Dissertations for Informatics (Knowledge and Data Management)

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Adaptive Secure Pipeline for Attacks Detection in Networks with set of Distribution Hosts
(The British University in Dubai (BUiD), 2022-01) ALSHAMSI, SUROUR
Currently, malware continues to represent one of the main computer security threats. It is difficult to have efficient detection systems to precisely separate normal behavior from malicious behavior, based on the analysis of network traffic. This is due to the characteristics of malicious and normal traffic, since normal traffic is very complex, diverse and changing; and malware is also changeable, migrates and hides itself pretending to be normal traffic. In addition, there is a large amount of data to analyze and the detection is required in real time to be useful. It is therefore necessary to have an effective mechanism to detect malware and attacks on the network. In order to benefit from multiple different classifiers, and exploit their strengths, the use of ensembling algorithms arises, which combine the results of the individual classifiers into a final result to achieve greater precision and thus a better result. This can also be applied to cybersecurity problems, in particular to the detection of malware and attacks through the analysis of network traffic, a challenge that we have raised in this thesis. The research work carried out, in relation to attack detection ensemble learning, mainly aims to increase the performance of machine learning algorithms by combining their results. Most of the studies propose the use of some technique, existing ensemble learning or created by the authors, to detect some type of attack in particular and not attacks in general. So far none addresses the use of Threat Intelligence (IT) data in Ensemble Learning algorithms to improve the detection process, nor does it work as a function of time, that is, taking into account what happens on the network in a limited time interval. The objective of this thesis is to propose a methodology to apply ensembling in the detection of infected hosts considering these two aspects. As a function of the proposed objective, ensembling algorithms applicable to network security have been investigated and evaluated, and a methodology for detecting infected PAGE 2 hosts using ensembling has been developed, based on experiments designed and tested with real datasets. This methodology proposes to carry out the process of detecting infected hosts in three phases. These phases are carried out each a certain amount of time. Each of them applies ensembling with different objectives. The first phase is done to classify each network flow belonging to the time window, as malware or normal. The second phase applies it to classify the traffic between an origin and a destination, as malicious or normal, indicating whether it is part of an infection. And finally, the third phase, in order to classify each host as infected or not infected, considering the hosts that originate the communications. The implementation in phases allows us to solve, in each one of them, one aspect of the problem, and in turn take the predictions of the previous phase, which are combined with the analysis of the phase itself to achieve better results. In addition, it implies carrying out the training and testing process in each phase. Since the best model is obtained from training, each time it is performed for a given phase, the model is adjusted to detect new attacks. This represents an advantage over tools based on firm rules or static rules, where you have to know the behavior to add new rules.
ALNER: ARABIC LOCATION NAMED ENTITIES
(The British University in Dubai (BUiD), 2010-10) KADDOURA, HAITHAM MOHAMAD
This dissertation describes a rule based approach carried out to determine Location Named Entities in Arabic. ALNER, an Arabic Location Named Entities Recognition system, implements the rule based approach and is introduced in this thesis. This research is the first of its type to specialize in Location NER as a stand-alone system from other named entity types. Such dedication on one named entities helps in investigating the performance of comprehensive NER systems. The Named Entity Recognition (NER) task has great influence on various Natural Language Processing (NLP) applications (e.g. Information Retrieval, Question Answering, etc.). Various research works conducted toward building language independent NER systems that will work on any language but very limited work has been done for NER systems to work with Arabic language. It is known that Arabic language has complex morphology as a language which makes the NER task more difficult. Readers will find an overview about the Arabic language morphology and how it is different from other languages. We also highlighted the key challenges in Arabic language for the NER task. In addition, overall presentation about previous work toward Arabic NER is presented. ALNER system using rule-based approach was evaluated and achieved accuracy of 87.27% and further investigation was conducted to study per module effectiveness and contribution.
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.
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.
Arabic Image Captioning (AIC): Utilizing Deep Learning and Main Factors Comparison and Prioritization.
