Performance Evaluation of Neural Network for Human Classification Using Blob Dataset

Title:Performance Evaluation of Neural Network for Human Classification Using Blob Dataset

Volume: 14 Issue: 5

Author(s): Monika Mehta *Madhulika Bhadauria

Affiliation:

• Department of Computer Science & Engineering, University School of Information, Communication & Technology, Guru Gobind Singh Indraprastha University Dwarka, New Delhi,India

Keywords: Computational intelligence, neural network, blob, classification, pattern recognition, underfitting, overfitting, hidden layer.

Abstract:

Background: Human Classification in public places is an emerging area in the applications of Computational Intelligence. Therefore, modeling of an optimal architecture of the neural network is required to classify them.

Methods: In this work for this purpose, blob dataset has been used to train the neural network. This dataset consists of 2408 features of a human blob.

Results: Further, analysis of this blob dataset has been done on the basis of various characteristic parameters for affirmation of actual training. During training and testing of this dataset, it has been observed that when nodes at hidden layer are below and above 10 then training of neural network is under fitted and overfitted respectively and works effectively when the nodes are 10 at the hidden layer.

Conclusion: From the experimental work performed in this study, an optimal neural network has been obtained to classify human using blob dataset.

Cite this article as:

Mehta Monika*, Bhadauria Madhulika, Performance Evaluation of Neural Network for Human Classification Using Blob Dataset, Recent Advances in Computer Science and Communications 2021; 14 (5) . https://dx.doi.org/10.2174/2666255813666190912101628