Innovative Research Award
HAMZA ABUBAKAR, PhD
Department of Mathematics, Isa Kaita College of Education, Dutsin-Ma, Nigeria
| HAMZA ABUBAKAR |
| Affiliation |
Isa Kaita College of Education |
| Country |
Nigeria |
| Scopus ID |
57217009001 |
| Documents |
30 |
| Citations |
350 |
| h-index |
9 |
| Subject Area |
Applied Mathematics, Financial Mathematics, Neural Networks, Body Area Network |
| Event |
Global Sensor Awards |
| ORCID |
0000-0002-9451-0401
|
Hamza Abubakar is a Nigerian applied mathematician and academic researcher specializing in financial mathematics, optimization algorithms, neural networks, and statistical modelling. He has contributed extensively to interdisciplinary mathematical research through scholarly publications, conference presentations, academic leadership, and funded research initiatives. His work integrates advanced computational techniques with applied statistical frameworks for solving practical problems in finance, engineering, healthcare analytics, and artificial intelligence.[1]
Abstract
This academic article presents the scholarly profile and research achievements of Hamza Abubakar, an applied mathematician with expertise in financial mathematics, neural networks, optimization algorithms, and statistical modelling. Over a professional academic career spanning more than fifteen years, he has contributed to higher education, interdisciplinary research, curriculum development, and mathematical applications in finance and artificial intelligence. His publications and conference presentations demonstrate sustained contributions to optimization theory, stochastic modelling, and machine learning-based analytical systems. His work has received visibility through peer-reviewed international journals and collaborative research activities across Nigeria and Malaysia.[2]
Keywords
Applied Mathematics; Financial Mathematics; Neural Networks; Optimization Algorithms; Statistical Modelling; Machine Learning; Risk Assessment; Weibull Distribution; Hopfield Neural Networks; Artificial Intelligence; Computational Mathematics; Mathematical Modelling.
Introduction
Applied mathematics continues to play an essential role in solving real-world scientific and financial challenges through computational modelling and algorithmic optimization. Researchers working at the intersection of mathematics, artificial intelligence, and financial analytics contribute significantly to modern predictive systems and decision-making frameworks. Hamza Abubakar has developed a research portfolio focused on the application of mathematical optimization techniques and intelligent computational models to finance, risk prediction, healthcare classification systems, and statistical estimation problems.[3]
His academic progression from assistant lecturer to principal lecturer reflects sustained professional growth and commitment to mathematics education and research leadership. In addition to teaching and supervision responsibilities, he has participated actively in professional associations and interdisciplinary collaborations within computational mathematics and artificial intelligence.[4]
Research Profile
Hamza Abubakar obtained his Bachelor of Science in Mathematics Education from the University of Abuja in 2006, followed by a Master of Science degree in Financial Mathematics from the same institution in 2015. He later completed a Doctor of Philosophy degree in Applied Mathematics at Universiti Sains Malaysia in 2022.[5]
His academic and professional engagements include positions at Isa Kaita College of Education, Annahda International University, Universiti Sains Malaysia, and Universiti Utara Malaysia. These appointments enabled him to contribute to teaching, research mentoring, curriculum implementation, and international academic collaboration across mathematics and quantitative sciences disciplines.[6]
Research Contributions
The research contributions of Hamza Abubakar are concentrated on optimization algorithms, generalized linear models, neural network systems, and probabilistic modelling techniques. His studies on Weibull and Gamma distribution parameter estimation introduced optimization-based frameworks that integrate heuristic and artificial intelligence algorithms for statistical inference.[2]
His publications also investigate the application of Hopfield neural networks and satisfiability logic in intelligent classification systems. These studies contribute to computational intelligence by combining neural computation with optimization strategies for financial risk prediction and healthcare-related classification tasks.[3]
Publications
The publication profile of Hamza Abubakar includes peer-reviewed journal articles, conference proceedings, books, and book chapters addressing applied mathematics, computational intelligence, optimization theory, and financial analytics.[5]
- Abubakar, H., & Sayed, A. A. I. (2025). Estimation of shifted Weibull distribution parameters using continuous Hopfield neural networks. Journal of Applied Statistics, 52(14), 1–33.
- Abubakar, H. (2025). Random Satisfiability Logic-Driven Approach in Hopfield Neural Networks. International Journal of Applied and Computational Mathematics, 11(3), 117.
- Ali, G. A., Abubakar, H., et al. (2023). Artificial dragonfly algorithm in the Hopfield neural network. PLOS ONE, 18(9), e0286874.
- Abubakar, H., & Sabri, S. R. M. (2023). A Bayesian Approach to Weibull Distribution. Journal of Reliability and Statistical Studies, 16(01), 1–24.
- Abubakar, H., & Madugu, A. (2025). Fundamentals of Mathematics in Finance: A Guide to Undergraduate Financial Mathematics. Ahmadu Bello University Press.
Research Impact
The research activities of Hamza Abubakar demonstrate interdisciplinary impact through the integration of mathematical theories with computational intelligence systems. His work contributes to broader developments in financial analytics, predictive modelling, and optimization-based machine learning approaches. Several of his studies have been indexed in internationally recognized journals and databases, increasing accessibility and scholarly visibility.[1]
In addition to research output, he has secured multiple institutional research grants under the TETFUND Institutional Based Research programme and contributed to academic administration and mentoring within the mathematics community in Nigeria.[1]
Award Suitability
Hamza Abubakar demonstrates suitability for recognition in applied mathematics and computational research due to his sustained academic contributions, interdisciplinary research portfolio, leadership in mathematics education, and involvement in international collaborations. His scholarly activities reflect a balance between theoretical mathematical development and practical computational applications.[2]
His publication record, funded projects, editorial roles, and conference participation collectively indicate active engagement in advancing quantitative sciences and intelligent computational systems. These contributions align with the objectives of international research excellence and innovation awards recognizing impactful academic scholarship.[3]
Conclusion
Hamza Abubakar has established a professional and scholarly profile grounded in applied mathematics, optimization techniques, financial modelling, and neural network systems. Through academic teaching, interdisciplinary research, conference engagement, and institutional leadership, he has contributed meaningfully to the advancement of quantitative sciences and computational methodologies. His body of work reflects ongoing dedication to mathematical innovation, research excellence, and higher education development within both regional and international academic communities.
External Links
References
- Elsevier. (n.d.). Scopus author details: HAMZA ABUBAKAR, Author ID 57217009001. Scopus.
https://www.scopus.com/authid/detail.uri?authorId=57217009001
- ORCID. (n.d.). HAMZA ABUBAKAR researcher profile.
https://orcid.org/0000-0002-9451-0401
- Abubakar, H., & Sayed, A. A. I. (2025). Estimation of shifted Weibull distribution parameters using continuous Hopfield neural networks. Journal of Applied Statistics.
- Abubakar, H. (2025). Random Satisfiability Logic-Driven Approach in Hopfield Neural Networks. International Journal of Applied and Computational Mathematics.
- Universiti Sains Malaysia. (2022). Doctor of Philosophy in Applied Mathematic