Expert Classification: Probabilistic Estimates

2021 ◽  
Author(s):  
Pavel I. Paderno ◽  
Evgeny A. Burkov ◽  
Elena A. Tolkacheva ◽  
Evgeny A. Lavrov ◽  
Olga E. Siryk
Keyword(s):  
Expert Classification

Investigating the role of Non-Technical Skills in Railway Traffic Operations through Expert Knowledge Elicitation

2021 ◽  
Vol 65 (1) ◽  
pp. 262-266
Author(s):  
Iga Jarosz* ◽  
Julia Lo ◽  
Jan Lijs
Keyword(s):  
Traffic Control ◽  
Expert Knowledge ◽  
Skill Training ◽  
Technical Skills ◽  
Knowledge Elicitation ◽  
Protective Function ◽  
Nontechnical Skills ◽  
Railway Traffic ◽  
The One ◽  
Expert Classification

Many high-risk industries identify non-technical skills as safety-critical abilities of the operational staff that have a protective function against human fallibility. Based on an established non-technical skills classification system, methods for expert knowledge elicitation were used to describe non-technical skills in the specific context of train traffic control in the Netherlands. The findings offer insights regarding the skill importance for good operational outcomes, skill difficulty, categorization, and attitudes based on subject matter experts’ opinions. Substantial overlap between the employed non-technical skills framework and the observed expert classification was found, which might indicate that the experts utilize a mental model of nontechnical skills similar to the one used. Furthermore, considerations concerning the organizational culture and the attitudes towards change provide a promising outlook when introducing novel solutions to non-technical skill training and assessment.

REMOTE-SENSING EXPERT CLASSIFICATION OF LAND USE/LAND COVER TYPES USING FRACTAL DIMENSIONS OVER A SUBTROPICAL HILLY REGION IN CHINA

Fractals ◽  
2011 ◽  
Vol 19 (04) ◽  
pp. 407-421
Author(s):  
JI ZHU ◽  
ZIYU LIN ◽  
XIAOZHOU LI
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Fractal Dimension ◽  
Land Cover ◽  
Fractal Dimensions ◽  
Land Use Land Cover ◽  
Surface Features ◽  
Land Cover Types ◽  
Hilly Region ◽  
Expert Classification

In the work, a simple and reliable algorithm is presented to calculate the fractal dimension of single pixel for the remote sensing images, and the fractal dimension values obtained by the algorithm proposed in this work have positive correlation with the complexity of surface features. On the basis of a scene of Landsat7 ETM+ (i.e., Enhanced Thematic Mapper Plus) data and the proposed algorithm, expert classification models and fractal technique were introduced to identify the ground objects in a Chinese subtropical hilly region, where surface features are very diverse and complex. In the work, the different land use/land cover types, especially the different vegetation categories were successfully identified using the ETM+ image, and this classification has an overall accuracy of 80.25% and a K hat of 0.7738, which are higher than those of the traditional supervised classification.

scholarly journals Automatic and Probabilistic Foehn Diagnosis with a Statistical Mixture Model

2014 ◽  
Vol 53 (3) ◽  
pp. 652-659 ◽  
Author(s):  
David Plavcan ◽  
Georg J. Mayr ◽  
Achim Zeileis
Keyword(s):  
Mixture Model ◽  
Potential Temperature ◽  
Statistical Mixture ◽  
Downslope Winds ◽  
Measured Time ◽  
Objective Classification ◽  
One Year ◽  
Expert Classification ◽  
Statistical Mixture Model

AbstractDiagnosing foehn winds from weather station data downwind of topographic obstacles requires distinguishing them from other downslope winds, particularly nocturnal ones driven by radiative cooling. An automatic classification scheme to obtain reproducible results that include information about the (un)certainty of the diagnosis is presented. A statistical mixture model separates foehn and no-foehn winds in a measured time series of wind. In addition to wind speed and direction, it accommodates other physically meaningful classifiers such as the (potential) temperature difference to an upwind station (e.g., near the crest) or relative humidity. The algorithm was tested for Wipp Valley in the central Alps against human expert classification and a previous objective method (Drechsel and Mayr 2008), which the new method outperforms. Climatologically, using only wind information gives nearly identical foehn frequencies as when using additional covariables. A data record length of at least one year is required for satisfactory results. The suitability of mixture models for objective classification of foehn at other locations will have to be tested in further studies.

