Staff Profile
Professor - Computer Science
Prof Boluwaji A. Akinnuwesi Email: bakinnuwesi@uniswa.sz Telephone: ---- Office: By Old Pension Fund Offices
Personal Details

Prof Boluwaji A. Akinnuwesi

Ph.D. Computer Science, MSc, BSc (Hons)

Professor - Computer Science

Ph.D Computer Science - Nigeria

Research Areas

  1. Software Engineering
  2. Soft Computing
  3. Health Informatics
  4. Database Systems
  5. Medical Informatics
  6. Machine Learning
  7. System Analysis and Design

Biography

Boluwaji Ade Akinnuwesi, Ph.D., C.ITP, DIA, is a Full Professor of Computer Science in the Department of Computer Science at the University of Eswatini (formerly the University of Swaziland). He holds a B.Sc., M.Sc., and Ph.D. in Computer Science and is a Chartered Information Technology Practitioner (C.ITP). In recognition of his contributions, he was awarded the title of Distinguished International Associate (DIA) by the Royal Academy of Engineering in London, UK, for 2022–2024. This award included a grant for his research on “Re-engineering Financial Inclusion for Sustainable Economic Development in the Kingdom of Eswatini,” in collaboration with the Centre for Financial Inclusion in Eswatini; Mount Royal University, Canada; and Coventry University, UK.

Prof. Akinnuwesi’s research focuses on Applied Software Engineering and Artificial Intelligence, where he uses software development methodologies and machine learning techniques to model and address real-world problems. He has developed several software frameworks to solve practical issues, with core applications in medical informatics, software development, business/finance, and technology adoption. His expertise includes utilizing soft computing, machine learning, and deep learning techniques to build medical diagnostic systems that enhance the accurate diagnosis of diseases with overlapping symptoms. Over the course of his career, he has successfully supervised the theses of 11 master’s and 1 doctoral student, all of whom have graduated. He has also served in various academic and administrative capacities, including roles as Head of Department, Director of the Computer Centre, and Member of the University Senate and several faculty boards and committees. Additionally, Prof. Akinnuwesi has developed curricula for undergraduate and postgraduate programs at the University of Eswatini and other institutions where he has served.

As an External Examiner, he has reviewed numerous Ph.D. theses and master’s dissertations across various universities and colleges. He is an active reviewer for several academic journals and conferences and has published numerous peer-reviewed articles, book chapters, and conference proceedings in his areas of research.

He is a professional member of several organizations, including the Computer Professional Registration Council of Nigeria (CPN), Nigeria Computer Society (NCS), Association of Computing Machinery (ACM), and the International Association of Engineers (IAENG). Prof. Akinnuwesi has also served as a research scholar at Southern University, Louisiana, USA.

His primary research interests include Software Engineering, Soft Computing, Database Systems, Machine Learning, Deep Learning, Medical Informatics, Financial Systems, System Analysis and Design, and Technology Adoption.

Research Profile

  1. Google Scholar
  2. Research Gate
  3. Scopus
  4. Web of Science
  5. Academia
  6. Orcid ID
  7. LinkedIn
  8. Loop Profile

