Relevant bibliographies by topics / Air quality-Artificial intelligence

Academic literature on the topic 'Air quality-Artificial intelligence'

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Published: 6 September 2023

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Journal articles on the topic "Air quality-Artificial intelligence"

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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.

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Artificial intelligence for air quality IntelliAQ is an ERC Advanced Grant project to explore the application of cutting-edge machine learning techniques to global air quality data in combination with high resolution geospatial and weather data. It combines novel data management and data science approaches to build the foundation for innovative air quality information services.
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Miguel, 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.

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Kulikova, 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.

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Air quality, integral to public health and environmental stability, necessitates innovative solutions for effective monitoring and control. Existing methodologies are often limited in their predictive accuracy, scalability, and cost-effectiveness. This paper explores the potential of Artificial Intelligence (AI) in transforming ambient air quality control. We conduct an in-depth review of current AI applications, examining various models’ strengths and weaknesses in predicting and controlling air quality. These include machine learning, deep learning, and other AI methodologies. Real-world case studies are analyzed to assess the practicality and effectiveness of AI applications. While AI presents promising capabilities, its implementation is not without challenges such as data requirements, interpretability, and scalability. We discuss these issues, propose possible solutions, and explore future prospects for AI in air quality control. The aim is to provide a comprehensive understanding of the role AI can play in environmental management and a pathway towards its enhanced application. This paper invites further research in harnessing AI’s potential to create sustainable and effective air quality control systems.
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Neo, 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.

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Background The environment has been significantly impacted by rapid urbanization, leading to a need for changes in climate change and pollution indicators. The 4IR offers a potential solution to efficiently manage these impacts. Smart city ecosystems can provide well-designed, sustainable, and safe cities that enable holistic climate change and global warming solutions through various community-centred initiatives. These include smart planning techniques, smart environment monitoring, and smart governance. An air quality intelligence platform, which operates as a complete measurement site for monitoring and governing air quality, has shown promising results in providing actionable insights. This article aims to highlight the potential of machine learning models in predicting air quality, providing data-driven strategic and sustainable solutions for smart cities. Methods This study proposed an end-to-end air quality predictive model for smart city applications, utilizing four machine learning techniques and two deep learning techniques. These include Ada Boost, SVR, RF, KNN, MLP regressor and LSTM. The study was conducted in four different urban cities in Selangor, Malaysia, including Petaling Jaya, Banting, Klang, and Shah Alam. The model considered the air quality data of various pollution markers such as PM2.5, PM10, O3, and CO. Additionally, meteorological data including wind speed and wind direction were also considered, and their interactions with the pollutant markers were quantified. The study aimed to determine the correlation variance of the dependent variable in predicting air pollution and proposed a feature optimization process to reduce dimensionality and remove irrelevant features to enhance the prediction of PM2.5, improving the existing LSTM model. The study estimates the concentration of pollutants in the air based on training and highlights the contribution of feature optimization in air quality predictions through feature dimension reductions. Results In this section, the results of predicting the concentration of pollutants (PM2.5, PM10, O3, and CO) in the air are presented in R2 and RMSE. In predicting the PM10 and PM2.5concentration, LSTM performed the best overall high R2values in the four study areas with the R2 values of 0.998, 0.995, 0.918, and 0.993 in Banting, Petaling, Klang and Shah Alam stations, respectively. The study indicated that among the studied pollution markers, PM2.5,PM10, NO2, wind speed and humidity are the most important elements to monitor. By reducing the number of features used in the model the proposed feature optimization process can make the model more interpretable and provide insights into the most critical factor affecting air quality. Findings from this study can aid policymakers in understanding the underlying causes of air pollution and develop more effective smart strategies for reducing pollution levels.
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P, 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.

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Air population is the primary concern in most urban areas because of its notable impact on the economy and health across the universe. The emergence of industry and automobiles made air pollution worldwide, which causes a highly critical issue and a more significant impact on humans' health than the contaminants. It causes health-related problems like lung-related diseases, namely respiratory problems and cardiovascular disease, and increases cancer. Accurate monitoring of air quality is of great importance to daily human life. The consuming time delay long-term delay is essential for long-term predictions. In this article, proposed Enriched Spatial-Temporal Sequence (EISAE-DL) improves the prediction accuracy by considering the long time delay based on locations and compared for experimental algorithm Improved Sparse Auto Encoder with Deep Learning (ISAE-DL) The experimental results show the effectiveness of the proposed EISAE-DL in terms of accuracy, precision, sensitivity, specificity, Area Under Curve (AUC), and Matthew's Correlation Coefficient (MCC).
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Mo, 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.

