Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Neural Network Algorithm

In recent days, Artificial Neural Network (ANN) can be applied to a vast majority of fields including business, medicine, engineering, etc. The most popular areas where ANN is employed nowadays are pattern and sequence recognition, novelty detection, character recognition, regression analysis, speec...

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Bibliographic Details
Published in:arXiv.org 2018-11
Main Authors: Md Abu Bakr Siddique, Mohammad Mahmudur Rahman Khan, Rezoana, Bente Arif, Ashrafi, Zahidun
Format: Article
Language:English
Subjects:
Artificial neural networks
Character recognition
Cybersecurity
Data processing
Digits
Electronic noses
Fault tolerance
Handwriting
Handwriting recognition
Image compression
Image detection
Learning theory
Maintenance
Neural networks
Object recognition
Pattern recognition
Regression analysis
Robotics
Speech recognition
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Summary:In recent days, Artificial Neural Network (ANN) can be applied to a vast majority of fields including business, medicine, engineering, etc. The most popular areas where ANN is employed nowadays are pattern and sequence recognition, novelty detection, character recognition, regression analysis, speech recognition, image compression, stock market prediction, Electronic nose, security, loan applications, data processing, robotics, and control. The benefits associated with its broad applications leads to increasing popularity of ANN in the era of 21st Century. ANN confers many benefits such as organic learning, nonlinear data processing, fault tolerance, and self-repairing compared to other conventional approaches. The primary objective of this paper is to analyze the influence of the hidden layers of a neural network over the overall performance of the network. To demonstrate this influence, we applied neural network with different layers on the MNIST dataset. Also, another goal is to observe the variations of accuracies of ANN for different numbers of hidden layers and epochs and to compare and contrast among them.
ISSN:2331-8422
DOI:10.48550/arxiv.1809.06188