scholarly journals Concept to recognize crisis of organizational and institutional separation by Artificial Intelligence System

2021 ◽  
Vol 10 (43) ◽  
pp. 59-71
Author(s):  
Oleg N. Dmitriev ◽  
Veronika A. Zolotova

The sphere of anti-crisis management is highlighted in relation to the open variety of organizational and institutional separations that are typical for the higher forms of industrial and post-industrial economies. This article shows the typicity and relevance of critical management situations associated with the emergence of crises. Furthermore, it justifies the objective orientation to a dense (not sparse) stream of crisis situations requiring identification, ranking, and classification. A strict management interpretation of the separation crisis is given through an assessment of the nature of the dynamics of the separation state indexes. Also, the document presents a generalized typological classification of crises. This article shows the necessity of using a high-level Artificial Intelligence System for this purpose, an indispensable component of which is the classification component.

2020 ◽  
pp. 1-10
Author(s):  
Kai Zhao ◽  
Wei Jiang ◽  
Xinlong Jin ◽  
Xuming Xiao

The traditional sports match analysis mostly adopts the method of manual observation and recording, which is not only time-consuming and laborious but also has the defects of subjectivity and inaccuracy in the judgment results, resulting in the deviation of the match data analysis and statistical results. The purpose of this paper is to study an artificial intelligence system that can automatically analyze and evaluate the effect of both sides in volleyball matches. In this paper, the system is divided into two steps: detection and tracking of moving objects, recognition, and classification of players’ behaviors and movements. About moving target detection and tracking, this paper proposes a moving target fast detection framework based on a mixture of mainstream technologies and a MeanShift target tracking method based on Kalman filtering and adaptive target region size. For behavior and action recognition and classification, this paper proposes a classifier combining BP neural network and support vector machine. Experimental results show that the proposed algorithm and classifier are effective. By analyzing the performance of the proposed classifier, the classification accuracy is 98%.


2021 ◽  
Author(s):  
Catherine Aiken ◽  

This brief explores the development and testing of artificial intelligence system classification frameworks intended to distill AI systems into concise, comparable and policy-relevant dimensions. Comparing more than 1,800 system classifications, it points to several factors that increase the utility of a framework for human classification of AI systems and enable AI system management, risk assessment and governance.


2021 ◽  
Vol 160 (6) ◽  
pp. S-64-S-65
Author(s):  
Ethan A. Chi ◽  
Gordon Chi ◽  
Cheuk To Tsui ◽  
Yan Jiang ◽  
Karolin Jarr ◽  
...  

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