PM2.5 Concentration Prediction Based on CNN-BiLSTM and Attention Mechanism

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Attention-based parallel networks (APNet) for PM2.5 spatiotemporal prediction

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Bidirectional LSTM with self-attention mechanism and multi-channel features for sentiment classification

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Interpreting network knowledge with attention mechanism for bearing fault diagnosis

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Sources, species and secondary formation of atmospheric aerosols and gaseous precursors in the suburb of Kitakyushu, Japan

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CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection

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Global burden of ischemic heart disease attributable to ambient PM2.5 pollution from 1990 to 2017

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Suspended fine particulate matter (PM2.5), microplastics (MPs), and polycyclic aromatic hydrocarbons (PAHs) in air: Their possible relationships and health implications

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Predicting PM2.5 in Well-Mixed Indoor Air for a Large Office Building Using Regression and Artificial Neural Network Models

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Change Point Detection for Airborne Particulate Matter (PM2.5, PM10) by Using the Bayesian Approach

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Constructing a PM2.5 concentration prediction model by combining auto-encoder with Bi-LSTM neural networks

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A hybrid multi-resolution multi-objective ensemble model and its application for forecasting of daily PM2.5 concentrations

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MFQE 2.0: A New Approach for Multi-Frame Quality Enhancement on Compressed Video

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Air pollution in China: Status and spatiotemporal variations

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Multiscale predictions of aviation-attributable PM 2.5 for U.S. airports modeled using CMAQ with plume-in-grid and an aircraft-specific 1-D emission model

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Estimating long-term PM2.5 concentrations in China using satellite-based aerosol optical depth and a chemical transport model

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Predicting submicron air pollution indicators: a machine learning approach

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PM2.5 concentration prediction using hidden semi-Markov model-based times series data mining

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