Olamilekan Shobayo
Lecturer
Summary
Olamilekan Shobayo is a dedicated lecturer at the School of Computing and Digital Technologies, where he imparts advanced knowledge in data management, healthcare data analytics, and big data technologies. With over seven years of teaching experience, he has imparted essential skills in data science and analytics, teaching modules on database management, big data analysis, Data mining, and project management where he has provided students with essential skills in R and Python programming, SQL, Hadoop and Apache Spark, JIRA, Tableau and Access. Some of these students have worked as data analysts, DB administrators, and Business Analyst for companies within the UK and abroad. He has mentored and supervised numerous master's and undergraduate projects, guiding students in analysing datasets and utilizing machine learning models to classify various health conditions such as fracture, depression cancer and stroke, credit loan default, stock price prediction, and providing clustering analysis for the retail sector with publications in several peer-reviewed open-source journal outlets.
About
Throughout my career, I have demonstrated a profound commitment to developing individuals across various academic and professional settings. I imparted essential skills in data science and analytics, teaching modules on database management, big data analysis, Data mining, and project management where I have provided students with essential skills in R and Python programming, SQL, Hadoop and Apache Spark, JIRA, Tableau and Access. Some of these students have gone ahead to work as data analysts, DB administrators, and Business Analyst for companies within the UK and abroad. I have mentored and supervised numerous master's and undergraduate projects, guiding students in analysing datasets and utilizing machine learning models to predict various health conditions and providing clustering analysis for the retail sector with publications in peer-reviewed open-source journal outlets. Additionally, as a PhD Tutor at The Brilliant Club, I designed and delivered courses to inspire young minds in STEM subjects, fostering a passion for learning and academic excellence. My extensive experience as a lecturer and facilitator underscores my dedication to nurturing talent and empowering individuals to excel in their academic and professional endeavours.
My involvement in AI research spans various domains, with a particular focus on healthcare applications. I have contributed to exploring AI in Emergency Medicine to underscore the immense potential of collaborative efforts between domain experts and data scientists. Through collaborative endeavours, I have created a framework to bridge the gap between AI research and clinical practice and provided key insights to support clinicians and researchers in navigating the complexities of AI model development in emergency care settings. One of my seminal works involves the development of a Convolutional Neural Network (CNN) aimed at classifying infrared thermal images of fractured wrists in paediatrics. This groundbreaking study leverages the power of CNNs to interpret infrared thermal images, offering a promising screening tool for diagnosing wrist bone fractures in paediatric patients. We achieved remarkable sensitivity and accuracy rates through meticulous data analysis and model development, paving the way for enhanced diagnostic capabilities in paediatric emergency care settings. I have also developed a model for the early prediction of stroke disease using ML models such as Decision trees and logistics regression based on using BMI and age variables with the highest principal components. I have participated in numerous data mining and analytics projects outside the healthcare industry, including the education sector and the retail sector using unsupervised clustering techniques such as GMM, DBSCAN, BIRCH and KNN. This contribution has been published in peer-reviewed open-sourced journal outlets and reputable conferences around the UK and abroad.
I have contributed to the wider research community by providing very thorough reviewed process for different journal outlets such as Electronics, Algorithms, Diagnostics, computation, computers and Multimodal technologies and interaction. For example, I was part of the reviewers for a work that proposes a systematic framework for extracting multiomics biomarkers associated with breast cancer before and after menopause, which uses MultiSig CV, PCA and SMOTE for the analysis of DNA methylation, gene expression, and copy number alteration data using a structured pipeline encompassing preprocessing, addressing class imbalance, dimensionality reduction, and classification. I also reviewed another body of work that evaluates the use of deep learning (DL) applications in gastric neoplasia detection from endoscopic images.
