Dissertations for Informatics (Knowledge and Data Management)

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Examining the Use of Generative Artificial Intelligence in Higher Education and Its Impact on Social Sustainability: An Integrated Model of UTAUT2 and T-EESST
(The British University in Dubai (BUiD), 2024-11) QUNNEIS, SAI’DAH MAHMOUD; Dr Mostafa Al-Emran
In this research, the author assesses and evaluates the degree to which the higher education (HE) sector has embraced generative artificial intelligence (GenAI), primarily focusing on the adoption and application of GenAI and its implications for social sustainability. The aim of this research entails the proposing and advancing of a model that enhances the adoption of GenAI in an educational setting that is based on Technology-Environmental, Economic, and Social Sustainability Theory (T-EESST) and the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model. This research employs structural equation modelling (SEM), in conjunction with artificial neural networks (ANNs), to examine the main constructs driving the intention to use GenAI and the nature of its impact on social sustainability (SS). The research’s results indicate four factors that significantly impact the adoption of GenAI in HE—habit (HB), hedonic motivation (HM), performance expectancy (PE), and perceived trust (PT)—essentially highlighting intrinsic motivators. However, effort expectancy (EE), social influence (SI), facilitating conditions (FC), price value (PV), and perceived risk (PR) were not statistically significant. Furthermore, the study demonstrates that adopting GenAI has a significant impact on social sustainability (SS) in education by promoting equitable, inclusive, and lifelong learning, as well as enhancing societal well-being. These outcomes provide essential awareness for policymakers and educational institutions, establishing a basis for developing socially sustainable learning environments that leverage the revolutionary capabilities of GenAI. Keywords: Generative AI, higher education, technology acceptance, UTAUT2, T-EESST, SEM, ANN, habit, hedonic motivation, perceived trust, social sustainability, perceived risk, performance expectancy, effort expectancy.
Metaverse Integration in Education: Empowering Diverse Learning Experiences
(The British University in Dubai (BUiD), 2024-06) ZAID, LINA M A; Professor Khaled Shaalan
This dissertation investigates the integration of the Metaverse into educational settings to enhance diverse learning experiences. The research aims to explore the transformative potential of leveraging immersive technologies in education (Zhang et al., 2022), particularly focusing on fostering inclusivity and accessibility. The methodology involves a comprehensive literature review to understand the technological components of the Metaverse, its evolution (Chen et al., 2023), benefits, challenges (Lin et al., 2022), and ethical considerations (Kaddoura et al., 2023). Qualitative and quantitative data collection methods are employed to gather insights from educators, students, and stakeholders regarding their attitudes, perceptions, and experiences with Metaverse integration. The major findings reveal that Metaverse integration holds promise for creating inclusive and adaptive learning environments, especially for students with special educational needs. However, challenges such as technological proficiency and ethical considerations need to be addressed (Lin et al., 2022). The implications of the findings underscore the importance of informed strategies and resources to support educators and students in leveraging the full potential of the Metaverse (Zhang et al., 2022). The dissertation concludes with recommendations for future research and the imperative for continued commitment to inclusivity, ethical considerations (Kaddoura et al., 2023), and pedagogical innovation in Metaverse integration within education.
Deep Learning for the Extraction of Aspects in Textual Opinions
(The British University in Dubai (BUiD), 2023-07) ALSEREIDI, MOHAMED SOHAIL
This study aimed to explore the effectiveness of deep learning models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer-based models, and Graph Neural Networks (GNNs), in aspect-based sentiment analysis (ABSA) of textual opinions. The research conducted a comprehensive literature review to analyse existing studies and articles in the field, focusing on comparing the performance of deep learning models with traditional rule-based or lexicon-based approaches. The findings indicated that deep learning models demonstrated promising results and surpassed the performance of traditional methods in ABSA. CNN-based models, in particular, achieved remarkable outcomes on benchmark datasets. Transformer-based models, such as BERT and RoBERTa, also exhibited strong performance across various natural language processing tasks, including sentiment analysis. Additionally, GNNs showcased potential in leveraging text structure to improve aspect and sentiment extraction. The research identified a research gap, emphasizing the need for further exploration and advancements in the utilization of deep learning models for ABSA. This study contributes to the understanding of the potential of deep learning in ABSA and provides insights for future research in this field.