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The problem of air pollution is a persistent issue for mankind and becoming increasingly serious in recent years, which has drawn worldwide attention. Establishing a scientific and effective air quality early-warning system is really significant and important. Regretfully, previous research didn’t thoroughly explore not only air pollutant prediction but also air quality evaluation, and relevant research work is still scarce, especially in China. Therefore, a novel air quality early-warning system composed of prediction and evaluation was developed in this study. Firstly, the advanced data preprocessing technology Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with the powerful swarm intelligence algorithm Whale Optimization Algorithm (WOA) and the efficient artificial neural network Extreme Learning Machine (ELM) formed the prediction model. Then the predictive results were further analyzed by the method of fuzzy comprehensive evaluation, which offered intuitive air quality information and corresponding measures. The proposed system was tested in the Jing-Jin-Ji region of China, a representative research area in the world, and the daily concentration data of six main air pollutants in Beijing, Tianjin, and Shijiazhuang for two years were used to validate the accuracy and efficiency. The results show that the prediction model is superior to other benchmark models in pollutant concentration prediction and the evaluation model is satisfactory in air quality level reporting compared with the actual status. Therefore, the proposed system is believed to play an important role in air pollution control and smart city construction all over the world in the future.
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Schürholz, 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.

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Ali, 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.

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Nowadays, Artificial Intelligence (AI) plays a primary role in different applications like medicine, science, health, and finance. In the past five decades, the development and progress of technology have allowed artificial intelligence to take an essential role in human life. Air quality classification is an excellent example of this role. The use of AI in this domain allows humans to predict whether the air is polluted or not. In effect, monitoring air quality and providing periodic and direct statistics are essential requirements to ensure good air quality for individuals in the community. For this reason, a decision-making system is built to decide whether the air is clean or not. Based on this system's decision, necessary practices and measures are taken to improve air quality and ensure air sustainability. In this paper, the multinomial logistic regression technique is used to detect the air pollution level. The proposed method is applied to a real dataset that consists of 145 responses recorded from an air quality multi-sensor device containing chemical sensors. The used device was placed in New York City, USA, from 1/1/2021 to 7/1/2021 (one week) and is freely available for air quality sensors deployed in the field. The result shows the efficacity of this method in air pollution prediction.
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Li, 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.

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Rahardja, 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.

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Artificial intelligence techniques to forecasts based on the Community Multiscale Air Quality (PM2.5) operational model can be known using TensorFlow. TensorFlow was used in this study to assess the scores of the Recurrent Neural Networks (RNN) input variables on the 6-hour forecast for July-October 2022. The relevance scores for the one- and two-day forecasts are represented by the sum of the relevance scores across the target prediction timeframe 2–5 and 4–7 previous time steps. The initial selection of input variables was based on their correlation coefficient with the measured PM2.5 concentration. Still, the order of contribution of the input variables measured by TensorFlow was different from the order of their correlation coefficients, which indicated an inconsistency between the linear and nonlinear variables of the method. It was found that the retraining of the RNN model using a subset of variables with a high relevance score resulted in a predictive ability similar to the initial set of input variables.
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Dissertations / Theses on the topic "Air quality-Artificial intelligence"

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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.

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Riffelli, Stefano. "Sustainable comfort in indoor environments: global comfort indices and virtual sensors." Doctoral thesis, Urbino, 2022. http://hdl.handle.net/11576/2700929.

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Maimury, 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.

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碩士
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.
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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.

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碩士
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.
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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.

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Books on the topic "Air quality-Artificial intelligence"

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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.

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Smart cities and villages have enhanced the quality of lives of residents. Various computer-assisted technologies have been harnessed for the development of smart cities and villages in order to provide solutions for common and niche urban problems. The development of smart environments has been possible due to advances in computing power and artificial intelligence (AI) that have allowed the deployment of scalable technologies. Artificial Intelligence for Smart Cities and Villages: Advanced Technologies, Development, and Challenges summarizes the role of AI in planning and designing smart solutions for urban and rural environments. This book is divided into three sections to impart a better understanding of the topics to readers. These sections are: 1) Demystifying smart cities and villages: A traditional perspective, 2) Smart innovations for rural lifestyle management solutions, and 3) Case studies. Through this book, readers will be able to understand various advanced technologies that are vital to the development of smart cities and villages. The book presents 15 chapters that present effective solutions to urban and rural challenges. Concepts highlighted in chapters include smart farms, indoor object classification systems, smart transportation, blockchains for medical information, humanoid robots for rural education, IoT devices for farming, and much more. This book is intended for undergraduate and graduate engineering students across all disciplines, security providers in the IT and related fields, and trainees working for infrastructure management companies. Researchers and consultants at all levels working in the areas of artificial intelligence, machine learning, IoT, blockchain, network security, and cloud computing will also find the contents beneficial for planning projects involving smart environments.
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Book chapters on the topic "Air quality-Artificial intelligence"

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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.

