The number of private vehicles is still increasing from year to year. In order to limit environmental damage, a proper way of dealing with this trend is the introduction of intelligent automotive infrastructure. Besides traffic management solutions, smart parking guidance systems are important for reducing unnecessary traffic. For this, a key prerequisite are sensor networks that provide information about the occupancy state of every single parking spot in the parking infrastructure of high traffic targets e.g. nearby an airport or shopping mall.


A reasonable approach to cope with increasing car traffic is the application of large-scale car traffic management solutions. Dense and widely applied car traffic monitoring is an important key prerequisite for this.

Established solutions like e.g. induction loops, video-camera-based systems, or radar, do not suit all the needs with regard to installation effort, privacy, and cost efficiency.


These datasets are used for epidemilogical modeling using artifical neural network.


The Bluetooth 5.1 Core Specification brought Angle of Arrival (AoA) based Indoor Localization to the Bluetooth Standard. This dataset is the result of one of the first comprehensive studies of static Bluetooth AoA-based Indoor Localization in a real-world testbed using commercial off-the-shelf Bluetooth chipsets.

The positioning experiments were carried out on a 100 m² test area using four stationary Bluetooth sensor devices each equipped with eight antennas. With this setup, a median localization accuracy of up to 18 cm was achieved.


Each voice sample is stored as a .WAV file, which is then pre-processed for acoustic analysis using the specan function from the WarbleR R package. Specan measures 22 acoustic parameters on acoustic signals for which the start and end times are provided.

The output from the pre-processed WAV files were saved into a CSV file, containing 3168 rows and 21 columns (20 columns for each feature and one label column for the classification of male or female).


The dataset consists of reviews for various hotels throughout the world and data columns range from Location, Trip Type to various parameters of reviewing with individual review score. The data can be preprocessed and used for various purposes ranging from review categorization, topic extraction, sentiment analysis, location based quality calculation etc. Trustworthy real world data comes handy now-a-days and is tough to get a grasp on. So this dataset will be a good contribution for the researcher community as well as professionals.