Dynamic detection of emergencies based on a hybrid architecture of multi-view clustering and multilayer perceptron

Key Points

• The aim is to improve emergency detection systems through a hybrid machine learning architecture.
• Implemented a hybrid architecture combining multi-view clustering and multilayer perceptron for enhanced performance.
• Designed to analyze data from multiple perspectives to improve detection accuracy.
• Utilized algorithms to automatically adapt to changing emergency scenarios.
• Demonstrated improved detection rates compared to traditional methods.
• Achieved greater accuracy in real-time emergency situations.
• Showed the system's capability to learn from diverse data inputs.