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Статті в журналах з теми "Air quality-Artificial intelligence"
Schultz, Martin. "Artificial intelligence for air quality." Project Repository Journal 12, no. 1 (January 31, 2022): 70–73. http://dx.doi.org/10.54050/prj1218384.
Повний текст джерелаMiguel, B. J., C. M. Guadalupe, B. F. Santiago, A. Diego, and V. Antonio. "Air Quality Index Estimation Applying Artificial Intelligence." Epidemiology 18, Suppl (September 2007): S60. http://dx.doi.org/10.1097/01.ede.0000276612.47104.00.
Повний текст джерелаKulikova, Elena, Vladimir Sulimin, and Vladislav Shvedov. "Artificial intelligence for ambient air quality control." E3S Web of Conferences 419 (2023): 03011. http://dx.doi.org/10.1051/e3sconf/202341903011.
Повний текст джерелаNeo, En Xin, Khairunnisa Hasikin, Khin Wee Lai, Mohd Istajib Mokhtar, Muhammad Mokhzaini Azizan, Hanee Farzana Hizaddin, Sarah Abdul Razak, and Yanto. "Artificial intelligence-assisted air quality monitoring for smart city management." PeerJ Computer Science 9 (May 24, 2023): e1306. http://dx.doi.org/10.7717/peerj-cs.1306.
Повний текст джерелаP, ShreeNandhini, Amudha P, and Sivakumari S. "Comparative Analysis of Air Quality Prediction Using Artificial Intelligence Techniques." ECS Transactions 107, no. 1 (April 24, 2022): 6059–66. http://dx.doi.org/10.1149/10701.6059ecst.
Повний текст джерелаMo, Zhang, Li, and Qu. "A Novel Air Quality Early-Warning System Based on Artificial Intelligence." International Journal of Environmental Research and Public Health 16, no. 19 (September 20, 2019): 3505. http://dx.doi.org/10.3390/ijerph16193505.
Повний текст джерелаSchürholz, Daniel, Sylvain Kubler, and Arkady Zaslavsky. "Artificial intelligence-enabled context-aware air quality prediction for smart cities." Journal of Cleaner Production 271 (October 2020): 121941. http://dx.doi.org/10.1016/j.jclepro.2020.121941.
Повний текст джерелаAli, Ahmad Najim, Ghalia Nassreddine, and Joumana Younis. "Air Quality prediction using Multinomial Logistic Regression." Journal of Computer Science and Technology Studies 4, no. 2 (September 29, 2022): 71–78. http://dx.doi.org/10.32996/jcsts.2022.4.2.9.
Повний текст джерелаLi, Yanzhao, Ju-e. Guo, Shaolong Sun, Jianing Li, Shouyang Wang, and Chengyuan Zhang. "Air quality forecasting with artificial intelligence techniques: A scientometric and content analysis." Environmental Modelling & Software 149 (March 2022): 105329. http://dx.doi.org/10.1016/j.envsoft.2022.105329.
Повний текст джерелаRahardja, Untung, Qurotul Aini, Po Abas Sunarya, Danny Manongga, and Dwi Julianingsih. "The Use of TensorFlow in Analyzing Air Quality Artificial Intelligence Predictions PM2.5." Aptisi Transactions on Technopreneurship (ATT) 4, no. 3 (October 31, 2022): 313–24. http://dx.doi.org/10.34306/att.v4i3.282.
Повний текст джерелаДисертації з теми "Air quality-Artificial intelligence"
Kadiyala, Akhil. "Development and Evaluation of an Integrated Approach to Study In-Bus Exposure Using Data Mining and Artificial Intelligence Methods." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1341257080.
Повний текст джерелаRiffelli, Stefano. "Sustainable comfort in indoor environments: global comfort indices and virtual sensors." Doctoral thesis, Urbino, 2022. http://hdl.handle.net/11576/2700929.
Повний текст джерелаMaimury, Yona, and 鍾如娜. "Monitoring and Forecasting of the Air Quality Using Artificial Intelligence Approaches." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/5ja4rs.
