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Abstract. Radar observations of winter storms often exhibit locally enhanced linear features in reflectivity, sometimes labeled as snow bands. We have developed a new, objective method for detecting locally enhanced echo features in radar data from winter storms. In comparison to convective cells in warm season precipitation, these features are usually less distinct from the background echo and often have more fuzzy or feathered edges. This technique identifies both prominent, strong features and more subtle, faint features. A key difference from previous radar reflectivity feature detection algorithms is the combined use of two adaptive differential thresholds, one that decreases with increasing background values and one that increases with increasing background values. The algorithm detects features within a snow rate field rather than reflectivity and incorporates an underestimate and overestimate of feature areas to account for uncertainties in the detection. We demonstrate the technique on several examples from the US National Weather Service operational radar network. The feature detection algorithm is highly customizable and can be tuned for a variety of data sets and applications.
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Spammer Detection via Combined Neural Network
Social networks, as an indispensable part of our daily lives, provide ideal platforms for entertainment and communication. However, the appearance of spammers who spread malicious information pollutes a network’s reliability. Unlike email spammers detection, a social network account has several types of attributes and complicated behavior patterns, which require a more sophisticated detection mechanism. To address the above challenges, we propose several efficient profiles and behavioral features to describe a social network account and a combined neural network to detect the spammers. The combined neural network can process the features separately based on their mutual correlation and handle data with missing features. In experiments, the combined neural network outperforms several classical machine learning approaches and achieves 97.5% accuracy on real data. The proposed features and the combined neural network have already been applied commercially.
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- Award ID(s):
- 1704204
- PAR ID:
- 10099590
- Date Published:
- Journal Name:
- Perner P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science
- Volume:
- 10934
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-319-96136-1_28