(The British University in Dubai (BUiD), 2022-02) HEJAZI, HANI DAOUD
Captioning of images has been a major concern for the last decade, with most of the efforts aimed at English captioning. Due to the lack of work done for Arabic, relying on translation as an alternative to creating Arabic captions will lead to accumulating errors during translation and caption prediction. When working with Arabic datasets, preprocessing is crucial, and handling Arabic morphological features such as Nunation requires additional steps. We tested 32 different variables combinations that affect caption generation, including preprocessing, deep learning techniques (LSTM and GRU), dropout, and features extraction (Inception V3, VGG16). Moreover, our results on the only publicly available Arabic Dataset outperform the best result with BLEU-1=36.5, BLEU-2=21.4, BLEU-3=12 and BLEU4=6.6. As a result of this study, we demonstrated that using Arabic preprocessing and VGG16 image features extraction enhanced Arabic caption quality, but we saw no measurable difference when using Dropout or LSTM instead of GRU.
Arabic Parser Evaluation and Improvements
(The British University in Dubai (BUiD), 2016-06) Ezzeldin, Khaled Mohamed Khaled
This thesis focuses on comparing between two famous Arabic parsers Stanford Parser and Bikel parser using Arabic Treebank (ATB) for model training and testing and for this purpose we created a software that enables us to convert the ATB format to grammar format, convert the Arabic Morphological tags to Penn tags, and evaluate the parsers output by calculating the Precision, Recall, F-Score, and Tag Accuracy. We also modify Bikel Parser to use the Penn tags in training to improve the Precision, Recall, F-Score, and Tag Accuracy results from the parse output.
Arabic Question Answering from diverse data sources
(The British University in Dubai (BUiD), 2018-07) KHATER, FERAS
Currently, Arabic users are still forced to extract manually the accurate answers of their questions, which is a difficult task with a vast amount of information available on the Internet. Actually, the existing Arabic Question Answering (QA) systems do not meet the users’ needs in terms of performance and scope that cover all types of questions. The motivation behind this research is the need for new approaches to handle all types of questions and answer them beyond the factoid questions. Therefore, we present in this paper a new design of the linguistic approach to develop a reliable Arabic QA system and data source with the ability to address the following challenges: (i) handle both factoid and complex questions in Arabic language, (ii) extract the precise answer from available resources, (iii) evaluate the proposed QA system based on a gold standard data set, and (iv) provide an Arabic Corpus of Occupations (ACO) corpus that has been made freely and publicly available for research purposes. Our QA system is a web application that helps us to get an answer to the question posed from different data sources. Accordingly, we conducted experiments on a set of 230 question from the previously published resources, TREC, CLEF, and Arabic Corpus of Occupations (ACO) corpus. The system performance shows an average precision of 36%, by answering 72 questions, as well as the Recall was 78% and F-Measure was 51%. Besides, the aim that attracted us to build the Arabic Corpus of Occupations (ACO) corpus was the lack of free, annotated and large-scale Arabic resources that can be used in training and testing Arabic QA systems. In this paper, we provide ACO corpus of one million words written in Modern Standard Arabic (MSA). The corpus contains 700 occupations which are analyzed carefully and manually annotated. We use Cohen's Kappa coefficient method to evaluate the reliability of the tagged content. The corpus content has been tagged and assessed by two different groups of taggers. Accordingly, the inter-annotator agreement indicates that the reliability of ACO corpus is almost perfect agreement. As well as, the content of the corpus is highly confidence and reliable according to the result achieved by 90%.
Arabic Sentiment Analysis for Gulf Opinion Leaders using a Deep Learning Approach Case Study: Covid-19-22
(The British University in Dubai (BUiD), 2023-07) ALKETBI, SULTAN
The COVID-19 pandemic has had a profound impact on global health and has affected various populations worldwide. In the Arab world, social media has emerged as a critical platform for expressing opinions, sharing information, and disseminating news related to COVID-19. However, the proliferation of false information and the spread of fear and panic on social media have created a significant problem. This study aims to investigate how Arab populations, including both opinion leaders and the general public, have responded to the COVID-19 pandemic on Twitter. The research focuses on analysing sentiment and developing a deep learning model to detect real news associated with the pandemic in Arabic text. By gathering and analyzing data from Gulf countries, the study provides insights into the sentiments expressed and contributes to understanding how opinion leaders and the general public engage with COVID-19 on Twitter. Additionally, the study evaluates the efficacy of the deep learning model in combating misinformation and highlights the significance of sentiment analysis and news detection in the Arabic language. Data collection was conducted using Twitter's API, focusing on Arabic tweets from Gulf opinion leaders, utilizing specific keywords, hashtags, and user accounts related to COVID-19. The testing phase involved collecting 100,000 tweets from January to June 2022, with an emphasis on quality and relevance, including opinion leaders with significant follower counts and those recognized for their expertise or influence in the field. Overall, this research contributes to understanding the response to COVID-19 on Twitter and provides valuable insights into sentiment analysis and the detection of real news in Arabic text.
