Outputs (209)

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.

An examination of daily CO2 emissions prediction through a comparative analysis of machine learning, deep learning, and statistical models (2025)
Journal Article
Ajala, A. A., Adeoye, O. L., Salami, O. M., & Jimoh, A. Y. (2025). An examination of daily CO2 emissions prediction through a comparative analysis of machine learning, deep learning, and statistical models. Environmental science and pollution research, 32(5), 2510-2535. https://doi.org/10.1007/s11356-024-35764-8

Human-induced global warming, primarily attributed to the rise in atmospheric CO2,poses a substantial risk to the survival of humanity. While most research focuses on predicting annual CO2emissions, which are crucial for setting long-term emission mi... Read More about An examination of daily CO2 emissions prediction through a comparative analysis of machine learning, deep learning, and statistical models.

The Galaxy Zoo catalogues for Galaxy and Mass Assembly (GAMA) survey (2024)
Journal Article
Holwerda, B. W., Robertson, C., Cook, K., Pimbblet, K., Casura, S., Sansom, A. E., Patel, D., Butrum, T. A., Glass, D. H. W., Kelvin, L. S., Baldry, I. K., De Propris, R., Bamford, S., Masters, K., Stone, M. B., Hardin, T., Walmsley, M., Liske, J., & Adnan, S. M. (2024). The Galaxy Zoo catalogues for Galaxy and Mass Assembly (GAMA) survey. Publications / Astronomical Society of Australia, 41, Article e115. https://doi.org/10.1017/pasa.2024.109

Galaxy Zoo is an online project to classify morphological features in extra-galactic imaging surveys with public voting. In this paper, we compare the classifications made for two different surveys, the Dark Energy Spectroscopic Instrument (DESI) ima... Read More about The Galaxy Zoo catalogues for Galaxy and Mass Assembly (GAMA) survey.

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.

Deep Learning-Based Colorectal Cancer Image Segmentation and Classification: A Concise Bibliometric Analysis (2024)
Presentation / Conference Contribution
Fagbola, T. M., Aderemi, E. T., & Thakur, C. S. (2024, August). Deep Learning-Based Colorectal Cancer Image Segmentation and Classification: A Concise Bibliometric Analysis. Presented at 7th International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), Mauritius

The use of Deep Learning (DL)-based methods for Colorectal Cancer (CRC) classification and segmentation has gained significant attention in recent times. This study employs a bibliometric analysis to investigate the state-of-The-art research on DL-ba... Read More about Deep Learning-Based Colorectal Cancer Image Segmentation and Classification: A Concise Bibliometric Analysis.

DeepCAI-V3: Improved Brain Tumor Classification from Noisy Brain MR Images using Convolutional Autoencoder and Inception-V3 Architecture (2024)
Presentation / Conference Contribution
Babaferi, E. V., Fagbola, T. M., & Thakur, C. S. (2024, August). DeepCAI-V3: Improved Brain Tumor Classification from Noisy Brain MR Images using Convolutional Autoencoder and Inception-V3 Architecture. Presented at 7th International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, Mauritius

Brain tumors are abnormal cell growths within the brain tissues, necessitating their early detection towards effective treatment. To achieve this, high-quality brain images via medical imaging techniques, such as Magnetic Resonance Imaging (MRI), are... Read More about DeepCAI-V3: Improved Brain Tumor Classification from Noisy Brain MR Images using Convolutional Autoencoder and Inception-V3 Architecture.

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.

Constraining SN Ia Progenitors from the Observed Fe-peak Elemental Abundances in the Milky Way Dwarf Galaxy Satellites (2024)
Preprint / Working Paper
Alexander, R., & Vincenzo, F. Constraining SN Ia Progenitors from the Observed Fe-peak Elemental Abundances in the Milky Way Dwarf Galaxy Satellites

Chemical abundances of iron-peak elements in the red giants of ultra-faint dwarf galaxies (UFD) and dwarf spheroidal galaxies (dSph) are among the best diagnostics in the cosmos to probe the origin of Type Ia Supernovae (SNe Ia). We incorporate metal... Read More about Constraining SN Ia Progenitors from the Observed Fe-peak Elemental Abundances in the Milky Way Dwarf Galaxy Satellites.

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.

Tests of subgrid models for star formation using simulations of isolated disc galaxies (2024)
Journal Article
Nobels, F. S. J., Schaye, J., Schaller, M., Ploeckinger, S., Chaikin, E., & Richings, A. J. (2024). Tests of subgrid models for star formation using simulations of isolated disc galaxies. Monthly notices of the Royal Astronomical Society, 532(3), 3299-3321. https://doi.org/10.1093/mnras/stae1390

We use smoothed particle hydrodynamics simulations of isolated Milky Way-mass disc galaxies that include cold, interstellar gas to test subgrid prescriptions for star formation (SF). Our fiducial model combines a Schmidt law with a gravitational inst... Read More about Tests of subgrid models for star formation using simulations of isolated disc galaxies.