A Comparative Analysis of Deep Learning Models for Facial Expression Recognition at COP28
(The British University in Dubai (BUiD), 2024-07) ABDELQADER, KHALED J.; Professor Khaled Shaalan; Dr Manar Al Khatib
Facial expression recognition (FER) has primarily been studied in controlled environments, which do not adequately represent the dynamic conditions of real-world events. This study aims to bridge this gap by conducting a comparative analysis of deep learning models tailored to the context of COP28, a large-scale public event in the UAE. The research evaluated several deep learning architectures, including the standard convolutional neural network (CNN), expanded CNN, multi-path CNN, pre-trained transfer learning Visual Geometry Group 16 (VGG16), recurrent neural network (RNN), long short-term memory (LSTM), gat-ed recurrent unit (GRU), and vision transformer (ViT) ones, using the Karolinska Directed Emotional Faces (KDEF) dataset for training and validation. Performance was then tested on unseen images from COP28. The findings revealed that CNN-based models, particularly the multi-path CNN and VGG16, outperformed sequential models such as the RNN, LSTM, and GRU, with the multi-path CNN achieving the highest accuracy (71.70%) on COP28 test im-ages. The expanded CNN showed a strong performance on the KDEF dataset, achieving a training accuracy of 97.7%. Although most models performed exceptionally well in con-trolled settings, their performance varied when applied to COP28, indicating areas for improvement in generalization. The limitations included dataset constraints and performance variability across different expressions. Future research should focus on diverse datasets, advanced data augmentation, ensemble methods, newer architectures, multimodal approaches, and understanding cultural impacts on FER while considering ethical implications. This work underscores the importance of adapting FER models to real-world scenarios and provides insights for improving FER accuracy and generalization.
Arabic Dialect Speech-Text Recognition Using Deep Learning
(The British University in Dubai (BUiD), 2024-09) RAEIALBOOM, OMAR SALEH DARWISH; Dr Manar Al Khatib
Recently, the dominant utilization of media networking has emphasised the importance of precisely identifying users’ feelings, covering a spectrum from contentment to dissatisfaction, in the domain of online communications. The dissertation addresses the challenges of accurately transcribing Arabic speech due to the language’s complexity, limited audio resources, and diverse regional variations. Traditional speech recognition models struggle in this domain, prompting an exploration of deep learning approaches, specifically the TestRCNN and Hybrid TestRCNN-CNN Models. The research begins with a comprehensive preprocessing process, which involves loading audio data, extracting features using Mel-Frequency Cepstral Coefficients (MFCCs), and encoding labels. Both models are trained and evaluated on a curated dataset of Arabic speech samples, capturing the spatial and temporal features. The TestRCNN Model combines convolutional layers for local feature extraction and recurrent layers to capture temporal dependencies. It achieves an accuracy of 93% and a word error rate (WER) of 0.0986, but faces difficulties in distinguishing closely related phonetic sounds. To address these limitations, a hybrid approach is proposed, combining the TestRCNN and CNN architectures. This hybrid model leverages the CNN’s ability to extract detailed spatial features and the TestRCNN’s proficiency in capturing long-term dependencies. The Hybrid TestRCNN-CNN Model outperforms the TestRCNN, achieving an accuracy of 94% and significantly reducing the (WER) to 0.0460. The dissertation provides a detailed comparison of these models’ operational features, hyperparameters, and outcomes. Through extensive experimentation, the study highlights the hybrid approach’s advantages in accurately transcribing Arabic speech and contributes valuable insights to the field of Arabic speech recognition research.
