Browsing by Author "Alqaryouti, Omar"

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    Customs Trade Facilitation and Compliance for Ecommerce using Blockchain and Data Mining
    (The British University in Dubai (BUiD), 2021-07) Alqaryouti, Omar
    Electronic commerce (ecommerce) has penetrated every society, organization, business and household and changed consumers’ habits. It enabled businesses in some nations to trade beyond local borders and reach global proportions. This led to the explosive growth in demands for ecommerce platforms over the last few years and the increased popularity in cross-border trade interactions. The popularity became more evident in times of crisis such as COVID-19 for critical food and medical supplies and products. However, it was disrupted in other markets due to societies going on lockdown, which were further accentuated by borders being shut down. Ecommerce cross-border trade is impacted by regulations of each country. The challenges facing global trade and Customs administrations in particular cover many dimensions. Customs being tasked with protecting society and the smooth trade flow can no longer rely on traditional practices. A coordinated and consorted effort is required to disrupt illegitimate activities and support the mission of Customs. This work first aims to determine factors that drive the adoption of Blockchain technology. Blockchain is characterized for providing visibility, integrity, provenance and immutability across participants through the shared ledger capabilities. Therefore, blockchain technology is used in this study to build a framework to enhance trade facilitation and increase compliance while eliminating risks. This framework will provide advance access to information from various sources and will enable real-time discovery of risks. Accordingly, two off-chain clustering algorithms are proposed to determine value manipulation in ecommerce transactions and increase the efficiency of Customs Audit process. The Software Development Life Cycle (SDLC) methodology is adopted to build the framework. An integrated web application is developed to mock up the end-to-end process in ecommerce. Additionally, the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology is employed for modelling the two proposed clustering algorithms to identify transactional risks. The usability of the proposed framework is evaluated using the System Usability Scale (SUS) resulting in overall high acceptability levels across all users. Furthermore, accuracy measures are used to evaluate performance of the proposed clustering algorithms, reaching 86% for valuation assessment and 87% for risk identification in customs audit. The proposed framework will revolutionize the way trade supply chain is handled. It will create a shift from reactive limited visibility to proactive full visibility mode and properly manage various scenarios such as the current health hurdles and any future challenges lurching around.
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    Mining government tweets to identify and predict citizens engagement
    (ScienceDirect, 2019) Siyam, Nur; Alqaryouti, Omar; Abdallah, Sherief
    The rise of social media offered new channels of communication between a government and its citizens. The social media channels are interactive, inclusive, low-cost, and unconstrained by time or place. This two-way communication between governments and citizens is referred to as electronic citizen participation, or e-participation. E-participation in the age of technology is considered as a mean for citizens to express their opinions and as a new input to be integrated by policy makers to take decisions. Governments and policy makers always aim to increase such participation not only to utilize public expertise and experience, but also to increase the transparency, trust, and acceptability of government decisions. In this research we investigate how governments can increase citizens e-participation on social media. We collected 55,809 tweets over a period of one year from Twitter accounts of a progressive government in the Arab world. This was followed by statistical analysis of posts characteristics (Type, Day, Time) and their impact on citizens’ engagement. Then, we evaluated how well can different machine learning techniques predict user engagement. Results of the statistical analysis confirmed that post type (video, image, link, and status) impacted citizens’ engagement, with videos and images having the highest positive impact on engagement. Furthermore, posting government tweets on weekdays obtained higher citizens’ engagement than weekends. Conversely, time of post had a weak effect on engagement. The results from the machine learning experiments show that two techniques (Random Forest and Adaboost) produced more accurate predictions, particularly when tweet textual contents were also used in the prediction. These results can help governments increase the engagement of their citizens.