SHORT CIRCUIT INDICATOR USING ARDUINO

Abstract

With the goal of preventing unauthorized access into places where people live, this study offers a
novel method for identifying different animal species based on their footprints. The suggested technique consists
of a series of steps that include warning system, probabilistic neural network (PNN) classification, Canny edge
detection algorithm, and picture processing.
During the first stage, footprints that are gathered close to places of residence are processed to improve their
quality and extract pertinent information. Then the Canny algorithm is used to find edges and identify certain
patterns in the footprints. These processed footprints are sent into the PNN, a powerful classification method
that can identify complex patterns and reliably predict species.
Using a large dataset of known animal footprints, the PNN is trained to identify certain traits across different
species. When a new footprint is seen, the PNN quickly examines its distinct characteristics and offers a species
categorization. This categorization is then used to activate a warning system that notifies locals of the possible
existence of a certain animal species close to their place of residence.
Homeowners may take preventative action and avoid unintentional interactions with wildlife thanks to this allinclusive
method that provides a proactive plan to reduce human-wildlife conflicts. This work promotes safety
and upholds the integrity of both environments by using edge detection, machine learning, image processing,
and real-time alerts to foster a happy coexistence between people and animals