Predicting and Interpreting Student Performance Using Machine Learning in Blended Learning Environments in a Jordanian School Context
(The British University in Dubai (BUiD), 0024-06) SALIM, MAHA JAWDAT; Professor Khaled Shaalan
The rise of the Internet has led to the widespread adoption of digital learning platforms, revolutionising the creation, access, and delivery of digital educational resources. These platforms enhance academic performance by fostering collaborative learning environments and generating extensive data from every user interaction. Machine learning algorithms can process large and complex datasets to identify patterns and trends that may not be immediately apparent. By analysing the data generated from these learning platforms with ML techniques, we can uncover detailed insights into student performance. Accurately predicting student performance can help educators tailor teaching methods and interventions to individual needs. This study focuses on predicting and interpreting student performance in a blended learning environment using ML in a Jordanian school context. The primary aim of this research is to employ machine learning models and SHAP (SHapley Additive exPlanations) to predict and understand student performance. A dataset generated by a digital learning platform used by a private school in Jordan is utilised. Various ML algorithms, such as Support Vector Machines, Logistic Regression, K-Nearest Neighbors, Naïve Bayes, Decision Trees, Random Forest, AdaBoost, Bagging, and Artificial Neural Networks are applied to predict student performance. SHAP values are used to interpret these predictions, offering insights into the factors most impacting student outcomes. Key findings indicate that ensemble methods like Random Forest and Bagging outperform other models in predicting student performance, achieving higher accuracy at 95.90% and 95.48%, respectively, as well as balanced precision and recall, which are crucial for accurately identifying both high- and low-performing students. The findings suggest that using these ensemble methods allows for more reliable predictions and better-informed educational strategies. The analysis reveals that individual features, such as engagement with learning materials and worksheets, significantly influence student performance. By understanding these specific factors and their impacts, educators can tailor interventions more effectively to individual needs, thereby enhancing the educational outcomes and supporting personalised learning. The findings underscore the potential of data-driven strategies to enhance educational outcomes and support personalised learning.
Enhancing Personalized Learning Experiences through AI-driven Analysis of xAPI Data
(The British University in Dubai (BUiD), 2024-03) ODEH, HANEEN; Professor Sherief Abdullah
In today's evolving educational arena, Adaptive learning experiences to individual needs has become a focal point. The Experience API (xAPI) provides a comprehensive mechanism to document all types of learning interactions, storing this stream of data into the Learning Record Store (LRS). This dissertation explores the fusion of Artificial Intelligence (AI) techniques with the obtained xAPI data. There is a gap of research in utilizing xAPI and AI integration in addressing learning objectives and understanding learners cognitive state and the utilization of data in actionable manner. This paper recommends a competency-aware framework for integrating xAPI and AI that predicts the pass/fail status of every student and provides personalized actionable feedback in an autonomous manner and in human-friendly language. To achieve this goal the CRISP-DM methodology was utilized. The analysis examined an eLearning lesson with 153 records and 51 participants, it concluded that blooms-level and pre-assessments are reliable predictors of student performance. The classification algorithms were able to predict the pass/fail statues with up to 93.5% accuracy. These predictions were fed to ChatGPT that provided personalized actionable feedback to students. The findings of this study can offer valuable insights for Educators, e-learning professionals, and AI researchers, showcasing the potential of AI in transforming the future of adaptive learning.
