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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The architecture of Artificial Neural Network laid the foundation as a powerful technique in handling problems such as pattern recognition and data analysis. Its data driven, self-adaptive, and non-linear capabilities channel it for use in processing at high speed and ability to learn the solution to a problem from a set of examples. Neural network training has been a dynamic area of research, with the Multi-Layer Perceptron (MLP) trained with Back Propagation (BP) mostly worked on by various researchers. In this study, a performance analysis based on BP training algorithms; gradient descent and gradient descent with momentum, both using the sigmoidal and hyperbolic tangent activation functions, coupled with pre-processing techniques are executed. The Min-Max, Z-Score, and Decimal Scaling pre-processing techniques are analyzed. Results generated from the simulations reveal that pre-processing the data greatly increase the ANN convergence, with Z-Score producing the overall best performance on all datasets

international journal on advanced science, engineering and information technology/international journal of advanced science, engineering and information technology

The Effect of Pre-Processing Techniques and Optimal Parameters selection on Back Propagation Neural Networks