Повний текст джерела元智大學
工業工程與管理學系
107
Today, the world where we live has significantly altered from the decades ago, where natural habitat such as forest has been replaced with highly-populated settlement areas, factories, commercial centers, and busy road with a lot of vehicles. As a result, not many green spaces are left to filter out the dust, smoke and other dangerous substances which lead to the air pollution problem. Air pollution accounts for 1.3 million deaths annually according to the WHO report, pointing out the high urgency that this issue holds. Many researchers had attempted to predict the occurrence of the bad air quality, but most of the researches produced were only satisfied with couple-years dataset. A couple-years dataset only would not be sufficient to explain all the possible seasonality that resemble the real case in the air pollution problem. Several prediction models that utilize an eleven years’ dataset gathered from the Environmental Protection Administration (EPA) Taiwan were proposed to fill the gap from the limited dataset. Machine learning methods including Random forest, AdaBoost, SVM, ANN, and stacking ensemble learning will be trained to learn 11 years’ data. The results show that machine learning is qualified to be applied in the prediction of AQI level especially in Taiwan, considering that the results are quite promising. From 9 experiments through 3 different datasets and target predictions, top 3 algorithms are always among stacking algorithm, AdaBoost, and random forest. Stacking and AdaBoost are competing each other in which superiority of R2 and RMSE score can be always found in stacking model, while the best MAE is usually obtained by AdaBoost. Additionally, data from EPA will be used for the other purpose, in which together with the other data from CWB (Central Weather Bureau), this information will be compared to our own dataset, obtained from an air pollution monitoring device we deployed. To ensure the reliability of data it generates, the calibration process was conducted on the reading of the temperature-humidity sensor (DHT-11) and PM10/dust sensor (GPY2Y1010AU0F) that are installed to the device. Machine learning algorithms are also adopted into the calibration setting. The resulting calibration models indicate that it had successfully corrected both temperature and humidity reading, even though only mediocre results were found for the humidity. As a contrast, PM10 sensor reading appears to be highly irrelevant with the benchmark values. By combining the observation in the field and the data summary for dust reading, the outcome for PM10 calibration signals that either the sensor has a random error or a technical limitation problem, hence the preferable step was to replace the sensor into a more reliable one. The whole scheme including the preparation of AQI forecasting model as well as deployment of air monitoring device are part of the endeavor to develop an Air Pollution Early Warning and Monitoring System. The ultimate goal of such system in the end is to promote a low-cost air pollution EWMS to complement or even substitute the current expensive monitoring system.
Huang, Yen-Chi, and 黃彥齊. "Application of artificial neural network for air quality forecast and anomaly detection of intelligent air quality sensors." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/y36sc4.
Повний текст джерела國立交通大學
環境工程系所
106
Artificial neural network(ANN) is a mathematical model that can be used to solve the problem such as classification and regression. This study is divided into two parts: The forecast of PM2.5 concentration of air quality monitoring station and the anomaly detection of air quality sensors network based on ANN. For the air quality forecast, the ANN models were trained with data from November 2013 to April 2014, then the data from November 2015 to April 2016 and November 2016 to April 2017 were used to test the trained models. The target station of forecast is Xitun. The features suitable for forecast was selected first. Then long short term memory network(LSTM) and back propagation neural network(BPN) were used to forecast the PM2.5 concentration of next 1 to 4 hours in Xitun. The results showed that the overall accuracy of LSTM was better than that of BPN. The R2 values of forecast were from 0.92(1hr) to 0.66(4hrs), which decreased with longer forecast interval. The accurate rate of forecast on whether the concentration exceed the air quality standard(>35.4 g/m3 or >54.4 g/m3) was higher than 84%. In terms of anomaly detection, the sensor data from NCTU(National Chiao Tung University) and CTSP(Central Taiwan Science Park) from May 2017 to May 2018 were taken as the research subject. The data would be detected as anomaly if the gap between prediction and measurement of target sensor was higher than the threshold. And for CTSP sensors, some conditions were added to separate the anomaly to different pollution sources. The results showed that the method proposed in this study can effectively detect the failure of sensors and identify the pollution source within 90 minutes of pollution occurrence. Most of the pollution in CTSP in April 2018 came from the sea area.
