A list of awesome research on log analysis, anomaly detection, fault localization, and AIOps
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Updated
Dec 31, 2023
A list of awesome research on log analysis, anomaly detection, fault localization, and AIOps
Detecting Frauds in Online Transactions using Anamoly Detection Techniques Such as Over Sampling and Under-Sampling as the ratio of Frauds is less than 0.00005 thus, simply applying Classification Algorithm may result in Overfitting
A research project of anomaly detection on dataset IoT-23
CICIDS2017 dataset
Anamoly Detection for Detecting Defected Manufactured Semi-Conductors, as in this case of Classification, the Defected Chips would be very less in comparison to perfect Chips so we have apply either Over-Sampling or Under-Sampling.
It is Based on Anamoly Detection and by Using Deep Learning Model SOM which is an Unsupervised Learning Method to find patterns followed by the fraudsters.
This is an highly imbalanced data with only 1.72% minority and 98.28% majority class, i will be explaining Up and down sampling and effect of sampling before and while doing cross validation. Model has been evaluated using precision recall curve.
The official implementation for ICLR23 paper "GNNSafe: Energy-based Out-of-Distribution Detection for Graph Neural Networks"
The official repository of TeamGabru.
Multimodal Subspace Support Vector Data Description
Subspace Support Vector Data Description
This Project is detect outliers in sensor networks. We are using ISSNIP Single hop dataset for this.
Showing outliers and novelty detection in a datasets, from Scikit-learn
A Python Module for Outliers Detection, Visualization and Treatment in Oil Well Datasets
Android app which will help patients with Alzheimer and dementia
A basic implementation of an autoencoder using Tensorflow. Trained and tested on an ECG dataset
Explore anomaly detection methods using the Isolation Forest approach in this GitHub project. Learn preprocessing techniques like one-hot encoding and timestamp conversion to enhance data analysis. Apply the algorithm to identify anomalies effectively. Adapt these insights to your own projects.
This project focuses on the detection of credit card fraud using various data science and machine learning techniques. The dataset includes a record of credit card transactions over a specific period, with the goal of accurately identifying fraudulent activities. 🚀✨
Stock Analysis, Predective Modelling using LSTM and Anamoly Detection.
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