Blockchain-Enabled Federated Learning on Kubernetes for Air Quality Prediction Applications

The biggest realization of the Machine Learning (ML) in societal applications, including air quality prediction, has been the inclusion of novel learning techniques with the focus on solving privacy and scalability issues which capture the inventiveness of tens of thousands of data scientists. Transferring learning models across multi-regions or locations has been a considerable challenge as sufficient technologies were not adopted in the recent past. This paper proposes a Blockchain- enabled Federated Learning Air Quality Prediction (BFL-AQP) framework on Kubernetes cluster which transfers the learning model parameters of ML algorithms across distributed cluster nodes and predicts the air quality parameters of different locations. Experiments were carried out to explore the frame- work and transfer learning models of air quality prediction parameters. Besides, the performance aspects of increasing the Kubernetes cluster nodes of blockchains in the federated learning environment were studied; the time taken to establish seven blockchain organizations on top of the Kubernetes cluster while investigating into the federated learning algorithms namely Federated Random Forests (FRF) and Federated Linear Regression (FLR) for air quality predictions, were revealed in the paper.

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Air Quality Prediction by Classification of Supervised Machine Learning

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.d6782.049420 ◽

2020 ◽

Vol 9 (4) ◽

pp. 516-521

Keyword(s):

Machine Learning ◽

Air Pollution ◽

User Interface ◽

Air Quality ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Model Parameters ◽

Quality Prediction ◽

Air Quality Prediction

Generally, air pollution refer to the release of various pollutants into the air which are threatening the human health and planet as well. The air pollution is the major dangerous vicious to the humanity ever faced. It causes major damage to animals, plants etc., if this keeps on continuing, the human being will face serious situations in the upcoming years. The major pollutants are from the transport and industries. So, to prevent this problem major sectors have to predict the air quality from transport and industries .In existing project there are many disadvantages. The project is about estimating the PM2.5 concentration by designing a photograph based method. But photographic method is not alone sufficient to calculate PM2.5 because it contains only one of the concentration of pollutants and it calculates only PM2.5 so there are some missing out of the major pollutants and the information needed for controlling the pollution .So thereby we proposed the machine learning techniques by user interface of GUI application. In this multiple dataset can be combined from the different source to form a generalized dataset and various machine learning algorithms are used to get the results with maximum accuracy. From comparing various machine learning algorithms we can obtain the best accuracy result. Our evaluation gives the comprehensive manual to sensitivity evaluation of model parameters with regard to overall performance in prediction of air high quality pollutants through accuracy calculation. Additionally to discuss and compare the performance of machine learning algorithms from the dataset with evaluation of GUI based user interface air quality prediction by attributes.

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Improving air quality prediction accuracy at larger temporal resolutions using deep learning and transfer learning techniques

Atmospheric Environment ◽

10.1016/j.atmosenv.2019.116885 ◽

2019 ◽

Vol 214 ◽

pp. 116885 ◽

Cited By ~ 27

Author(s):

Jun Ma ◽

Jack C.P. Cheng ◽

Changqing Lin ◽

Yi Tan ◽

Jingcheng Zhang

Keyword(s):

Deep Learning ◽

Air Quality ◽

Transfer Learning ◽

Prediction Accuracy ◽

Quality Prediction ◽

Air Quality Prediction ◽

Learning Techniques

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Air Quality Prediction based on Supervised Machine Learning Methods

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i1132.0789s419 ◽

2019 ◽

Vol 8 (9S4) ◽

pp. 206-212

Keyword(s):

Machine Learning ◽

Air Quality ◽

Decision Tree ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Support Vector ◽

Quality Prediction ◽

Validation Data ◽

Air Quality Prediction ◽

Learning Techniques

Generally, Air pollution alludes to the issue of toxins into the air that are harmful to human well being and the entire planet. It can be described as one of the most dangerous threats that the humanity ever faced. It causes damage to animals, crops, forests etc. To prevent this problem in transport sectors have to predict air quality from pollutants using machine learning techniques. Subsequently, air quality assessment and prediction has turned into a significant research zone. The aim is to investigate machine learning based techniques for air quality prediction. The air quality dataset is preprocessed with respect to univariate analysis, bi-variate and multi-variate analysis, missing value treatments, data validation, data cleaning/preparing. Then, air quality is predicted using supervised machine learning techniques like Logistic Regression, Random Forest, K-Nearest Neighbors, Decision Tree and Support Vector Machines. The performance of various machine learning algorithms is compared with respect to Precision, Recall and F1 Score. It is found that Decision Tree algorithm works well for predicting air quality. This application can help the meteorological Department in predicting air quality. In future, this work can be optimized by applying Artificial Intelligence techniques.

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