Application of Artificial Neural Networks to Forecast ITK Inhibitor Activity Data

Authors

  • Rama Devi Chalasani Research Scholar, Department of Computer Science and Engineering, GITAM School of Technology, GITAM( Deemed to-be University) Visakhapatnam-530045, A.P, India.
  • Radhika Y. Professor, Department of Computer Science and Engineering, GITAM School of Technology, GITAM( Deemed to-be University) Visakhapatnam-530045, A.P, India

Keywords:

Back propagation, hidden layer, Neurons, Neural Network

Abstract

An innovative method of testing the artificial neural network's effectiveness for ITK inhibitor data prediction was used. As a comparison, a multiple linear regression model was also developed. Using back propagation training, a multilayer perceptron MLP neural network was given the bioactivity estimate task. It was determined that there were enough buried neurons and that the learning rate was enough based on changes in RMSE. Thus, the final neural network consists of one output variable as the output layer, six input variables, eight hidden neurons, three nodes for bias accounting, and a 0.55 learning rate. To assess the robustness of the neural network model, test set data were forecasted, and forecast accuracy was measured.

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Mean square error calculation between data and output from variations with the number of neurons in the hidden layer (A) and variations with the learning rate (B).

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Published

16.01.2023

How to Cite

Chalasani , R. D. ., & Radhika Y. (2023). Application of Artificial Neural Networks to Forecast ITK Inhibitor Activity Data. International Journal of Intelligent Systems and Applications in Engineering, 11(1), 70–78. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2445

Issue

Vol. 11 No. 1 (2023)

Section

Research Article

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