Professor Sherief Abdallah

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https://bspace.buid.ac.ae/handle/1234/2425
Dr Sherief Abdallah is Professor of Artificial Intelligence at the British University in Dubai and CAA certified reviewer. He holds a PhD in Computer Science (AI) from the University of Massachusetts at Amherst, the United States. Dr Abdallah’s research focuses on applying machine learning to real and novel problems, including education, network management, and information retrieval. He collaborated with world-class researchers in the United States, South America, and Europe. He worked on research projects funded by the National Science Foundation (USA), some of which involved hundred researchers from interdisciplinary areas.

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Browsing Professor Sherief Abdallah by Author "Siyam, Nur"

Now showing 1 - 3 of 3
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    A Pilot Study Investigating the Use of Mobile Technology for Coordinating Educational Plans in Inclusive Settings
    (SAGE journals, 2021) Siyam, Nur; Abdallah, Sherief
    Good coordination among school staff and families leads to increased learning quality and academic success for students with special education needs and disabilities (SEND). This pilot study aims to investigate the use of mobile technology for the coordination of therapy and learning for students with SEND. This study first follows a participatory design methodology to identify the key design principles required to inform the design of a coordination mobile app for special education. Then, a mobile app (IEP-Connect) is designed and implemented with the aim of facilitating information sharing between different parties involved in the intervention of students with SEND. The proposed app uses the Individualized Educational Plan (IEP) as the focal point of coordination. The evaluation of the app focused on students with autism spectrum disorder (ASD) as their learning requires sharing information from different distributed sources. Results from the usability study revealed that the app has “good” usability and that participants were satisfied with the use of the app for recording and sharing IEP information. The results of this study provide an understanding of the ways in which a coordination app for special education could be made easy and rewarding to use.
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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.
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    Toward automatic motivator selection for autism behavior intervention therapy
    (ProQuest Central, 2022) Siyam, Nur; Abdallah, Sherief
    Children with autism spectrum disorder (ASD) usually show little interest in academic activities and may display disruptive behavior when presented with assignments. Research indicates that incorporating motivational variables during interven tions results in improvements in behavior and academic performance. However, the impact of such motivational variables varies between children. In this paper, we aim to address the problem of selecting the right motivator for children with ASD using reinforcement learning by adapting to the most infuential factors impacting the efectiveness of the contingent motivator used. We model the task of selecting a motivator as a Markov decision process problem. The states, actions and rewards design consider the factors that impact the efectiveness of a motivator based on applied behavior analysis as well as learners’ individual preferences. We use a Q-learning algorithm to solve the modeled problem. Our proposed solution is then implemented as a mobile application developed for special education plans coordination. To evaluate the motivator selection feature, we conduct a study involving a group of teachers and therapists and assess how the added feature aids the participants in their decision-making process of selecting a motivator. Preliminary results indicated that the motivator selection feature improved the usability of the mobile app. Analysis of the algorithm performance showed promising results and indicated improvement of the recommendations over time.