Convolutional neural network has shown promising future object detection and recognition, particularly in images and videos. However, labels are required for learning as convolutional neural network is a supervised technique. For anomaly detection in videos, we propose a spatiotemporal architecture which ...
《Real-Time Anomaly Detection for Streaming Analytics》S Ahmad, S Purdy [Numenta, Inc] (2016) http://t.cn/R5DMFnG
Realtime Anomaly Detection Using Trajectory-Level Crowd Behavior Learning,程序员大本营,技术文章内容聚合第一站。
Dark Pools leverages AI and machine learning to provide real-time anomaly detection, predictive analytics and risk mitigation across financial services, gove...
In this post, we present a streaming time series anomaly detection algorithm based on matrix profiles and left-discords, inspired by Lu et al., 2022, with Apache Flink, and provide a working example that will help you get started on a managed Apache...
Real-Time Anomaly Detection with Uncertainty Estimation The objective of this study is to proposed a real-time anomaly detection framework and assess the uncertainty associated with the proposed method. The anomaly detection framework for ICS data streams, using Docker containers for scalable and managea...
Ralyn J,Toledo T.Real-time road traffic anomaly detection. Journal of TransportationTechnologies . 2014Real-Time Road Traffic Anomaly Detection[J] . Jamal Raiyn,Tomer Toledo.Journal of Transportation Technologies . 2014 (03)Raiyn, J. and Toledo, T. (2014) Real-Time Road Traffic Anomaly ...
Unlike the anomaly detection capabilities in many other log management solutions, Loggly analyzes thousands of field values in parallel as it ingests your logs, determines the normal value ranges in your logs, and brings the ones with the biggest changes to your attention in near real-time. You...
Due to the outstanding performance of the DRM as shown in Table 1, we propose a real-time anomaly detection method with an adaptive threshold for VSTLF based on the DRM. First, the parameters of the DRM are estimated using the training dataset (i.e., part of historical data). Then the...
Although the recent load information is critical to very short-term load forecasting(VSTLF), power companies often have difficulties in collecting the most recent load values accurately and timely for VSTLF applications.This paper tackles the problem of real-time anomaly detection in most recent load...