Time series evaluation of the 3M™ Clean-Trace™ ATP detection device to confirm swab effectiveness

2015 ◽  
Vol 20 (3-4) ◽  
pp. 108-114 ◽  
Author(s):  
Erica M. Colbert ◽  
Harlan Sayles ◽  
John J. Lowe ◽  
Oleg Chaika ◽  
Philip W. Smith ◽  
...  
Keyword(s):  
Time Series ◽  
Series Evaluation ◽  
Atp Detection

scholarly journals Study on Time Series Evaluation of Tsunami Force Acting on Land Structures

2014 ◽  
Vol 70 (2) ◽  
pp. I_796-I_800 ◽  
Author(s):  
Yukinobu ODA ◽  
Takahide HONDA ◽  
Tomoyuki TAKABATAKE
Keyword(s):  
Time Series ◽  
Series Evaluation

Highs and Lows: An Interrupted Time-Series Evaluation of the Impact of North America’s Only Supervised Injection Facility on Crime

2017 ◽  
Vol 48 (1) ◽  
pp. 36-49 ◽  
Author(s):  
Andrew J. Myer ◽  
Linsey Belisle
Keyword(s):  
Time Series ◽  
Drug Users ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Supervised Injection Facility ◽  
Series Evaluation ◽  
Supervised Injection ◽  
Supervised Injection Facilities ◽  
The Impact ◽  
Combat Problems

North America is currently experiencing an opioid crisis. One proposed solution to combat problems associated with injection drug use is the use of supervised injection facilities. These facilities provide drug users a space to inject pre-obtained drugs without any legal repercussions. Research on these facilities has focused on public health outcomes, and generally found positive results. Far fewer studies have investigated the impact supervised injection facilities have on crime. The current study provides an interrupted time-series analysis on the impact of North America’s only supervised injection facility on crime. Analyses of city wide crime data evidence no impact of the supervised injection facility on crime. Disaggregated analyses indicate a significant decrease in crimes in the district where the supervised injection facility is located. Implications of the findings are discussed.

scholarly journals Machine Learning in Classification Time Series with Fractal Properties

Data ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 5 ◽  
Author(s):  
Lyudmyla Kirichenko ◽  
Tamara Radivilova ◽  
Vitalii Bulakh
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Random Forest ◽  
Denial Of Service ◽  
Probability Of Detection ◽  
Random Forest Method ◽  
Fractal Properties ◽  
Series Evaluation ◽  
Cascade Processes ◽  
Fractal Time

The article presents a novel method of fractal time series classification by meta-algorithms based on decision trees. The classification objects are fractal time series. For modeling, binomial stochastic cascade processes are chosen. Each class that was singled out unites model time series with the same fractal properties. Numerical experiments demonstrate that the best results are obtained by the random forest method with regression trees. A comparative analysis of the classification approaches, based on the random forest method, and traditional estimation of self-similarity degree are performed. The results show the advantage of machine learning methods over traditional time series evaluation. The results were used for detecting denial-of-service (DDoS) attacks and demonstrated a high probability of detection.

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