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Isaac Azure

Mathematics

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Research Summary

(inferred from publications by AI)

The researcher's research focuses on developing efficient iterative methods for solving nonlinear equations across diverse disciplines. Their work encompasses innovative algorithms in metrology and metrology systems, optimal experimental designs, photovoltaic system optimization techniques, anomaly detection methodologies, advanced measurement tools, and mathematics education strategies, all aimed at enhancing problem-solving capabilities in physical and social sciences.

Research Themes

All Papers

AI-Enhanced Performance Evaluation of Python, MATLAB, and Scilab for Solving Nonlinear Systems of Equations: A Comparative Study Using the Broyden Method(2023)
A Comparison of Newly Developed Broyden – Like Methods for Solving System of Nonlinear Equations(2021)
A New Trapezoidal-Simpson 3/8 Method for Solving Systems of Nonlinear Equations(2021)
An Analysis of Solutions of Nonlinear Equations Using AI Inspired Mathematical Packages(2023)
Error Analysis of Newly Developed Numerical Methods for Solving System of Nonlinear Equations(2023)
Derivation and Performance Analysis of Composite Trapezoidal Iterative Methods(2024)
Comparative Study of Numerical Methods for Solving Non-linear Equations Using Manual Computation(2019)
On the Selection of Optimal Balanced Incomplete Block Design using Different Types of Designs(2024)
Concept of Isomorphisms and Automorphisms of a Balanced Incomplete Block Design(2025)
Student Discourse, an Effective Way of Learning Mathematics: Case Study in Bongo Senior High School(2019)
Comprehensive Anomaly Detection in Ghana: Addressing Social, Economic, and Security Challenges Through Transfer Learning in Traffic Surveillance(2019)
An iterative method for robust solutions to nonlinear Volterra integral equations: Stability, convergence, and practical applications(2024)
Parameter estimation for photovoltaic systems: An enhanced Broyden-like approach(2024)
A 2^k Factorial Design on The Effect of Different Cholesterol Reducing Factors(2025)

Collaboration Network

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About This Profile

This profile is generated from publicly available publication metadata and is intended for research discovery purposes. Themes, summaries, and trajectories are inferred computationally and may not capture the full scope of the lecturer's work. For authoritative information, please refer to the official KNUST profile.