scholarly journals Rare events in complex systems: Understanding and prediction

2020 ◽  
Vol 30 (9) ◽  
pp. 090401
Author(s):  
Nishant Malik ◽  
Ugur Ozturk
Keyword(s):  
Complex Systems ◽  
Rare Events

scholarly journals Analysis and Simulation of Extremes and Rare Events in Complex Systems

2020 ◽  
pp. 151-182
Author(s):  
Meagan Carney ◽  
Holger Kantz ◽  
Matthew Nicol
Keyword(s):  
Complex Systems ◽  
Rare Events

HUMAN FACTORS IN COMPLEX SYSTEMS.

1967 ◽  
Author(s):  
FRANCIS H. THOMAS
Keyword(s):  
Complex Systems ◽  
Human Factors

A new definition of health derived from chaos and complex systems

2008 ◽  
Author(s):  
Stefan Topolski
Keyword(s):  
Complex Systems ◽  
Definition Of Health ◽  
Definition Of

Complex Systems are Relational Space

2013 ◽  
Author(s):  
Gus Koehler
Keyword(s):  
Complex Systems ◽  
Relational Space

Palaeoflood hydrology: insight into rare events and extreme flood discharges

2006 ◽  
pp. 91-96 ◽  
Author(s):  
Gerardo Benito ◽  
Varyl R. Thorndycraft
Keyword(s):  
Rare Events ◽  
Extreme Flood ◽  
Insight Into

Framework for rare event detection using Artificial Neural Network based context free grammar

2020 ◽  
Vol 39 (6) ◽  
pp. 8463-8475
Author(s):  
Palanivel Srinivasan ◽  
Manivannan Doraipandian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Detection ◽  
Performance Metrics ◽  
Rare Events ◽  
Rare Event ◽  
Video Stream ◽  
Context Free Grammar ◽  
Artificial Neural ◽  
Context Free

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

Complex systems-operational approaches

1990 ◽  
Vol 160 (7) ◽  
pp. 163-164
Author(s):  
Boris B. Kadomtsev
Keyword(s):  
Complex Systems
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