Description: Juvenile anomaly detection refers to techniques focused on identifying unusual behaviors or activities in data related to youth. This approach uses artificial intelligence (AI) algorithms to analyze behavior patterns, allowing researchers and professionals to detect deviations that may indicate issues such as bullying, violence, substance abuse, or school dropout. Anomaly detection is based on the premise that typical behaviors of youth can be modeled, and thus any significant deviation from these models can be considered an anomaly. This process involves collecting data from various sources, such as social media, educational records, and surveys, and analyzing it using machine learning techniques. The relevance of this technique lies in its ability to provide early and personalized interventions, thereby improving youth well-being and preventing social problems. Additionally, juvenile anomaly detection can contribute to academic research and the formulation of more effective public policies by offering a deeper understanding of the factors affecting youth in contemporary society.