Arabic Speech Sentiment Analysis Using Machine Learning: A Case Study of COP28
(The British University in Dubai (BUiD), 2024-06) ALMUALLA, SHEIKH ABDULAZIZ; Professor Khaled Shaalan; Dr Manar Alkhatib
In recent years, the UAE has played a pivotal role in advancing the global climate agenda by hosting significant events such as the COP28. COP28 served as a crucial platform for international dialogue and cooperation among nations to address climate change and accelerate efforts to mitigate its impacts. In an era characterized by rapid technological advancements, the development of Arabic speech recognition systems emerges as a crucial frontier in enhancing accessibility, efficiency, and usability across various domains. Despite significant advancements in speech recognition for languages like English, challenges persist in adapting these technologies effectively to accommodate the unique characteristics of Arabic. Within this context, exploring Arabic speech recognition within the framework of COP28 serves as a compelling case study. This research integrates speech recognition technologies at COP28 and holds the potential to streamline communication and enhance accessibility for Arabic-speaking delegates and stakeholders. Through a comprehensive investigation of various speech recognition models, including CNN, BI-LSTM, GRU, and hybrid architectures such as CNN-BI-LSTM and GRU-BI-LSTM, valuable insights can be gained into their performance and efficacy within the unique context of Arabic speech recognition. Analysing key metrics such as accuracy across different sentiment categories – positive, negative, and neutral – provides a nuanced understanding of each model's strengths and limitations. The hybrid GRU and BI-LSTM model takes the lead, showcasing outstanding performance with an accuracy rate of 94%. Close behind is the standalone GRU technique, achieving an accuracy of 93%. Subsequently, both the CNN-BI-LSTM and CNN models follow suit with accuracies of 91% and 90%, respectively. The results showed the robustness and the effectiveness of the proposed models.
Automatic Recognition of Poets for Arabic Poetry using Deep Learning Techniques (LSTM and Bi-LSTM)
(The British University in Dubai (BUiD), 2024-02) AL SHOUBAKI, HAMZA YOUNIS; Professor Sherief Abdallah
Arabic poetry with its beauty, deep cultural importance and linguistic features, has always been a subject of attraction for scholars and readers. It attracted numerous researchers and writers to analyze and extract deep poetic features from various poems. As the literature review shows, there are numerous successful attempts to identify these traits and characteristics such as categorizing the used poetry metric and identifying the poets behind these poems. In our research, we introduce a comprehensive approach to Arabic poetry text classification using deep learning techniques. We have used an almost one-million record dataset of Arabic poetry verses extracted from a poetry encyclopedia. These verses are labeled with different nine poets and cover both classical and modern poetic styles. Due to the complexity of Arabic poetry such as the excessive use of metaphors, figurative language, unlimited imagination, and the diversity of styles from one poet to another and from one poem to another, we tackle these challenges by careful employment of preprocessing steps, feature engineering and selection. We also explore a range of algorithms, including traditional classifiers and deep learning models, to determine and select the most suitable and accurate models of identifying poets' names from the verses. We have decided to employ LSTSM and Bi-LSTM as our main baseline models. The reason behind selecting such models is observing a concentration on RNN (Recurrent Neural Network) and its variants when it comes to text classification. LSTM has proven its capability for sequential data analysis in many different languages. Our reported results have shown promising classification accuracy with an average of 92.35%. This sheds some light on the feasibility of automating the classification of a morphologically complex language text (Arabic). Bi-LSTM has slightly outperformed the classic LSTM in normal situation with average accuracy of 92.15% and 92.56% for LSTM and Bi-LSTM respectively. We discuss what would be the impact of our research findings on Arabic literature in particularly Arabic poetry. We also address the challenges associated with this study.
