Constant time, fixed memory, zero false negative error logging for low power wearable devices

2017 ◽  
Author(s):  
Saffiyah Doolan ◽  
Nicholas Hosein ◽  
Patrick Hosein ◽  
Devika Bhagwandin
Keyword(s):  
Low Power ◽  
False Negative ◽  
Wearable Devices ◽  
Constant Time ◽  
Negative Error ◽  
False Negative Error

Error Rates in Competency Testing When Test Retaking Is Permitted

1990 ◽  
Vol 15 (1) ◽  
pp. 39-52 ◽  
Author(s):  
Huynh Huynh
Keyword(s):  
Conditional Probability ◽  
False Positive ◽  
False Negative ◽  
Error Rates ◽  
Rasch Models ◽  
Positive Error ◽  
False Positive Error ◽  
Competency Testing ◽  
Negative Error ◽  
False Negative Error

False positive and false negative error rates are studied for competency testing where examinees are permitted to retake the test if they fail to pass. Formulae are provided for the beta-binomial and Rasch models, and estimates based on these two models are compared for several typical situations. Although Rasch estimates are expected to be more accurate than beta-binomial estimates, differences among them are found not to be substantial in a number of practical situations. Under relatively general conditions and when test retaking is permitted, the probability of making a false negative error is zero. Under the same situation, and given that an examinee is a true nonmaster, the conditional probability of making a false positive error for this examinee is one.

scholarly journals “Missed” Mild Cognitive Impairment: High False-Negative Error Rate Based on Conventional Diagnostic Criteria

2016 ◽  
Vol 52 (2) ◽  
pp. 685-691 ◽  
Author(s):  
Emily C. Edmonds ◽  
Lisa Delano-Wood ◽  
Amy J. Jak ◽  
Douglas R. Galasko ◽  
David P. Salmon ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Diagnostic Criteria ◽  
Error Rate ◽  
False Negative ◽  
Negative Error ◽  
False Negative Error

scholarly journals High resolution analysis of cervical cells--a progress report.

1977 ◽  
Vol 25 (7) ◽  
pp. 689-695 ◽  
Author(s):  
R S Poulsen ◽  
L H Oliver ◽  
R L Cahn ◽  
C Louis ◽  
G Toussaint
Keyword(s):  
High Resolution ◽  
False Positive ◽  
False Negative ◽  
Error Rates ◽  
Cell Segmentation ◽  
Negative Error ◽  
False Negative Error ◽  
High Resolution Analysis ◽  
Resolution Analysis ◽  
Cervical Cells

This paper presents preliminary results of research toward the development of a high resolution analysis stage for a dual resolution image processing-based prescreening device for cervical cytology. Experiments using both manual and automatic methods for cell segmentation are described. In both cases, 1500 cervical cells were analyzed and classified as normal or abnormal (dysplastic or malignant) using a minimum Mahalanobis distance classifier with eight subclasses of normal cells, and five subclasses of abnormal cells. With manual segmentation, false positive and false negative error rates of 2.98 and 7.73% were obtained. Similar experiments using automatic cell segmentation methods yielded false positive and false negative error rates of 3.90 and 11.56%, respectively. In both cases, independent training and testing data were used.

False-Negative Error

2013 ◽  
pp. 759-759
Author(s):  
Lindy Clemson ◽  
J. Rick Turner ◽  
J. Rick Turner ◽  
Farrah Jacquez ◽  
Whitney Raglin ◽  
...  
Keyword(s):  
False Negative ◽  
Negative Error ◽  
False Negative Error

Optimizing the balance between false positive and false negative error probabilities of confirmatory methods for the detection of veterinary drug residues†

The Analyst ◽  
1999 ◽  
Vol 124 (2) ◽  
pp. 109-114 ◽  
Author(s):  
Waldo J. de Boer ◽  
Hilko van der Voet ◽  
Wil G. de Ruig ◽  
J. A. (Hans) van Rhijn ◽  
Kevin M. Cooper ◽  
...  
Keyword(s):  
False Positive ◽  
False Negative ◽  
Veterinary Drug ◽  
Veterinary Drug Residues ◽  
Drug Residues ◽  
Negative Error ◽  
False Negative Error ◽  
Error Probabilities
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