scholarly journals Accounting for heterogeneity in false‐positive detection rate in southeastern beach mouse habitat occupancy models

Ecosphere ◽  
2019 ◽  
Vol 10 (10) ◽  
Author(s):  
Eric D. Stolen ◽  
Donna M. Oddy ◽  
Shanon L. Gann ◽  
Karen G. Holloway‐Adkins ◽  
Stephanie A. Legare ◽  
...  
Keyword(s):  
False Positive ◽  
Detection Rate ◽  
Occupancy Models ◽  
Habitat Occupancy ◽  
Positive Detection Rate ◽  
False Positive Detection ◽  
False Positive Detection Rate

Preventing Tracking-Tube False Detections in Occupancy Modeling of Southeastern Beach Mouse

2014 ◽  
Vol 5 (2) ◽  
pp. 270-281 ◽  
Author(s):  
Eric D. Stolen ◽  
Donna M. Oddy ◽  
Mike L. Legare ◽  
David R. Breininger ◽  
Shanon L. Gann ◽  
...  
Keyword(s):  
False Positive ◽  
Limited Range ◽  
Negative Influence ◽  
Occupancy Models ◽  
Probability Of Error ◽  
Bayesian Hierarchical ◽  
Peromyscus Polionotus ◽  
Habitat Occupancy ◽  
Kennedy Space Center ◽  
Kennedy Space

Abstract Quantifying habitat occupancy of the southeastern beach mouse Peromyscus polionotus niveiventris is important for managing this threatened species throughout its limited range. Tracking tubes were used to detect the southeastern beach mouse in coastal areas on the federal lands of the Kennedy Space Center, Cape Canaveral Air Force Station, and Canaveral National Seashore. Because this method relied on observations of footprints, detections of beach mice were confounded by the co-occurrence of cotton mice Peromyscus gossypinus, which have wider but slightly overlapping footprint widths. Mice of both species were captured and footprinted using tracking tubes to collect a database of footprints of known identity. These data were used to develop a Bayesian hierarchical model of the cutoff width at which a print could be assigned as a beach mouse with a known probability of error. Specifically, within the model, observed footprint widths were used to estimate a mean and variance of footprint width for each species, while accounting for variation between individual mice. Then, a distribution of new footprint widths was generated for each species by drawing from their modeled distributions. Finally, the new footprints were compared with a range of potential cutoff widths to evaluate the proportion of times the correct decision to exclude or accept the footprint was made. We graphically evaluated the performance of the cutoff widths and chose one that traded off between reducing false positives and retaining more correct detections for use in occupancy models. We explored the use of the cutoff width using occupancy models that allow for false-positive detections, and found that the use of the cutoff performed as expected. Over 40% of primary dune habitat on the Kennedy Space Center was occupied by beach mice during the period sampled. The proportion of vegetated habitat at a site had a negative influence on detection probability. No ecological covariates had a measurable influence on beach mouse occupancy, probably due to the limited range of environmental variation in the sampled region. The use of a cutoff for footprint width resulted in a reliable method to deal with false-positive detections in tracking tubes with small mammals and allowed the use of occupancy models that rely on certain detection.

Investigation and Resolution of Interference in the LC-QTOF-MS Detection of 4-MePPP

2021 ◽  
Author(s):  
Darren R Allen ◽  
Christopher Warnholtz ◽  
Brett C McWhinney
Keyword(s):  
Reference Material ◽  
Spectral Data ◽  
False Positive ◽  
In Silico ◽  
Spectral Library ◽  
In Silico Fragmentation ◽  
False Positive Detection ◽  
Product Ions ◽  
Synthetic Cathinone ◽  
Recent Addition

Abstract An interference resulting in the false-positive detection of the synthetic cathinone 4-MePPP in urine was suspected following the recent addition of 4-MePPP spectral data to an LC-QTOF-MS drug library. Although positive detection criteria were achieved, it was noted that all urine samples suspected of containing 4-MePPP also concurrently contained high levels of tramadol and its associated metabolites. Using QTOF-MS software elucidation tools, candidate compounds for the suspected interference were proposed. To provide further confidence in the identity of the interference, in silico fragmentation tools were used to match product ions generated in the analysis with product ions predicted from the theoretical fragmentation of candidate compounds. The ability of the suspected interference to subsequently produce the required product ions for spectral library identification of 4-MePPP was also tested. This information was used to provide a high preliminary confidence in the compound identity prior to purchase and subsequent confirmation with certified reference material. A co-eluting isobaric interference was identified and confirmed as an in-source fragment of the tramadol metabolite, N,N-bisdesmethyltramadol. Proposed resolutions for this interference are also described and subsequently validated by retrospective interrogation of previous cases of suspected interference.

