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Related Concept Videos

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Related Experiment Video

Updated: Jan 10, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

885

AiM: urban air quality forecasting with grid-embedded recurrent MLP model.

Kalyan Chatterjee1, Bhoomeshwar Bala1, Mudassir Khan2

  • 1Computer Science & Engineering, Nalla Malla Reddy Engineering College, Hyderabad, Telangana, 500088, India.

Scientific Reports
|November 29, 2025
PubMed
Summary

A new hybrid model, AiM, improves urban air quality forecasting by integrating spatial and temporal data. This advanced system offers more accurate predictions and faster processing for smart city applications.

Keywords:
Air quality forecastingGrid-embedded architectureIoTRecurrentSmart citiesSpatiotemporal

Related Experiment Videos

Last Updated: Jan 10, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

885

Area of Science:

  • Environmental Science
  • Computer Science
  • Data Science

Background:

  • Urban air pollution presents significant public health and environmental challenges.
  • Accurate, low-latency air quality forecasting is crucial for real-world smart city infrastructure.

Purpose of the Study:

  • To develop and evaluate a novel hybrid model, the Grid-Embedded Recurrent Multi-Layer Perceptron (AiM), for enhanced urban air quality forecasting.
  • To improve the accuracy and efficiency of air quality predictions by considering spatial and temporal factors.

Main Methods:

  • A hybrid model combining a recurrent Multi-Layer Perceptron (R-MLP) with a Grid-Embedded framework.
  • Grid-based partitioning of urban areas to capture localized pollutant dispersion patterns.
  • Feature engineering incorporating pollutant interactions, meteorological data, and grid adjacency for cross-regional correlation analysis.

Main Results:

  • AiM demonstrated superior forecasting accuracy over conventional models (LSTM, GRU, CNN-RNN hybrids), reducing RMSE by up to 12.4%.
  • Achieved a 35% reduction in inference latency on edge devices.
  • The model showed high scalability and suitability for real-time deployment in smart city air quality management.

Conclusions:

  • The AiM model offers a significant advancement in urban air quality forecasting, balancing accuracy and efficiency.
  • Its ability to integrate with IoT nodes makes it a viable solution for real-time smart city environmental monitoring.