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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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Ciric, Ivan, Zarko Cojbasic, Vlastimir Nikolic, Predrag Zivkovic, and Mladen Tomic. "Air quality estimation by computational intelligence methodologies." Thermal Science 16, suppl. 2 (2012): 493–504. http://dx.doi.org/10.2298/tsci120503186c.
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The subject of this study is to compare different computational intelligence methodologies based on artificial neural networks used for forecasting an air quality parameter - the emission of CO2, in the city of Nis. Firstly, inputs of the CO2 emission estimator are analyzed and their measurement is explained. It is known that the traffic is the single largest emitter of CO2 in Europe. Therefore, a proper treatment of this component of pollution is very important for precise estimation of emission levels. With this in mind, measurements of traffic frequency and CO2 concentration were carried out at critical intersections in the city, as well as the monitoring of a vehicle direction at the crossroad. Finally, based on experimental data, different soft computing estimators were developed, such as feed forward neural network, recurrent neural network, and hybrid neuro-fuzzy estimator of CO2 emission levels. Test data for some characteristic cases presented at the end of the paper shows good agreement of developed estimator outputs with experimental data. Presented results are a true indicator of the implemented method usability.
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Faleh, Rabeb, Souhir Bedoui, and Abdennaceur Kachouri. "Review on Smart Electronic Nose Coupled with Artificial Intelligence for Air Quality Monitoring." Advances in Science, Technology and Engineering Systems Journal 5, no. 2 (2020): 739–47. http://dx.doi.org/10.25046/aj050292.
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Younes, Mohammad K., Ghassan Sulaiman, and Ali Al-Mashni. "Integration of Traffic Management and an Artificial Intelligence to Evaluate Urban Air Quality." Asian Journal of Atmospheric Environment 14, no. 3 (September 30, 2020): 225–35. http://dx.doi.org/10.5572/ajae.2020.14.3.225.
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Zhang, Yongli. "Seasonal Disparity in the Effect of Meteorological Conditions on Air Quality in China Based on Artificial Intelligence." Atmosphere 12, no. 12 (December 13, 2021): 1670. http://dx.doi.org/10.3390/atmos12121670.
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Air contamination is identified with individuals’ wellbeing and furthermore affects the sustainable development of economy and society. This paper gathered the time series data of seven meteorological conditions variables of Beijing city from 1 November 2013 to 31 October 2017 and utilized the generalized regression neural network optimized by the particle swarm optimization algorithm (PSO-GRNN) to explore seasonal disparity in the impacts of mean atmospheric humidity, maximum wind velocity, insolation duration, mean wind velocity and rain precipitation on air quality index (AQI). The results showed that in general, the most significant impacting factor on air quality in Beijing is insolation duration, mean atmospheric humidity, and maximum wind velocity. In spring and autumn, the meteorological diffusion conditions represented by insolation duration and mean atmospheric humidity had a significant effect on air quality. In summer, temperature and wind are the most significant variables influencing air quality in Beijing; the most important reason for air contamination in Beijing in winter is the increase in air humidity and the deterioration of air diffusion condition. This study investigates the seasonal effects of meteorological conditions on air contamination and suggests a new research method for air quality research. In future studies, the impacts of different variables other than meteorological conditions on air quality should be assessed.
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Damanhuri, Endang Agus, Yusni Ikhwan Siregar, and Elfizar Elfizar. "PENERAPAN MODEL BERBASIS ARTIFICIAL NEURAL NETWORK UNTUK MEMPREDIKSI KUALITAS AIR DI SUNGAI SUBAYANG KABUPATEN KAMPAR." Jurnal Ilmu Lingkungan 14, no. 1 (March 18, 2020): 18. http://dx.doi.org/10.31258/jil.14.1.p.18-28.
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Water quality management is very important to do, because water is an inseparable part of everyday human life. Monitoring water quality is a way to maintain the quality of waters, especially rivers. River quality monitoring that is usually done requires a lot of equipment, effort and expertise so that its application becomes expensive and complicated. Technology that is growing rapidly nowadays puts forward artificial intelligence as the backbone of the Industrial Revolution 4.0 which promises many conveniences for industry and government. One of artificial intelligence technology is machine learning with Artificial Neural Network algorithm which is commonly used to predict or forecast a future value. This artificial neural network can be used to help monitor river water quality. The objective of this research to develop Artificial Neural Networks (ANN) model to predict the paramater of river quality (DO, pH, turbidity, temperature, water flow, conductivity) in the Subayang River, Kampar Regency, using software Rapidminer. The performance of the ANN models was evaluated using root mean squared error (RMSE) and correlation squared (R2) as a second comparison, then the results of the testing implementation are compared with direct measurements in the field. With the RMSE values obtained in the test results of each parameter DO = 1.613, pH = 0.098, turbidity = 4.730, temperature = 0.493, water flow = 0.121 and conductivity = 0.909. The lower the RMSE level, the closer it is to Artificial Neural Network accuracy for value prediction.
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Mileva-Karova, Milena Nikolova, Tsvetelin Angelov Petrov, Kristian Ivanov Ivanov, Nayden Nikolaev Nikolov, and Tony Angelov Gadzhev. "System for assessment and forecast of air quality in populated areas." ANNUAL JOURNAL OF TECHNICAL UNIVERSITY OF VARNA, BULGARIA 7, no. 1 (June 30, 2023): 52–60. http://dx.doi.org/10.29114/ajtuv.vol7.iss1.267.
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The paper provides an account of a system for collecting data, forecasting and assessing the quality of ambient air in a given locality. The developed system allows for extremely sustainable analysis of the results and due consideration of the utilization of artificial intelligence algorithms and methods for the development of accurate forecasts. The obtained results are expected to detect the problems related to the quality of air before their actual occurrence.
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Pandey, Shirish, S. Hasan Saeed, and N. R. Kidwai. "Simulation and optimization of genetic algorithm-artificial neural network based air quality estimator." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 2 (August 1, 2020): 775. http://dx.doi.org/10.11591/ijeecs.v19.i2.pp775-783.
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In this work intelligent model for estimation of the concentration of carbon monoxide in a polluted environment is developed on mat Lab platform. The results are validated using data collected from repository linked to University of California. The data records are over the duration of one year using E nose sensor placed in main city of Italy. The records are rectified and segmented at different length to extract the Base and Divergence Values features. An Artificial Neural Network Model (ANN) is developed and the result is validated manually. Another optimized Genetic Algorithm-Artificial Neural Network based air quality estimation model is developed which validate the result using artificial intelligence technique to get a better performance network.
