Oliver Kornyo

Computer Science

About

Dr. Kornyo has over 14 years of experience in the energy sector and IoT Sensors Automation including Advanced Metering Infrastructure (AMI) Solutions and smart metering application management. He has developed state-of-the-art Database Management, software testing in the field of smart metering billing, data mining and simulation techniques in the Utility distribution applications.Currently a Lecturer in KNUST-Computer Science Department. Specialised in System Security and fraud detection techniques. A Lead IT Project Consultant. Was the CEO and a Project Manager with E.E.K Consults & Electricals Limited, in KUMASI. The project Manager for the installation of ‘’Mini- off grid Renewable Solar PROJECT for total A.M.I energy solution in Yeboahkrom and Onaa, a project under the German government and UK Funding respectively, a community in the Ejisu-Juaben Municipal in Ghana. A total monitoring network system, control energy usage from the user point (demand-side) and manage the Solar Energy generation (supply-side) on community households to avoid energy wastage. Oliver also developed the National net metering solution that is being implemented by Electricity Company of Ghana for fraud detection as the company moves towards pre-paid meters. Previously, Oliver worked for the Electricity Supply Company, Ghana and the Ashanti Region Database Administrator for GEDAP Projects. This included System Energy Management of smart metering End To-End solutions, training on Energy conservation and Tariff Calculation principles for energy billing system and application of Artificial Intelligence (AI) in Smart Metering Solutions, database Security Management, controlling billing records for management. He is part of the Ghana GOALS team, surveying, designing and installing the Mini grids with solar e-cooking in 4 Senior High Schools and one community. A research fellow at TCC-KNUST Ghana.

Research Summary

(inferred from publications by AI)

The researcher's work integrates machine learning into various fields within physical sciences, focusing on enhancing energy systems' efficiency, improving network security, advancing data storage technologies, detecting and preventing malware, addressing smart grid vulnerabilities, enhancing cybersecurity, and fostering privacy solutions. Their studies aim to leverage AI/ML across these areas to solve complex challenges, from predicting solar PV energy impacts to developing secure smart grids and managing advanced adversaries with sophisticated algorithms.

Research Themes

All (12 Papers)

Physical Sciences (11 Papers)

Social Sciences (1 Papers)

All Papers

Machine learning of redundant energy of a solar PV Mini-grid system for cooking applications(2023)

Integration of Advanced Metering Infrastructure for Mini-Grid Solar PV Systems in Off-Grid Rural Communities (SoAMIRural)(2023)

Botnet attacks classification in AMI networks with recursive feature elimination (RFE) and machine learning algorithms(2023)

Enhancing Port Scans Attack Detection Using Principal Component Analysis and Machine Learning Algorithms(2022)

Intrusion Detection System Based on Artificial Immune System: A Review(2021)

A Fraud Prevention and Secure Cognitive SIM Card Registration Model(2022)

Enhancing AMI network security with STI model: A mathematical perspective(2024)

Machine Learning-Assisted Innovative Charging Strategy for E-Mobility in Rural Communities Operated by Redundant Energy on Solar Pv Mini-Grids(2025)

Rsencarver": Enhancing File Carving Techniques with Error Correction Using the Reed Solomon Algorithm(2023)

Deit-Mi: Advancing Malware Detection and Classification with Data-Efficient Image Transformers(2023)

A Hybrid Integrated High Availability Cluster (Ihac) for Enhancing System Resilience and Addressing Persistent Cyber Threats(2025)

Hybrid framework of differential privacy and secure multi-party computation for privacy-preserving entity resolution(2025)

Collaboration Network

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Research Collaboration Map

Collaboration Frequency

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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.

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