Performance Metrics for Intelligent Systems (PerMIS) 2006Workshop: Summary and Review

2006 ◽  
Author(s):  
R. Madhavan ◽  
E. Messina
Keyword(s):  
Intelligent Systems ◽  
Performance Metrics

scholarly journals Beyond Tracking in Crowd: Analyzing Crowd based on Physical Characteristics

2020 ◽  
Author(s):  
Muhammad Siraj
Keyword(s):  
Intelligent Systems ◽  
Performance Metrics ◽  
Physical Characteristics ◽  
Shopping Mall ◽  
Subway Station ◽  
Event Analysis ◽  
Challenging Problem ◽  
Unique Model ◽  
Effective System ◽  
Physical Elements

The safety of people is an important phenomenon nowadays. This importance arises due to the crowded places including subway station, universities, colleges, airport, shopping mall and square, and city squares. Therefore, the development of an effective system based on physical characteristics of crowd layout is of significant demand. In this paper, we proposed a novel automated and intelligent systems for crowd event analysis based on a set of physical elements. For this purpose, we take into account optical flow and spatial-time gradient, contour features, and Gaussian processes. Our method combine these characteristics into a unique model to deal with the challenging problem of crowd event analysis. For evaluating our proposed method, we consider a benchmark dataset and a number of different performance metrics. These analysis demonstrate the robustness and effectiveness of our proposed method.

A Formal Model for Metacognitive Reasoning in Intelligent Systems

2014 ◽  
Vol 8 (3) ◽  
pp. 70-86 ◽  
Author(s):  
Manuel F. Caro ◽  
Darsana P. Josyula ◽  
Jovani A. Jiménez
Keyword(s):  
Intelligent Systems ◽  
Performance Metrics ◽  
Formal Model ◽  
Predicate Logic ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Level Control ◽  
Tutoring System ◽  
Meta Level ◽  
Proposed Model

This paper presents a formal model of metacognitive reasoning in intelligent systems (IS). The proposed model was named fM2 and uses predicate logic to represent a cycle of reasoning about failures generated in reasoning tasks in an IS. fM2 has mechanisms such as introspective monitoring and meta-level control to perform metacognitive reasoning. fM2 was implemented and validated on an intelligent tutoring system named FUNPRO. The performance metrics of FUNPRO indicate the capacity of fM2 to drastically decrease the reasoning failures produced in the recommendations of FUNPRO. Thus, this paper demonstrates the efficacy of fM2 as a valid tool to improve the performance of the reasoning processes of IS.

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