Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses

By Mingjing Wang, Huiling Chen, Bo-Seok Yang, Xuehua Zhao, Lufeng Hu, Zhennao Cai, Hui Huang, Changfei Tong. Neurocomputing, 2017

433 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/d3e263dc7bb5df39793c6b43a9c1d2cc6c3a745a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Mohammad Shehab
2. Mohammad Alshinwan
3. Hamzeh Alabool
4. Laith Mohammad Abualigah
5. Ali Diabat

Citations in the corpus

• Moth–flame optimization algorithm: variants and applications (2019, methodology): “On account of these merits, MFO was successfully applied to various optimization problems, for instance, scheduling [38], inverse problem and parameter estimation [11, 52], classification [134], economic [121], medical [125], power energy [133], and image processing [37].”