Unpacking Developer Intentions: Assessing the Behavioural Factors Influencing ChatGPT Adoption in Software Development
(The British University in Dubai (BUiD), 2023-11) SALAMEH, MOHAMMAD KHALEEL JABER; Dr Piyush Maheshwari; Professor Khaled Shaalan
Emerging AI tools such as ChatGPT have the potential to revolutionize software development. To provide a comprehensive study, our research combined two influential frameworks: the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and the Task-Technology Fit (TTF). This convergence allowed for a comprehensive evaluation that encompassed individual behavioral motivations as well as the compatibility between the technology's capabilities and the particulars of development tasks. Our research methodology was multifaceted. Beginning with a Measurement Model Assessment to confirm our constructs, we moved on to a Structural Model Assessment to uncover underlying relationships. Using the capabilities of Artificial Neural Networks (ANN), we implemented additional Root Mean Squared Error (RMSE) and Sensitivity Analysis evaluations to enhance the accuracy of our insights. Ten-fold cross-validation was rigorously applied to a dataset containing 461 observations. Our comprehensive study sought to understand the factors influencing the adoption of ChatGPT in the software development domain. Central to our findings was the pivotal role of Performance Expectancy (PE), indicating developers' inclination towards tools that enhance their efficiency and streamline processes. Similarly, the importance of Social Influence (SI) underscored the collective nature of the developer community, where endorsements from peers or influential figures can significantly bolster adoption. Habit Behavior (HB) emerged as a defining factor, emphasizing the relevance of ingrained routines in the adoption of new technologies. Moreover, Task-Technology Fit (TTF) and its interplay with PE highlighted the importance of aligning AI tools with specific developer tasks to amplify expected outcomes. Conversely, factors traditionally deemed significant in technology adoption, such as Effort Expectancy (EE), Facilitating Conditions (FC), and Hedonic Motivation (HM), did not exert a considerable influence in the context of ChatGPT.
Building a Management Information System for Juvenile Welfare Centre in the UAE and Investigate the Effectiveness of its Implementation
(The British University in Dubai (BUiD), 2017-04) ZAZA, SARWEEN; Dr Cornelius Ncube
This project investigates the use of a management information software system for juvenile welfare centre in UAE by assessing the staff’s performance in operating the project and examining the viability of the software system implementation in applying the work- streams. A review of the juvenile share system was carried out to understand how different government handles the juvenile offensive cases and the need for further actions. In any case, is was noted from research made that juvenile beneath age 18, that are found to commit serious offences are taken to correction facilities to be evaluated, and certain measures are taken by the government to ensure such children are reoriented. The research demonstrated the software system by collecting and analysing data after building the software interface. The interview was carried out with the staff of Al Mafraq Juvenile Welfare Centre to crevice the efficiency of the software. The outcome of the exercise was limited to the computer literacy or operability of the staff’s.
Analysing Pneumonia Disease Depending on X-Ray Images of Chest Using Deep Learning
(The British University in Dubai (BUiD), 2023-10) Khamees, Ahmed; Professor Khaled Shalaan
Using Machine Learning (ML) in industry has vast applications, however using it in medical domain alerts a priority to help doctors determine unseen or hidden indicators of any probable illness or medical condition, which if not treated urgently may affect patient health. In this paper, the author aims to review and enhance Image recognition and classification using ML methodologies. The data input of X-ray images taken for medical proposes, used to gain better outcomes through advanced analysis of the training data, this includes specifying the average amount of data needed for training to make a good enough predictions using deep learning (DL) in order to save costs. In addition, exploring training data by applying data cleaning techniques to gain a well-balanced model for classification purposes. Author shown that setting 1600 x-ray images or more, as a training data input, tend to enforce a steady percentage of accuracy greater than 90%. Moreover, author described the results of using dirty (unclean) or unbalanced data to the ML model, which showed a clearly drop in precision, recall and F1 score percentages. Overall, our proposed experiments showed the importance of having a quality training data in achieving higher performance results.
