A long short-term memory approach to predicting air quality based on social media data

2020 ◽  
Vol 237 ◽  
pp. 117411
Author(s):  
Weixin Zhai ◽  
Chengqi Cheng
Keyword(s):  
Social Media ◽  
Air Quality ◽  
Short Term Memory ◽  
Short Term ◽  
Social Media Data ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Media Data

scholarly journals Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models

2020 ◽  
Vol 12 (6) ◽  
pp. 2570 ◽  
Author(s):  
Thanongsak Xayasouk ◽  
HwaMin Lee ◽  
Giyeol Lee
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Short Term Memory ◽  
Batch Size ◽  
Quality Data ◽  
Short Term ◽  
Optimal Learning ◽  
Term Memory ◽  
Batch Sizes ◽  
Long Short Term Memory

Many countries worldwide have poor air quality due to the emission of particulate matter (i.e., PM10 and PM2.5), which has led to concerns about human health impacts in urban areas. In this study, we developed models to predict fine PM concentrations using long short-term memory (LSTM) and deep autoencoder (DAE) methods, and compared the model results in terms of root mean square error (RMSE). We applied the models to hourly air quality data from 25 stations in Seoul, South Korea, for the period from 1 January 2015, to 31 December 2018. Fine PM concentrations were predicted for the 10 days following this period, at an optimal learning rate of 0.01 for 100 epochs with batch sizes of 32 for LSTM model, and DAEs model performed best with batch size 64. The proposed models effectively predicted fine PM concentrations, with the LSTM model showing slightly better performance. With our forecasting model, it is possible to give reliable fine dust prediction information for the area where the user is located.

scholarly journals Detecting Fake News Over Job Posts via Bi-Directional Long Short-Term Memory (BIDLSTM)

2021 ◽  
Vol 16 (6) ◽  
pp. 1-18
Author(s):  
T. V. Divya ◽  
Barnali Gupta Banik
Keyword(s):  
Social Media ◽  
Performance Metrics ◽  
Short Term Memory ◽  
Word Embedding ◽  
Support Vector ◽  
Fake News ◽  
Short Term ◽  
Term Memory ◽  
Online Social Media ◽  
Long Short Term Memory

Fake news detection on job advertisements has grabbed the attention of many researchers over past decade. Various classifiers such as Support Vector Machine (SVM), XGBoost Classifier and Random Forest (RF) methods are greatly utilized for fake and real news detection pertaining to job advertisement posts in social media. Bi-Directional Long Short-Term Memory (Bi-LSTM) classifier is greatly utilized for learning word representations in lower-dimensional vector space and learning significant words word embedding or terms revealed through Word embedding algorithm. The fake news detection is greatly achieved along with real news on job post from online social media is achieved by Bi-LSTM classifier and thereby evaluating corresponding performance. The performance metrics such as Precision, Recall, F1-score, and Accuracy are assessed for effectiveness by fraudulency based on job posts. The outcome infers the effectiveness and prominence of features for detecting false news. .

scholarly journals Comparative Analysis of Deep Learning Techniques for the Classification of Hate Speech

2021 ◽  
Vol 4 (1) ◽  
pp. 121-128
Author(s):  
A Iorliam ◽  
S Agber ◽  
MP Dzungwe ◽  
DK Kwaghtyo ◽  
S Bum
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Deep Learning ◽  
Hate Speech ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Learning Techniques ◽  
Or Groups ◽  
Long Short Term Memory

Social media provides opportunities for individuals to anonymously communicate and express hateful feelings and opinions at the comfort of their rooms. This anonymity has become a shield for many individuals or groups who use social media to express deep hatred for other individuals or groups, tribes or race, religion, gender, as well as belief systems. In this study, a comparative analysis is performed using Long Short-Term Memory and Convolutional Neural Network deep learning techniques for Hate Speech classification. This analysis demonstrates that the Long Short-Term Memory classifier achieved an accuracy of 92.47%, while the Convolutional Neural Network classifier achieved an accuracy of 92.74%. These results showed that deep learning techniques can effectively classify hate speech from normal speech.

Air quality modelling using long short-term memory (LSTM) over NCT-Delhi, India

2019 ◽  
Vol 12 (8) ◽  
pp. 899-908 ◽  
Author(s):  
Mrigank Krishan ◽  
Srinidhi Jha ◽  
Jew Das ◽  
Avantika Singh ◽  
Manish Kumar Goyal ◽  
...  
Keyword(s):  
Air Quality ◽  
Short Term Memory ◽  
Air Quality Modelling ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Prediction of Ambient PM2.5 Concentrations Using a Correlation Filtered Spatial-Temporal Long Short-Term Memory Model

2019 ◽  
Vol 10 (1) ◽  
pp. 14 ◽  
Author(s):  
Yuexiong Ding ◽  
Zheng Li ◽  
Chengdian Zhang ◽  
Jun Ma
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Quality Prediction ◽  
Short Term ◽  
Concentration Data ◽  
Term Memory ◽  
Air Quality Prediction ◽  
Long Short Term Memory

Due to the increasingly serious air pollution problem, air quality prediction has been an important approach for air pollution control and prevention. Many prediction methods have been proposed in recent years to improve the prediction accuracy. However, most of the existing methods either did not consider the spatial relationships between monitoring stations or overlooked the strength of the correlation. Excluding the spatial correlation or including too much weak spatial inputs could influence the modeling and reduce the prediction accuracy. To overcome the limitation, this paper proposes a correlation filtered spatial-temporal long short-term memory (CFST-LSTM) model for air quality prediction. The model is designed based on the original LSTM model and is equipped with a spatial-temporal filter (STF) layer. This layer not only takes into account the spatial influence between stations, but also can extract highly correlated sequential data and drop weaker ones. To evaluate the proposed CFST-LSTM model, hourly PM2.5 concentration data of California are collected and preprocessed. Several experiments are conducted. The experimental results show that the CFST-LSTM model can effectively improve the prediction accuracy and has great generalization.

Sign in / Sign up

Export Citation Format

Share Document