  The pervasiveness of GPS-enabled mobile devices and the widespread use of
location-based services have resulted in the generation of massive amounts of
geo-tagged data. In recent times, the data analysis now has access to more
sources, including reviews, news, and images, which also raises questions about
the reliability of Point-of-Interest (POI) data sources. While previous
research attempted to detect fake POI data through various security mechanisms,
the current work attempts to capture the fake POI data in a much simpler way.
The proposed work is focused on supervised learning methods and their
capability to find hidden patterns in location-based data. The ground truth
labels are obtained through real-world data, and the fake data is generated
using an API, so we get a dataset with both the real and fake labels on the
location data. The objective is to predict the truth about a POI using the
Multi-Layer Perceptron (MLP) method. In the proposed work, MLP based on data
classification technique is used to classify location data accurately. The
proposed method is compared with traditional classification and robust and
recent deep neural methods. The results show that the proposed method is better
than the baseline methods.