Arabic Sentiment Analysis using Machine Learning
(The British University in Dubai (BUiD), 2016-09) ATIYAH, SASI FUAD
Sentiment Analysis is a rising field that is gaining popularity every day due to its importance in mining the public opinions, the immense amount of generated data every second over the Internet via social network, microblogs, blogs, forums, consumer websites and other presents a rich field of opinions that are ready to be populated, aggregated and summarized and based on that decision are made. The applications are wide from the classical problems like political campaigns, product reviews to more sophisticated usage in Human Machine Interaction where the detection of the human sentiment plays an important role in a successful machine interaction. In this research we investigated the problem of sentiment analysis in the Arabic language and focus on how to utilize the machine learning-based approach to its maximum by conducting several experiments on several multi-domain dataset and optimize the trained model using parameter optimization and using the findings to establish a predefined best parameter settings to be used on new datasets. The research showed that through parameter optimization, basic machine learning classifiers achieved higher results than other more complex hybrid approaches, in addition, the overall parameters settings were tested on two new datasets and provided very promising results indicating that performance weren’t as a cause of overfitting. The research also explains the issues of testing such well-trained models on an unseen dataset from different sources in the same domain and how it can be solved. The work was concluded by the possible enhancements that can be applied to the work done and a new path for future work that promises a more generalized solution.
Arabic Sign Language Recognition: A Deep Learning Approach
(The British University in Dubai (BUiD), 2022-05) ALMAHRI, HAMDA GHALIB AWADH ALI
With more than 300 sign languages across the world, sign interprets are not always available to translate spoken words into sign language and vice versa. As people with hearing and speech impairments rely on Sign Language for communication, this would limit their communication with others. A solution for this would be utilizing Sign Language Recognition systems, which allow for communication between users of the sign language and those who do not without the need for interpreters. As we consider the success of Deep Learning for Computer Vision tasks, we observe the advantage it can provide for Arabic Sign Language Recognition. For this research, we have two aims. First, we would like to review the current status of research in Arabic Sign Language Recognition using Deep Learning and find research gaps. Second, we aim to build a Sign Language Recognition system that bridges the gap. We achieve this through a systematic review that identifies primary studies using deep learning models for Arabic Sign Language Recognition. Out of 414 identified studies, 67 were deemed of relevance to our topic. Out of those, 32 studies passed our full selection procedure. We were able to discover patterns in research and find that the biggest issue is data collection as current datasets don’t offer enough variety and are not representative of real-life scenarios. Current methods are either too expensive, or easily affected by the surrounding environment. Thus, for the second part, we offer a solution for data collection using MediaPipe, which allow us to collect data directly through the webcam. We are able to leverage this framework to build a recognition system for Emirati Sign Language that recognizes the signs for the seven Emirates. We used an LSTM model and achieve an accuracy of 100% in the testing dataset.
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.
