The digital age is marked by an extraordinary growth in connected devices, leading to a massive influx of data through the Internet [12]. This data is primarily managed by cloud infrastructures. The proliferation of smart devices such as smartphones, tablets, smartwatches, and fitness trackers has transformed them into essential aspects of daily life [8]. These devices accumulate extensive contextual information about users, encompassing their location, activities, and environmental conditions [5]. This information is crucial for applications in predicting user behavior and providing personalized experiences. Mobile crowdsourcing has emerged as a significant phenomenon, where individuals collectively contribute data through various digital channels [32]. Applications in this domain, like traffic monitoring systems, utilize crowd-sourced data to offer real-time insights. However, the process often raises concerns about the privacy of individual contributors. The transparency in data usage and the potential risk of sensitive information being accessed by unauthorized entities are issues that need addressing [11, 26].

Recommending points of interest (POIs) is a challenging task that requires extracting comprehensive location data from location-based social media platforms. To provide effective location-based recommendations, it's important to analyze users' historical behavior and preferences. In this study, we present a sophisticated location-aware recommendation system that uses Bidirectional Encoder Representations from Transformers (BERT) to offer personalized location-based suggestions. Our model combines location information and user preferences to provide more relevant recommendations compared to models that predict the next POI in a sequence. Our experiments on two benchmark dataset show that our BERT-based model outperforms various state-of-the-art sequential models. Moreover, we see the effectiveness of the proposed model for quality through additional experiments.

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.