Open AccessTuning Neural Networks for Geofencing Applications
Author(s) -
George Eldho John,
Rohit Joseph Mamutil
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1831/1/012017
Subject(s) - computer science , artificial neural network , key (lock) , field (mathematics) , global positioning system , artificial intelligence , point (geometry) , range (aeronautics) , data mining , machine learning , telecommunications , computer security , engineering , geometry , mathematics , pure mathematics , aerospace engineering
Neural networks have been used in a wide range of applications such as Image processing, Object detection, Weather and Economic Forecast and many other areas where algorithms which could learn from data have been revolutionizing. This study elaborates on the usage of neural networks in the field of geofencing. Ever since the advent of satellite-based Maps, Geofencing has been mentioned as one of the key possible features. With the booming usage of smartphones and IoT based devices, Geofence has a key role to play in location based advertising, marketing, GPS tracking, etc. This paper explains how to use neural networks for detecting whether a point is in geofence or not. It is proved in this study that neural networks perform better over commonly used techniques such as the Ray casting method in some scenarios.