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Incident Alert Priority Levels Classification in Command and Control Centre Using Word Embedding Techniques

Abstract:

This research analyzes the textual content of emergency calls that arrive at command and control centers to classify them according to their priority. The aim is to respond promptly and make appropriate decisions in situations that require immediate attention. Text mining techniques construct a computational model with preprocessing techniques such as tokenization, case folding, and stop words removal. The calls are then represented using Word Embeddings with the Skip-Gram architecture to obtain word vectorization, and the Clustering algorithm is applied for classification. The results show an improvement in classification accuracy, achieving 95% precision in classification tests using high and low-priority categories and 81% in classification tests using four alert categories. These techniques enhance the syntactic and semantic understanding of emergency calls and reduce the risk of loss of human life.