scholarly journals Cell type classification and unsupervised morphological phenotype identification from low-res images with deep learning

2019 ◽  
Author(s):  
Kai Yao ◽  
Nash D. Rochman ◽  
Sean X. Sun
Keyword(s):  
Single Cell ◽  
Cell Density ◽  
Semantic Segmentation ◽  
Cell Interaction ◽  
Support Network ◽  
Cell Type ◽  
A Cell ◽  
Convnet Feature ◽  
Expert Classification ◽  
Type Classification

AbstractConvolutional neural networks (ConvNets) have been used for both classification and semantic segmentation of cellular images. Here we establish a method for cell type classification utilizing images taken on a benchtop microscope directly from cell culture flasks eliminating the need for a dedicated imaging platform. Significant flask-to-flask heterogeneity was discovered and overcome to support network generalization to novel data. Cell density was found to be a prominent source of heterogeneity even within the single-cell regime indicating the presence of morphological effects due to diffusion-mediated cell-cell interaction. Expert classification was poor for single-cell images and excellent for multi-cell images suggesting experts rely on the identification of characteristic phenotypes within subsets of each population and not ubiquitous identifiers. Finally we introduce Self-Label Clustering, an unsupervised clustering method relying on ConvNet feature extraction able to identify distinct morphological phenotypes within a cell type, some of which are observed to be cell density dependent.Author summaryK.Y., N.D.R., and S.X.S. designed experiments and computational analysis. K.Y. performed experiments and ConvNets design/training, K.Y., N.D.R and S.X.S wrote the paper.

scholarly journals Impact of spectrograms on the classification of wheezes and crackles in an educational setting. An interrater study

2019 ◽  
Author(s):  
JC. Aviles-Solis ◽  
I Storvoll ◽  
Vanbelle Sophie ◽  
H. Melbye
Keyword(s):  
Positive Impact ◽  
Random Order ◽  
The Arctic ◽  
Educational Setting ◽  
Lung Sounds ◽  
Online Questionnaire ◽  
Chest Auscultation ◽  
Technological Developments ◽  
Expert Classification

AbstractBackgroundChest auscultation is a widely used method in the diagnosis of lung diseases. However, the interpretation of lung sounds is a subjective task and disagreements arise. New technological developments like the use of visual representation of sounds through spectrograms could improve the agreement when classifying lung sounds, but this is not yet known.AimsTo test if the use of spectrograms improves the agreement when classifying wheezes and crackles.MethodsWe used 30 lung sounds recordings. The sample contained 15 normal recordings and 15 with wheezes or crackles. We produced spectrograms of the recordings. Twenty-three third to fifth-year medical students at UiT the Arctic University of Norway classified the recordings using an online questionnaire. We first showed the students examples of how wheezes and crackles looked in the spectrogram. Then, we played the recordings in a random order two times, first without the spectrogram, then with live spectrograms displayed. We asked them to classify the sounds for the presence of wheezes and crackles. We calculated kappa values for the agreement between each student and the expert classification with and without display of spectrograms and tested for significant improvement. We also calculated Fleiss kappa for the 23 observers with and without the spectrogram.ResultsWhen classifying wheezes 13/23 (1 with p<.05) students had a positive change in k, and 16/23 (2 with p<.05). All the statistically significant changes were in the direction of improved kappa values (.52 - .75). Fleiss kappa values were k=.51 and k=.56 (p=.63) for wheezes without and with spectrograms. For crackles, these values were k=.22 and k=.40 (p=<0.01) in the same order.ConclusionsThe use of spectrograms had a positive impact on the inter-rater agreement and the agreement with experts. We observed a higher improvement in the classification of crackles compared to wheezes.

Expert Classification: Resource-Based Approach

2021 ◽  
Author(s):  
Pavel I. Paderno ◽  
Evgeny A. Burkov ◽  
Elena A. Tolkacheva ◽  
Evgeny A. Lavrov ◽  
Olga E. Siryk
Keyword(s):  
Expert Classification
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