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ai_anomaly_detection [2025/05/24 14:38] – [Applications] eagleeyenebulaai_anomaly_detection [2025/06/26 18:20] (current) – [AI Anomaly Detection] eagleeyenebula
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 ====== AI Anomaly Detection ====== ====== AI Anomaly Detection ======
-* **[[https://autobotsolutions.com/god/templates/index.1.html|More Developers Docs]]**:+[[https://autobotsolutions.com/aurora/wiki/doku.php?id=ai_anomaly_detection|Wiki]]: [[https://autobotsolutions.com/god/templates/ai_anomaly_detection.html|Framework]]: [[https://github.com/AutoBotSolutions/Aurora/blob/Aurora/ai_anomaly_detection.py|GitHub]]: [[https://autobotsolutions.com/artificial-intelligence/anomaly-detection-module-proactive-insights-for-data-consistency/|Article]]: 
 The **AI Anomaly Detection** system is a Python-based utility that identifies outliers in datasets using statistical principles like standard deviation. This function is essential for finding anomalous data points that deviate significantly from the dataset's normal range. The **AI Anomaly Detection** system is a Python-based utility that identifies outliers in datasets using statistical principles like standard deviation. This function is essential for finding anomalous data points that deviate significantly from the dataset's normal range.
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 +{{youtube>0m1Ivadc2PM?large}}
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 ===== Overview ===== ===== Overview =====
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 **Threshold for Anomalies**: **Threshold for Anomalies**:
 Data points are considered anomalies if they fall outside the range: Data points are considered anomalies if they fall outside the range:
 +<code>
 [mean - (3 * standard deviation), mean + (3 * standard deviation)] [mean - (3 * standard deviation), mean + (3 * standard deviation)]
 +</code>
 ==== 2. Logging Information ==== ==== 2. Logging Information ====
  
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 **Example Log Messages**: **Example Log Messages**:
 +<code>
 INFO: Detecting anomalies in the data... INFO: Anomalies detected: [120, -45] INFO: Detecting anomalies in the data... INFO: Anomalies detected: [120, -45]
 +</code>
 ====== Function Details ====== ====== Function Details ======
  
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 **Signature**: **Signature**:
-   python+<code> 
 +python
 def detect_anomalies(data: List[float]) -> List[float]: def detect_anomalies(data: List[float]) -> List[float]:
     """     """
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     :return: List of anomalies detected     :return: List of anomalies detected
     """     """
 +</code>
 ===== Examples ===== ===== Examples =====
  
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 3. **Visualization**: 3. **Visualization**:
 Combine detection results with visualizations for better interpretability. Combine detection results with visualizations for better interpretability.
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---- 
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 ===== Conclusion ===== ===== Conclusion =====
  
 The **AI Anomaly Detection** framework provides a robust, flexible, and extensible mechanism for outlier detection in numerical datasets. With applications ranging from real-time monitoring to preprocessing for AI pipelines, the system is a valuable tool for automated anomaly analysis. By leveraging advanced usage patterns like visualization and threshold adjustments, the functionality can be tailored to a wide range of industry applications. The **AI Anomaly Detection** framework provides a robust, flexible, and extensible mechanism for outlier detection in numerical datasets. With applications ranging from real-time monitoring to preprocessing for AI pipelines, the system is a valuable tool for automated anomaly analysis. By leveraging advanced usage patterns like visualization and threshold adjustments, the functionality can be tailored to a wide range of industry applications.
ai_anomaly_detection.1748097491.txt.gz · Last modified: 2025/05/24 14:38 by eagleeyenebula