ArgDF: Arguments on the semantic web
(The British University in Dubai (BUiD), 2007-02) Zablith, Fouad
Aspect-Based Sentiment Analysis for Government Smart Applications Customers’ Reviews
(The British University in Dubai (BUiD), 2017-07) ALQARYOUTI, OMAR HARB ABDELKARIM
Nowadays, sharing opinions has been made easier with the evolvement of Web 2.0. People can share their opinions on their daily activities and consider others’ opinions to decide whether to buy a product or install an app or use a service. Therefore, the public opinion on the web has become a norm in the modern world. Government agencies and business owners are keen to understand the publics’ opinions towards their services and products. This is a key input for these organizations decision making process in terms of understanding the customers’ needs in order to enhance the product or improve the service or introduce new features. This dissertation presents a holistic review on a variety of recent articles that commences with a background on Sentiment Analysis (SA) as well as it touches on numerous SA techniques, issues, challenges and real-life applications with focus on governmental services and smart apps. In this study, the government smart applications aspects that can be used in aspect-based SA were defined based on written standards with emphasis on customer experience as an important aspect. The proposed aspects include User Interface, User Experience, Functionality and Performance, Security, as well as Support and Updates. For studying SA of government smart applications customers’ reviews, a novel domain-specific annotated dataset has been constructed. It involves government apps in the United Arab Emirates (UAE) as well as its corresponding aspects terms and opinion lexicons. This was done with the help of a proposed Government Apps Reviews Sentiment Analyser (GARSA) which is a responsive web tool that we have developed in order to facilitate the annotation process in a flexible, organized, efficient and tracked manner. Aspect-based SA is considered as one of the challenging tasks in SA. In this regard, an integrated lexicon and rule-based approach was employed to extract explicit and implicit aspects and their sentiment classification. This model utilized the manually generated lexicons in this dissertation with hybrid rules to handle some of the key challenges in aspect-based SA in particular and SA in general. This approach reported high performance results through an integrated lexicon and rule-based model. The approach confirmed that integrating sentiment and aspects lexicons with various rules settings that handle various challenges in SA such as handling negation, intensification, downtoners, repeated characters and special cases of negation-opinion rules outperformed the lexicon baseline and other rules combinations.
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.
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.
Clustering Tweets to Discover Trending Topics about دبي (Dubai)
(The British University in Dubai (BUiD), 2018-03) ALYALYALI, SALAMA KHAMIS SALEM KHAMIS
Nowadays, a lot of people targeting social networks to learn what are the trending topics and the news alongside the huge flow of texts posted daily in social networks. One of these social networks is Twitter - a microblogging hub and rich environment of data. Scanning tweets online is a hard task and searching effortlessly to find intended topic from huge amount of data is also time consuming. This paper is intended to propose a solution of collecting Twitter of the corpus دبي (Dubai) by using Zapier website and storing them in Google sheet. Then, creating a word vector to the tweets by using TF-IDF methodology. After this, log results into k- mean clustering algorithm with cosine similarity to measure similarity between objects of each cluster. The results demonstrate that internal evaluation techniques failed to evaluate quality of the cluster. In addition to that, interesting topics was found about دبي (Dubai). Moreover, better results achieved by using Filter Tokens (by Region) than without using it. The data were collected for the experiment at several periods to ensure getting the most trending topics about دبي (Dubai). All of the results found in this paper tested with real tweets.
Colon cancer classification using microarray data
(The British University in Dubai (BUiD), 2010-03) Tariq Khan, Saima
A thesis presented on the classification of cancerous and normal tissue samples using microarray data. In treating cancer time is of the essence and early detection can dramatically increase the chances of survival. Imaging techniques, which are the prevalent method of detection and diagnosis, are only useful once the cancerous growth has become visible.However, if techniques that detect cancerous processes at a genetic level are utilized then the cancerous tissues could be identified, and the disease diagnosed much earlier, thus giving a far better prognosis.Therefore, the aim of this thesis is to evaluate the performance of a variety of different classification methods with a particular dataset containing genetic samples of both normal and cancerous biopsies of the colon tissue.A classifier will be recommended which is able to learn the patterns within the microarray data that best determines the classification of the samples.
