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Taxonomy of Polymer Samples Using Machine Learning Algorithms

  • Kothapalli SwathiEmail author
  • Sambu Ravali
  • Thadisetty Shravani Sagar
  • Katta Sugamya
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 8)

Abstract

The rapid growth in technology has led to the decrease in manual work and is creating most of the objects in various industries of machine-driven. One such automation need is found in the chemical industry where machine driven package needed for the classification of various kinds of plastics supported their absorbance values. One of the efficient algorithms used for cataloguing is through support a vector machine which provides a classification model that is trained and tested. A solution to automate the sorting of various kinds of plastic by using the Fisher iris data set (which is a result of Near Infrared Spectroscopy (NIRS)). Plastics are everyday used non-biodegradable materials once not disposed properly have adverse effects on the atmosphere. For recycling of plastics totally different sorts of plastics (polymers) need to be known and separate. For economic reasons plastics must known and sorted instantly. The Fisher Iris data set that can be employed by us is a result of NIRS. The NIRS techniques have been used for the instantaneous identification of plastics. Measurements made by NIRS are quite accurate and fast. The necessary algorithm needed to process the NIRS data and to obtain information on the polymer category is written on the general purpose, high-level programming language Python as well as on MATLAB. In order to extend the efficiency of this process we also implement KS algorithm.

Keywords

Machine learning SVM KS algorithm 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Kothapalli Swathi
    • 1
    Email author
  • Sambu Ravali
    • 1
  • Thadisetty Shravani Sagar
    • 1
  • Katta Sugamya
    • 1
  1. 1.Department of ITChaitanya Bharathi Institute of TechnologyHyderabadIndia

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