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Safira, Aretha, L. M. Sarudi As., Afifa Puspitasari, Nur Mayke Eka Normasari, and Achmad Pratama Rifai. "PENGEMBANGAN NEURAL NETWORK UNTUK PREDIKSI KUALITAS AIR." Jurnal Rekavasi 10, no. 2 (February 14, 2023): 30–36. http://dx.doi.org/10.34151/rekavasi.v10i2.4014.
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Research on artificial intelligence to determine water quality has been widely developed as a human endeavor toimprove the quality of life. This study employs an artificial neural network (ANN) to determine the optimalclassification model for determining the safety of water. This study uses existing Kaggle generic datasets. Numerouspreprocesses were performed on the dataset starting from cleaning the data from missing values and outliers toequalizing the weights of each parameter with the min-max scaler. This study compares the accuracy of ANN modelin various scenarios constructed with 10, 15, 20, and 30 neurons. Scaled Conjugate Gradient is implemented as thelearning algorithm for developing the prediction model. The obtained results of the experiments vary betweenscenarios. Overall accuracy increases when the number of neurons is between 10 and 20, and decreases when thenumber of neurons is between 20 and 30.
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Sankar Ganesh, S., Pachaiyappan Arulmozhivarman, and Rao Tatavarti. "Forecasting Air Quality Index Using an Ensemble of Artificial Neural Networks and Regression Models." Journal of Intelligent Systems 28, no. 5 (November 18, 2017): 893–903. http://dx.doi.org/10.1515/jisys-2017-0277.
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Abstract Air is the most essential constituent for the sustenance of life on earth. The air we inhale has a tremendous impact on our health and well-being. Hence, it is always advisable to monitor the quality of air in our environment. To forecast the air quality index (AQI), artificial neural networks (ANNs) trained with conjugate gradient descent (CGD), such as multilayer perceptron (MLP), cascade forward neural network, Elman neural network, radial basis function (RBF) neural network, and nonlinear autoregressive model with exogenous input (NARX) along with regression models such as multiple linear regression (MLR) consisting of batch gradient descent (BGD), stochastic gradient descent (SGD), mini-BGD (MBGD) and CGD algorithms, and support vector regression (SVR), are implemented. In these models, the AQI is the dependent variable and the concentrations of NO2, CO, O3, PM2.5, SO2, and PM10 for the years 2010–2016 in Houston and Los Angeles are the independent variables. For the final forecast, several ensemble models of individual neural network predictors and individual regression predictors are presented. This proposed approach performs with the highest efficiency in terms of forecasting air quality index.
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Pan, Zhengxiang, Han Yu, Chunyan Miao, and Cyril Leung. "Crowdsensing Air Quality with Camera-Enabled Mobile Devices." Proceedings of the AAAI Conference on Artificial Intelligence 31, no. 2 (February 11, 2017): 4728–33. http://dx.doi.org/10.1609/aaai.v31i2.19102.
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Crowdsensing of air quality is a useful way to improve public awareness and supplement local air quality monitoring data. However, current air quality monitoring approaches are either too sophisticated, costly or bulky to be used effectively by the mass. In this paper, we describe AirTick, a mobile app that can turn any camera enabled smart mobile device into an air quality sensor, thereby enabling crowdsensing of air quality. AirTick leverages image analytics and deep learning techniques to produce accurate estimates of air quality following the Pollutant Standards Index (PSI). We report the results of an initial experimental and empirical evaluations of AirTick. The AirTick tool has been shown to achieve, on average, 87% accuracy in day time operation and 75% accuracy in night time operation. Feedbacks from 100 test users indicate that they perceive AirTick to be highly useful and easy to use. Our results provide a strong positive case for the benefits of applying artificial intelligence techniques for convenient and scalable crowdsensing of air quality.
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Oh, Kyuetaek, Mintaek Yoo, Nayoung Jin, Jisu Ko, Jeonguk Seo, Hyojin Joo, and Minsam Ko. "A Review of Deep Learning Applications for Railway Safety." Applied Sciences 12, no. 20 (October 19, 2022): 10572. http://dx.doi.org/10.3390/app122010572.
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Railways speedily transport many people and goods nationwide, so railway accidents can pose immense damage. However, the infrastructure of railways is so complex that its maintenance is challenging and expensive. Therefore, using artificial intelligence for railway safety has attracted many researchers. This paper examines artificial intelligence applications for railway safety, mainly focusing on deep learning approaches. This paper first introduces deep learning methods widely used for railway safety. Then, we investigated and classified earlier studies into four representative application areas: (1) railway infrastructure (catenary, surface, components, and geometry), (2) train body and bogie (door, wheel, suspension, bearing, etc.), (3) operation (railway detection, railroad trespassing, wind risk, train running safety, etc.), and (4) station (air quality control, accident prevention, etc.). We present fundamental problems and popular approaches for each application area. Finally, based on the literature reviews, we discuss the opportunities and challenges of artificial intelligence for railway safety.
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Gulkari, Shruti S. "A Review on Advanced Home Automation System using GSM and AI." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 10, 2021): 459–61. http://dx.doi.org/10.22214/ijraset.2021.34882.
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In this proposed work, we are going to connect home appliances to user mobile so that they can handle their appliances by remote distances. Smart home and artificial technology are the trending technology which is developing rapidly. This smart home product improves the quality of living for occupants. In this system, we explore full home control or home automation by using GSM (Global System for Mobile Communication). User home appliances such as LED bulb, air conditioner etc. is controlled by GSM (Global System for Mobile Communication) modem via SMS (Short Message Services) text message is presented in this paper. In this project, we are using AI (Artificial Intelligence) to operate devices. AI (Artificial Intelligence) is relatively a new idea that is easily accessible today by anyone who has access to a computer or a smartphone. This (Artificial Intelligence) also becoming a powerful presence in technology, has been swiftly dominating the home automation market. This allows us to integrate smart solutions into our everyday tasks. In this work, we are going to execute a continuous process to home appliances control system without any supervisor and later it will co-ordinate appliances and other devices through Short Message Service using GSM(Global System for Mobile Communication) and AI(Artificial Intelligence)
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Jufriansah, Adi, Azmi Khusnani, Yudhiakto Pramudya, Nursina Sya’bania, Kristina Theresia Leto, Hamzarudin Hikmatiar, and Sabarudin Saputra. "AI Big Data System to Predict Air Quality for Environmental Toxicology Monitoring." Journal of Novel Engineering Science and Technology 2, no. 01 (April 4, 2023): 21–25. http://dx.doi.org/10.56741/jnest.v2i01.314.
