Jonathan Kwaku Afriyie
The researcher's work is centered around innovative data analysis techniques and computational methods that address challenges in diverse scientific domains. Their research spans a range of areas including imbalanced classification problems, complex systems modeling, time series forecasting, and anomaly detection. By developing robust methodologies for data analysis, the researcher contributes to advancements in fields such as fraud detection in financial transactions, environmental monitoring through time series studies, and understanding the dynamics of infectious diseases. This work reflects a commitment to enhancing predictive and explanatory capabilities across physical and social sciences through interdisciplinary approach and cutting-edge computational techniques.
Anomaly Detection Techniques and Applications
Complex Systems and Time Series Analysis
COVID-19 epidemiological studies
Imbalanced Data Classification Techniques
A supervised machine learning algorithm for detecting and predicting fraud in credit card transactions
Open AccessA hybrid forecasting technique for infection and death from the mpox virus
Open AccessSupervised Machine Learning Algorithm Approach to Detecting and Predicting Fraud in Credit Card Transactions
Open AccessComparison of outlier detection techniques in non-stationary time series data
Open AccessEvaluating the Performance of Unit Root Tests in Single Time Series Processes
Open AccessDepartment of Statistics and Actuarial Science
Kwame Nkrumah University of Science and Technology