Academic Qualifications
PhD, Howard University, Washington, DC, USA, 2009
Master of Engineering, Howard University, Washington, DC, USA, 2005
Bachelor of Science in Electrical and Electronic Engineering (First Class Honours) – July 2000
Professional Membership
Awards
Research Interest
The Department of Computer Engineering has three core thematic areas of research for cross-disciplinary and interdisciplinary research collaborations, namely:
I belong to the Control Systems and Automation group but conducts research in the other core areas as well. My main research interest include;
Current Research
Past Research
This research project proposed the design and development of a fuzzy logic-based multi-sensor fire detection and a web-based notification system with trained Convolutional Neural Networks for both proximity and wide-area fire detection. Until recently, most consumer-grade fire detection systems relied solely on smoke detectors. These offer limited protection due to the type of fire present and the detection technology at use. To solve this problem, we present a multi-sensor data fusion with Convolutional Neural Networks (CNN) fire detection and notification technology. Convolutional Neural Networks are mainstream methods of deep learning due to their ability to perform feature extraction and classification in the same architecture. The system is designed to enable early detection of fire in residential, commercial, and industrial environments by using multiple fire signatures such as flames, smoke, and heat. The incorporation of the Convolutional Neural Networks enables broader coverage of the area of interest, using visuals from surveillance cameras. With access granted to the web-based system, the Fire and Rescue Crew get notified in real-time with location information. The efficiency of the fire detection and notification system employed by standard fire detectors and the multi-sensor remote-based notification approach adopted in this paper showed significant improvements with timely fire detection, alerting, and response time for firefighting. The final experimental and performance evaluation results showed that the accuracy rate of CNN was 94%, and that of the fuzzy logic unit is 90%.
Fraud in health insurance claims has become a significant problem whose rampant growth has deeply affected the global delivery of health services. In addition to financial losses incurred, patients who genuinely need medical care suffer because service providers are not paid on time as a result of delays in the manual vetting of their claims and are therefore unwilling to continue offering their services. Health insurance claims fraud is committed through service providers, insurance subscribers, and insurance companies. The need for the development of a decision support system (DSS) for accurate, automated claim processing to offset the attendant challenges faced by the National Health Insurance Scheme cannot be overstated. This paper utilized the National Health Insurance Scheme claims dataset obtained from hospitals in Ghana for detecting health insurance fraud and other anomalies. Genetic support vector machines (GSVMs), a novel hybridized data mining and statistical machine learning tool, which provides a set of sophisticated algorithms for the automatic detection of fraudulent claims in these health insurance databases, are used. The experimental results have proven that the GSVM possessed better detection and classification performance when applied using SVM kernel classifiers. Three GSVM classifiers were evaluated, and their results compared. Experimental results show a significant reduction in computational time on claims processing while increasing classification accuracy via the various SVM classifiers (linear (80.67%), polynomial (81.22%), and radial basis function (RBF) kernel (87.91%).
Recent Publications