152. Industrial Hygiene Program Performance Metrics (Indicators)

Routine on-site reviews should focus primarily on facilities that are at risk of harming human subjects. Using human research protection program performance metric data from 107 facilities, we defined a facility to be at risk when one of its noncompliance/incident rates was among the top three highest rates of that performance metric. Based on 14 performance metrics with noncompliance and incidents in 2017, 27 facilities were identified to be at risk. These 27 facilities at risk, while constituting only 25% of all facilities, contributed to 70% ± 25% ( M ± SD; range = 32%-100%) of all reported noncompliance/incidents. Thus, performance metric data can be used to guide compliance oversight activities.

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Program performance standards: supporting home language and English acquisition

PsycEXTRA Dataset ◽

10.1037/e506182006-001 ◽

2005 ◽

Author(s):

Michele Plutro

Keyword(s):

Performance Standards ◽

Home Language ◽

Program Performance

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Criterion setting for objective, fourier analysis based pilot performance metrics

PsycEXTRA Dataset ◽

10.1037/e577062012-033 ◽

2004 ◽

Author(s):

Nicholas R. Johnson ◽

Esa M. Rantanen ◽

Donald A. Talleur

Keyword(s):

Fourier Analysis ◽

Performance Metrics ◽

Pilot Performance ◽

Criterion Setting

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

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189164 ◽

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.

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