Outputs (264)

Crafting Tomorrow's Evaluations: Assessment Design Strategies in the Era of Generative AI (2024)
Presentation / Conference Contribution
Kadel, R., Mishra, B. K., Shailendra, S., Abid, S., Rani, M., & Mahato, S. P. (2024, July). Crafting Tomorrow's Evaluations: Assessment Design Strategies in the Era of Generative AI. Presented at 2024 International Symposium on Educational Technology, ISET 2024, Macau

In recent years, no other technology has revolutionised our life as Generative Artificial Intelligence (GenAI). GenAI has gained the attention of a myriad of users in almost every profession. Its advancement has had an intense impact on education, si... Read More about Crafting Tomorrow's Evaluations: Assessment Design Strategies in the Era of Generative AI.

A Responsible AI Perspective to implementing Generative AI in Personalized Healthcare: Implications, Challenges and Future Directions (2024)
Presentation / Conference Contribution
Fagbola, T. M., Dhiman, A., Mboli, J., & Mishra, B. (2024, October). A Responsible AI Perspective to implementing Generative AI in Personalized Healthcare: Implications, Challenges and Future Directions. Paper presented at 1st International Workshop on Responsible AI (RAI) for Healthcare and Net Zero, IIT Madras, Chennai, India

Generative AI (GenAI) is transforming personalized healthcare by enabling customized treatment plans, advancing drug discovery, and offering targeted diagnostic support. While these advancements offer significant potential, they also present complex... Read More about A Responsible AI Perspective to implementing Generative AI in Personalized Healthcare: Implications, Challenges and Future Directions.

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.

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.

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.

Ensemble Supervised Learning-based Approaches for Mobile Network Coverage and Quality Predictions in a University Setting (2024)
Presentation / Conference Contribution
Okiemute Osiezagha, M., Kumar Mishra, B., & Fagbola, T. M. (2024, August). Ensemble Supervised Learning-based Approaches for Mobile Network Coverage and Quality Predictions in a University Setting. Paper presented at International Conference on Intelligent Systems with Applications in Communications, Computing and IoT (ICISCCI-2K24), Vardhaman College of Engineering, Hyderabad, India

This research explores the application of predictive analytics through Machine Learning (ML) algorithms to enhance Mobile Network Key Performance Indicators (KPIs), specifically focusing on Reference Signal Received Power (RSRP) as coverage and Refer... Read More about Ensemble Supervised Learning-based Approaches for Mobile Network Coverage and Quality Predictions in a University Setting.

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.

The evolution of democratic peace in animal societies (2024)
Journal Article
Hunt, K. L., Patel, M., Croft, D. P., Franks, D. W., Green, P. A., Thompson, F. J., Johnstone, R. A., Cant, M. A., & Sankey, D. W. (2024). The evolution of democratic peace in animal societies. Nature communications, 15(1), Article 6583. https://doi.org/10.1038/s41467-024-50621-5

A major goal in evolutionary biology is to elucidate common principles that drive human and other animal societies to adopt either a warlike or peaceful nature. One proposed explanation for the variation in aggression between human societies is the d... Read More about The evolution of democratic peace in animal societies.

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.