TY - GEN
T1 - An Application of linear Regression on Air Pollution Detection Using Deep Learning
AU - Mustafa, M. Mohammed
AU - Umamaheswari, S.
AU - Khalifa, Ahmed A.
AU - Mokhtar, Bassem
AU - Cengiz, Korhan
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - A regularly expanding number of urban occupants think about the criticalness of the air quality to their prosperity, especially who are living in the colossal urban territories are genuinely sabotaged through air pollution. Air quality observing assumes an undeniably significant job in giving exact air contamination information to evaluating the effects of air contamination on general wellbeing. Advancement of appropriate sensor systems, by sending the correct air contamination sensors at the ideal put in, so as to address the issues of various gatherings in the city and give the genuinely necessary open administrations, merits cautious consideration, particularly when savvy city improvement is to be measured. Notwithstanding, air quality observation is an exorbitant degree. To handle such a test, air contamination sensor position can be deliberately intended to accomplish definite ideal resident driven goals without field data, and application of the linear regression to it.
AB - A regularly expanding number of urban occupants think about the criticalness of the air quality to their prosperity, especially who are living in the colossal urban territories are genuinely sabotaged through air pollution. Air quality observing assumes an undeniably significant job in giving exact air contamination information to evaluating the effects of air contamination on general wellbeing. Advancement of appropriate sensor systems, by sending the correct air contamination sensors at the ideal put in, so as to address the issues of various gatherings in the city and give the genuinely necessary open administrations, merits cautious consideration, particularly when savvy city improvement is to be measured. Notwithstanding, air quality observation is an exorbitant degree. To handle such a test, air contamination sensor position can be deliberately intended to accomplish definite ideal resident driven goals without field data, and application of the linear regression to it.
KW - Air Quality
KW - Deep Learning
KW - IOT
KW - Python
KW - Regression
UR - http://www.scopus.com/inward/record.url?scp=85133979463&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85133979463&partnerID=8YFLogxK
U2 - 10.1109/HORA55278.2022.9800042
DO - 10.1109/HORA55278.2022.9800042
M3 - Conference contribution
AN - SCOPUS:85133979463
T3 - HORA 2022 - 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings
BT - HORA 2022 - 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2022
Y2 - 9 June 2022 through 11 June 2022
ER -
