{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:15:02Z","timestamp":1750220102589,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":16,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T00:00:00Z","timestamp":1656028800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,6,24]]},"DOI":"10.1145\/3548608.3559203","type":"proceedings-article","created":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T17:47:01Z","timestamp":1665769621000},"page":"257-264","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Principal Component Analysis-Improved Fuzzy Genetic Algorithm"],"prefix":"10.1145","author":[{"given":"Tao","family":"Lu","sequence":"first","affiliation":[{"name":"Information School of Nanning University, Nanning University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minjun","family":"Cen","sequence":"additional","affiliation":[{"name":"Information School of Nanning University, Nanning University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sicong","family":"Huo","sequence":"additional","affiliation":[{"name":"Information School of Nanning University, Nanning University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ling","family":"Luo","sequence":"additional","affiliation":[{"name":"Cultures Languages Guangxi University of Foreign Languages, Guangxi University of Foreign Languages, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,10,14]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"12","article-title":"Introducing the Green Infrastructure for Roadside Air Quality (GI4RAQ) Platform: Estimating Site-Specific Changes in the Dispersion of Vehicular Pollution Close to Source[J]","author":"Mackenzie A R","year":"2021","unstructured":"Mackenzie A R . Introducing the Green Infrastructure for Roadside Air Quality (GI4RAQ) Platform: Estimating Site-Specific Changes in the Dispersion of Vehicular Pollution Close to Source[J] . Forests , 2021 , 12 . Mackenzie A R . Introducing the Green Infrastructure for Roadside Air Quality (GI4RAQ) Platform: Estimating Site-Specific Changes in the Dispersion of Vehicular Pollution Close to Source[J]. Forests, 2021, 12.","journal-title":"Forests"},{"key":"e_1_3_2_1_2_1","volume-title":"SO2 and NO2 during Diwali at Chennai, India[J]. Natural Hazards","author":"Krishnan M A","year":"2020","unstructured":"Krishnan M A , Devaraj T , Velayutham K , Statistical evaluation of PM2.5 and dissemination of PM2.5 , SO2 and NO2 during Diwali at Chennai, India[J]. Natural Hazards , 2020 (9). Krishnan M A , Devaraj T , Velayutham K , Statistical evaluation of PM2.5 and dissemination of PM2.5, SO2 and NO2 during Diwali at Chennai, India[J]. Natural Hazards, 2020(9)."},{"key":"e_1_3_2_1_3_1","volume-title":"Using Machine Learning to Predict Air Quality Index in New Delhi[J]","author":"Bhattacharya S","year":"2021","unstructured":"Bhattacharya S , Shahnawaz S . Using Machine Learning to Predict Air Quality Index in New Delhi[J] . 2021 . Bhattacharya S , Shahnawaz S . Using Machine Learning to Predict Air Quality Index in New Delhi[J]. 2021."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_4_1","DOI":"10.3390\/ijerph18031333"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_5_1","DOI":"10.1007\/s42979-020-00384-9"},{"key":"e_1_3_2_1_6_1","volume-title":"An Air Pollution Prediction Scheme Using Long Short Term Memory Neural Network Model[J]. E3S Web of Conferences","author":"Kim","year":"2021","unstructured":"J Kim . An Air Pollution Prediction Scheme Using Long Short Term Memory Neural Network Model[J]. E3S Web of Conferences , 2021 . J Kim. An Air Pollution Prediction Scheme Using Long Short Term Memory Neural Network Model[J]. E3S Web of Conferences, 2021."},{"key":"e_1_3_2_1_7_1","first-page":"13","author":"Kong T , D","year":"2021","unstructured":"Kong T , D Choi , Lee G , Air Pollution Prediction Using an Ensemble of Dynamic Transfer Models for Multivariate Time Series[J] . Sustainability , 2021 , 13 . Kong T , D Choi, Lee G , Air Pollution Prediction Using an Ensemble of Dynamic Transfer Models for Multivariate Time Series[J]. Sustainability, 2021, 13.","journal-title":"Sustainability"},{"issue":"15","key":"e_1_3_2_1_8_1","first-page":"1","article-title":"The Attention and Autoencoder Hybrid Learning Model[J]","volume":"2021","author":"Tu X Y","year":"2021","unstructured":"Tu X Y , Zhang B , Jin Y P , Longer Time Span Air Pollution Prediction : The Attention and Autoencoder Hybrid Learning Model[J] . Mathematical Problems in Engineering , 2021 , 2021 ( 15 ): 1 - 16 . Tu X Y , Zhang B , Jin Y P , Longer Time Span Air Pollution Prediction: The Attention and Autoencoder Hybrid Learning Model[J]. Mathematical Problems in Engineering, 2021, 2021(15):1-16.","journal-title":"Mathematical Problems in Engineering"},{"key":"e_1_3_2_1_9_1","first-page":"12","article-title":"Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models[J]","author":"Xayasouk T","year":"2020","unstructured":"Xayasouk T , Lee H M , Lee G . Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models[J] . Sustainability , 2020 , 12 . Xayasouk T , Lee H M , Lee G . Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models[J]. Sustainability, 2020, 12.","journal-title":"Sustainability"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_10_1","DOI":"10.1088\/1757-899X\/872\/1\/012030"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_11_1","DOI":"10.1088\/1757-899X\/725\/1\/012124"},{"key":"e_1_3_2_1_12_1","volume-title":"2021 6th International Conference on Inventive Computation Technologies (ICICT).","author":"Sunori S K","year":"2021","unstructured":"Sunori S K , Negi P B , Maurya S , K- Means Clustering of Ambient Air Quality Data of Uttarakhand, India during Lockdown Period of Covid-19 Pandemic[C]\/\/ 2021 6th International Conference on Inventive Computation Technologies (ICICT). 2021 . Sunori S K , Negi P B , Maurya S , K-Means Clustering of Ambient Air Quality Data of Uttarakhand, India during Lockdown Period of Covid-19 Pandemic[C]\/\/ 2021 6th International Conference on Inventive Computation Technologies (ICICT). 2021."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_13_1","DOI":"10.1051\/matecconf\/202133607002"},{"key":"e_1_3_2_1_14_1","volume-title":"Air Quality Atmosphere & Health","author":"Hentabli M .","year":"2020","unstructured":"Hentabli M . Prediction of the concentrations of PM1, PM2.5, PM4, and PM10 by using the hybrid dragonfly-SVM algorithm[J] . Air Quality Atmosphere & Health , 2020 . Hentabli M . Prediction of the concentrations of PM1, PM2.5, PM4, and PM10 by using the hybrid dragonfly-SVM algorithm[J]. Air Quality Atmosphere & Health, 2020."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_15_1","DOI":"10.1109\/ACCESS.2021.3093430"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_16_1","DOI":"10.1177\/00131640121971491"}],"event":{"acronym":"ICCIR 2022","name":"ICCIR 2022: 2022 2nd International Conference on Control and Intelligent Robot","location":"Nanjing China"},"container-title":["Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3548608.3559203","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3548608.3559203","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:10:21Z","timestamp":1750183821000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3548608.3559203"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,24]]},"references-count":16,"alternative-id":["10.1145\/3548608.3559203","10.1145\/3548608"],"URL":"https:\/\/doi.org\/10.1145\/3548608.3559203","relation":{},"subject":[],"published":{"date-parts":[[2022,6,24]]},"assertion":[{"value":"2022-10-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}