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Pollutants in the air have a detrimental effect on both human existence and the environment. Because it is closely linked to climate change and the effects of global warming, research on air quality is currently receiving attention from a variety of disciplines. The science of forecasting air quality has evolved over time, and the actions of different gases (hazardous elements) and other components directly affect the health of the ecosystem. This study aims to present the development of a prediction system based on artificial intelligence models using a database of air quality sensors.This study develops a prediction model using machine learning (ML) and a Decision Tree (DT) algorithm that can enable decision harmonization across different industries with high accuracy. Based on pollutant levels and the classification outcomes from each cluster's analysis, statistical forecasting findings with a model accuracy of 0.95 have been achieved. This may act as a guiding factor in the development of air quality policies that address global consequences, international rescue efforts, and the preservation of the gap in air quality index standardization.
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Piltan, Farzin, Mansour Bazregar, Marzieh Kamgari, Mojdeh Piran, and Mehdi Akbari. "Quality Model and Artificial Intelligence Base Fuel Ratio Management with Applications to Automotive Engine." IAES International Journal of Artificial Intelligence (IJ-AI) 3, no. 1 (March 1, 2014): 36. http://dx.doi.org/10.11591/ijai.v3.i1.pp36-48.
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<p class="Default">In this research, manage the Internal Combustion (IC) engine modeling and a multi-input-multi-output artificial intelligence baseline chattering free sliding mode methodology scheme is developed with guaranteed stability to simultaneously control fuel ratios to desired levels under various air flow disturbances by regulating the mass flow rates of engine PFI and DI injection systems. Nevertheless, developing a small model, for specific controller design purposes, can be done and then validated on a larger, more complicated model. Analytical dynamic nonlinear modeling of internal combustion engine is carried out using elegant Euler-Lagrange method compromising accuracy and complexity. The fuzzy inference baseline sliding methodology performance was compared with a well-tuned baseline multi-loop PID controller through MATLAB simulations and showed improvements, where MATLAB simulations were conducted to validate the feasibility of utilizing the developed controller and state estimator for automotive engines. The proposed tracking method is designed to optimally track the desired FR by minimizing the error between the trapped in-cylinder mass and the product of the desired FR and fuel mass over a given time interval.</p>
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Aldakheel, Joud, Myriam Bahrar, and Mohamed El Mankibi. "Indoor environmental quality evaluation of smart/artificial intelligence techniques in buildings – a review." E3S Web of Conferences 396 (2023): 01101. http://dx.doi.org/10.1051/e3sconf/202339601101.
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The built environment sector is responsible for around one-third of the world's final energy consumption. Smart technologies play an essential role in strengthening existing regulations and facilitating energy efficiency targets. Smart Buildings allow the response to the external conditions of buildings including grid and climatic conditions, and internal building needs such as user requirements achieved through real-time monitoring and real-time interaction which are resembled the smart buildings concept. The optimal management of occupant comfort plays a crucial role in the built environment since the occupant's productivity and health are highly influenced by Indoor Environmental Quality. This work explores the application of real-time monitoring and interaction to achieve optimal Indoor Environmental Quality, occupant comfort and energy savings in relation to smart buildings and smart technologies. To better address and indoor air quality issues, ventilation needs to become smarter. It is crucial to understand first the Key Performance Indicators of evaluating smart ventilation. In parallel, Artificial Intelligence techniques such as machine and deep learning have been increasingly and successfully applied to develop solutions for the built environment. Thus, this paper provides a review on the existing Key Performance Indicators that allows smart ventilation in smart buildings. Then, it reviews the existing literature on the machine and deep learning methods and software for assessing the smart ventilation. Finally, it shows the most recent technologies for performing experimental evaluation on the main indicators for smart ventilation. This work is expected to highlight the selection of the most optimal ventilation metrics, proper indicators, machine learning and deep learning models and measurement technologies to achieve excellent Indoor Environmental Quality and energy efficiency levels.
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Petry, L., T. Meiers, D. Reuschenberg, S. Mirzavand Borujeni, J. Arndt, L. Odenthal, T. Erbertseder, et al. "DESIGN AND RESULTS OF AN AI-BASED FORECASTING OF AIR POLLUTANTS FOR SMART CITIES." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences VIII-4/W1-2021 (September 3, 2021): 89–96. http://dx.doi.org/10.5194/isprs-annals-viii-4-w1-2021-89-2021.
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Abstract. This paper presents the design and the results of a novel approach to predict air pollutants in urban environments. The objective is to create an artificial intelligence (AI)-based system to support planning actors in taking effective and adequate short-term measures against unfavourable air quality situations. In general, air quality in European cities has improved over the past decades. Nevertheless, reductions of the air pollutants particulate matter (PM), nitrogen dioxide (NO2) and ground-level ozone (O3), in particular, are essential to ensure the quality of life and a healthy life in cities. To forecast these air pollutants for the next 48 hours, a sequence-to-sequence encoder-decoder model with a recurrent neural network (RNN) was implemented. The model was trained with historic in situ air pollutant measurements, traffic and meteorological data. An evaluation of the prediction results against historical data shows high accordance with in situ measurements and implicate the system’s applicability and its great potential for high quality forecasts of air pollutants in urban environments by including real time weather forecast data.
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Nadiri, Ataallah, Marwa M. Hassan, and Somayeh Asadi. "Supervised Intelligence Committee Machine to Evaluate Field Performance of Photocatalytic Asphalt Pavement for Ambient Air Purification." Transportation Research Record: Journal of the Transportation Research Board 2528, no. 1 (January 2015): 96–105. http://dx.doi.org/10.3141/2528-11.
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The ability of a titanium dioxide (TiO2) photocatalytic nanoparticle to trap and to decompose organic and inorganic air pollutants makes it a promising technology as a pavement coating to mitigate the harmful effects of vehicle emissions. Statistical models and artificial intelligence (AI) models are two applicable methods to quantify photocatalytic efficiency. The objective of this study was to develop a model based on field-collected data to predict the nitrogen oxide (NOx) reduction. To achieve this objective, the supervised intelligent committee machine (SICM) method as a combinational black box model was used to predict NOx concentration at the pavement level before and after TiO2 application on the pavement surface. SICM predicts NOx concentration by a nonlinear combination of individual AI models through an artificial intelligent system. Three AI models—Mamdani fuzzy logic, artificial neural network, and neuro-fuzzy—were used to predict NOx concentration in the air as a function of traffic count and climatic conditions, including humidity, temperature, solar radiation, and wind speed before and after the application of TiO2. In addition, an intelligent committee machine model was developed by combining individual AI model output linearly through a set of weights. Results indicated that the SICM model could provide a better prediction of NOx concentration as an air pollutant in the complex and multidimensional air quality data analysis with less residual mean square error than that given by multivariate regression models.
