AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The findings highlight the potential of HFL with fog computing as a scalable, cost-effective, and privacy-preserving solution for real-time urban air quality monitoring in India, particularly in resource-constrained smart city environments.
Experimental results confirm competitive prediction accuracy, real-time responsiveness, and seamless scalability, affirming the IQMMS system's suitability for next-generation smart city deployments.
There are significant gaps that remain in terms of model interpretability and the ability to generalize across climate variations, so this article provides a relatively comprehensive overview of the application of federated learning in air quality forecasting and monitoring.
This review emphasizes the vital role of civil engineers in promoting resilient and sustainable urban development, advancing effective air quality management, and safeguarding public health through interdisciplinary collaboration between data scientists and policymakers.
An end-to-end Internet of Things framework designed for real-time water quality monitoring and predictive pollution modeling and a hybrid machine learning architecture—combining Long Short-Term Memory (LSTM) networks for time-series forecasting and Random Forest models for anomaly classification—is proposed.
Air quality information is commonly communicated through the air quality index (AQI), which residents often access through the nearest available monitoring site. Motivated by this real-world use pattern, we evaluated how well different monitoring networks represent local AQI across California’s San Joaquin Valley, one...
Air pollution is a global environmental and health issue that has adverse effects on climate change and human health and contributes to ecosystem degradation and biodiversity loss. Air quality monitoring is therefore crucial to understand the sources and impacts of air pollution and to assess mitigation interventions....
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