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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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PM₂.₅ Monitoring: Use Information Abundance Measurement and Wide and Deep Learning.

Ke Gu, Hongyan Liu, Zhifang Xia

    IEEE Transactions on Neural Networks and Learning Systems
    |August 30, 2021
    PubMed
    Summary

    This study introduces a novel photograph-based model to estimate real-time PM2.5 concentrations, offering a wide-reaching alternative to traditional sensors. This advancement aids atmospheric monitoring and public health initiatives.

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    Area of Science:

    • Environmental Science
    • Computer Science
    • Data Science

    Background:

    • Current electrochemical sensor methods for PM2.5 monitoring suffer from limited spatial coverage and delayed readings.
    • Accurate real-time PM2.5 monitoring is crucial for atmospheric forecasting, pollution control, and public health, including mitigating disease outbreaks like COVID-19.

    Purpose of the Study:

    • To develop a photograph-based model for estimating real-time PM2.5 concentrations.
    • To overcome the limitations of existing sensor-based monitoring techniques.
    • To provide a widely applicable method for PM2.5 monitoring in urban environments.

    Main Methods:

    • A novel monitoring model, Information Abundance and Wide and Deep learning (IAWD), was developed.
    • Features were extracted in a new DS transform space to measure Information Abundance (IA), which correlates inversely with PM2.5 concentration.
    • A Wide and Deep neural network was employed to learn the mapping between extracted features and groundtruth PM2.5 levels.

    Main Results:

    • The proposed IAWD model demonstrated effectiveness in estimating PM2.5 concentrations using photographs.
    • Experiments with over 100,000 images validated the superiority of the IAWD model compared to existing methods.
    • The extracted features in the DS transform space proved effective for PM2.5 monitoring.

    Conclusions:

    • The photograph-based IAWD model offers a viable and effective solution for real-time PM2.5 monitoring.
    • This method provides a high-density, low-delay alternative to conventional PM2.5 sensors.
    • The approach supports improved atmospheric monitoring and informed decision-making for environmental and public health strategies.