Identification of outliers in pollution concentration levels using anomaly detection

被引：0

作者：

Anandharajan, T. R. V. ^{[1
]}

Vignajeth, K. K. ^{[1
]}

Hariharan, G. Abhishek ^{[1
]}

Jijendiran, R. ^{[1
]}

机构：

[1] Velammal Inst Technol, Madras, Tamil Nadu, India

来源：

2016 INTERNATIONAL CONFERENCE ON COMPUTATIONAL TECHNIQUES IN INFORMATION AND COMMUNICATION TECHNOLOGIES (ICCTICT) | 2016年

关键词：

AirPollution; MachineLearning; Anomaly detection; Air quality Index;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Anomaly detection is generally an identification of any odd or anomalous data sometimes even called as an outlier from a give pattern of data. It involves machine learning technique to learn the data and determine the outliers based on a probability condition. Machine learning, a branch of artificial intelligence plays a vital role in analyzing the data and identifies the outliers with a good probability. The objective of this paper is to determine the outlier of pollutant's concentration based on anomaly detection techniques and describe the air quality standards of the particular area.

引用

页数：6

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