Recent Published Journal Articles

  1. Chukwudi Obinna Nwokoro, Udoinyang G. Inyang, Imo J. Eyoh, Faith-Michael Uzoka, Boluwaji Ade Akinnuwesi, and Paul Augustine Ejegwa (2024). “Meta-Analysis of Predictive Modelling Approaches and Systematic Reviews for Maternal Healthcare Outcomes”. Asian Journal of Science and Applied Technology, Vol.13 No.1, pp.19-35. DOI: https://doi.org/10.70112/ajsat-2024.13.1.4110
  2. Olusola Olabanjo, Ashiribo Wusu, Edwin Aigbokhan, Olufemi Olabanjo, Oseni Afisi, Boluwaji Akinnuwesi (2024). “A novel graph convolutional networks model for an intelligent network traffic analysis and classification”. International Journal of Information Technology, pp. 1-13. DOI: https://doi.org/10.1007/s41870-024-02032-4
  3. Olabanjo, O., A. Wusu, O. Afisi and B. Akinnuwesi, (2024). “Stroke risk factor prediction using machine learning techniques: A systematic review”. Journal of Applied Sciences, Vol. 24, Issue 1, pp1-15. DOI: https://doi.org/10.3923/jas.2024.1.15.
  4. Boluwaji A. Akinnuwesi, Kehinde A. Olayanju, Benjamin S. Aribisala, Stephen G. Fashoto, Elliot Mbunge, Moses Okpeku, and Patrick Owate (2023). “Application of Support Vector Machine Algorithm for Early Differential Diagnosis of Prostate Cancer”. Data Science and management, Vol. 6, Issue 1, pp 1-12. DOI: https://doi.org/10.1016/j.dsm.2022.10.001.
  5. Fashoto S.G, Akinnuwesi B.A, Mbunge E, and Metfula A.S. (2023). “Financial Inclusion Dataset Classification in Eswatini using Support Vector Machine and Logistic Regression”. International Journal of Business Information Systems. Vol. 43, No. 4, pp 507-527 DOI: http://dx.doi.org/10.1504/IJBIS.2020.10035234.
  6. Boluwaji A. Akinnuwesi, Fashoto, S. G., Mbunge, E., Mashwama, P., & Owate, P. A. (2022). “A SWOT Analysis of Software Requirement Validation Techniques”. International Journal of Software Innovation (IJSI), 10(1), 1-24. URL: https://www.igi-global.com/article/a-swot-analysis-of-software-requirement-validation-techniques/297132.
  7. Boluwaji A. Akinnuwesi, Faith-Michael E. Uzoka, Stephen G. Fashoto, Elliot Mbunge, Adedoyin Odumabo, Oluwaseun O. Amusa, Moses Okpeku, and Olumide Owolabi (2022). “A Modified UTAUT Model for the Acceptance and Use of Digital Technology for Tackling COVID-19”. Sustainable Operations and Computers, Vol. 3, pp. 118-135. DOI: https://doi.org/10.1016/j.susoc.2021.12.001.
  8. Priyanka Govender, Stephen Gbenga Fashoto, Leah Maharaj, Matthew A. Adeleke, Elliot Mbunge, Jeremiah Olamijuwon, Boluwaji Akinnuwesi, Moses Okpeku (2022). “The application of machine learning to predict genetic relatedness using human mtDNA hypervariable region I sequences”. PLoS ONE 17(2): e0263790, pp 1-19. https://doi.org/10.1371/journal.pone.0263790 [Published by Public Library of Science.
  9. Boluwaji A. Akinnuwesi, Stephen G. Fashoto, Elliot Mbunge, Adedoyin Odumabo, Andile S. Metfula, Petros Mashwama, Faith-Michael Uzoka, Olumide Owolabi, Moses Okpeku, and Oluwaseun O. Amusa (2021). “Application of intelligence-based computational techniques for classification and early differential diagnosis of COVID-19 disease”. Data Science and Management, Vol. 4, pp. 10 – 18. DOI: https://doi.org/10.1016/j.dsm.2021.12.001. - Won Best Paper Award)
  10. Boluwaji A. Akinnuwesi, Blessing O. Yama, Alade M. Rahman and Stephen G. Fashoto (2021). “Berth Allocation Model for Container Terminal using Genetic Algorithm Technique: Case of Apapa Wharf, Lagos, Nigeria”. Journal of Computer Science and its Application (An International journal of Nigeria Computer Society), Vol. 28, No. 1, pp. 1-14. Nigeria. DOI: 10.4314/jcsia.v28i1.1.
  11. F.M.E. Uzoka, C. Akwaowo, C. Nwafor‑Okoli, V. Ekpin, C. Nwokoro, M. El Hussein, J. Osuji, F. Aladi, B. Akinnuwesi and T.F. Akpelishi (2021). “Risk factors for some tropical diseases in an African country”. BMC Public Health. Vol. 21, No. 2261, Pp 1 – 10. DOI: https://doi.org/10.1186/s12889-021-12286-3.
  12. Elliot Mbunge, Stephen G. Fashoto, Boluwaji Akinnuwesi, Andile Metfula, Sakhile Simelane, and Nzuza Ndumiso (2021). “Ethics for integrating emerging technologies to contain COVID-19 in Zimbabwe”. Human Behavior and Emerging Technologies, Vol. 3, No. 5, pp. 876-890. DOI: http://dx.doi.org/10.1002/hbe2.277.
  13. Babafemi Oluropo Macaulay, Benjamin Segun Aribisala, Soji Alabi Akande, Boluwaji Ade Akinnuwesi, and Olusola Aanu Olabanjo. (2021). “Breast Cancer Risk Prediction in African Women using Random Forest Classifier”. Cancer Treatment and Research Communications, Vol. 28(100396), pp. 1-7. DOI: https://doi.org/10.1016/j.ctarc.2021.100396. United Kingdom.