DS-kNN

2021 ◽  
Vol 15 (2) ◽  
pp. 131-144
Author(s):  
Redha Taguelmimt ◽  
Rachid Beghdad
Keyword(s):  
Intrusion Detection ◽  
False Positive ◽  
Detection Rate ◽  
Nearest Neighbors ◽  
The Other ◽  
Intrusion Detection Systems ◽  
K Nearest Neighbors ◽  
Detection Systems ◽  
Knn Classifier ◽  
Better Than

On one hand, there are many proposed intrusion detection systems (IDSs) in the literature. On the other hand, many studies try to deduce the important features that can best detect attacks. This paper presents a new and an easy-to-implement approach to intrusion detection, named distance sum-based k-nearest neighbors (DS-kNN), which is an improved version of k-NN classifier. Given a data sample to classify, DS-kNN computes the distance sum of the k-nearest neighbors of the data sample in each of the possible classes of the dataset. Then, the data sample is assigned to the class having the smallest sum. The experimental results show that the DS-kNN classifier performs better than the original k-NN algorithm in terms of accuracy, detection rate, false positive, and attacks classification. The authors mainly compare DS-kNN to CANN, but also to SVM, S-NDAE, and DBN. The obtained results also show that the approach is very competitive.

scholarly journals False Positive Decremented Research for Fire and Smoke Detection in Surveillance Camera using Spatial and Temporal Features Based on Deep Learning

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1167 ◽  
Author(s):  
Yeunghak Lee ◽  
Jaechang Shim
Keyword(s):  
Deep Learning ◽  
False Positive ◽  
Learning Algorithm ◽  
Detection Performance ◽  
Economic Losses ◽  
Smoke Detection ◽  
Temporal Features ◽  
False Positive Detection ◽  
Surveillance Cameras ◽  
Fire Flame

Fire must be extinguished early, as it leads to economic losses and losses of precious lives. Vision-based methods have many difficulties in algorithm research due to the atypical nature fire flame and smoke. In this study, we introduce a novel smoke detection algorithm that reduces false positive detection using spatial and temporal features based on deep learning from factory installed surveillance cameras. First, we calculated the global frame similarity and mean square error (MSE) to detect the moving of fire flame and smoke from input surveillance cameras. Second, we extracted the fire flame and smoke candidate area using the deep learning algorithm (Faster Region-based Convolutional Network (R-CNN)). Third, the final fire flame and smoke area was decided by local spatial and temporal information: frame difference, color, similarity, wavelet transform, coefficient of variation, and MSE. This research proposed a new algorithm using global and local frame features, which is well presented object information to reduce false positive based on the deep learning method. Experimental results show that the false positive detection of the proposed algorithm was reduced to about 99.9% in maintaining the smoke and fire detection performance. It was confirmed that the proposed method has excellent false detection performance.

Hemisphere Function and Vigilance

1971 ◽  
Vol 23 (4) ◽  
pp. 443-448 ◽  
Author(s):  
Stuart Dimond ◽  
Graham Beaumont
Keyword(s):  
Left Hemisphere ◽  
False Positive ◽  
Cerebral Hemisphere ◽  
Detection Rate ◽  
Experimental Period ◽  
Vigilance Task ◽  
Tunnel Vision ◽  
Detection Of Signals

A vigilance task in which successive signals were presented to one or other hemiretina, and therefore to one or other cerebral hemisphere, revealed no differences between the hemispheres in terms of detections, although detections declined overall during the experimental period. False positive responses also declined, but consistently more arose from the left hemisphere. There was also a difference in the detection of signals received through the nasal and temporal hemiretinae, the temporal hemiretina showing superiority in detection rate throughout the experiment. This finding may provide a new and more economical approach to the tunnel vision phenomenon.

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