Abstract:
According to the Center for Homeland Defense and Security, in the first half of 2022, 2 active school shooter and 151
non-active shooter events resulted in 150 victims; the previous year’s statistic the highest it has been since 1970. Most students
displayed signs of mental illness and troubled behavior that was often overlooked. This research seeks to identify signs of a threat in
order to distinguish and assist students who are at risk for violent behavior. 30 randomly selected shooters were analyzed through the
processing of news reports to identify recurring psychosocial attributes using a WordCloud generator. A feed forward neural network
then uses these traits to recognize and categorize potential growing threats in a student body. Data is collected through deep learning
graphological parameters in students’ handwriting using a 2D convolutional neural network. This model, with an overall accuracy of
97%, classifies cases based on the combination of 28 features that appeared in the initially studied cases. It generates an accessible
report that quickly identifies students in need of immediate support, reducing the number of active-shooter incidents. The School Threat
Assessment System (STAS) is available online to school systems working to increase the safety of their students from within
