Feed forward neural network for solid waste image classification

W. Zailah, M. A. Hannan, Abdulla Al Mamun

Research output: Contribution to journalArticle

1 Citation (Scopus)


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. © Maxwell Scientific Organization, 2013.
Original languageEnglish
Pages (from-to)1466-1470
Number of pages1318
JournalResearch Journal of Applied Sciences, Engineering and Technology
Publication statusPublished - 14 Feb 2013
Externally publishedYes

Fingerprint Dive into the research topics of 'Feed forward neural network for solid waste image classification'. Together they form a unique fingerprint.

  • Cite this