Where's the Psychology? A Commentary on “Unique Characteristics of Diagnostic Classification Models: A Comprehensive Review of the Current State-of-the-Art”

2008 ◽  
Vol 6 (4) ◽  
pp. 272-275
Author(s):  
Jacqueline P. Leighton
Keyword(s):  
State Of The Art ◽  
Diagnostic Classification ◽  
Classification Models ◽  
Comprehensive Review ◽  
Diagnostic Classification Models ◽  
Current State

scholarly journals A Robust Context-Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Juan F. Ramirez Rochac ◽  
Nian Zhang ◽  
Lara A. Thompson ◽  
Tolessa Deksissa
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Mineral Exploration ◽  
Classification Models ◽  
Noise Resistance ◽  
Deep Convolutional Neural Networks ◽  
Current State ◽  
Feature Augmentation ◽  
Active Research ◽  
Hyperspectral Classification

Hyperspectral imaging is an area of active research with many applications in remote sensing, mineral exploration, and environmental monitoring. Deep learning and, in particular, convolution-based approaches are the current state-of-the-art classification models. However, in the presence of noisy hyperspectral datasets, these deep convolutional neural networks underperform. In this paper, we proposed a feature augmentation approach to increase noise resistance in imbalanced hyperspectral classification. Our method calculates context-based features, and it uses a deep convolutional neuronet (DCN). We tested our proposed approach on the Pavia datasets and compared three models, DCN, PCA + DCN, and our context-based DCN, using the original datasets and the datasets plus noise. Our experimental results show that DCN and PCA + DCN perform well on the original datasets but not on the noisy datasets. Our robust context-based DCN was able to outperform others in the presence of noise and was able to maintain a comparable classification accuracy on clean hyperspectral images.

scholarly journals The Reliability of the Posterior Probability of Skill Attainment in Diagnostic Classification Models

2019 ◽  
Vol 45 (1) ◽  
pp. 5-31
Author(s):  
Matthew S. Johnson ◽  
Sandip Sinharay
Keyword(s):  
Posterior Probability ◽  
Real Data ◽  
Exact Calculation ◽  
Diagnostic Classification ◽  
Classification Models ◽  
Diagnostic Classification Models ◽  
Reliability Measure ◽  
Skill Mastery

One common score reported from diagnostic classification assessments is the vector of posterior means of the skill mastery indicators. As with any assessment, it is important to derive and report estimates of the reliability of the reported scores. After reviewing a reliability measure suggested by Templin and Bradshaw, this article suggests three new measures of reliability of the posterior means of skill mastery indicators and methods for estimating the measures when the number of items on the assessment and the number of skills being assessed render exact calculation computationally burdensome. The utility of the new measures is demonstrated using simulated and real data examples. Two of the suggested measures are recommended for future use.

scholarly journals A Review of Extended Reality (XR) Technologies for Manufacturing Training

Technologies ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 77
Author(s):  
Sanika Doolani ◽  
Callen Wessels ◽  
Varun Kanal ◽  
Christos Sevastopoulos ◽  
Ashish Jaiswal ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Augmented Reality ◽  
Manufacturing Industry ◽  
Mixed Reality ◽  
State Of The Art ◽  
Comprehensive Review ◽  
Assembly Task ◽  
Practical Applications ◽  
Current State ◽  
Research Publications

Recently, the use of extended reality (XR) systems has been on the rise, to tackle various domains such as training, education, safety, etc. With the recent advances in augmented reality (AR), virtual reality (VR) and mixed reality (MR) technologies and ease of availability of high-end, commercially available hardware, the manufacturing industry has seen a rise in the use of advanced XR technologies to train its workforce. While several research publications exist on applications of XR in manufacturing training, a comprehensive review of recent works and applications is lacking to present a clear progress in using such advance technologies. To this end, we present a review of the current state-of-the-art of use of XR technologies in training personnel in the field of manufacturing. First, we put forth the need of XR in manufacturing. We then present several key application domains where XR is being currently applied, notably in maintenance training and in performing assembly task. We also reviewed the applications of XR in other vocational domains and how they can be leveraged in the manufacturing industry. We finally present some current barriers to XR adoption in manufacturing training and highlight the current limitations that should be considered when looking to develop and apply practical applications of XR.

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