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Anomaly Detection-Structured (57 results) showing 31 - 40



Machine Learning model performance degrades over time and the drift associated with data is one of the main reasons for the gradual drop in model accuracy. Drift Detection in Categorical variables aids in finding the extent of drift observed in data with respect to a reference dataset. The solution...

Model Package - Fulfilled on Amazon SageMaker


This solution utilizes unsupervised outlier detection methods to detect anomalous health events from multiple sensors providing their heart rate and steps data sequenced over a period of time. Such an approach does not require labelling, as the model learns to detect anomalous values from the...

Algorithm - Fulfilled on Amazon SageMaker

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Machine-Learning-based Network Intrusion Detection System (NIDS) meant to be used with NetFlow traffic. Given an input flow, this will return the threat type alongside the confidence of the prediction. It is capable of detecting 4 main network traffic classes: Benign, Brute Force, DDoS, and...

Model Package - Fulfilled on Amazon SageMaker


DeepInsights Card Fraud Analyzer is a Deep-Learning powered classification solution that provides valuable insights from any data that is highly skewed with relevant class (e.g. fraudulent transactions) being represented by less than 1% of data. The solution works with numerical data and provides...

Model Package - Fulfilled on Amazon SageMaker


The Wizata Platform is a state-of-the-art IIoT application designed to help users improve operation management on all levels. It's a software solution that allows users to improve manufacturing, locate bottlenecks, and make better data-driven business decisions. With the use of advanced AI,...


Botnet Detector is a cutting-edge supervised machine learning model specifically trained on network traffic data to detect botnet activity with unparalleled accuracy. Botnets pose a significant threat to networks worldwide, causing damages worth billions. However, with this solution, you can...

Model Package - Fulfilled on Amazon SageMaker


Data evolves over time, causing a change in the distributions and interpretation. This is known as drift and causes a degradation in ML model performance. The Drift Detector detects changes in the incoming data, and provides useful insights to the user with respect to the data and model behavior....

Model Package - Fulfilled on Amazon SageMaker


This solution takes a deep learning-based approach to learn and understand the patterns in Temperature sensor data. It aims at learning the normal behavior patterns of the sensor data during training process using generative algorithms. Once trained, the model can monitor and identify abnormal...

Algorithm - Fulfilled on Amazon SageMaker