K-Clustering Methods for Investigating Social-Environmental and Natural-Environmental Features Based on Air Quality Index

Victor Chang; Pin Ni; Yuming Li

doi:10.1109/MITP.2020.2993851

K-Clustering Methods for Investigating Social-Environmental and Natural-Environmental Features Based on Air Quality Index

Victor Chang^*, Pin Ni, Yuming Li

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

Air pollution has caused environmental and health hazards across the globe, particularly in emerging countries such as China. In this article, we propose the use of air quality index and the development of advanced data processing, analysis, and visualization techniques based on the AI-based k-clustering method. We analyze the air quality data based on seven key attributes and discuss its implications. Our results provide meaningful values and contributions to the current research. Our future work will include the use of advanced AI algorithms and big data techniques to ensure better performance, accuracy and real-time checks.

Original language	English
Pages (from-to)	28-34
Number of pages	7
Journal	IT Professional
Volume	22
Issue number	4
DOIs	https://doi.org/10.1109/MITP.2020.2993851
Publication status	Published - 17 Jul 2020

Bibliographical note

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Access to Document

10.1109/MITP.2020.2993851

ITPro_Air_quality_submission_final
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript, 359 KB

Cite this

@article{77089164deaf4d02b90dddfdd7a55141,

title = "K-Clustering Methods for Investigating Social-Environmental and Natural-Environmental Features Based on Air Quality Index",

abstract = "Air pollution has caused environmental and health hazards across the globe, particularly in emerging countries such as China. In this article, we propose the use of air quality index and the development of advanced data processing, analysis, and visualization techniques based on the AI-based k-clustering method. We analyze the air quality data based on seven key attributes and discuss its implications. Our results provide meaningful values and contributions to the current research. Our future work will include the use of advanced AI algorithms and big data techniques to ensure better performance, accuracy and real-time checks. ",

author = "Victor Chang and Pin Ni and Yuming Li",

note = "{\textcopyright} 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.",

year = "2020",

month = jul,

day = "17",

doi = "10.1109/MITP.2020.2993851",

language = "English",

volume = "22",

pages = "28--34",

journal = "IT Professional",

issn = "1520-9202",

publisher = "IEEE",

number = "4",

}

TY - JOUR

T1 - K-Clustering Methods for Investigating Social-Environmental and Natural-Environmental Features Based on Air Quality Index

AU - Chang, Victor

AU - Ni, Pin

AU - Li, Yuming

N1 - © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2020/7/17

Y1 - 2020/7/17

N2 - Air pollution has caused environmental and health hazards across the globe, particularly in emerging countries such as China. In this article, we propose the use of air quality index and the development of advanced data processing, analysis, and visualization techniques based on the AI-based k-clustering method. We analyze the air quality data based on seven key attributes and discuss its implications. Our results provide meaningful values and contributions to the current research. Our future work will include the use of advanced AI algorithms and big data techniques to ensure better performance, accuracy and real-time checks.

AB - Air pollution has caused environmental and health hazards across the globe, particularly in emerging countries such as China. In this article, we propose the use of air quality index and the development of advanced data processing, analysis, and visualization techniques based on the AI-based k-clustering method. We analyze the air quality data based on seven key attributes and discuss its implications. Our results provide meaningful values and contributions to the current research. Our future work will include the use of advanced AI algorithms and big data techniques to ensure better performance, accuracy and real-time checks.

UR - http://www.scopus.com/inward/record.url?scp=85088709842&partnerID=8YFLogxK

UR - https://ieeexplore.ieee.org/document/9143256

U2 - 10.1109/MITP.2020.2993851

DO - 10.1109/MITP.2020.2993851

M3 - Article

AN - SCOPUS:85088709842

SN - 1520-9202

VL - 22

SP - 28

EP - 34

JO - IT Professional

JF - IT Professional

IS - 4

ER -

K-Clustering Methods for Investigating Social-Environmental and Natural-Environmental Features Based on Air Quality Index

Abstract

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this