Gupta, Dinesh Kumar. "Modeling the relationship between air quality and intelligent transportation systems (ITS) with artificial neural networks /." 2008. http://digital.library.louisville.edu/cgi-bin/showfile.exe?CISOROOT=/etd&CISOPTR=840&filename=841.pdf.
Повний текст джерелаКниги з теми "Air quality-Artificial intelligence"
Bhushan, Megha, Sailesh Iyer, Ashok Kumar, Tanupriya Choudhury, and Arun Negi, eds. Artificial Intelligence for Smart Cities and Villages: Advanced Technologies, Development, and Challenges. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150492511220101.
Повний текст джерелаЧастини книг з теми "Air quality-Artificial intelligence"
Sowmya, Vattam, and Shravya Ragiphani. "Air Quality Monitoring System Based on Artificial Intelligence." In Lecture Notes in Electrical Engineering, 267–73. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5550-1_26.
Повний текст джерелаPatel, Divya, Mridu Kulwant, Saba Shirin, Ankit Kumar, Mohammad Aurangzeb Ansari, and Akhilesh Kumar Yadav. "Artificial Intelligence for Air Quality and Control Systems." In Modeling and Simulation of Environmental Systems, 133–52. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003203445-9.
Повний текст джерелаSaini, Jagriti, Maitreyee Dutta, and Gonçalo Marques. "Predicting Indoor Air Quality: Integrating IoT with Artificial Intelligence." In Internet of Things for Indoor Air Quality Monitoring, 51–67. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82216-3_4.
Повний текст джерелаNur, Salman Ahmed, Refik Alemdar, Ufuk Süğürtin, Adem Taşın, and Muhammed Kürşad Uçar. "An Artificial Intelligence-Based Air Quality Health Index Determination: A Case Study in Sakarya." In 4th International Conference on Artificial Intelligence and Applied Mathematics in Engineering, 630–39. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-31956-3_53.
Повний текст джерелаLuculescu, Marius Cristian, Luciana Cristea, Constantin Sorin Zamfira, Attila Laszlo Boer, and Sebastian Pop. "Distributed IoT System for Indoor Air Quality Monitoring." In Artificial Intelligence and Online Engineering, 288–99. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17091-1_30.
Повний текст джерелаAggarwal, Apeksha, and Ajay Agarwal. "A Hybrid Ensemble Prediction Method for Analyzing Air Quality Data." In Artificial Intelligence and Technologies, 663–71. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6448-9_63.
Повний текст джерелаJiang, Zhifang, Shanxiang Zhang, Ruobo Xin, Shenghui Cheng, and Ning Li. "Research of the Urban Air Quality Forecast Method Based on Resource Allocation Network." In Artificial Intelligence and Computational Intelligence, 650–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33478-8_80.
Повний текст джерелаSahoo, Limali, Bani Bhusan Praharaj, and Manoj Kumar Sahoo. "Air Quality Prediction Using Artificial Neural Network." In Advances in Intelligent Systems and Computing, 31–37. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7394-1_3.
Повний текст джерелаScarpiniti, Michele, Danilo Comminiello, Federico Muciaccia, and Aurelio Uncini. "Quaternion Widely Linear Forecasting of Air Quality." In Progresses in Artificial Intelligence and Neural Systems, 393–403. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5093-5_35.
Повний текст джерелаCarbajal Hernández, José Juan, Luis Pastor Sánchez Fernández, and Pablo Manrique Ramírez. "Environmental Pattern Recognition for Assessment of Air Quality Data with the Gamma Classifier." In Advances in Artificial Intelligence, 436–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16761-4_38.
