基于支持向量机的垃圾分类系统

V. Akshita, R. Pushkala, R. Nivetha, S. S. Subashka
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引用次数: 0

摘要

垃圾桶是满的,半满的或空的。这些垃圾桶里有各种各样的垃圾,从金属、塑料到玻璃。这些倾倒垃圾的收集没有进行分类,因此当提出消除方法时,该方法效率不高。生物垃圾也被倾倒到垃圾填埋场。为了提高系统的效率,本文提出了系统早期隔离的方法。该系统使用一种称为支持向量机(SVM)的机器学习算法,以向量形式进行图像比较。此外,电容式接近传感器用于下一级隔离,其识别具有介电效应的湿或干废物类型。因此,该系统利用机器学习和电容效应协同分离废物。分离后的生物废物被转化为生物燃料,以达到经济目的。塑料废物可以给废品工业。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Garbage Segregation System using Support Vector Machine
The garbage bins are full, half full or empty. These bins include various types of garbage ranging from metals, plastics to glasses. The collection of these dump waste are not segregated and so when the eliminating method is proposed the method is not efficient. The bio waste also gets dumped into landfills. To make the system efficient this paper proposes method to segregate system at its early stage. The system uses a Machine Learning Algorithm called Support Vector Machine (SVM) which performs image comparison in the vector form. Also, capacitive proximity sensors is used for next level of segregation which identifies type of waste either wet or dry with dielectric effect. Thus, this system collaboratively separates the waste using Machine learning and capacitance effect. The separated bio waste is converted to bio fuel for economic purposes. The plastic wastes can be given to scrap industries.
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