Understanding the Impact of Smartwatch Use on Environmental Sustainability Using a Hybrid SEM-ANN Approach
(The British University in Dubai, 2023-06-26) Almheiri, Eiman
While smartwatches have many advantages for physical activity and health, their use can also result in electronic waste and the depletion of resources. The study aims to fill the current knowledge gap about the relationship between smartwatch use and environmental sustainability via an extended UTAUT2 theoretical model and subsequently validated and evaluated using a hybrid SEM-ANN technique to achieve this. Three models were deployed in the ANN (Use, Effort Expectancy, and Performance Expectancy). Most participants in this study, comprising 303 smartwatch owners from the United Arab Emirates, reported using their watches mostly for fitness-related activities. Of the 13 hypotheses put forth by the study, the model supports 10 of them while rejecting the other 3. The findings of this research hold strategic importance for researchers and professionals involved in developing smartwatches. The insights gained from this study can guide and inform future research and development efforts in the field. Understanding the impact of smartwatch usage on environmental sustainability is particularly relevant and timely, considering the growing concerns about ecological balance and resource conservation. By identifying the specific constructs that significantly influence the adoption and use of smartwatches and their relationship with environmental sustainability, this study provides valuable insights for designing more sustainable and user-friendly smartwatch technologies. Researchers and developers can leverage these findings to inform smartwatches’ design, marketing, and implementation strategies to promote greater acceptance and sustainable usage patterns.
The Impact of Industry 5.0 Technologies on the Performance within the Healthcare Sector through the Integration of Gamification-Based Learning Programs
(The British University in Dubai (BUiD), 2023-06) Alsuwaidi, Laila
Education continues to evolve in response to its influence in a world increasingly dominated by technology. This study thoroughly explores applying and accepting Gamification-Based Learning Programs within the healthcare sector. This extensive study was conducted through the lens of Gamification-based Learning Programs in the healthcare industry with the primary objective of realizing how learning techniques open the way for integrating technology into many professional fields. This study presented novel findings based on the Unified Theory of Acceptance and Usage of Technology (UTAUT2), embracing the importance of mobility, facilitating conditions, habit, and availability in encouraging the use of GBLPs. However, traditional considerations such as performance expectancy, effort expectancy, social influence, hedonic motivation, and price value have little effect on the decision to accept technology, necessitating a reevaluation of the reasons for technological use. This research used a hybrid SEM-ANN strategy, highlighting its potential to make accurate and consequential projections regarding the future of Gamification-based Learning Programs applications. While this study focused on the healthcare industry, the lessons learned apply to other professional learning settings and may help spark a revolution in how we know in many fields. The report continues by pointing out its limitations and calling for more research into this area from various professions. The research could cover the way for future research into the model, encouraging confidence in its ability to forecast technological uptake and serve as a roadmap for crafting more exciting and effective educational environments.
Evaluation of I4.0 Technologies adoption approaches within the SMEs operations based on Multi-Criteria Analysis
(The British University in Dubai (BUiD), 2023-03) ABU-LAIL, DAREEN
Exploring the Impact of Explainable Artificial Intelligence on Decision-making in Healthcare
(The British University in Dubai, 2023-07) MOHAMMAD, AHMAD HASAN
As artificial intelligence (AI) advances in healthcare, there is an increasing need to understand how AI-driven decision-making affects healthcare workers and patients. The development of explainable artificial intelligence (XAI) systems, which attempt to give visible and interpretable explanations for AI algorithms' judgements, is a vital part of AI in healthcare. This study investigates the influence of XAI on healthcare decision-making and its potential to improve trust, acceptance, and collaboration between AI systems and human decision-makers. The study analyses the benefits and limitations of applying XAI in healthcare decision-making processes through an exhaustive analysis of current literature and empirical data. It investigates how XAI might increase AI algorithm transparency, allowing healthcare practitioners to better comprehend the reasoning behind AI-generated suggestions or forecasts. Furthermore, it investigates how XAI might help to enhance trust among healthcare professionals, patients, and other stakeholders, leading to better informed and collaborative decision-making processes. The study also tackles possible barriers to XAI deployment in healthcare. The complexity of AI algorithms, the interpretability of XAI explanations, and the integration of XAI systems into conventional healthcare procedures are among the hurdles. Furthermore, ethical aspects like as privacy, security, and bias mitigation are studied to guarantee that XAI is used responsibly in healthcare decision-making. The outcomes of this study lead to a better understanding of the influence of XAI on healthcare decision-making. This research seeks to give insights for policymakers, healthcare practitioners, and AI developers to support the responsible and successful integration of XAI into healthcare systems by shedding light on the benefits and issues connected with XAI. The ultimate objective is to use XAI to improve healthcare decision-making processes, improve patient outcomes, and allow the ethical and trustworthy deployment of AI in the healthcare sector.