Повний текст джерелаТези доповідей конференцій з теми "Air quality-Artificial intelligence"
Bazzi, Tony, and Mohamed Zohdy. "Artificial Intelligence For Air Quality Control Systems: A Holistic Approach." In 2018 Twentieth International Middle East Power Systems Conference (MEPCON). IEEE, 2018. http://dx.doi.org/10.1109/mepcon.2018.8635295.
Повний текст джерелаS.R, Ashokkumar, Harihar R, Subhashini R, and Naveen Prasaath S. "A Literature Survey on Artificial Intelligence-Based Smart City Automation Using LoRa and IOT for Street Lights and Air Quality Check." In 2022 International Conference on Computer, Power and Communications (ICCPC). IEEE, 2022. http://dx.doi.org/10.1109/iccpc55978.2022.10072071.
Повний текст джерелаBlom, David, Ilya Berchenko, Farid Samie, and Darian Frajberg. "Shell Autonomous Integrity Recognition - Machine Vision Application for Inspections." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211838-ms.
Повний текст джерелаHunter, Aaron, and Rodrigo Mora. "Knowledge-based Analysis of Residential Air Quality." In 12th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009102908010805.
Повний текст джерелаSilva, Carolina, Bruno Fernandes, Pedro Oliveira, and Paulo Novais. "Using Machine Learning to Forecast Air and Water Quality." In 13th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010379312101217.
Повний текст джерелаWarter, Sven, Christian Laubichler, Constantin Kiesling, Martin Kober, Andreas Wimmer, Marco Coppo, Danilo Laurenzano, and Claudio Negri. "Data-Driven Prediction of Key Combustion Parameters Based on an Intelligent Diesel Fuel Injector for Large Engine Applications." In WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2023. http://dx.doi.org/10.4271/2023-01-0291.
Повний текст джерелаLin, Hai, Jianbing Jin, and Jaap van den Herik. "Air Quality Forecast through Integrated Data Assimilation and Machine Learning." In 11th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007555207870793.
Повний текст джерелаBaran, Burhan. "Prediction of Air Quality Index by Extreme Learning Machines." In 2019 International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2019. http://dx.doi.org/10.1109/idap.2019.8875910.
Повний текст джерелаLyu, Linjie, Jingyi Kong, and Yingyi Peng. "Urban Ambient Air Quality Data Mining and Visualisation." In 2022 International Conference on Artificial Intelligence of Things and Crowdsensing (AIoTCs). IEEE, 2022. http://dx.doi.org/10.1109/aiotcs58181.2022.00101.
Повний текст джерелаConea, Sorin Ionut, and Gloria Cerasela Crisan. "Green air quality monitoring system based on Arduino." In 2022 14th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE, 2022. http://dx.doi.org/10.1109/ecai54874.2022.9847421.
Повний текст джерелаЗвіти організацій з теми "Air quality-Artificial intelligence"
Musser, Micah, Rebecca Gelles, Catherine Aiken, and Andrew Lohn. “The Main Resource is the Human”. Center for Security and Emerging Technology, April 2023. http://dx.doi.org/10.51593/20210071.
Повний текст джерелаArnold, Zachary, Joanne Boisson, Lorenzo Bongiovanni, Daniel Chou, Carrie Peelman, and Ilya Rahkovsky. Using Machine Learning to Fill Gaps in Chinese AI Market Data. Center for Security and Emerging Technology, February 2021. http://dx.doi.org/10.51593/20200064.
Повний текст джерелаPieterson, Willem, Dulce Baptista, David Rosas-Shady, and Andrés Franco. The digital transformation of public employment services across Latin America and the Caribbean. Inter-American Development Bank, August 2023. http://dx.doi.org/10.18235/0005084.
Повний текст джерелаDaudelin, Francois, Lina Taing, Lucy Chen, Claudia Abreu Lopes, Adeniyi Francis Fagbamigbe, and Hamid Mehmood. Mapping WASH-related disease risk: A review of risk concepts and methods. United Nations University Institute for Water, Environment and Health, December 2021. http://dx.doi.org/10.53328/uxuo4751.
Повний текст джерела