Document Detail

Development of hybrid genetic-algorithm-based neural networks using regression trees for modeling air quality inside a public transportation bus.
MedLine Citation:
PMID:  23472304     Owner:  NLM     Status:  MEDLINE    
IMPLICATIONS: The novelty of this research is the development of a novel approach to modeling vehicular indoor air quality by integration of the advanced methods of genetic algorithms, regression trees, and the analysis of variance for the monitored in-vehicle gaseous and particulate matter contaminants, and comparing the results obtained from using the developed approach with conventional artificial intelligence techniques of back propagation networks and radial basis function networks. This study validated the newly developed approach using holdout and threefold cross-validation methods. These results are of great interest to scientists, researchers, and the public in understanding the various aspects of modeling an indoor microenvironment. This methodology can easily be extended to other fields of study also.
Akhil Kadiyala; Devinder Kaur; Ashok Kumar
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.; Validation Studies    
Journal Detail:
Title:  Journal of the Air & Waste Management Association (1995)     Volume:  63     ISSN:  1096-2247     ISO Abbreviation:  J Air Waste Manag Assoc     Publication Date:  2013 Feb 
Date Detail:
Created Date:  2013-03-11     Completed Date:  2013-04-04     Revised Date:  2014-07-31    
Medline Journal Info:
Nlm Unique ID:  9503111     Medline TA:  J Air Waste Manag Assoc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  205-18     Citation Subset:  IM    
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MeSH Terms
Air Pollution, Indoor / analysis*
Environmental Monitoring*
Models, Theoretical*
Motor Vehicles
Neural Networks (Computer)
Regression Analysis

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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