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Fu, Leiming, Junlong Li, and Yifei Chen. "An innovative decision making method for air quality monitoring based on big data-assisted artificial intelligence technique." Journal of Innovation & Knowledge 8, no. 2 (April 2023): 100294. http://dx.doi.org/10.1016/j.jik.2022.100294.
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Yigitcanlar, Tan, and Federico Cugurullo. "The Sustainability of Artificial Intelligence: An Urbanistic Viewpoint from the Lens of Smart and Sustainable Cities." Sustainability 12, no. 20 (October 15, 2020): 8548. http://dx.doi.org/10.3390/su12208548.
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The popularity and application of artificial intelligence (AI) are increasing rapidly all around the world—where, in simple terms, AI is a technology which mimics the behaviors commonly associated with human intelligence. Today, various AI applications are being used in areas ranging from marketing to banking and finance, from agriculture to healthcare and security, from space exploration to robotics and transport, and from chatbots to artificial creativity and manufacturing. More recently, AI applications have also started to become an integral part of many urban services. Urban artificial intelligences manage the transport systems of cities, run restaurants and shops where every day urbanity is expressed, repair urban infrastructure, and govern multiple urban domains such as traffic, air quality monitoring, garbage collection, and energy. In the age of uncertainty and complexity that is upon us, the increasing adoption of AI is expected to continue, and so its impact on the sustainability of our cities. This viewpoint explores and questions the sustainability of AI from the lens of smart and sustainable cities, and generates insights into emerging urban artificial intelligences and the potential symbiosis between AI and a smart and sustainable urbanism. In terms of methodology, this viewpoint deploys a thorough review of the current status of AI and smart and sustainable cities literature, research, developments, trends, and applications. In so doing, it contributes to existing academic debates in the fields of smart and sustainable cities and AI. In addition, by shedding light on the uptake of AI in cities, the viewpoint seeks to help urban policymakers, planners, and citizens make informed decisions about a sustainable adoption of AI.
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Soares, Paulo Henrique, Johny Paulo Monteiro, Fernando José Gaioto, Luciano Ogiboski, and Cid Marcos Gonçalves Andrade. "Use of Association Algorithms in Air Quality Monitoring." Atmosphere 14, no. 4 (March 30, 2023): 648. http://dx.doi.org/10.3390/atmos14040648.
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Over the years, there has been a gradual increase in the emission of pollutants, and it is imperative to establish mechanisms to monitor air quality. In addition to carbon dioxide (CO2), particulate matter (PM) is considered one of the main types of air pollution. However, there is a wide variety of pollutants, and high investment is required to carry out detailed air quality monitoring. We present the third version of a previously proposed air quality monitoring platform based on CO2 concentration measurements. In this new version, a specific sensor for PM measurements and an artificial intelligence algorithm were added. The added algorithm traced associations between measurements of CO2 and PM concentrations. Thus, the measurement of a pollutant can be used for estimating the concentration of another. This can contribute to the development of a simpler and cheaper monitoring system. The acquisition of CO2 and PM concentrations was carried out daily over a period of one month. Pollutant measurements were taken in three strategic locations in a Brazilian city. It was possible to determine a correlation between pollutant concentrations for the monitored locations. Thus, it would be possible to efficiently estimate the PM concentration based on the measured CO2 concentration.
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Shah, Sayed Khushal, Zeenat Tariq, Jeehwan Lee, and Yugyung Lee. "Event-Driven Deep Learning for Edge Intelligence (EDL-EI)." Sensors 21, no. 18 (September 8, 2021): 6023. http://dx.doi.org/10.3390/s21186023.
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Edge intelligence (EI) has received a lot of interest because it can reduce latency, increase efficiency, and preserve privacy. More significantly, as the Internet of Things (IoT) has proliferated, billions of portable and embedded devices have been interconnected, producing zillions of gigabytes on edge networks. Thus, there is an immediate need to push AI (artificial intelligence) breakthroughs within edge networks to achieve the full promise of edge data analytics. EI solutions have supported digital technology workloads and applications from the infrastructure level to edge networks; however, there are still many challenges with the heterogeneity of computational capabilities and the spread of information sources. We propose a novel event-driven deep-learning framework, called EDL-EI (event-driven deep learning for edge intelligence), via the design of a novel event model by defining events using correlation analysis with multiple sensors in real-world settings and incorporating multi-sensor fusion techniques, a transformation method for sensor streams into images, and lightweight 2-dimensional convolutional neural network (CNN) models. To demonstrate the feasibility of the EDL-EI framework, we presented an IoT-based prototype system that we developed with multiple sensors and edge devices. To verify the proposed framework, we have a case study of air-quality scenarios based on the benchmark data provided by the USA Environmental Protection Agency for the most polluted cities in South Korea and China. We have obtained outstanding predictive accuracy (97.65% and 97.19%) from two deep-learning models on the cities’ air-quality patterns. Furthermore, the air-quality changes from 2019 to 2020 have been analyzed to check the effects of the COVID-19 pandemic lockdown.
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Ashikyan, Oganes, Donald Chan, Daniel S. Moore, Uma Thakur, and Avneesh Chhabra. "Quality of Hand Radiograph Collimation Determined by Artificial Intelligence Algorithm Correlates with Radiograph Quality Scores Assigned by Radiologists." Radiation 1, no. 2 (April 8, 2021): 116–22. http://dx.doi.org/10.3390/radiation1020010.
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Providing direct feedback to technologists has become challenging for radiologists due to geographic separation and other reasons. As such, there is a need for automated solutions to solve quality issues in radiography. We evaluated the feasibility of using a computer vision artificial intelligence (AI) algorithm to classify hand radiographs into quality categories in order to automate quality assurance processes in radiology. A bounding box was placed over the hand on 300 hand radiographs. These inputs were employed to train the computational neural network (CNN) to automatically detect hand boundaries. The trained CNN detector was used to place bounding boxes over the hands on an additional 100 radiographs, independently of the training or validation sets. A computer algorithm processed each output image to calculate unused air spaces. The same 100 images were classified by two musculoskeletal radiologists into four quality categories. The correlation between the AI-calculated unused space metric and radiologist-assigned quality scores was determined using the Spearman correlation coefficient. The kappa statistic was used to calculate the inter-reader agreement. The best negative correlation between the AI-assigned metric and the radiologists’ assigned quality scores was achieved using the calculation of the unused space at the top of the image. The Spearman correlation coefficients were −0.7 and −0.6 for the two radiologists. The kappa correlation coefficient for interobserver agreement between the two radiologists was 0.6. Automatic calculation of the percentage of unused space or indirect collimation at the top of hand radiographs correlates moderately well with radiographic collimation quality.
