Optimal neuro‐fuzzy control of hepatitis C virus integrated by genetic algorithm

A Fractional Order Hepatitis C Mathematical Model with Mittag-Leffler Kernel

“…As better theories are discovered, a lack of concordance between mathematical models that are theoretical and experimental findings frequently leads to significant advancements. Many mathematical models were formulated for the Hepatitis C diseases to understand the dynamics of the diseases and control the spreading of diseases; we refer to [11][12][13][14].…”

Computational Approach For Real-Time Interval Type-2 Fuzzy Kalman Filtering and Forecasting via Unobservable Spectral Components of Experimental Data

“… 2020a ; Zhao and Lin 2019 ) and also for health area (Khodaei-Mehr et al. 2018 ; Pham and Berger 2011 ). The successful applications of fuzzy systems are due to its structure based on rules, where the antecedent propositions of the rules define fuzzy operation regions and the consequent describes a corresponding physical behavior in those regions, and its capability of approximate functions as well as treat nonlinearities and uncertainties (Serra 2012 ).…”

Chaotic emperor penguin optimised extreme learning machine for microarray cancer classification

“…Due to the random selection of input weights and hidden biases, the output weight matrix of ELM may not show full column rank leading to ill‐conditioning [17] of the system which produces non‐optimal solutions. Therefore, to enhance the conditioning of ELM and to guarantee the optimal solutions, evolutionary algorithms, namely, genetic algorithm (GA) [18], particle swarm optimisation (PSO) [19], and differential evolution (DE) [20] have been embedded with ELM. Some of the evolutionary ELM methods, namely, GA‐based ELM [21], PSO‐based ELM [22], DE‐based ELM [23], artificial bee colony(ABC)‐based ELM [24], and cuckoo search‐based ELM [15] have been developed in the literature in the recent past.…”