Lecturer
Teaching
College of Business, Technology and Engineering
Digital Analytics and Technologies
Courses
Msc Computing
Msc Big Data Analytics
Msc Data analytics with Banking and Finance
Degree Apprentices
Foundations in Computing
Modules
Advanced Data Management Project
Introduction to Databases and Big Data
Healthcare Knowledge and Data Management
Research Skills for Computing
Study Skills and Project Management
Databases (Foundation)
Reflective Practice for Apprentice Professional Development
Publications
Journal articles
Mamillapalli, A., Ogunleye, B., Timoteo Inacio, S., & Shobayo, O. (2024). GRU vader: Sentiment-Informed Stock Market Prediction. Mathematics, 12 (23). http://doi.org/10.3390/math12233801
Tanimola, O., Shobayo, O., Popoola, O., & Okoyeigbo, O. (2024). Breast Cancer Classification Using Fine-Tuned SWIN Transformer Model on Mammographic Images. Analytics, 3 (4), 461-475. http://doi.org/10.3390/analytics3040026
Akinjole, A., Shobayo, O., Popoola, J., Okoyeigbo, O., & Ogunleye, B. (2024). Ensemble-Based Machine Learning Algorithm for Loan Default Risk Prediction. Mathematics, 12 (21). http://doi.org/10.3390/math12213423
Shobayo, O., Adeyemi-Longe, S., Popoola, O., & Ogunleye, B. (2024). Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach. Big Data and Cognitive Computing, 8 (11). http://doi.org/10.3390/bdcc8110143
Ogunleye, B., Sharma, H., & Shobayo, O. (2024). Sentiment Informed Sentence BERT-Ensemble Algorithm for Depression Detection. Big Data and Cognitive Computing, 8 (9). http://doi.org/10.3390/bdcc8090112
Shobayo, O., Sasikumar, S., Makkar, S., & Okoyeigbo, O. (2024). Customer Sentiments in Product Reviews: A Comparative Study with GooglePaLM. Analytics, 3 (2), 241-254. http://doi.org/10.3390/analytics3020014
Shobayo, O., Saatchi, R., & Ramlakhan, S. (2024). Convolutional Neural Network to Classify Infrared Thermal Images of Fractured Wrists in Pediatrics. Healthcare, 12 (10). http://doi.org/10.3390/healthcare12100994
John, J.M., Shobayo, O., & Ogunleye, B. (2023). An Exploration of Clustering Algorithms for Customer Segmentation in the UK Retail Market. Analytics, 2 (4), 809-823. http://doi.org/10.3390/analytics2040042
Shobayo, O., Zachariah, O., Odusami, M.O., & Ogunleye, B. (2023). Prediction of stroke disease with demographic and behavioural data using random forest algorithm. Analytics, 2 (3), 604-617. http://doi.org/10.3390/analytics2030034
Shobayo, O., Saatchi, R., & Ramlakhan, S. (2022). Infrared thermal imaging and artificial neural networks to screen for wrist fractures in pediatrics. Technologies, 10 (19). http://doi.org/10.3390/technologies10060119
Ramlakhan, S.L., Saatchi, R., Sabir, L., Ventour, D., Shobayo, O., Hughes, R., & Singh, Y. (2022). Building artificial intelligence and machine learning models : a primer for emergency physicians. Emergency medicine journal : EMJ, 39 (5), e1. http://doi.org/10.1136/emermed-2022-212379
Ramlakhan, S., Saatchi, R., Sabir, L., Singh, Y., Hughes, R., Shobayo, O., & Ventour, D. (2022). Understanding and interpreting artificial intelligence, machine learning and deep learning in Emergency Medicine. Emergency Medicine Journal. http://doi.org/10.1136/emermed-2021-212068
Ramlakhan, S., Saatchi, R., Sabir, L., Ventour, D., Hughes, R., Shobayo, O., & Singh, Y. (2022). Building artificial intelligence and machine learning models : a primer for emergency physicians. Emergency Medical Journal. http://doi.org/10.1136/emermed-2022-212379
Okoyeigbo, O., Ibhaze, A.E., Olajube, A., Shobayo, O., Somefun, T., & Steve-Essi, O. (2021). Design and analysis of a broadband microwave amplifier. Indonesian Journal of Electrical Engineering and Informatics, 9 (1), 210-219. http://doi.org/10.11591/ijeei.v9i1.2708
