eprintid: 10159900
rev_number: 7
eprint_status: archive
userid: 699
dir: disk0/10/15/99/00
datestamp: 2022-11-21 11:57:51
lastmod: 2022-11-21 11:57:51
status_changed: 2022-11-21 11:57:51
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Chang, Victor
creators_name: Ni, Pin
creators_name: Li, Yuming
title: K-Clustering Methods for Investigating Social-Environmental and Natural-Environmental Features Based on Air Quality Index
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F44
keywords: Science & Technology, Technology, Computer Science, Information Systems, Computer Science, Software Engineering, Telecommunications, Computer Science, PM2.5 CONCENTRATIONS, POLLUTION, NUMBER
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
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.
date: 2020-07-01
date_type: published
publisher: IEEE COMPUTER SOC
official_url: https://doi.org/10.1109/MITP.2020.2993851
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1946861
doi: 10.1109/MITP.2020.2993851
lyricists_name: Ni, Pin
lyricists_id: PNIXX47
actors_name: Ni, Pin
actors_id: PNIXX47
actors_role: owner
funding_acknowledgements: VCR 000011 []
full_text_status: public
publication: IT Professional
volume: 22
number: 4
pagerange: 28-34
pages: 7
citation:        Chang, Victor;    Ni, Pin;    Li, Yuming;      (2020)    K-Clustering Methods for Investigating Social-Environmental and Natural-Environmental Features Based on Air Quality Index.                   IT Professional , 22  (4)   pp. 28-34.    10.1109/MITP.2020.2993851 <https://doi.org/10.1109/MITP.2020.2993851>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10159900/1/ITPro_Air_quality_submission_final.pdf