Unlocking the Power of Edge AI Anomaly Detection with K-Nearest Neighbors (K-NN)
In today's era of IoT and real-time data streaming, Edge AI has emerged as a game-changer in anomaly detection. By processing data on the edge, we can significantly reduce latency and improve real-time decision-making. In this post, we'll explore how to leverage K-Nearest Neighbors (K-NN) for anomaly detection on the edge.
What is K-NN?
K-NN is a popular machine learning algorithm that works by identifying the most similar data points to a new input. In the context of anomaly detection, K-NN can be used to identify data points that are significantly different from the majority.
Code Example
python
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
# Load and normalize data
X = np.random.rand(100, 5) * 10 # 100 data points with 5 features each
y = (np.random.rand(100) > 0.5).astype(int) # 0 or 1 labels for anomaly or normal data
# Split data into training (80%) ...
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*This post was originally shared as an AI/ML insight. Follow me for more expert content on artificial intelligence and machine learning.*
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