Imo Eyoh

542 total citations
22 papers, 333 citations indexed

About

Imo Eyoh is a scholar working on Artificial Intelligence, Statistics and Probability and Information Systems. According to data from OpenAlex, Imo Eyoh has authored 22 papers receiving a total of 333 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 11 papers in Statistics and Probability and 3 papers in Information Systems. Recurrent topics in Imo Eyoh's work include Fuzzy Logic and Control Systems (14 papers), Neural Networks and Applications (12 papers) and Fuzzy Systems and Optimization (10 papers). Imo Eyoh is often cited by papers focused on Fuzzy Logic and Control Systems (14 papers), Neural Networks and Applications (12 papers) and Fuzzy Systems and Optimization (10 papers). Imo Eyoh collaborates with scholars based in United Kingdom, Nigeria and China. Imo Eyoh's co-authors include Robert John, Geert De Maere, Udoinyang G. Inyang, Erdal Kayacan, Moses E. Ekpenyong, Julius U. Akpabio, Okorie Ekwe Agwu, Jerry M. Mendel, Roy Kalawsky and Li Bai and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Fuzzy Systems and Journal of Petroleum Science and Engineering.

In The Last Decade

Imo Eyoh

22 papers receiving 327 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Imo Eyoh United Kingdom 9 153 101 78 61 55 22 333
Naresh Iyer United States 11 81 0.5× 30 0.3× 71 0.9× 6 0.1× 34 0.6× 31 284
Liang-Sian Lin Taiwan 11 127 0.8× 7 0.1× 18 0.2× 57 0.9× 50 0.9× 34 323
Durga Rao Karanki Switzerland 8 20 0.1× 12 0.1× 37 0.5× 17 0.3× 37 0.7× 13 445
Víctor Henrique Alves Ribeiro Brazil 10 171 1.1× 21 0.2× 23 0.3× 3 0.0× 46 0.8× 41 332
Guo Bo China 7 28 0.2× 18 0.2× 57 0.7× 49 0.8× 7 0.1× 50 276
E. McCormick United States 3 395 2.6× 9 0.1× 30 0.4× 52 0.9× 31 0.6× 6 533
Wojciech Cholewa Poland 5 94 0.6× 14 0.1× 65 0.8× 17 0.3× 53 1.0× 17 322
K. Durga Rao India 5 17 0.1× 18 0.2× 22 0.3× 12 0.2× 33 0.6× 6 365

Countries citing papers authored by Imo Eyoh

Since Specialization
Citations

This map shows the geographic impact of Imo Eyoh's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Imo Eyoh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Imo Eyoh more than expected).

Fields of papers citing papers by Imo Eyoh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Imo Eyoh. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Imo Eyoh. The network helps show where Imo Eyoh may publish in the future.

