A new feature selection method based on machine learning technique for air quality dataset

Sethi, Jasleen Kaur; Mittal, Mamta

doi:10.1080/09720510.2019.1609726

Cited by 40 publications

(15 citation statements)

References 13 publications

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“…As a result of the four methods, NO 2 and CO contribute the most in forecasting PM 2.5 concentrations. Sethi and Mittal (2019) [8] denote measuring the relationship between air pollutants and PM 2.5 play a vital role in predict accurately the concentration of PM 2.5 . The dependencies among features can also decide the relevance of the feature in the dataset, determined using linear regression.…”

Section: Feature Selectionsmentioning

confidence: 99%

The Prediction of Influenza-like Illness and Respiratory Disease Using LSTM and ARIMA

Tsan

Chen

Liu

et al. 2022

IJERPH

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This paper proposed the forecasting model of Influenza-like Illness (ILI) and respiratory disease. The dataset was extracted from the Taiwan Environmental Protection Administration (EPA) for air pollutants data and the Centers for Disease Control (CDC) for disease cases from 2009 to 2018. First, this paper applied the ARIMA method, which trained based on the weekly number of disease cases in time series. Second, we implemented the Long short-term memory (LSTM) method, which trained based on the correlation between the weekly number of diseases and air pollutants. The models were also trained and evaluated based on five and ten years of historical data. Autoregressive integrated moving average (ARIMA) has an excellent model in the five-year dataset of ILI at 2564.9 compared to ten years at 8173.6 of RMSE value. This accuracy is similar to the Respiratory dataset, which gets 15,656.7 in the five-year dataset and 22,680.4 of RMSE value in the ten-year dataset. On the contrary, LSTM has better accuracy in the ten-year dataset than the five-year dataset. For example, on average of RMSE in the ILI dataset, LSTM has 720.2 RMSE value in five years and 517.0 in ten years dataset. Also, in the Respiratory disease dataset, LSTM gets 4768.6 of five years of data and 3254.3 of the ten-year dataset. These experiments revealed that the LSTM model generally outperforms ARIMA by three to seven times higher model performance.

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Section: Feature Selectionsmentioning

confidence: 99%

The Prediction of Influenza-like Illness and Respiratory Disease Using LSTM and ARIMA

Tsan

Chen

Liu

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…The results suggested an integrated use of linguistic, syntactic and semantic features. Mittal et al [8][9][10][11] have presented the role of machine learning in predicting Crime rates, air quality as well as different data mining techniques exists and how they can be used in different domains. Shastri SN Computer Science et al [12] have presented artificial intelligence technique for prediction of stock market.…”

Section: Related Workmentioning

confidence: 99%

Fake News Detection Using a Blend of Neural Networks: An Application of Deep Learning

Agarwal

Mittal²,

Pathak

et al. 2020

SN COMPUT. SCI.

Self Cite

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Fake news and its consequences carry the potential of impacting different aspects of different entities, ranging from a citizen's lifestyle to a country's global relations, there are many related works for collecting and determining fake news, but no reliable system is commercially available. This study aims to propose a deep learning model which predicts the nature of an article when given as an input. It solely uses text processing and is insensitive to history and credibility of the author or the source. In this paper, authors have discussed and experimented using word embedding (GloVe) for text pre-processing in order to construct a vector space of words and establish a lingual relationship. The proposed model which is the blend of convolutional neural network and recurrent neural networks architecture has achieved benchmark results in fake news prediction, with the utility of word embeddings complementing the model altogether. Further, to ensure the quality of prediction, various model parameters have been tuned and recorded for the best results possible. Among other variations, addition of dropout layer reduces overfitting in the model, hence generating significantly higher accuracy values. It can be a better solution than already existing ones, viz: gated recurrent units, recurrent neural networks or feed-forward networks for the given problem, which generates better precision values of 97.21% while considering more input features.

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“…Linear Regression represents a dependent variable based on the linear combination of various independent variables. 27 A scatter plot is constructed and a correlation is computed between the response variable and the predictors. The regression coefficients that is the intercept and slope coefficient are finally calculated to find the regression line which determines the predicted value of the response variable.…”

Section: Linear Regressionmentioning

confidence: 99%

Monitoring the Impact of Air Quality on the COVID-19 Fatalities in Delhi, India: Using Machine Learning Techniques

Sethi

Mittal²

2020

Disaster med. public health prep.

Self Cite

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Objective: The focus of this study is to monitor the effect of lockdown on the various air pollutants due to COVID-19 pandemic and identify the ones that affect COVID-19 fatalities so that measures to control the pollution could be enforced. Methods: Various machine learning techniques: Decision Trees, Linear Regression and Random Forest have been applied to correlate air pollutants and COVID-19 fatalities in Delhi. Furthermore, a comparison between the concentration of various air pollutants and the air quality index during lockdown period and last two years 2018 and 2019 has been presented. Results: From the experimental work, it has been observed that the pollutants Ozone and Toluene have increased during the lockdown period. It has also been deduced that the pollutants that may impact the mortalities due to COVID-19 are Ozone, NH3, NO2, and PM10. Conclusions: The novel corona virus has led to environmental restoration due to lockdown. However, there is a need to impose measures to control Ozone pollution as there has been a significant increase in its concentration and it also impacts the COVID-19 mortality rate.

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A new feature selection method based on machine learning technique for air quality dataset

Cited by 40 publications

References 13 publications

The Prediction of Influenza-like Illness and Respiratory Disease Using LSTM and ARIMA

The Prediction of Influenza-like Illness and Respiratory Disease Using LSTM and ARIMA

Fake News Detection Using a Blend of Neural Networks: An Application of Deep Learning

Monitoring the Impact of Air Quality on the COVID-19 Fatalities in Delhi, India: Using Machine Learning Techniques

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