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Deep Convolutional Neural Network For Indoor Regio by Hamza OKD is a document available to read on EtoBox.

What is Deep Convolutional Neural Network For Indoor Regio about?

This document proposes a deep learning model called STA-CFPNet to predict indoor regional crowd flow. STA-CFPNet uses convolutional neural networks to capture spatial correlations between indoor regions and temporal dependencies in crowd flow data. It has four branches to model temporal closeness, periodicity, tendency, and external factors that influence crowd flow. STA-CFPNet incorporates attention mechanisms and feature fusion to improve prediction accuracy. An empirical study shows STA-CFPNet outperform

Author

Hamza OKD

Language

EN