Artificial Intelligence; Dataset; Machine Learning

The dataset is generated from the ice-cream factory simulation environmen that is composed of six modules (Mixer, Pasteurizer, Homogenizer, Aeging Cooling, Dynamic Freezer, and Hardening). The values of analog sensors for level and temperature are modified using three anomaly injection options: freezing value, step change and ramp change. The dataset is composed of 1000 runs, out of which 258 were executed without anomalies.

Link to github: https://github.com/vujicictijana/MIDAS

 

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The dataset contains the data collected using an Arduino Nano 33 BLE Sense for several classification tasks: color detection, keyword spotting, sound frequency recognition, vibration pattern detection, hand-gesture recognition, and vibration intensity detection. 

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Currency recognition and classification is one essential task to do. Both paper and coin currency play important role in transactions in everyday life. But provided there are many datasets available of paper currency, and very less datasets are available of coin currency.  Coin currency recognition becomes important because even though the amount for which people do coin transactions is small but inaccuracy in recognition can lead to huge loss. Following are the objectives to create this dataset:

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The goal of our research is to identify malicious advertisement URLs and to apply adversarial attack on ensembles. We extract lexical and web-scrapped features from using python code. And then 4 machine learning algorithms are applied for the classification process and then used the K-Means clustering for the visual understanding. We check the vulnerability of the models by the adversarial examples. We applied Zeroth Order Optimization adversarial attack on the models and compute the attack accuracy.

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