Ruxandra Stoean

TL;DR: This paper aims to present a novel technique that integrates the conservation of the best successive local individuals with a topological subpopulations separation instead of the common but problematic radius-triggered manner.

TL;DR: A two-step hybridized methodology is put forward, where learning is accurately performed by the SVMs and a comprehensible emulation of the resulting decision model is generated by EAs in the form of propositional rules, while referring only those indicators that highly influence the class separation.

TL;DR: In this paper, the authors proposed an enhanced version of the firefly algorithm that corrects the acknowledged drawbacks of the original method by an explicit exploration mechanism and a chaotic local search strategy, theoretically tested on two sets of bound-constrained benchmark functions from the CEC suites and practically validated for automatically selecting the optimal dropout rate for the regularization of deep neural networks.

TL;DR: The use of the evolutionary technique for fibrosis degree prediction triggers simplicity and offers a direct expression of the influence of dynamically selected indicators on the corresponding stage, which significantly surpasses the classical support vector machines, which are both widely used and technically sound.

TL;DR: An automatic tool capable to learn from a patients data set with 24 medical indicators characterizing each sample and to subsequently use the acquired knowledge to differentiate between five degrees of liver fibrosis is presented.