Outputs (15)

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.

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.

UCTH Breast Cancer Dataset (2023)
Data
Eteng, I., Bisong, E., Fagbola, T., Ibrahim, M., Udosen, J., & Akpotuzor, S. (2023). UCTH Breast Cancer Dataset. [Data]. https://doi.org/10.17632/63fpbc9cm4.2

Research Hypothesis:
This study hypothesizes that there are significant associations between the diagnostic characteristics of patients, including age, menopause status, tumor size, presence of invasive nodes, affected breast, metastasis status, br... Read More about UCTH Breast Cancer Dataset.

Smart Face Masks for Covid-19 Pandemic Management: A Concise Review of Emerging Architectures, Challenges and Future Research Directions (2022)
Journal Article
Fagbola, T. M., Fagbola, F. I., Aroba, O. J., Doshi, R., Hiran, K. K., & Thakur, S. C. (2023). Smart Face Masks for Covid-19 Pandemic Management: A Concise Review of Emerging Architectures, Challenges and Future Research Directions. IEEE sensors journal, 23(2), 877-888. https://doi.org/10.1109/JSEN.2022.3225067

Smart sensing technology has been playing tremendous roles in digital healthcare management over time with great impacts. Lately, smart sensing has awoken the world by the advent of Smart Face Masks (SFM) in the global fight against the deadly Corona... Read More about Smart Face Masks for Covid-19 Pandemic Management: A Concise Review of Emerging Architectures, Challenges and Future Research Directions.

In silico identification of chemical compounds in Spondias mombin targeting aldose reductase and glycogen synthase kinase 3β to abate diabetes mellitus (2022)
Journal Article
Ajiboye, B. O., Fagbola, T. M., Folorunso, I. M., Salami, A. W., Aletile, O. N., Akomolede, B. A., Ayemoni, F. I., Akinfemiwa, K. I., Anwo, V. O., Ojeleke, M. I., & Oyinloye, B. E. (2023). In silico identification of chemical compounds in Spondias mombin targeting aldose reductase and glycogen synthase kinase 3β to abate diabetes mellitus. Informatics in Medicine Unlocked, 36, Article 101126. https://doi.org/10.1016/j.imu.2022.101126

Aldose reductase and glycogen synthase kinase 3β (GSK3β) represent two of the ideal drug targets in diabetes due to their role in the pathogenesis of diabetes. Studies have shown that plant compounds provide therapeutics in diabetes management. This... Read More about In silico identification of chemical compounds in Spondias mombin targeting aldose reductase and glycogen synthase kinase 3β to abate diabetes mellitus.

Lexicon-Based Sentiment Analysis and Emotion Classification of Climate Change Related Tweets (2022)
Presentation / Conference Contribution
Fagbola, T. M., Abayomi, A., Mutanga, M. B., & Jugoo, V. (2021, December). Lexicon-Based Sentiment Analysis and Emotion Classification of Climate Change Related Tweets. Presented at 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021), Online

The concerns for a potential future climate jeopardy has steered actions by youths globally to call the governments to immediately address challenges relating to climate change. In this paper, using natural language processing techniques in data scie... Read More about Lexicon-Based Sentiment Analysis and Emotion Classification of Climate Change Related Tweets.

Lexicon-based bot-aware public emotion mining and sentiment analysis of the Nigerian 2019 presidential election on Twitter (2019)
Journal Article
Fagbola, T. M., & Thakur, S. C. (2019). Lexicon-based bot-aware public emotion mining and sentiment analysis of the Nigerian 2019 presidential election on Twitter. International journal of advanced computer science and applications : IJACSA, 10(10), 329-336. https://doi.org/10.14569/ijacsa.2019.0101047

Online social networks have been widely engaged as rich potential platforms to predict election outcomes' in several countries of the world. The vast amount of readily-available data on such platforms, coupled with the emerging power of natural langu... Read More about Lexicon-based bot-aware public emotion mining and sentiment analysis of the Nigerian 2019 presidential election on Twitter.

News article classification using Kolmogorov complexity distance measure and artificial neural network (2019)
Journal Article
Fagbola, T. M., Thakur, C. S., & Olugbara, O. (2019). News article classification using Kolmogorov complexity distance measure and artificial neural network. International Journal of Technology, 10(4), 710-720. https://doi.org/10.14716/ijtech.v10i4.2339

News article classification is a recently growing area of interest in text classification because of its associated multiple matching categories. However, the weak reliability indices and ambiguities associated with state-of-the-art classifiers often... Read More about News article classification using Kolmogorov complexity distance measure and artificial neural network.