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A deep learning approach for prediction of air quality index in smart city
Adel Binbusayyis
, Muhammad Attique Khan
, Mohamed Mustaq Ahmed A
, W. R.Sam Emmanuel
Software Engineering
University of Engineering and Technology, Taxila
University of Madras
Manonmaniam Sundaranar University
Research output
:
Contribution to journal
›
Article
›
peer-review
28
Scopus citations
Overview
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Dive into the research topics of 'A deep learning approach for prediction of air quality index in smart city'. Together they form a unique fingerprint.
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Engineering
Accurate Prediction
12%
Air Pollution
12%
Air Quality
100%
Comparative Analysis
12%
Daily Basis
12%
Deep Learning Method
100%
Fossil Fuel
12%
Input Data
12%
Learning Approach
100%
Mean Absolute Error
25%
Mean Square Error
25%
Metrics
12%
Pollution Control
12%
Processed Data
12%
Quality Data
12%
Quality Index
100%
Real Data
12%
Root Mean Square Error
25%
Smart City
100%
Mathematics
Absolute Error
50%
Accurate Prediction
25%
Coefficient of Determination (R2)
25%
Deep Learning Method
100%
Input Data
25%
Mean Square Error
100%
Missing Value
25%
Real Data
25%
Smart City
100%
Earth and Planetary Sciences
Air Pollution
12%
Fossil Fuel
12%
India
25%
Pollution Control
12%
Preprocessing
12%
Root-Mean-Square Error
25%
Smart City
100%
Chemical Engineering
Deep Learning Method
100%
Material Science
Fossil Fuel
100%