Anomaly Detection Techniques and Applications
Anomaly Detection Techniques and Applications is a research topic within Artificial Intelligence. Science Explorer counts 55k research works in it since 1950. 9.8% of them reached the world's top 10% most cited for their field and year.
This cluster of papers focuses on the detection of anomalies in high-dimensional data, particularly in the context of video analysis, surveillance, and time series data. It covers a wide range of techniques including unsupervised learning, outlier detection, deep learning, and novelty detection for identifying abnormal patterns and events.
- Anomaly Detection
- Unsupervised
- Outlier Detection
- Deep Learning
- High-Dimensional Data
- Video Analysis
- Neural Networks
- Novelty Detection
- Surveillance
- Time Series
- Research works
- 55k fractional, since 1950
- In the world top 10%
- 5.3k per year above
- Top-10% rate
- 9.8% share of its works in the world top 10%
- Growth, 2018-21 → 2022-25
- +93% the tick is no change
- Largest producer
China
48% of the 10 largest, 2022-now
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