Conference papers

  1. Faremi, A.S., Mbunge, E, Akinnuwesi, B., Mashwama, P., Fashoto, S.G., Ncube, P.Z., Batani, J., Sanni, S.A., Faremi, Y.A. and Metfula, A. (2024), "Machine Learning Models for Identifying Factors Influencing and Predicting Malaria Among Children Under Five Years in Nigeria," 2024 Conference on Information Communications Technology and Society (ICTAS) (IEEE), Durban, South Africa, 2024, pp. 88-94, doi: 10.1109/ICTAS59620.2024.10507142.
  2. Mbunge, E., Batani, J., Fashoto, S. G., Akinnuwesi, B., Gurajena, C., Opeyemi, O. G., Metfula A. & Ncube, Z. P. (2023). The Future of Next Generation Web: Juxtaposing Machine Learning and Deep Learning-Based Web Cache Replacement Models in Web Caching Systems. In Computer Science On-line Conference (pp. 426-450). Cham: Springer International Publishing.
  3. Faith-Michael Uzoka, Mugisha Gift, Kingsley Attai, Boluwaji A. Akinnuwesi, Samali V. Mlay, Peter Zeh, Arnold Kiirya, Christine Muhumuza, Justine N. Bukenya, Stephen Fashoto, Daniel Asuquo, Christie Akwaowo, Oryina Kingsley Akputu, Mercy E. Edoho, Ifiok Udo, Lucy Amaniyo, Andile S. Metfula, Gorretti Kyeyune (2022). “Tackling Occupational and Nosocomial Infection using Vitex-Medical Assistant Tool”. In IST-Africa 2022 Conference Proceedings, Miriam Cunningham and Paul Cunningham (Eds), IST-Africa Institute and IIMC, 2022, Pp 1-9. ISBN: 978-1-905824-68-7. [Published by IST-Africa Institute and IIMC]
  4. Elliot Mbunge, John Batani, Racheal Mafumbate, Caroline Gurajena, Stephen Fashoto, Talent Rugube, Boluwaji Akinnuwesi & Andile Metfula (2022). “Predicting Student Dropout in Massive Open Online Courses Using Deep Learning Models - A Systematic Review”. In: Silhavy, R. (eds) Cybernetics Perspectives in Systems. CSOC 2022. Lecture Notes in Networks and Systems, Vol 503, Pp 212-231. DOI: https://doi.org/10.1007/978-3-031-09073-8_20. [Published by Springer Nature, Cham].
  5. Akinnuwesi A. Boluwaji, Fashoto S.G., Metfula A.S., Akinnuwesi A.N. (2020) Experimental Application of Machine Learning on Financial Inclusion Data for Governance in Eswatini. In Hattingh M., Matthee M., Smuts H., Pappas I., Dwivedi Y., Mäntymäki M. (eds) Responsible Design, Implementation and Use of Information and Communication Technology. I3E 2020. Lecture Notes in Computer Science, Vol 12067, pp 414-425. Online ISBN: 978-3-030-45002-1. DOI: https://doi.org/10.1007/978-3-030-45002-1_36. Published by Springer Nature, Cham, Switzerland AG 2020.