Outputs (747)

Evaluating and implementing machine learning models for personalised mobile health app recommendations (2025)
Journal Article
Morenigbade, H., Al Jaber, T., Gordon, N., & Eke, G. (2025). Evaluating and implementing machine learning models for personalised mobile health app recommendations. PLoS ONE, 20(3), e0319828. https://doi.org/10.1371/journal.pone.0319828

This paper focuses on the evaluation and recommendation of healthcare applications in the mHealth field. The increase in the use of health applications, supported by an expanding mHealth market, highlights the importance of this research. In this stu... Read More about Evaluating and implementing machine learning models for personalised mobile health app recommendations.

Comprehensive Health Tracking Through Machine Learning and Wearable Technology (2025)
Journal Article
Yusuf, A., Jaber, T. A., & Gordon, N. (online). Comprehensive Health Tracking Through Machine Learning and Wearable Technology. Journal of Data Science and Intelligent Systems, https://doi.org/10.47852/bonviewjdsis52023588

The accurate interpretation of data from wearable devices is paramount in advancing personalized healthcare and disease prevention. This study explores the application of machine learning techniques to improve the interpretation of health metrics fro... Read More about Comprehensive Health Tracking Through Machine Learning and Wearable Technology.

Teaching and Assessing at Scale: The Use of Objective Rubrics and Structured Feedback Teaching and Assessing at Scale: The Use of Objective Rubrics and Structured Feedback (2024)
Journal Article
Grey, S., & Gordon, N. (2024). Teaching and Assessing at Scale: The Use of Objective Rubrics and Structured Feedback Teaching and Assessing at Scale: The Use of Objective Rubrics and Structured Feedback. New Directions in the Teaching of Physical Sciences, 19(1), 1-9. https://doi.org/10.29311/ndtns.vi19.4103

It is widely recognised that feedback is an important part of learning: effective feedback should result in a meaningful change in student behaviour (Morris et al., 2021). However, individual feedback takes time to produce, and for large cohorts-typi... Read More about Teaching and Assessing at Scale: The Use of Objective Rubrics and Structured Feedback Teaching and Assessing at Scale: The Use of Objective Rubrics and Structured Feedback.

Digital Health and Indoor Air Quality: An IoT- Driven Human-Centred Visualisation Platform for Behavioural Change and Technology Acceptance (2024)
Presentation / Conference Contribution
Kureshi, R. R., Mazumdar, S., Mishra, B. K., Li, X., & Thakker, D. (2023, December). Digital Health and Indoor Air Quality: An IoT- Driven Human-Centred Visualisation Platform for Behavioural Change and Technology Acceptance. Presented at 4th International Conference on Distributed Sensing and Intelligent Systems (ICDSIS 2023), Dubai, UAE

The detrimental effects of air pollutants on human health have prompted increasing concerns regarding indoor air quality (IAQ). The emergence of digital health interventions and citizen science initiatives has provided new avenues for raising awarene... Read More about Digital Health and Indoor Air Quality: An IoT- Driven Human-Centred Visualisation Platform for Behavioural Change and Technology Acceptance.

Devising a Responsible Framework for Air Quality Sensor Placement (2024)
Presentation / Conference Contribution
Westcarr, J., Gunturi, V. M. V., Cabaneros, S. M., Raja, R., Thakker, D., & Porter, A. (2024, July). Devising a Responsible Framework for Air Quality Sensor Placement. Presented at 2024 IEEE International Conference on Omni-layer Intelligent Systems (COINS), London, United Kingdom

A major challenge faced when developing smart, sustainable urban environments is the reduction of air pollutants that adversely impact citizens' health. The UK has implemented strategies such as clean air zones (CAZs) coupled with the use of sensor t... Read More about Devising a Responsible Framework for Air Quality Sensor Placement.

A new class of zero-truncated counting models and its application (2024)
Journal Article
Tang, X. P., Tian, Y. Z., Wu, C. H., Wang, Y., & Mian, Z. B. (2024). A new class of zero-truncated counting models and its application. Communications in Statistics - Simulation and Computation, https://doi.org/10.1080/03610918.2024.2384561

Count data is a type of data derived from the number of times an event occurs per unit of time, and zero-truncated count data refers to count data without zero, which often appears in various fields. In this paper, a new zero-truncated Bell (ZTBell)... Read More about A new class of zero-truncated counting models and its application.

Enquiry-based learning pedagogy – Design, development and delivery of a reproducible robotics framework (2024)
Presentation / Conference Contribution
Walker, A., Diaz, K. R. V., McKie, D., & Iqbal, J. (2024, February). Enquiry-based learning pedagogy – Design, development and delivery of a reproducible robotics framework. Presented at Ninth International Congress on Information and Communication Technology (ICICT 2024), London

Hardware-inspired enquiry-based learning (EBL) is an emerging pedagogy to develop transferable engineering skills in students. This paper is aimed at unleashing the potential of this pedagogy via the multidisciplinary domain of robotics to learn the... Read More about Enquiry-based learning pedagogy – Design, development and delivery of a reproducible robotics framework.

A hybrid contextual framework to predict severity of infectious disease: COVID-19 case study (2024)
Journal Article
Azam, M. M. B., Anwaar, F., Khan, A. M., Anwar, M., Ghani, H. B. A., Eisa, T. A. E., & Abdelmaboud, A. (2024). A hybrid contextual framework to predict severity of infectious disease: COVID-19 case study. Egyptian Informatics Journal, 27, Article 100508. https://doi.org/10.1016/j.eij.2024.100508

Infectious disease is a particular type of disorder triggered by organisms and transmitted directly or indirectly from an infected one like COVID-19. The global economy and public health are immensely affected by COVID-19, a recently emerging infecti... Read More about A hybrid contextual framework to predict severity of infectious disease: COVID-19 case study.

Global Knowledge, Local Impact: Domain Adaptation and Classification for Obesity in the UAE (2024)
Presentation / Conference Contribution
Raza, M., Khattak, A., Abbas, W., & Khan, A. (2024, June). Global Knowledge, Local Impact: Domain Adaptation and Classification for Obesity in the UAE. Presented at 37th IEEE International Symposium on Computer-Based Medical Systems (CBMS), Guadalajara, Mexico

Obesity, a global public health concern, is escalating rapidly, especially in the Middle East, with the United Arab Emirates (UAE) witnessing one of the highest prevalence rates among adults and children. This multifactorial health issue is influence... Read More about Global Knowledge, Local Impact: Domain Adaptation and Classification for Obesity in the UAE.

User Engagement Triggers in Social Media Discourse on Biodiversity Conservation (2024)
Journal Article
Dethlefs, N., & Cuayáhuitl, H. (online). User Engagement Triggers in Social Media Discourse on Biodiversity Conservation. ACM Transactions on Social Computing, https://doi.org/10.1145/3662685

Studies in digital conservation have increasingly used social media in recent years as a source of data to understand the interactions between humans and nature, model and monitor biodiversity, and analyse online discourse about the conservation of s... Read More about User Engagement Triggers in Social Media Discourse on Biodiversity Conservation.