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Arroyo, Patricia, Jesús Lozano, and José Suárez. "Evolution of Wireless Sensor Network for Air Quality Measurements." Electronics 7, no. 12 (November 22, 2018): 342. http://dx.doi.org/10.3390/electronics7120342.
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This study addresses the development of a wireless gas sensor network with low cost, small size, and low consumption nodes for environmental applications and air quality detection. Throughout the article, the evolution of the design and development of the system is presented, describing four designed prototypes. The final proposed prototype node has the capacity to connect up to four metal oxide (MOX) gas sensors, and has high autonomy thanks to the use of solar panels, as well as having an indirect sampling system and a small size. ZigBee protocol is used to transmit data wirelessly to a self-developed data cloud. The discrimination capacity of the device was checked with the volatile organic compounds benzene, toluene, ethylbenzene, and xylene (BTEX). An improvement of the system was achieved to obtain optimal success rates in the classification stage with the final prototype. Data processing was carried out using techniques of pattern recognition and artificial intelligence, such as radial basis networks and principal component analysis (PCA).
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Krishnan, Srivatsan, Behzad Boroujerdian, William Fu, Aleksandra Faust, and Vijay Janapa Reddi. "Air Learning: a deep reinforcement learning gym for autonomous aerial robot visual navigation." Machine Learning 110, no. 9 (July 7, 2021): 2501–40. http://dx.doi.org/10.1007/s10994-021-06006-6.
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AbstractWe introduce Air Learning, an open-source simulator, and a gym environment for deep reinforcement learning research on resource-constrained aerial robots. Equipped with domain randomization, Air Learning exposes a UAV agent to a diverse set of challenging scenarios. We seed the toolset with point-to-point obstacle avoidance tasks in three different environments and Deep Q Networks (DQN) and Proximal Policy Optimization (PPO) trainers. Air Learning assesses the policies’ performance under various quality-of-flight (QoF) metrics, such as the energy consumed, endurance, and the average trajectory length, on resource-constrained embedded platforms like a Raspberry Pi. We find that the trajectories on an embedded Ras-Pi are vastly different from those predicted on a high-end desktop system, resulting in up to $$40\%$$ 40 % longer trajectories in one of the environments. To understand the source of such discrepancies, we use Air Learning to artificially degrade high-end desktop performance to mimic what happens on a low-end embedded system. We then propose a mitigation technique that uses the hardware-in-the-loop to determine the latency distribution of running the policy on the target platform (onboard compute on aerial robot). A randomly sampled latency from the latency distribution is then added as an artificial delay within the training loop. Training the policy with artificial delays allows us to minimize the hardware gap (discrepancy in the flight time metric reduced from 37.73% to 0.5%). Thus, Air Learning with hardware-in-the-loop characterizes those differences and exposes how the onboard compute’s choice affects the aerial robot’s performance. We also conduct reliability studies to assess the effect of sensor failures on the learned policies. All put together, Air Learning enables a broad class of deep RL research on UAVs. The source code is available at: https://github.com/harvard-edge/AirLearning.
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Demmler, Joanne C., Ákos Gosztonyi, Yaxing Du, Matti Leinonen, Laura Ruotsalainen, Leena Järvi, and Sanna Ala-Mantila. "A novel approach of creating sustainable urban planning solutions that optimise the local air quality and environmental equity in Helsinki, Finland: The CouSCOUS study protocol." PLOS ONE 16, no. 12 (December 2, 2021): e0260009. http://dx.doi.org/10.1371/journal.pone.0260009.
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Background Air pollution is one of the major environmental challenges cities worldwide face today. Planning healthy environments for all future populations, whilst considering the ongoing demand for urbanisation and provisions needed to combat climate change, remains a difficult task. Objective To combine artificial intelligence (AI), atmospheric and social sciences to provide urban planning solutions that optimise local air quality by applying novel methods and taking into consideration population structures and traffic flows. Methods We will use high-resolution spatial data and linked electronic population cohort for Helsinki Metropolitan Area (Finland) to model (a) population dynamics and urban inequality related to air pollution; (b) detailed aerosol dynamics, aerosol and gas-phase chemistry together with detailed flow characteristics; (c) high-resolution traffic flow addressing dynamical changes at the city environment, such as accidents, construction work and unexpected congestion. Finally, we will fuse the information resulting from these models into an optimal city planning model balancing air quality, comfort, accessibility and travelling efficiency.
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Liu, Ruifang, Lixia Pang, Yidian Yang, Yuxing Gao, Bei Gao, Feng Liu, and Li Wang. "Air Quality—Meteorology Correlation Modeling Using Random Forest and Neural Network." Sustainability 15, no. 5 (March 3, 2023): 4531. http://dx.doi.org/10.3390/su15054531.
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Under the global warming trend, the diffusion of air pollutants has intensified, causing extremely serious environmental problems. In order to improve the air quality–meteorology correlation model’s prediction accuracy, this work focuses on the management strategy of the environmental ecosystem under the Artificial Intelligence (AI) algorithm and explores the correlation between air quality and meteorology. Xi’an city is selected as an example. Then, the theoretical knowledge is explained for Random Forest (RF), Backpropagation Neural Network (BPNN), and Genetic Algorithm (GA) in AI. Finally, GA is used to optimize and predict the weights and thresholds of the BPNN. Further, a fusion model of RF + BP + GA is proposed to predict the air quality and meteorology correlation. The proposed air quality–meteorology correlation model is applied to forest ecosystem management. Experimental analysis reveals that average temperature positively correlates with Air Quality Index (AQI), while relative humidity and wind speed negatively correlate with AQI. Moreover, the proposed RF + BP + GA model’s prediction error for AQI is not more than 0.32, showing an excellently fitting effect with the actual value. The air-quality prediction effect of the meteorological correlation model using RF is slightly lower than the real measured value. The prediction effect of the BP–GA model is slightly higher than the real measured value. The prediction effect of the air quality–meteorology correlation model combining RF and BP–GA is the closest to the real measured value. It shows that the air quality–meteorology correlation model using the fusion model of RF and BP–GA can predict AQI with the utmost accuracy. This work provides a research reference regarding the AQI value of the correlation model of air quality and meteorology and provides data support for the analysis of air quality problems.
