Gideon Opoku
Kwame Nkrumah University of Science and Technology
Publishing since 2023, latest 2026
- Electrical and Electronic Engineering
- Biomedical Engineering
- Artificial Intelligence
- Atomic and Molecular Physics, and Optics
- Computer Networks and Communications
- Information Systems
In their own words
- Plasmonic sensors
- Photonic Crystal Fibres
- Surface Plasmon Resonance
- Applied Machine Learning
- photonic crystal fiber
- Artificial intelligence
- Publications
- 42023–2026
- Citations
- 48to those papers
- h-index
- 3i10-index 2
- Field-weighted impact
- 1.821.00 is the world average
- World top 10%
- 25%1 of 4 scored papers
- Open access
- 25%1 papers free to read
5 other works — preprints, datasets and the like — are listed but not counted.
Google Scholar shows 65 citations, h-index 3 and i10-index 2 (65 citations and h-index 3 since 2021), as of October 10, 2026. It also counts theses, books and web pages, so it runs higher.
By year
Papers, and the citations to them
Publications, left axisCitations to that year’s papers, right axis2026 still in progress
Citations received each year
PeerfordGoogle Scholar
To all their work, old papers included. Google Scholar also counts citations from theses, books and web pages, so its line runs higher.
What they work on
From the classification of each paper
Subfields
- Electrical and Electronic Engineering375%
- Biomedical Engineering
Positions
- Computer EngineeringKwame Nkrumah University of Science and Technology
Education
- Master of Philosophy, Computer EngineeringKwame Nkrumah University of Science and Technology2020–2022
- Bachelor of Science, Computer EngineeringUniversity of Energy and Natural Resources2015–2019
On their papers
Publications
4 in all
- A comparative analysis of ten machine learning models for performance prediction in a numerically simulated photonic crystal fibre-based surface plasmon resonance sensor
2026Journal of Electromagnetic Waves and ApplicationsJournalFirst authorCorresponding1 citation10.1080/09205071.2026.2656347
- A Systematic Review of Machine Learning and Deep Learning Techniques for DDoS Attack Mitigation in IoT and Edge Computing Systems