Co-authorship network of co-authors of Imo Eyoh

This figure shows the co-authorship network connecting the top 25 collaborators of Imo Eyoh. A scholar is included among the top collaborators of Imo Eyoh based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Imo Eyoh. Imo Eyoh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Inyang, Udoinyang G., et al.. (2023). A Dataset-Driven Parameter Tuning Approach for Enhanced K-Nearest Neighbour Algorithm Performance. International Journal on Advanced Science Engineering and Information Technology. 13(1). 380–391. 9 indexed citations
2.
Eyoh, Imo, et al.. (2021). Prediction of COVID-19 Time Series – Case Studies of South Africa and Egypt using Interval Type-2 Fuzzy Logic System. International Journal of Advanced Trends in Computer Science and Engineering. 10(2). 627–635. 1 indexed citations
3.
Agwu, Okorie Ekwe, et al.. (2021). A critical review of drilling mud rheological models. Journal of Petroleum Science and Engineering. 203. 108659–108659. 72 indexed citations
4.
Agwu, Okorie Ekwe, et al.. (2021). A comprehensive review of laboratory, field and modelling studies on drilling mud rheology in high temperature high pressure (HTHP) conditions. Journal of Natural Gas Science and Engineering. 94. 104046–104046. 50 indexed citations
5.
Eyoh, Imo, et al.. (2021). Hybrid intelligent telemedical monitoring and predictive systems. International Journal of Hybrid Intelligent Systems. 17(1-2). 43–57. 3 indexed citations
6.
Eyoh, Imo, et al.. (2021). Software Fault Prediction Based on Interval Type-2 Intuitionistic Fuzzy Logic System. International Journal of Advances in Scientific Research and Engineering. 7(5). 10–24. 1 indexed citations
7.
Eyoh, Imo, et al.. (2021). Optimization of Interval Type-2 Intuitionistic Fuzzy Logic System for Prediction Problems. International Journal of Computational Intelligence and Applications. 20(4). 2 indexed citations
8.
Eyoh, Imo, et al.. (2020). A Sliding Mode Control Learning of Interval Type-2 Intuitionistic Fuzzy Logic for Non-Linear System Prediction. Solid State Technology. 63(6). 7793–7811. 1 indexed citations
9.
Eyoh, Imo, et al.. (2020). Derivative-Based Learning of Interval Type-2 Intuitionistic Fuzzy Logic Systems for Noisy Regression Problems. International Journal of Fuzzy Systems. 22(3). 1007–1019. 7 indexed citations
10.
Eyoh, Imo, et al.. (2020). Interval Type-2 Intuitionistic Fuzzy Logic System for Time Series and Identification Problems - A Comparative Study. Zenodo (CERN European Organization for Nuclear Research). 10(1). 1–17. 3 indexed citations
11.
Eyoh, Imo, et al.. (2020). Optimization of Interval Type-2 Fuzzy Logic System for Software Reliability Prediction. International Journal of Engineering Research and Advanced Technology. 6(11). 1–12. 2 indexed citations
12.
Mendel, Jerry M., Imo Eyoh, & Robert John. (2019). Comparing Performance Potentials of Classical and Intuitionistic Fuzzy Systems in Terms of Sculpting the State Space. IEEE Transactions on Fuzzy Systems. 28(9). 2244–2254. 8 indexed citations
13.
Eyoh, Imo, Robert John, Geert De Maere, & Erdal Kayacan. (2018). Hybrid Learning for Interval Type-2 Intuitionistic Fuzzy Logic Systems as Applied to Identification and Prediction Problems. IEEE Transactions on Fuzzy Systems. 26(5). 2672–2685. 77 indexed citations
14.
Eyoh, Imo, Robert John, & Geert De Maere. (2017). Time series forecasting with interval type-2 intuitionistic fuzzy logic systems. Repository@Nottingham (University of Nottingham). 1 indexed citations
15.
Eyoh, Imo, Robert John, & Geert De Maere. (2017). Interval Type-2 A-Intuitionistic Fuzzy Logic for Regression Problems. IEEE Transactions on Fuzzy Systems. 26(4). 2396–2408. 39 indexed citations
16.
Eyoh, Imo, Robert John, & Geert De Maere. (2017). Extended Kalman filter-based learning of interval type-2 intuitionistic fuzzy logic system. Nottingham ePrints (University of Nottingham). 728–733. 8 indexed citations
17.
Eyoh, Imo, Robert John, & Geert De Maere. (2017). Time series forecasting with interval type-2 intuitionistic fuzzy logic systems. 1–6. 19 indexed citations
18.
Ehling, Josef, et al.. (2016). 3D segmentation of the whole heart vasculature using improved multi-threshold Otsu and white top-hat scale space hessian based vessel filter. Repository@Nottingham (University of Nottingham). 80. 1–7. 1 indexed citations
19.
Eyoh, Imo, Robert John, & Geert De Maere. (2016). Interval type-2 intuitionistic fuzzy logic system for non-linear system prediction. 1063–1068. 24 indexed citations
20.
Eyoh, Imo, et al.. (2009). Object Oriented Database Management System: A UML Design Approach.. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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