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Patel, 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.

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Saini, 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.

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Nur, 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.

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Luculescu, 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.

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Aggarwal, 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.

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Jiang, 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.

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Sahoo, 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.

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Scarpiniti, 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.

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Carbajal 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.

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Conference papers on the topic "Air quality-Artificial intelligence"

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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.

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S.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.

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Blom, 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.

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Abstract Shell Autonomous Integrity Recognition (AIR) on C3 AI Platform is an Artificial Intelligence (AI) application that allows pressure equipment and structural steel inspectors to quickly and easily make use of automated image capture and evaluation to support execution of external integrity inspections. By processing data in the cloud coming from inspections carried out with handheld devices, drones and robots, this application enables inspectors to objectively evaluate issues, identify items that may have been overlooked, reduce the time needed to generate reports, and improve inputs to maintenance planning. By using Shell Autonomous Integrity Recognition on C3 AI Platform, users can improve the quality, efficiency, and standardization of visual inspections.
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Hunter, 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.

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Silva, 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.

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Warter, 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.

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<div class="section abstract"><div class="htmlview paragraph">Digital technologies are capable of making a significant contribution to improving large internal combustion engine technology. In particular, methods from the field of artificial intelligence are opening up new avenues. So-called “intelligent” engine components rely on advanced instrumentation and data analytics to create value-added data, which in turn can serve as the basis for applications such as condition monitoring, predictive maintenance and controls. For related components and systems, these data may also allow for novel condition monitoring approaches. This paper describes the use of value-added data from an intelligent diesel fuel injection valve that give detailed information about the injection process for real-time prediction of key combustion parameters such as indicated mean effective pressure, maximum cylinder pressure and combustion phasing. These parameters are usually involved in combustion controls and power unit condition monitoring and normally acquired using in-cylinder pressure indication systems, which are costly and prone to wear. On the one hand, a data-driven model for key combustion parameters based on an intelligent fuel injection valve could replace an indication system. On the other hand, such a model may enable backup functionality and mutual condition monitoring of the fuel injection valve and the indication system. The data required for model building were acquired from a medium-speed four-stroke single-cylinder research engine with a displacement of approximately 15.7 dm<sup>3</sup>. Different machine learning methods are compared to obtain an accurate yet reliable model for each of the desired combustion parameters. In addition to the value-added injection data, readily available parameters on production engines serve as model inputs (e.g., engine speed, charge air and exhaust gas pressures). Based on the results, the quality of the model predictions is evaluated, and it is assessed whether the approach might be useful for series engine applications.</div></div>
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Lin, 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.

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Baran, 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.

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Lyu, 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.

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Conea, 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.

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Reports on the topic "Air quality-Artificial intelligence"

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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.

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Progress in artificial intelligence (AI) depends on talented researchers, well-designed algorithms, quality datasets, and powerful hardware. The relative importance of these factors is often debated, with many recent “notable” models requiring massive expenditures of advanced hardware. But how important is computational power for AI progress in general? This data brief explores the results of a survey of more than 400 AI researchers to evaluate the importance and distribution of computational needs.
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Arnold, 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.

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In this proof-of-concept project, CSET and Amplyfi Ltd. used machine learning models and Chinese-language web data to identify Chinese companies active in artificial intelligence. Most of these companies were not labeled or described as AI-related in two high-quality commercial datasets. The authors' findings show that using structured data alone—even from the best providers—will yield an incomplete picture of the Chinese AI landscape.
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Pieterson, 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.