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Spatola, Nicolas, and Karl F. Macdorman. "Why Real Citizens Would Turn to Artificial Leaders." Digital Government: Research and Practice 2, no. 3 (July 2021): 1–24. http://dx.doi.org/10.1145/3447954.
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Governments are increasingly using artificial intelligence to improve workflows and services. Applications range from predicting climate change, crime, and earthquakes to flu outbreaks, low air quality, and tax fraud. Artificial agents are already having an impact on eldercare, education, and open government, enabling users to complete procedures through a conversational interface. Whether replacing humans or assisting them, they are the technological fix of our times. In two experiments and a follow-up study, we investigate factors that influence the acceptance of artificial agents in positions of power, using attachment theory and disappointment theory as explanatory models. We found that when the state of the world provokes anxiety, citizens perceive artificial agents as a reliable proxy to replace human leaders. Moreover, people accept artificial agents as decision-makers in politics and security more willingly when they deem their leaders or government to be untrustworthy, disappointing, or immoral. Finally, we discuss these results with respect to theories of technology acceptance and the delegation of duties and prerogatives.
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Arroyo, Patricia, José Herrero, José Suárez, and Jesús Lozano. "Wireless Sensor Network Combined with Cloud Computing for Air Quality Monitoring." Sensors 19, no. 3 (February 8, 2019): 691. http://dx.doi.org/10.3390/s19030691.
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Low-cost air pollution wireless sensors are emerging in densely distributed networks that provide more spatial resolution than typical traditional systems for monitoring ambient air quality. This paper presents an air quality measurement system that is composed of a distributed sensor network connected to a cloud system forming a wireless sensor network (WSN). Sensor nodes are based on low-power ZigBee motes, and transmit field measurement data to the cloud through a gateway. An optimized cloud computing system has been implemented to store, monitor, process, and visualize the data received from the sensor network. Data processing and analysis is performed in the cloud by applying artificial intelligence techniques to optimize the detection of compounds and contaminants. This proposed system is a low-cost, low-size, and low-power consumption method that can greatly enhance the efficiency of air quality measurements, since a great number of nodes could be deployed and provide relevant information for air quality distribution in different areas. Finally, a laboratory case study demonstrates the applicability of the proposed system for the detection of some common volatile organic compounds, including: benzene, toluene, ethylbenzene, and xylene. Principal component analysis, a multilayer perceptron with backpropagation learning algorithm, and support vector machine have been applied for data processing. The results obtained suggest good performance in discriminating and quantifying the concentration of the volatile organic compounds.
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Blagovechshenskiy, Viktor, Akhmetkal Medeu, Tamara Gulyayeva, Vitaliy Zhdanov, Sandugash Ranova, Aidana Kamalbekova, and Ulzhan Aldabergen. "Application of Artificial Intelligence in the Assessment and Forecast of Avalanche Danger in the Ile Alatau Ridge." Water 15, no. 7 (April 6, 2023): 1438. http://dx.doi.org/10.3390/w15071438.
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The assessment and forecast of avalanche danger are very important means of preventing avalanche fatalities, especially in recreational areas. The use of artificial intelligence methods for these purposes significantly increases the accuracy of avalanche forecasts. The purpose of this re-search was to improve the methods for assessing and forecasting avalanche danger in the Ile Alatau Ridge. To create a training sample, the data from three meteorological and two avalanche stations for the period from 2002 to 2022 were used. The following predictors were chosen: air temperature, snow cover depth, precipitation, and snowpack stability index. The subject of the assessment and forecasts was the level of avalanche danger, assessed on a five-point scale. The program Statistica StatSoft was used as a neurosimulator. When forecasting avalanche danger, the predictive values of air temperature and precipitation, obtained from numerical weather forecast models, were used. The model correctly assessed the current level of avalanche danger in 90% of cases. The forecast of avalanche danger was justified in 80% of cases. The artificial intelligence program helped the avalanche forecaster to improve the forecast quality. This method is currently being used for compiling an avalanche bulletin for two river basins in the Ile Alatau.
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Przybył, Krzysztof, and Krzysztof Koszela. "Applications MLP and Other Methods in Artificial Intelligence of Fruit and Vegetable in Convective and Spray Drying." Applied Sciences 13, no. 5 (February 25, 2023): 2965. http://dx.doi.org/10.3390/app13052965.
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The seasonal nature of fruits and vegetables has an immense impact on the process of seeking methods that allow extending the shelf life in this category of food. It is observed that through continuous technological changes, it is also possible to notice changes in the methods used to examine and study food and its microbiological aspects. It should be added that a new trend of bioactive ingredient consumption is also on the increase, which translates into numerous attempts that are made to keep the high quality of those products for a longer time. New and modern methods are being sought in this area, where the main aim is to support drying processes and quality control during food processing. This review provides deep insight into the application of artificial intelligence (AI) using a multi-layer perceptron network (MLPN) and other machine learning algorithms to evaluate the effective prediction and classification of the obtained vegetables and fruits during convection as well as spray drying. AI in food drying, especially for entrepreneurs and researchers, can be a huge chance to speed up development, lower production costs, effective quality control and higher production efficiency. Current scientific findings confirm that the selection of appropriate parameters, among others, such as color, shape, texture, sound, initial volume, drying time, air temperature, airflow velocity, area difference, moisture content and final thickness, have an influence on the yield as well as the quality of the obtained dried vegetables and fruits. Moreover, scientific discoveries prove that the technology of drying fruits and vegetables supported by artificial intelligence offers an alternative in process optimization and quality control and, even in an indirect way, can prolong the freshness of food rich in various nutrients. In the future, the main challenge will be the application of artificial intelligence in most production lines in real time in order to control the parameters of the process or control the quality of raw materials obtained in the process of drying.
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Veiga, Tiago, Arne Munch-Ellingsen, Christoforos Papastergiopoulos, Dimitrios Tzovaras, Ilias Kalamaras, Kerstin Bach, Konstantinos Votis, and Sigmund Akselsen. "From a Low-Cost Air Quality Sensor Network to Decision Support Services: Steps towards Data Calibration and Service Development." Sensors 21, no. 9 (May 5, 2021): 3190. http://dx.doi.org/10.3390/s21093190.
