AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
A Temporal-Aware Multi-Task Learning (TMTL-AQI) framework to assess urban air quality via structured data that outperforms single-task and baseline multi-task models with an accuracy of 0.7605 and an F1-score of 0.7422.
Air pollution in Delhi has reached dangerous levels, necessitating accurate and dependable Air Quality Index (AQI) forecasts for timely intervention. However, traditional monitoring methods and stand-alone machine learning algorithms frequently fail to capture the complex, nonlinear relationships between contaminants a...
An intelligent system that assigns an observation to one of six air quality index (AQI) classes, used as a proxy for the level of harm to population health, together with a comparative evaluation of the six models behind it is presented.
Comparative analysis indicates that hybrid and Transformer-based models consistently achieve superior predictive performance, while RMSE, MAE, and R² remain the most widely adopted evaluation metrics.
The efficiency of the proposed DNN model is proved, and the comparative analysis with the baseline models, such as Linear Regression and Support Vector Regression, demonstrates that the proposed model is more effective than traditional techniques in identifying nonlinear dependencies in air pollution data.
The experimental results reveal that the XGBoost model outperforms the other models, while hyperparameter tuning further improves the effectiveness of both the Decision Tree and KNN models.
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