English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The purpose of this study is to develop a metaheuristic design for primary parameters and architectures of two models of artificial neural network (ANN) in predicting a cargo aircraft’s exergo-emissions (exergy destruction ratio,  r ex,dest   , and waste exergy ratio,  r wex   ) at different flight stages. Hybrid genetic algorithm (GA)-ANN models have been accomplished utilizing real databases of  r ex,dest    and  r wex    at various powers. Implementing a metaheuristics-based optimization on multilayer perceptron (MLP)-ANNs has produced the most favourable initial weights, step-size, biases, and training algorithm’s back-propagation (BP) momentum rate in addition to optimum number of neurons in the hidden layer(s). In accordance with an error assessment, a close fit linking real data and  r wex    (linear correlation ratio, R, value of 0.999851) as well as  r ex,dest    (R value of 0.999985) predicted values is found. In the  r ex, dest    estimation model, the accuracy among single-hidden-layer networks has been confirmed to be higher; whereas, highly accurate testing outcomes have been obtained in two-hidden-layer networks as far as modeling of  r wex    is concerned. ANN models’ optimization by GAs has increased the accuracy of the resulting models (R value of 0.999987 and 0.999869 for  r ex,dest    and  r wex   , in that order ascertaining a drop-off in the testing stage errors).

Metaheuristics Optimized Machine Learning Modelling of Environmental Exergo-Emissions for an Aero-Engine