The automatic detection of surface-level irregularities—defects or anomalies—in 3D data is of significant interest for various real-world purposes, such as industrial quality inspection, ...
CAPE CANAVERAL—The upper stage of a SpaceX Falcon 9 rocket failed to execute a planned engine burn to deorbit after successfully deploying a batch of Starlink satellites launched from California, the ...
src/ # Core system ├── monitoring/ # Resource collection ├── preprocessing/ # Data cleaning & feature engineering ├── anomaly_detection/ # ML model (Isolation Forest) ├── adaptive_engine/ # Decision ...
Hybrid Dual-Heterogeneous Knowledge Distillation Network for Anomaly Detection in Retinal OCT Images
Abstract: Unsupervised medical anomaly detection aims to identify abnormal images by training exclusively on normal samples, thereby enabling the detection of disease related irregularities without ...
School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, China. The rapid expansion of the Internet of Things (IoT), cloud computing, and remote work ...
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Build a deep neural network from scratch in Python
We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write the code, but it's entirely from scratch in python. We will code Deep Neural ...
This document specifies a machine learning system for network intrusion detection that implements both supervised classification and unsupervised anomaly detection methodologies. The unsupervised ...
Abstract: Anomaly detection is a key technology in quality control for automated production lines. Currently, 2D-based anomaly detection methods fail to identify geometric structure anomalies in ...
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