Feed forward neural network for solid waste image classification

W. Zailah, Hannan M A, Abdulla Al Mamun

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This study deals with the Feed Forward Neutral Network (FFNN) model to classify the level content of waste based on teaching and learning concept. An FFNN with twenty images is used for testing the input samples through the neural network learning to compute the sum squared error to ensure the performance of the model. After several training the neural network was able to learn and match the target. Thirty images for each class are used as a fullest of inputs samples for classifying. Result from the neural network and the rules decision are used to build the Receiver Operating Characteristic (ROC) graph. Decision graph show the performance of the system based on Area Under Curve (AUC) for the solid waste system is classified as WS-Class equal to 0.9875 and as WS-grade equal to 0.8293. The system has been successfully designated with the motivation of waste been monitoring system, to escalate the results that can applied to wide variety of local municipal authorities system.

Original languageEnglish
Pages (from-to)1466-1470
Number of pages5
JournalResearch Journal of Applied Sciences, Engineering and Technology
Volume5
Issue number4
Publication statusPublished - 2013

Fingerprint

Image classification
Feedforward neural networks
Solid wastes
Neural networks
Teaching
Monitoring
Testing

Keywords

  • Artificial neural network
  • Hough transforms
  • Image classification
  • Solid waste

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science(all)

Cite this

Feed forward neural network for solid waste image classification. / Zailah, W.; M A, Hannan; Mamun, Abdulla Al.

In: Research Journal of Applied Sciences, Engineering and Technology, Vol. 5, No. 4, 2013, p. 1466-1470.

Research output: Contribution to journalArticle

Zailah, W. ; M A, Hannan ; Mamun, Abdulla Al. / Feed forward neural network for solid waste image classification. In: Research Journal of Applied Sciences, Engineering and Technology. 2013 ; Vol. 5, No. 4. pp. 1466-1470.
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