaps as I detail below. Since Instagram is one of the most popular social media platforms in UAE, I constructed a dataset of the Emirati dialect from the Instagram platform. My dataset consists of 216,000 posts, of which 70,000 posts were manually annotated by three human annotators. Each post is annotated into (Positive/ Negative/Neutral), and it is further annotated into (Emirati Dialect/ Arabic Dialect/ MSA). In order for the dataset to be used as a benchmark, the inter-annotator agreement (IAA) was measured using Fleiss's Kappa coefficient. The findings reveal that the overall Fleiss Kappa coefficient is = 0.93, indicating an almost-perfect agreement amongst the three annotators. Once the dataset was constructed and validated, I then conducted a performance evaluation and comparison of various basic Machine Learning algorithms, Deep Learning models, and stacking deep learning models on different datasets of Sentiment Analysis of Arabic Dialects. For the basic machine learning algorithms, LR, NB, SVM, RF, DT, MLP, AdaBoost, GBoost, and an ensemble model of machine learning classifiers were used. For deep-learning models, CNN, Bi-LSTM, Bi-GRU, as well as Hybrid deep-learning models were used for Sentiment Analysis. In order to improve performance further, I have proposed three ensemble-stacking deep-learning models with meta-learner layers of classifiers. The first stacking deep learning model combined 2 of the used deep learning models that produced the best results in terms of accuracy, the second stacking deep learning model combined 4 of the used deep learning models that produced the best results in terms of accuracy, and the final stacking deep-learning model combined all the trained deep learning models in this research. The proposed ensemble stacking model was evaluated using three datasets: the ESAAD Emirati Sentiment Analysis Annotated Dataset (which is one of this thesis contributions), and two other benchmark datasets (A Twitter-based Benchmark Arabic Sentiment Analysis Dataset ASAD and Arabic Company Reviews dataset). Experimental results show that my proposed ensemble stacking model outperformed existing deep learning models and achieved an accuracy of 95.54% for the ESAAD dataset, 96.71% for the benchmark ASAD, and 96.65% for the Arabic Company Reviews Dataset.