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Air pollution is a widespread problem due to its impact on both humans and the environment. Providing decision makers with artificial intelligence based solutions requires to monitor the ambient air quality accurately and in a timely manner, as AI models highly depend on the underlying data used to justify the predictions. Unfortunately, in urban contexts, the hyper-locality of air quality, varying from street to street, makes it difficult to monitor using high-end sensors, as the cost of the amount of sensors needed for such local measurements is too high. In addition, development of pollution dispersion models is challenging. The deployment of a low-cost sensor network allows a more dense cover of a region but at the cost of noisier sensing. This paper describes the development and deployment of a low-cost sensor network, discussing its challenges and applications, and is highly motivated by talks with the local municipality and the exploration of new technologies to improve air quality related services. However, before using data from these sources, calibration procedures are needed to ensure that the quality of the data is at a good level. We describe our steps towards developing calibration models and how they benefit the applications identified as important in the talks with the municipality.
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Hong, Hyunsu, IlHwan Choi, Hyungjin Jeon, Yumi Kim, Jae-Bum Lee, Cheong Hee Park, and Hyeon Soo Kim. "An Air Pollutants Prediction Method Integrating Numerical Models and Artificial Intelligence Models Targeting the Area around Busan Port in Korea." Atmosphere 13, no. 9 (September 9, 2022): 1462. http://dx.doi.org/10.3390/atmos13091462.
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Exposure to air pollutants, such as PM2.5 and ozone, has a serious adverse effect on health, with more than 4 million deaths, including early deaths. Air pollution in ports is caused by exhaust gases from various elements, including ships, and to reduce this, the International Maritime Organization (IMO) is also making efforts to reduce air pollution by regulating the sulfur content of fuel used by ships. Nevertheless, there is a lack of measures to identify and minimize the effects of air pollution. The Community Multiscale Air Quality (CMAQ) model is the most used to understand the effects of air pollution. In this paper, we propose a hybrid model combining the CMAQ model and RNN-LSTM, an artificial neural network model. Since the RNN-LSTM model has very good predictive performance, combining these two models can improve the spatial distribution prediction performance of a large area at a relatively low cost. In fact, as a result of prediction using the hybrid model, it was found that IOA improved by 0.235~0.317 and RMSE decreased by 4.82~8.50 μg/m3 compared to the case of using only CMAQ. This means that when PM2.5 is predicted using the hybrid model, the accuracy of the spatial distribution of PM2.5 can be improved. In the future, if real-time prediction is performed using the hybrid model, the accuracy of the calculation of exposure to air pollutants can be increased, which can help evaluate the impact on health. Ultimately, it is expected to help reduce the damage caused by air pollution through accurate predictions of air pollution.
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Et. al., D. Saravanan ,. "Predict and Measure Air Quality Monitoring System Using Machine Learning." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 10, 2021): 2562–71. http://dx.doi.org/10.17762/turcomat.v12i2.2217.
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This article looks at how artificial intelligence can help expect the hourly consolidation of air toxinSulphur ozone, element matter (PM2.5), and Sulphur dioxide. As one of the most excellently procedures, AI can efficiently prepare a model on a large amount of data by using large-scale streamlining computations. Even thoughseveral works use AI to predict air quality, most of the earlier studies are limited to long-term data and easilyinstruct regular relapse designs (direct or nonlinear) to expect the hourly air pollution focus. This paper suggestsadvanced analysis to simulate the hourly environmental change focus based on previous days' weather-related data by calculating the expectation for more than 24 hours as an execute multiple tasks learning (MTL) issue. This allows us to choose a suitable model with a variety of regularization strategies. We suggest a useful regularization that maintains the assumption patterns of concurrent hours to be nearby to each other, and we evaluate it to a few common MTL expect completion such as normal Frobenius standard regularization, normal atomicregularization, and '2,1-standard regularization. Our tests revealed that the suggested boundary declining concepts and constant hour-related regularizations outperform open product relapse models and regularizations in terms of execution.
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Shan, Yulong, Ren Zhang, Ismail Gultepe, Yaojia Zhang, Ming Li, and Yangjun Wang. "Gridded Visibility Products over Marine Environments Based on Artificial Neural Network Analysis." Applied Sciences 9, no. 21 (October 23, 2019): 4487. http://dx.doi.org/10.3390/app9214487.
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The reconstruction and monitoring of visibility over marine environments is critically important because of a lack of observations. To travel safely in marine environments, a high quality of visibility data is needed to evaluate navigation risk. Currently, although visibility is available through numerical weather prediction models as well as ground and spaceborne remote sensing platforms and ship measurements, issues still exist over the remote marine environments and northern latitudes. To improve visibility prediction and reduce navigational risks, gridded visibility data based on artificial neural network analysis can be used over marine environments, and the problem can be regarded as an air quality prediction problem based on machine learning algorithms. This new method based on artificial intelligence techniques developed here is tested over the Indian Ocean. The mean error of the inferred visibility from the artificial neural network analysis is found to be less than 8.0%. The results suggested that satellite-based optical thickness and numerical model-based reanalysis data can be used to infer gridded visibility values based on artificial neural network analysis, and that could help us reconstruct and monitor surface gridded visibility values over marine and remote environments.
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Čorný, Ivan. "Possibilities of Application of Computational Intelligence in Monitoring of Heat Production and Supply." Key Engineering Materials 669 (October 2015): 560–67. http://dx.doi.org/10.4028/www.scientific.net/kem.669.560.
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The article deals with on-line monitoring of heat sources with application of predictive system equipped with computational intelligence. In particular, an emphasis is given especially on efficiency, optimization of operation, predictability, and synergistic effects. The operation effectiveness will be evaluated from several perspectives such as the thermal properties of the objects, characteristics and properties of resources, and internal air quality. The proposed system based on the analytical/static approaches (e.g. heat loss models of heated objects) and applying the techniques of computational intelligence (e.g. artificial neural networks) creates a dynamic environment that can predict the amount of heat delivered not only on the basis of the energy requirements for the thermal balance of the heated objects and the current weather forecast, but the use of the data base and universal approximators in the field of computational intelligence on the behavior of objects in different operating conditions.
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Sobrino García, Itziar. "Innovative cities for E-governments. Artificial Intelligence initiatives in the public sector and the conflicts with privacy." Revista de Direito Administrativo e Infraestrutura | RDAI 6, no. 21 (May 29, 2022): 215–30. http://dx.doi.org/10.48143/rdai.21.isobrinho.
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Local governments across the world are in the middle of technological and economic developments that come together in the catch-all label of smart cities or innovative cities. In a smart city, ICT-infused infrastructures enable the extensive monitoring and steering of city maintenance, mobility, air and water quality, energy usage, among others. The effect of growing population and the challenges regarding urbanization and environmental sustainability have led the European Union to adopt different policies and initiatives to promote this new city model. Nevertheless, such processes use and produce massive amounts of data, which could affect people's privacy. Countries like Spain have begun to invest in smart cities and Artificial Intelligence projects to improve efficiency in the public sector. However, the use of artificial intelligence can generate several problems such as opacity, legal uncertainty, or breaches of personal data protection. Therefore, the goal of this article is to identify the main legal challenges for public administrations derived from the development of innovative cities and the use of AI regarding to privacy.