Understanding the Intention to Use the Metaverse in IT Companies Using a Hybrid SEM-ANN Approach.
(The British University in Dubai, 2023-07-01) ZAMMAR, AHMAD KHALED
While embracing the metaverse within Information Technology (IT) companies could present unique opportunities, it also brings about challenges in adoption behavior. However, research on the factors influencing intentional behavior to use the metaverse in IT companies is scarce. To bridge this gap, this study develops a research model that integrates elements from the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), Task-Technology Fit (TTF), and awareness studies, and hypothesizes key variables such as performance expectancy, effort expectancy, and social influence. Through a comprehensive survey of 234 participants, the research model is evaluated employing a unique combination of Structural Equation Modeling (SEM) and Artificial Neural Network (ANN), which serve as advanced modeling techniques. The SEM and ANN analyses elucidate intricate relationships and make predictions about adoption behavior, while uncovering patterns and insights into metaverse adoption in IT companies. Although the primary focus is on SEM and ANN, this study also utilizes Partial Least Squares (PLS) in the research design. It identifies and discusses key findings from descriptive analysis, measurement model assessments, and structural model assessments. Furthermore, the ANN results and sensitivity analysis paint a more nuanced picture of metaverse adoption behavior in the IT sector, providing valuable predictions and insights. In addition to the theoretical contributions, the findings offer practical implications for IT companies and suggest future research directions to help them make informed decisions related to the implementation and use of the metaverse. Overall, this study contributes to the growing body of literature on the metaverse and its application in the business landscape, with specific emphasis on IT companies.
Examining the impact of Industry 5.0 on economic sustainability: A hybrid SEM-ANN approach
(The British University in Dubai (BUiD), 2023-05) ALSHAMSI, SAIF RASHED
While there is an existence of a significant amount of literature studies on industrial revolution there exist a scarce knowledge about examining the impact of Industry 5.0 on economic sustainability using a hybrid SEM-ANN approach. The study aims at understanding the impact of industrial 5.0 on economic sustainability. A Quantitative analysis was done with data collected from over 363 participants. Two stage analytical techniques were applied in this work which involves the combination of ANN and with the PLS-SEM. First, PLS-SEM is used to enable understand the predictors and their significant influence on the sustained use of industrial 5.0 revolution. The results show that there is a positive and non-significant relationship between predictors and industrial 5.0 revolution on sustainability. The outcome of this study as provided valuable insight for those countries willing to adopt industrial 5.0 revolution to understand its impact on economic sustainability.