Adekitan, A.I., & Shobayo, O. (2020). Gender-based comparison of students’ academic performance using regression models. Engineering and Applied Science Research, 47 (3), 241-248. http://doi.org/10.14456/easr.2020.27
Shobayo, O., Olajube, A., Ohere, N., Odusami, M., & Okoyeigbo, O. (2020). Development of Smart Plate Number Recognition System for Fast Cars with Web Application. Applied Computational Intelligence and Soft Computing, 2020. http://doi.org/10.1155/2020/8535861
Adekitan, A.I., Abolade, J., & Shobayo, O. (2019). Data mining approach for predicting the daily Internet data traffic of a smart university. Journal of Big Data, 6 (11). http://doi.org/10.1186/s40537-019-0176-5
Okokpujie, K., Emmanuel, C., Shobayo, O., Noma-Osaghae, E., Okokpujie, I., & Odusami, M. (2019). Comparative analysis of the performance of various active queue management techniques to varying wireless network conditions. International Journal of Electrical and Computer Engineering, 9 (1), 359-368. http://doi.org/10.11591/ijece.v9i1.pp359-368
Okokpujie, K., Shobayo, O., Noma-Osaghae, E., Okokpujie, I.P., & Okoyeigbo, O. (2018). Performance of MPLS-based virtual private networks and classic virtual private networks using advanced metrics. Telkomnika (Telecommunication Computing Electronics and Control), 16 (5), 2073-2081. http://doi.org/10.12928/TELKOMNIKA.v16i5.7326
Okoyeigbo, O., Okokpujie, K., Noma-Osaghae, E., Ndujiuba, C.U., Shobayo, O., & Jeremiah, A. (2018). Comparative study of MIMO-OFDM channel estimation in wireless systems. International Review on Modelling and Simulations, 11 (3), 158-165. http://doi.org/10.15866/iremos.v11i3.13884
Rodrigues, M., & Shobayo, O. (2017). Design and Implementation of a Low-Cost Low Interaction IDS/IPS System Using Virtual Honeypot Approach. Covenant Journal of Informatics & Communication Technology, 5 (1), 48-64. https://journals.covenantuniversity.edu.ng/index.php/cjict/article/view/452
Conference papers
Shobayo, O., Saatchi, R., Reed, C., & Ramlakhan, S. (2023). Correlation of skin temperature with time since injury in paediatric wrist injuries: An infrared thermal image analysis. 60th Annual Conference of the British Institute of Non-Destructive Testing, NDT 2023, 167-177. http://doi.org/10.1784/ndt2023.4b5
Okoyeigbo, O., Olajube, A.A., Shobayo, O., Aligbe, A., & Ibhaze, A.E. (2021). Wireless power transfer: a review. IOP Conference Series: Earth and Environmental Science, 655. http://doi.org/10.1088/1755-1315/655/1/012032
Shobayo, O., Olajube, A., Okoyeigbo, O., & Ogbonna, J. (2021). Design and Implementation of an IoT Based Baggage Tracking System. Communications in Computer and Information Science, 1350 (1350), 618-631. http://doi.org/10.1007/978-3-030-69143-1_47
Shobayo, O., Abayomi-Alli, O., Odusami, M., Misra, S., & Safiriyu, M. (2020). Modeling and simulation of impedance-based algorithm on overhead power distribution network using matlab. Lecture Notes in Electrical Engineering, 672 (672), 335-345. http://doi.org/10.1007/978-981-15-5558-9_31
Abayomi-Alli, O., Odusami, M., Ojinaka, D., Shobayo, O., Misra, S., Damasevicius, R., & Maskeliunas, R. (2018). Smart-Solar Irrigation System (SMIS) for Sustainable Agriculture. Communications in Computer and Information Science, 942 (942), 198-212. http://doi.org/10.1007/978-3-030-01535-0_15
Odusami, M., Abayomi-Alli, O., Misra, S., Shobayo, O., Damasevicius, R., & Maskeliunas, R. (2018). Android Malware Detection: A Survey. Communications in Computer and Information Science, 942 (942), 255-266. http://doi.org/10.1007/978-3-030-01535-0_19
Book chapters
Odusami, M., Misra, S., Abayomi-Alli, O., Shobayo, O., & Moses, C. (2022). An enhanced IoT-Based array of sensors for Monitoring Patients’ Health. In Intelligent Internet of Things for Healthcare and Industry. Springer: https://link.springer.com/chapter/10.1007/978-3-030-81473-1_5