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Fault Detection in SPS Using Image Encoding and Deep Learning

Publication Type : Conference Paper

Publisher : Springer Nature Singapore

Source : Lecture Notes on Data Engineering and Communications Technologies

Url : https://doi.org/10.1007/978-981-16-3728-5_41

Campus : Coimbatore

School : School of Computing

Year : 2021

Abstract : India is the second most populated country in the world and it is still facing hindrances to its development on waste management. It is believed that 10 million tons of waste is produced just by the metropolitan cities in India. In this work, a way to classify the waste and find the category of it is proposed with a well-defined and labelled data set of images consisting of categories (plastic, paper, cardboard, metals) using Convolutional Neural Network (CNN). Images are categorized based on their properties by the help of a self-learning neural network. The designed classifier learns from the image data provided for training purpose. The classifier uses the method of supervised learning where the algorithm learns from a labelled data set. With this method a testing accuracy of 76% is achieved.

Cite this Research Publication : P. Hari Prasad, N. S. Jai Aakash, T. Avinash, S. Aravind, M. Ganesan, R. Lavanya, Fault Detection in SPS Using Image Encoding and Deep Learning, Lecture Notes on Data Engineering and Communications Technologies, Springer Nature Singapore, 2021, https://doi.org/10.1007/978-981-16-3728-5_41

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