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Analysis of Multiple Hidden Layer vs. Accuracy in Performance using Back Propagation Neural Network

Affiliations

  • CSE Department, Suresh Gyan Vihar University, Jagatpura, Jaipur, Rajasthan, India
  • SECE, Shri Mata Vaishno Devi University, Katra, Jammu - 182320, India

Abstract


In this paper it has been analyzed that the proportionality among hidden layers neurons plays very important role in determining the accuracy of the target output. Better proportionality of neurons with appropriate number of hidden layers result in higher accuracy. In general, in any neural network at most two hidden layers are enough to train the network. But in some cases where accuracy is chief criteria then hidden layer plays vital role. This problem gets crucial when someone have to deal with multiscript numeral recognition where many numerals have similar shape but different values. For example ‘0’ shape in Arabic resembles numeric “Five” whereas in Hindi, English and many other scripting languages it resembles numeric “Zero”. This crucial problem usually arises when a person writes any number/pin code in two or more scripting language like Hindi and Farsi. These types of problems are taken into consideration and accuracy is considered chief criteria.

Keywords

Multiple Hidden Layers, Multiscript Pin Code Recognition, Neural Network.

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