TY - GEN
T1 - Environmental pattern recognition for assessment of air quality data with the gamma classifier
AU - Carbajal Hernández, José Juan
AU - Sánchez Fernández, Luis Pastor
AU - Manrique Ramírez, Pablo
N1 - Funding Information:
The authors of the present paper would like to thank the following institutions for their support to develop this work: National Polytechnic Institute, Mexico, SIMAT and CONACyT.
PY - 2010
Y1 - 2010
N2 - Nowadays efficient methods for air quality assessment are needed in order to detect negative problems in human health. A new computational model is developed in order to evaluate toxic compounds in air of urban areas that can be harmful in sensitive people, affecting their normal activities. Using the Gamma classifier (Γ), environmental variables are assessed determining their negative impact in air quality based on their toxicity limits, the average of the frequency and the deviations of toxic tests. A fuzzy inference system uses the environmental classifications providing an air quality index, which describes the pollution levels in five stages: excellent, good, regular, bad and danger respectively.
AB - Nowadays efficient methods for air quality assessment are needed in order to detect negative problems in human health. A new computational model is developed in order to evaluate toxic compounds in air of urban areas that can be harmful in sensitive people, affecting their normal activities. Using the Gamma classifier (Γ), environmental variables are assessed determining their negative impact in air quality based on their toxicity limits, the average of the frequency and the deviations of toxic tests. A fuzzy inference system uses the environmental classifications providing an air quality index, which describes the pollution levels in five stages: excellent, good, regular, bad and danger respectively.
KW - Artificial intelligence
KW - air quality
KW - fuzzy inference systems
KW - pollution
UR - http://www.scopus.com/inward/record.url?scp=78650034771&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-16761-4_38
DO - 10.1007/978-3-642-16761-4_38
M3 - Contribución a la conferencia
SN - 3642167608
SN - 9783642167607
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 436
EP - 445
BT - Advances in Artificial Intelligence - 9th Mexican International Conference on Artificial Intelligence, MICAI 2010, Proceedings
T2 - 9th Mexican International Conference on Artificial Intelligence, MICAI 2010
Y2 - 8 November 2010 through 13 November 2010
ER -