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Ravikiran Chimatapu
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2020 – today
- 2021
- [b1]Ravikiran Chimatapu:
An explainable artificial intelligence approach based on deep type-2 fuzzy logic system. University of Essex, Colchester, UK, 2021 - [c7]Ravikiran Chimatapu, Hani Hagras, Mathias Kern, Gilbert Owusu:
Enhanced Deep Type-2 Fuzzy Logic System For Global Interpretability. FUZZ-IEEE 2021: 1-8 - 2020
- [j1]Jerry M. Mendel, Ravikiran Chimatapu, Hani Hagras:
Comparing the Performance Potentials of Singleton and Non-singleton Type-1 and Interval Type-2 Fuzzy Systems in Terms of Sculpting the State Space. IEEE Trans. Fuzzy Syst. 28(4): 783-794 (2020) - [c6]Ravikiran Chimatapu, Hani Hagras, Mathias Kern, Gilbert Owusu:
Hybrid Deep Learning Type-2 Fuzzy Logic Systems For Explainable AI. FUZZ-IEEE 2020: 1-6
2010 – 2019
- 2018
- [c5]Ravikiran Chimatapu, Hani Hagras, Andrew Starkey, Gilbert Owusu:
Stacked Auto Encoder Based Hybrid Genetic Algorithm for Workforce Optimization. CEEC 2018: 236-241 - [c4]Ravikiran Chimatapu, Hani Hagras, Andrew Starkey, Gilbert Owusu:
A Big-Bang Big-Crunch Type-2 Fuzzy Logic System for Generating Interpretable Models in Workforce Optimization. FUZZ-IEEE 2018: 1-8 - [c3]Ravikiran Chimatapu, Hani Hagras, Andrew Starkey, Gilbert Owusu:
Interval Type-2 Fuzzy Logic Based Stacked Autoencoder Deep Neural Network For Generating Explainable AI Models in Workforce Optimization. FUZZ-IEEE 2018: 1-8 - [c2]Ravikiran Chimatapu, Hani Hagras, Andrew Starkey, Gilbert Owusu:
Enhancing Human Decision Making for Workforce Optimisation Using a Stacked Auto Encoder Based Hybrid Genetic Algorithm. SGAI Conf. 2018: 63-75 - [c1]Ravikiran Chimatapu, Hani Hagras, Andrew Starkey, Gilbert Owusu:
Explainable AI and Fuzzy Logic Systems. TPNC 2018: 3-20
Coauthor Index
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