Imo Eyoh

Orcid: 0000-0002-6548-7644

According to our database1, Imo Eyoh authored at least 12 papers between 2016 and 2021.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2021
Hybrid intelligent telemedical monitoring and predictive systems.
Int. J. Hybrid Intell. Syst., 2021

Optimization of Interval Type-2 Intuitionistic Fuzzy Logic System for Prediction Problems.
Int. J. Comput. Intell. Appl., 2021

2020
Comparing Performance Potentials of Classical and Intuitionistic Fuzzy Systems in Terms of Sculpting the State Space.
IEEE Trans. Fuzzy Syst., 2020

Derivative-Based Learning of Interval Type-2 Intuitionistic Fuzzy Logic Systems for Noisy Regression Problems.
Int. J. Fuzzy Syst., 2020

2018
Interval type-2 Atanassov-intuitionistic fuzzy logic for uncertainty modelling.
PhD thesis, 2018

Hybrid Learning for Interval Type-2 Intuitionistic Fuzzy Logic Systems as Applied to Identification and Prediction Problems.
IEEE Trans. Fuzzy Syst., 2018

Interval Type-2 A-Intuitionistic Fuzzy Logic for Regression Problems.
IEEE Trans. Fuzzy Syst., 2018

Interval Type-2 Intuitionistic Fuzzy Logic Systems - A Comparative Evaluation.
Proceedings of the Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations, 2018

2017
Extended Kalman filter-based learning of interval type-2 intuitionistic fuzzy logic system.
Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics, 2017

Time series forecasting with interval type-2 intuitionistic fuzzy logic systems.
Proceedings of the 2017 IEEE International Conference on Fuzzy Systems, 2017

2016
3D segmentation of the whole heart vasculature using improved multi-threshold Otsu and white top-hat scale space hessian based vessel filter.
Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence, 2016

Interval type-2 intuitionistic fuzzy logic system for non-linear system prediction.
Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics, 2016


  Loading...