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Public employment services have a crucial role in the search for quality jobs, worker placement, labor market adaptation, and mitigating impacts during economic transitions. Globally, these services have been leveraging on digital technologies to transform, and those in Latin America and the Caribbean are no exception. These technologies are enabling the creation of new channels to expand outreach and service delivery, centralizing, and sharing data, facilitating collaboration, and improving processes. However, rapid technological advancements also pose risks in terms of access and equity. For instance, improper use of artificial intelligence (AI) may exacerbate existing labor market inequalities. Therefore, it is essential for public employment services to harness the potential of digital technologies while mitigating associated risks. To address these challenges, these institutions must consider five key dimensions. Firstly, they need to be aware of the strategic implications of digital technologies. Secondly, they need to manage the impact of technology on their administrative operations. Thirdly, they must effectively utilize technology in their interactions with the public. Fourthly, they should undergo organizational changes to enhance agility and adopt different structures, skills, and cultures. Lastly, they must grow into their role of data processing organizations to take advantage of new opportunities and tackle new challenges. This document provides relevant information on the opportunities and challenges of digitalization in public employment services in Latin America and the Caribbean across these dimensions, as well as their level of digital maturity. The data and results presented in this study are based on a survey conducted by the IDB's SEALC Network in 2019, which was expanded in 2022 to include fifteen countries in the region. Additionally, the effect of the COVID-19 pandemic on the digitalization efforts of public employment services in the region is evaluated. This information is relevant for public employment services as it enables them to identify strengths and weaknesses in their digital transformation processes.
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Daudelin, 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.

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The report provides a review of how risk is conceived of, modelled, and mapped in studies of infectious water, sanitation, and hygiene (WASH) related diseases. It focuses on spatial epidemiology of cholera, malaria and dengue to offer recommendations for the field of WASH-related disease risk mapping. The report notes a lack of consensus on the definition of disease risk in the literature, which limits the interpretability of the resulting analyses and could affect the quality of the design and direction of public health interventions. In addition, existing risk frameworks that consider disease incidence separately from community vulnerability have conceptual overlap in their components and conflate the probability and severity of disease risk into a single component. The report identifies four methods used to develop risk maps, i) observational, ii) index-based, iii) associative modelling and iv) mechanistic modelling. Observational methods are limited by a lack of historical data sets and their assumption that historical outcomes are representative of current and future risks. The more general index-based methods offer a highly flexible approach based on observed and modelled risks and can be used for partially qualitative or difficult-to-measure indicators, such as socioeconomic vulnerability. For multidimensional risk measures, indices representing different dimensions can be aggregated to form a composite index or be considered jointly without aggregation. The latter approach can distinguish between different types of disease risk such as outbreaks of high frequency/low intensity and low frequency/high intensity. Associative models, including machine learning and artificial intelligence (AI), are commonly used to measure current risk, future risk (short-term for early warning systems) or risk in areas with low data availability, but concerns about bias, privacy, trust, and accountability in algorithms can limit their application. In addition, they typically do not account for gender and demographic variables that allow risk analyses for different vulnerable groups. As an alternative, mechanistic models can be used for similar purposes as well as to create spatial measures of disease transmission efficiency or to model risk outcomes from hypothetical scenarios. Mechanistic models, however, are limited by their inability to capture locally specific transmission dynamics. The report recommends that future WASH-related disease risk mapping research: - Conceptualise risk as a function of the probability and severity of a disease risk event. Probability and severity can be disaggregated into sub-components. For outbreak-prone diseases, probability can be represented by a likelihood component while severity can be disaggregated into transmission and sensitivity sub-components, where sensitivity represents factors affecting health and socioeconomic outcomes of infection. -Employ jointly considered unaggregated indices to map multidimensional risk. Individual indices representing multiple dimensions of risk should be developed using a range of methods to take advantage of their relative strengths. -Develop and apply collaborative approaches with public health officials, development organizations and relevant stakeholders to identify appropriate interventions and priority levels for different types of risk, while ensuring the needs and values of users are met in an ethical and socially responsible manner. -Enhance identification of vulnerable populations by further disaggregating risk estimates and accounting for demographic and behavioural variables and using novel data sources such as big data and citizen science. This review is the first to focus solely on WASH-related disease risk mapping and modelling. The recommendations can be used as a guide for developing spatial epidemiology models in tandem with public health officials and to help detect and develop tailored responses to WASH-related disease outbreaks that meet the needs of vulnerable populations. The report’s main target audience is modellers, public health authorities and partners responsible for co-designing and implementing multi-sectoral health interventions, with a particular emphasis on facilitating the integration of health and WASH services delivery contributing to Sustainable Development Goals (SDG) 3 (good health and well-being) and 6 (clean water and sanitation).
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