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


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.

Publication DOI: https://doi.org/10.1109/MITP.2020.2993851
Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: © 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.
Uncontrolled Keywords: Software,Hardware and Architecture,Computer Science Applications
Publication ISSN: 1520-9202
Last Modified: 19 Jun 2024 07:21
Date Deposited: 10 Jun 2022 08:53
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://ieeexpl ... ocument/9143256 (Publisher URL)
PURE Output Type: Article
Published Date: 2020-07-17
Authors: Chang, Victor (ORCID Profile 0000-0002-8012-5852)
Ni, Pin
Li, Yuming



Version: Accepted Version

| Preview

Export / Share Citation


Additional statistics for this record