Arabic Hotel Reviews Sentiment Analysis Using Deep Learning
(The British University in Dubai (BUiD), 2023-06) ALMANSOORI, MOHAMMAD
Arabic Hotel Feedback sentiment analysis plays a significant role in understanding the opinions and sentiments expressed by customers in their reviews. With the growing popularity of online platforms and social media, Arabic Hotel Feedback have become a valuable source of information for both hotel owners and potential customers. Sentiment analysis techniques aim to automatically classify the sentiment polarity of these reviews as positive, negative, or neutral, providing valuable insights into customer satisfaction and areas of improvement for hotels. In this study, we present a comprehensive analysis of Arabic Hotel Reviews sentiment analysis. We collected a large dataset of Arabic hotel Feedback from various online platforms, encompassing a wide range of hotels and customer experiences. The dataset was carefully annotated with sentiment labels by human annotators to serve as ground truth for training and evaluation purposes. We employed state-of-the-art machine learning and natural language processing techniques to develop sentiment analysis models specifically tailored for the Arabic language. Our models utilized advanced text preprocessing, feature extraction, and classification algorithms to accurately predict sentiment polarity in Arabic hotel reviews. We evaluated the performance of our models using various evaluation metrics, including accuracy, precision, recall, and F1-score, to assess their effectiveness in sentiment classification. The results of our study demonstrate the viability and effectiveness of sentiment analysis in Arabic Hotel Reviews. Our models achieved high accuracy and robust performance in sentiment classification, enabling hotel owners to gain valuable insights into customer sentiments and make informed decisions to enhance customer satisfaction and improve their services. CNN model demonstrated superior performance in terms of precision, recall, F1-score, and accuracy, consistently achieving a score of 74% across all evaluation metrics. The SVM model closely followed with a score of 73% for the same metrics. The LSTM model exhibited slightly lower performance, achieving values between 70% and 71%. On the other hand, the DT model had the lowest scores among all the models, with values of 66% and 68%. The findings of this study contribute to the growing body of research in sentiment analysis and provide valuable insights into sentiment patterns specific to Arabic hotel reviews. Overall, this study highlights the importance of sentiment analysis in the context of Arabic Hotel Feedback and provides a foundation for future research and applications in the field. The insights gained from sentiment analysis can empower hotel owners, marketers, and decision-makers to better understand customer sentiments, address concerns, and optimize their services to meet customer expectations in the dynamic and competitive hotel industry.
Detecting Arabic Cyberbullying Tweets in Arabic Social Using Deep Learning
(The British University in Dubai (BUiD), 2023-06) ALFALASI, FARIS Jr
The widespread engagement with social media platforms in recent years has made cyberbullying a significant concern. Individuals may have catastrophic side effects from that as well, including despair, anxiety, and even suicide. Due to the difficulty of manually detecting and categorizing vast volumes of electronic text data, conventional methods for recognizing and combating cyberbullying have not proven successful. As a consequence, deep learning methods have become a potential solution for this situation. Artificial neural networks and other deep learning approaches can automatically identify patterns and features from a massive quantity of data. These methods may be applied to electronic text data analysis to spot cyberbullying-related trends. Techniques for natural language processing may be used to text data to extract useful features like sentiment, emotion, and subjectivity. A sizable dataset of electronic text data was gathered from multiple social media platforms like Twitter, Instagram, YouTube, and many more sites in order to examine cyberbullying in social media using machine learning and deep learning techniques. The data needs to be initially prepared so that deep learning algorithms may be trained on it before cyberbullying analysis can be done. Manually annotated data from a corpus collection was used to label the information for deep learning purposes. Pre-processing is a vital part of the data preparation process for cyberbullying detection. There are several varieties of Arabic, but the three most common are dialect Arabic , Modern Standard Arabic, and Classical Arabic. Because of its widespread use on social media, DA Arabic is the subject of this essay. Based on the existence of cyberbullying, the data was then preprocessed and classified. In this work, two cases of classification were adapted. The first case was 2-classes classification where the data labeled as either cyberbullying or not cyberbullying. The second case was 6-classes classification which consists of six different cyberbullying types. To categorize electronic text in these two cases, deep learning models such as convolutional neural networks and recurrent neural networks and a combination of CNN-RNN were trained on this data. In an independent test set, the trained models were assessed, and they showed promise in identifying cyberbullying via social media. The results that obtained from 2-classes classification showed a superiority of LSTM in terms of accuracy with 95.59%, while the best accuracy in the 6-classes classification gained from implementing CNN with 78.75%. Meanwhile the f1-score results were the highest in LSTM for the 2-lasses and 6-classes classifications with 96.73% , and 89%, respectively. These findings emphasize the potential for deep learning techniques to be applied in the development of automated systems for identifying and combating cyberbullying on social media and show how well they work in detecting cyberbullying.

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