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A Survey on Existing Convolutional Neural Networks and Waste Management Techniques and an Approach to Solve Waste Classification Problem Using Neural Networks

  • Tejashwini Hiremath
  • S. RajarajeswariEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 906)

Abstract

In India, waste management has become one of the major crises with population explosion, coupled with improved lifestyle of people, results in increased generation of solid wastes in urban as well as rural areas of the country. It is well known that waste management policies, as they exist now, are not sustainable in the long term. Thus, waste management is undergoing drastic change to offer more options that are more sustainable. Most of the landfills are becoming full of waste in which most part is reusable and leading to spreading of disease damaging human body and leading to unpleasant air and only 5% of whole waste is actual waste. The government of Karnataka mandated system of 2 BIN 1 BAG to be adapted at every households in Bangalore, and 2 BIN 1 BAG is a color-coded system consisting of green bin which holds garden waste, and the wastes that are compostable, reject waste can be thrown in red bin, and finally a big category called as reusable bag which holds recyclable waste. Segregation of waste at source is best solution and should be done properly. Types of waste need to be remembered by members of home in order to put them to proper bins, and this may lead to human error. So our solution can answer this in good way, what if you just click picture of waste material and application says to which category it belongs. A convolutional neural network is trained with images of waste materials, and model can be inferred by giving waste-material image as input and get the perfect category of waste material in a second. This helps society in dealing with prime problem of segregating waste materials at source.

Keywords

Solid waste management (SWM) 2 BIN 1 BAG system Convolution neural networks (CNN) Stochastic gradient descent (SGD) Neural networks (NN) 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringRamaiah Institute of TechnologyBangaloreIndia

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