Haya El Ghalayini
Haya El Ghalayini received her PhD in computer science from University of the West of England, Bristol-UK. Her PhD was in reverse engineering bioinformatics ontologies to conceptual data models. Haya has published number of conference and journal papers related to the field of ontologies, conceptual modelling, social network analysis, project-based learning, and designing eLearning contents. Prior to Sheridan Haya was an assistant professor at Petra University, Jordan, teaching introductory and advanced courses in computer science and software engineering.
Contributions to the training of students and other talent
- Title: A Machine Learning approach to Audio-Driven Lip Syncing, Dec. 2019, by Daniel Picott. This research tests the possibility of a conditional Generative Adversarial Network (cGAN) learning speech structures and generating a corresponding facial pose as defined by 3D vertex positions of the face model is examined. Supervisors: Edward Sykes and Haya El Ghalayini.
- Title: Artificial Intelligence for Auto Captioning Images to Aid Visually Impaired People Use Social Media, Dec. 2019, by Gaurav Kanwar. The auto captioning system uses a convolutional neural network and combines the YOLO algorithm with the Viola Jones algorithm to detect faces, objects, and scenes. Supervisors: Edward Sykes and Haya El Ghalayini.
- Title: Forecasting Bitcoin Prices Using N-BEATS Deep Learning Architecture, Dec. 2020, by Ali Bulatov. This study evaluates the predictive power of the N-BEATS deep learning architecture trained on Bitcoin daily, hourly, and up-to-the-minute data in comparison with other popular time series forecasting methods such as LSTM and ARIMA.
Capstone Projects Supervision
- Study Snap App, April 2021, by Benjamin Sykes, Malik Talhat, and Liam Stickney. The Study System that allows the students to organize the resources with a smart and automatic resource search engine based on elastic search methods. The app uses various cloud computing services, natural language processing techniques, and Optical Character Recognition (OCR).
- Prescription Blockchain, Dec. 2022 by Ryan McCaluum, Skylar Garland, Anran Qin. It is a centralized blockchain-based prescription management system, which allows physicians to create and provide prescriptions to users. Prescriptions entered onto the blockchain and distributed to remote ledgers. The system proposes an auditable solution which provides physicians and pharmacies the ability to ensure the integrity of prescriptions from end-to-end.
Other evidence of impact and contributions
- Partial scholarship for PhD degree (Perta University, Amman- Jordan 2004-2007).
- Community engagement- Helping students to be computer literate (Jordan, Amman, 2011- 2014). Visiting Researcher- Ryerson University (August 2014- December-2014). Implementing an organic chemistry ontology in OWL.
- Sessional faculty at McMaster University, Hamilton from (2016 and 2018).
- Academic Adviser, School of Applied Computing, Sheridan College.
Contributions to research
Town of Oakville, Public Transit Mobility Project; Partner: Invoex. The overall goal of this project is to study the utilization of Oakville Go Station as the second busiest after Union Station, Toronto. The outcome of the project presents the origin-destination matrix for different modes of transportation from and to the station. These results will assist urban planners to understand how people currently travel to/ from Oakville Go Station to increase the use of sustainable travel modes and decrease the use of private vehicles. Therefore, these matrices will provide the most complete and accurate information about the utilization of a physical area of interest to understand the journey and mobility pattern. The project is implemented using different programming tools such as Python Pandas, Geopandas and Geoplot. In addition to python tools, Here.com API and TELUS insights API are used to convert location names to geospatial coordinates and to provide zone breakdown of cell device movements in Canada.
A review on ontology modularization techniques – a multi-dimensional perspective. Survey Research Paper between Sheridan College, McMaster University, and St. Francis Xavier University. Ontology modularization is the process of extracting a fragment, or “module”, from an ontology, based on predefined requirements. Due to diversity in ontological representations and motivations for modularizing, the body of research on ontology modularization techniques has become extremely large and may be intimidating to the novice ontology researcher. The objective of this research was to present a comprehensive, albeit high-level, review of ontology modularization techniques. A systematic literature review covering January 1st, 2000, to July 31st, 2020, was performed to find and classify papers on ontology modularization techniques. The classifications are intended to guide one to a suitable modularization process in accordance with the requirements. The limitations of ontology modularization techniques are highlighted in conclusion, and characteristics of a desirable framework for an ontology representation that would be best suited for modularization are suggested.
Developing Analytical and Critical Thinking in Designing Algorithms, Sheridan Creates 2021. This approach integrates mind maps and problem-solving models to assist students in understanding the problem definition and then design their correct algorithm as an answer. The approach has been applied to Data structures and algorithms development course. The survey results show the approach helped in solving problems.
Ontology and e-Learning. Designing blended courses, utilizing human-computer interaction and multimedia in the usability and effectiveness of the designed courses. The research papers are related to understanding the effectiveness of different technology in improving the learning process. In addition, another paper in this area of research utilizes project-based and networked learning methods. The system is developed using Moodle open-source platform that employs social constructivism learning theory. The main objective of this system is to manage and control activities such as the submission of project deliverables and grading in the graduation project course, including: students, supervisors, mentors, examiners and graduation project committee members. To highlight the advantages of using a web-based graduation-project system, a Social Network Analysis is applied to study the interaction messages among all participants using cohesion measures, including density and centrality. The results of analyzing the learning network of the graduation project system show that students interact and communicate as active learners. In contrast, the system can assist supervisors and other participants in coordinating and managing the processes of the graduation project course.
Ontology and Knowledge Based Systems. Several methodologies and methods.
Andrew LeClair, Alicia Marinache, Haya El Ghalayini, Wendy MacCaull and Ridha Khedri. (2022). "A review on ontology modularization techniques – a multi-dimensional perspective." Transactions on Knowledge and Data Engineering, pp. 1-28.
El Ghalayini, Haya, "Developing Analytical and Critical Thinking in Designing Algorithms (Teach Geeks)" (2021). Sheridan Creates. 53.
El-Ghalayini, H., Abu-Arqoub, M., Issa, G. and, Shubita, A. (2017). "Graduation-Project Management System: A Social Network Analysis Perspective." Journal of Theoretical and Applied Information Technology, Vol. 95. No 4. pp. 810–819.
El-Khalili, Haddad, B., El-Ghalayini. (2015). "Language Engineering for Creating Relevance Corpus," International Journal of Software Engineering and its Applications (IJSEIA), Vol. 9, No.2.
El-Khalili, N. and El-Ghalayini, H. (2014). "Comparison of Effectiveness of Different Learning Technologies," International Journal of Emerging Technologies in Learning (iJET), Vol. 9, No. 9, pp. 56.
Issa, G. El-Ghalayini, H., Shubita, A., and Abu-Arqoub, M. (2014). "A Framework for Collaborative Networked Learning in Higher Education: Design & Analysis." International Journal of Emerging Technologies in Learning (iJET), [S.l.], v. 9, n. 8, p. pp. 32-37, may. 2014. ISSN 1863-0383.