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Marzouk, Mohamed, and Mohamed Atef. "Assessment of Indoor Air Quality in Academic Buildings Using IoT and Deep Learning." Sustainability 14, no. 12 (June 8, 2022): 7015. http://dx.doi.org/10.3390/su14127015.
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Humans spend most of their lifetime indoors; thus, it is important to keep indoor air quality within acceptable levels. As a result, many initiatives have been developed by multiple research centers or through academic studies to address the harmful effects of increased indoor pollutants on public health. This research introduces a system for monitoring different air parameters to evaluate the indoor air quality (IAQ) and to provide real-time readings. The proposed system aims to enhance planning and controlling measures and increase both safety and occupants’ comfort. The system combines microcontrollers and electronic sensors to form an Internet of Things (IoT) solution that collects different indoor readings. The readings are then compared with outdoor readings for the same experiment period and prepared for further processing using artificial intelligence (AI) models. The results showed the high effectiveness of the IoT device in transferring data via Wi-Fi with minimum disruptions and missing data. The average readings for temperature, humidity, air pressure, CO2, CO, and PM2.5 in the presented case study are 30 °C, 42%, 100,422 pa, 460 ppm, 2.2 ppm, and 15.3 µ/m3, respectively. The developed model was able to predict multiple air parameters with acceptable accuracy. It can be concluded that the proposed system proved itself as a powerful forecasting and management tool for monitoring and controlling IAQ.
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Zhu, Yingbo, Shahriar Abdullah Al-Ahmed, Muhammad Zeeshan Shakir, and Joanna Isabelle Olszewska. "LSTM-Based IoT-Enabled CO2 Steady-State Forecasting for Indoor Air Quality Monitoring." Electronics 12, no. 1 (December 27, 2022): 107. http://dx.doi.org/10.3390/electronics12010107.
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Whether by habit or necessity, people tend to spend most of their time indoors. Built-up Carbon dioxide (CO2) can lead to a series of negative health effects such as nausea, headache, fatigue, and so on. Thus, indoor air quality must be monitored for a variety of health reasons. Various air quality monitoring systems are available on the market. However, since they are expensive and difficult to obtain, they are not commonly employed by the general population. With the advent of the Internet of Things (IoT), the Indoor Air Quality (IAQ) monitoring system has been simplified, and a number of studies have been conducted in order to monitor the IAQ using IoT. In this paper, we propose an improved IoT-based, low-cost IAQ monitoring system using Artificial Intelligence (AI) to provide recommendations. In our proposed system, the IoT sensors transmit data via Message Queuing Telemetry Transport (MQTT) protocol which can be visualised in real time on a user-friendly dashboard. Furthermore, the AI technique referred to as Long Short-Term Memory (LSTM) is applied to the collected CO2 data for the purpose of predicting future CO2 concentrations. Based on the predicted CO2 concentration, our system can compute CO2 steady state in advance with an error margin of 5.5%.
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Aryal, Yog. "Application of Artificial Intelligence Models for Aeolian Dust Prediction at Different Temporal Scales: A Case with Limited Climatic Data." AI 3, no. 3 (August 22, 2022): 707–18. http://dx.doi.org/10.3390/ai3030041.
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Accurately predicting ambient dust plays a crucial role in air quality management and hazard mitigation. Dust emission is a complex, non-linear response to several climatic variables. This study explores the accuracy of Artificial Intelligence (AI) models: an adaptive-network-based fuzzy inference system (ANFIS) and a multi-layered perceptron artificial neural network (mlp-NN), over the Southwestern United States (SWUS), based on the observed dust data from IMPROVE stations. The ambient fine dust (PM2.5) and coarse dust (PM10) concentrations on monthly and seasonal timescales from 1990–2020 are modeled using average daily maximum wind speed (W), average precipitation (P), and average air temperature (T) available from the North American Regional Reanalysis (NARR) dataset. The model’s performance is measured using correlation (r), root mean square error (RMSE), and percentage bias (% BIAS). The ANFIS model generally performs better than the mlp-NN model in predicting regional dustiness over the SWUS region, with r = 0.77 and 0.83 for monthly and seasonal fine dust, respectively. AI models perform better in predicting regional dustiness on a seasonal timescale than the monthly timescale for both fine dust and coarse dust. AI models better predict fine dust than coarse dust on both monthly and seasonal timescales. Compared to precipitation, air temperature is the more important predictor of regional dustiness on both monthly and seasonal timescales. The relative importance of air temperature is higher on the monthly timescale than the seasonal timescale for PM2.5 and vice versa for PM10. The findings of this study demonstrate that the AI models can predict monthly and seasonal fine and coarse dust, based on the limited climatic data, with good accuracy and with potential implications for research in data sparse regions.
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Spyrou, Evangelos D., Chrysostomos Stylios, and Ioannis Tsoulos. "Classification of CO Environmental Parameter for Air Pollution Monitoring with Grammatical Evolution." Algorithms 16, no. 6 (June 15, 2023): 300. http://dx.doi.org/10.3390/a16060300.
Full textAbstract:
Air pollution is a pressing concern in urban areas, necessitating the critical monitoring of air quality to understand its implications for public health. Internet of Things (IoT) devices are widely utilized in air pollution monitoring due to their sensor capabilities and seamless data transmission over the Internet. Artificial intelligence (AI) and machine learning techniques play a crucial role in classifying patterns derived from sensor data. Environmental stations offer a multitude of parameters that can be obtained to uncover hidden patterns showcasing the impact of pollution on the surrounding environment. This paper focuses on utilizing the CO parameter as an indicator of pollution in two datasets collected from wireless environmental monitoring devices in the greater Port area and the Town Hall of Igoumenitsa City in Greece. The datasets are normalized to facilitate their utilization in classification algorithms. The k-means algorithm is applied, and the elbow method is used to determine the optimal number of clusters. Subsequently, the datasets are introduced to the grammatical evolution algorithm to calculate the percentage fault. This method constructs classification programs in a human-readable format, making it suitable for analysis. Finally, the proposed method is compared against four state-of-the-art models: the Adam optimizer for optimizing artificial neural network parameters, a genetic algorithm for training an artificial neural network, the Bayes model, and the limited-memory BFGS method applied to a neural network. The comparison reveals that the GenClass method outperforms the other approaches in terms of classification error.