Comparative Study of Deep Learning Models for Unimodal & Multimodal Disaster Data for Effective Disaster Management
(The British University in Dubai (BUiD), 2021-07) MOHAMED, DENA AHMED
Multimodal data of text and images on social media posts hold valuable information that can be utilized during crisis events. Such information includes requests for help, rescue efforts, warnings, infrastructure damage, missing people, injured or dead individuals, volunteers, donations, and many more. Many studies focus only on the text modalities, single classification tasks and small-scale home-grown datasets when studying how useful social media data can be for emergency services. In this study, a multimodal deep learning system for automatic classification of disaster tweets was developed. Two classification tasks were tackled, which are informativeness and the humanitarian category. An extensive comparison between unimodal text-only, unimodal image-only and multimodal deep learning models across three different representative disaster datasets (CrisisMMD, CrisisNLP, and CrisisLex26) was done. Convolutional neural networks are utilized for defining the deep learning architectures. Experiments across the multiple settings and datasets show that multimodal models perform better than their unimodal counterparts. It was also found that mapping between the diverse humanitarian categories and consolidating smaller datasets with larger ones significantly improves the models’ performance when compared to individual datasets. The consolidated dataset can serve as a new baseline multimodal dataset for further research directions.
Covid-19 Spread Simulation based on control measures in UAE
(The British University in Dubai (BUiD), 2021-05) Yaseen, Amer
By February 2021, the total number of reported COVID-19 cases are over 110 million with 2.4 million death cases. In response to that, governments all over the world have imposed a variety of control measures including travel ban, lockdowns, events cancellation and closure of workspace and schools. In addition, continuous monitoring, contacts tracing and increasing the testing capacity were applied in most of the countries. With continuous growth of spread momentum, more strict rules are applied causing unprecedented disruption for society and economy. In this thesis, a SEIR compartmental model is proposed and developed by python-based program in order to analyze the virus transmission between different model compartments. The program executes the standard SEIR model differential equations over time under different combinations of control measures in order to examine their effectiveness on the virus development. Three control measures were discussed and analyzed in this thesis which namely are closure of schools, closure of universities and limitation of business capacity. Results show that if schools get closed, then the number of infections will surge rapidly starting from day 100 and reach a peak of 5% of population at day 150. Similarly, closing the universities will cause the number of infections to start surging at day 70 and reach a peak of 7% of population at day 120. Finally, forcing all employees to work remotely from home will lead to flattening the infection curve. Results show also that if we set the effectiveness value of control measure to 45%, then infections curve will get flattened and hence keep the infection rate under control. Finally, an optimized policy of control measures is proposed which will not only control the virus infection rate, but also will minimize the unnecessary control measures and keep the infected population below the capacity of the healthcare system.
Critical Success Factors for Implementing Artificial Intelligence (AI) Projects in Dubai Government United Arab Emirates (UAE) Health Sector: Applying the Extended Technology Acceptance Model (TAM)
(The British University in Dubai (BUiD), 2019-03) ALHASHMI, SHAIKHA ALI MOHSIN ALATTAR
Recently, the government of United Arab of Emirates (UAE) is focusing on Artificial Intelligence (AI) strategy for future projects that will serve various sectors. Health care sector is one of the significant sectors they are focusing on and the planned (AI) projects of it is aiming to minimize chronic and early prediction of dangerous diseases affecting human beings. Nevertheless, project success depends on the adoption and acceptance by the physicians, nurses, decision makers and patients. The main purpose of this dissertation is to explore out the critical success factors assist in implementing artificial intelligence projects in the health sector. Besides, the founded gap for this topic was explored as there is no enough sharing of multiple success factors that assist in implementing artificial intelligence projects in the health sector precisely. First of all, this dissertation analyze the mostly used external factors of the Technology Acceptance Model (TAM), by highlighting studies that address these factors, mainly Perceived Ease of Use, Perceived Usefulness, Attitude towards use and Behavioral intention to use. In order, to identify the most widely used factors a systematic review approach was conducted for 23 related research studies between 2015 and 2018 having quantitative and qualitative data. Second, a modified proposed model for this research was developed by using the extended TAM model and the most widely used factors. Third, to fit the purpose of this research a validation to the new model was used by the partial least squares-structural equation modelling (PLS-SEM). Data of this study was collected through survey from employees working in the health and IT sectors in UAE and total number of participants is 53 employees. The outcome of this questionnaire illustrated that managerial, organizational, operational and IT infrastructure factors have a positive impact on (AI) projects perceived ease of use and perceived usefulness.

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