Dr Tareq Al Jaber's Outputs (10)

Artificial Intelligence in Education: An automatic Rule-Based Chatbot to generate guidance from lecture recordings (2024)
Journal Article
Hing, W., Gordon, N., & Al Jaber, T. (2024). Artificial Intelligence in Education: An automatic Rule-Based Chatbot to generate guidance from lecture recordings. Acta Scientific Computer Sciences, 6(7), 64-74

In a new era of educational and research-based chatbots, implementing personalised interactive learning resources is critical in enhancing students' academic experiences. Whilst general purpose chatbots are now available with a range of platforms, th... Read More about Artificial Intelligence in Education: An automatic Rule-Based Chatbot to generate guidance from lecture recordings.

Assessing the Impact of Usability from Evaluating Mobile Health Applications (2024)
Journal Article
Busari, A., Jaber, T., Gordon, N., & Mian, Z. (2024). Assessing the Impact of Usability from Evaluating Mobile Health Applications. International Journal on Engineering Technologies and Informatics, 5(2), https://doi.org/10.51626/ijeti.2024.05.00074

Software applications that are used to monitor, track, and improve health are called Mobile Health Applications or mHAs. They are developed with or without the help of medical professionals to potentially aid health, achieve health goals and improve... Read More about Assessing the Impact of Usability from Evaluating Mobile Health Applications.

Application of Machine Learning Techniques for the Prediction of Heart Disease (2024)
Journal Article
Owodunni, A. A., Jaber, T., & Mian, Z. (2024). Application of Machine Learning Techniques for the Prediction of Heart Disease. Acta Scientific Computer Sciences, 6(3), 13-23

As important as the heart is to humans, unfortunately, 43% of death is from heart disease [2] declared by Global Burden of Disease research. By 2030, deaths from cardiovascular disease will reach 23.6 million where heart disease takes the lead [3]. A... Read More about Application of Machine Learning Techniques for the Prediction of Heart Disease.

Application of Artificial Intelligence and Data Science in Detecting the Impact of Usability from Evaluation of Mobile Health Applications (2024)
Journal Article
Kayode, O., Al Jaber, T., & Gordon, N. (2024). Application of Artificial Intelligence and Data Science in Detecting the Impact of Usability from Evaluation of Mobile Health Applications. International Journal on Engineering Technologies and Informatics, 5(1), 1-9. https://doi.org/10.51626/ijeti.2024.05.00070

Mobile health (mHealth) applications have demonstrated immense potential for facilitating preventative care and disease management through intuitive platforms. However, realizing transformational health objectives relies on creating accessible tools... Read More about Application of Artificial Intelligence and Data Science in Detecting the Impact of Usability from Evaluation of Mobile Health Applications.

Fairness, Bias, and Ethics in AI: Exploring the Factors Affecting Student Performance (2024)
Journal Article
Omughelli, D., Gordon, N., & Al Jaber, T. (2024). Fairness, Bias, and Ethics in AI: Exploring the Factors Affecting Student Performance. Journal of Intelligent Communication, 4(1), 100-110. https://doi.org/10.54963/jic.v4i1.306

The use of artificial intelligence (AI) as a data science tool for education has enormous potential for increasing student performance and course outcomes. However, the growing concern about fairness, bias, and ethics in AI systems requires a careful... Read More about Fairness, Bias, and Ethics in AI: Exploring the Factors Affecting Student Performance.

A literature review of fault diagnosis based on ensemble learning (2023)
Journal Article
Mian, Z., Deng, X., Dong, X., Tian, Y., Cao, T., Chen, K., & Jaber, T. A. (2024). A literature review of fault diagnosis based on ensemble learning. Engineering applications of artificial intelligence, 127, Article 107357. https://doi.org/10.1016/j.engappai.2023.107357

The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of... Read More about A literature review of fault diagnosis based on ensemble learning.

Why the Educational Metaverse Is Not All About Virtual Reality Apps (2023)
Presentation / Conference Contribution
Gordon, N., Brayshaw, M., Kambili-Mzembe, F., & Al Jaber, T. Why the Educational Metaverse Is Not All About Virtual Reality Apps. Presented at Learning and Collaboration Technologies : 10th International Conference, LCT 2023, Held as Part of the 25th HCI International Conference, HCII 2023, Copenhagan, Denmark

This paper explores how the Metaverse can be used in the context of learning and collaboration. In it we seek to dispel the story that the Metaverse is just another synonym for Virtual Reality and future technology. Instead we will argue that the Met... Read More about Why the Educational Metaverse Is Not All About Virtual Reality Apps.

Creating a Classification Module to Analysis the Usage of Mobile Health Apps (2022)
Journal Article
Azuma, K., Al Jaber, T., & Gordon, N. (2022). Creating a Classification Module to Analysis the Usage of Mobile Health Apps. Acta Scientific Computer Sciences, 4(12), 34-42

With an ageing society becoming a major issue for many countries, health-related concerns are growing and mobile health applications (MHAs) are rapidly gaining users. The applications available range from those that promote exercise to maintain healt... Read More about Creating a Classification Module to Analysis the Usage of Mobile Health Apps.

An evaluation framework and selection tool for education apps usability, with a case study from health education apps (2019)
Thesis
Aljaber, T. An evaluation framework and selection tool for education apps usability, with a case study from health education apps. (Thesis). University of Hull. https://hull-repository.worktribe.com/output/4223074

Mobile apps for health education are commonly utilised to support different users. The development of these apps is increasing rapidly. A critical evaluation framework is needed to ensure the usability and reliability of Mobile Health Education Appli... Read More about An evaluation framework and selection tool for education apps usability, with a case study from health education apps.

An evaluation framework for mobile health education software (2015)
Presentation / Conference Contribution
Aljaber, T., Gordon, N., Kambhampati, C., & Brayshaw, M. (2015, July). An evaluation framework for mobile health education software. Presented at 2015 Science and Information Conference (SAI), London

© 2015 IEEE. Mobile applications in general, and mobile applications for health education in particular, are commonly used to support patients, health professionals and other stakeholders. A critical evaluation framework is needed to ensure the usabi... Read More about An evaluation framework for mobile health education software.