Computerized systems validation strategy

In the present study we applied a validation strategy for implicit measures like the IAT, which complements multitrait-multimethod (MTMM) analyses. As the measurement method (implicit vs. explicit) and underlying representation format (associative vs. propositional) are often confounded, the validation of implicit measures has to go beyond MTMM analysis and requires substantive theoretical models. In the present study (N = 133), we employed such a model ( Hofmann, Gschwendner, Nosek, & Schmitt, 2005 ) and investigated two moderator constructs in the realm of anxiety: specificity similarity and content similarity. In the first session, different general and specific anxiety measures were administered, among them an Implicit Association Test (IAT) general anxiety, an IAT-spider anxiety, and an IAT that assesses speech anxiety. In the second session, participants had to deliver a speech and behavioral indicators of speech anxiety were measured. Results showed that (a) implicit and explicit anxiety measures correlated significantly only on the same specification level and if they measured the same content, and (b) specific anxiety measures best predicted concrete anxious behavior. These results are discussed regarding the validation of implicit measures.

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Targeting senescent chondrocytes in osteoarthritis: artificial intelligence powered target identification and hypothesis validation strategy

Osteoarthritis and Cartilage ◽

10.1016/j.joca.2021.02.542 ◽

2021 ◽

Vol 29 ◽

pp. S419-S420

Author(s):

J. Monserrat ◽

C. Hudson ◽

J. Small ◽

T. Mak ◽

P. Richardson ◽

...

Keyword(s):

Artificial Intelligence ◽

Target Identification ◽

Validation Strategy

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A Cross-Validation Strategy for the Identification of Electromechanical Oscillations in Real Ambient Data

Journal of Control Automation and Electrical Systems ◽

10.1007/s40313-021-00843-5 ◽

2021 ◽

Author(s):

Juliana Luiza Pereira ◽

Rodolfo Bialecki Leandro ◽

Ildemar Cassana Decker ◽

Guido Rossetto Moraes

Keyword(s):

Cross Validation ◽

Electromechanical Oscillations ◽

Ambient Data ◽

Validation Strategy

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A Novel Method for Gender and Age Detection Based on EEG Brain Signals

The International Arab Journal of Information Technology ◽

10.34028/iajit/18/5/10 ◽

2021 ◽

Vol 18 (5) ◽

Author(s):

Haitham Issa ◽

Sali Issa ◽

Wahab Shah

Keyword(s):

Cross Validation ◽

Image Feature ◽

Emotional States ◽

Time Frequency ◽

Brain Signals ◽

Average Accuracy ◽

Gender And Age ◽

Novel Method ◽

Fold Cross Validation ◽

Validation Strategy

This paper presents a new gender and age classification system based on Electroencephalography (EEG) brain signals. First, Continuous Wavelet Transform (CWT) technique is used to get the time-frequency information of only one EEG electrode for eight distinct emotional states instead of the ordinary neutral or relax states. Then, sequential steps are implemented to extract the improved grayscale image feature. For system evaluation, a three-fold-cross validation strategy is applied to construct four different classifiers. The experimental test shows that the proposed extracted feature with Convolutional Neural Network (CNN) classifier improves the performance of both gender and age classification, and achieves an average accuracy of 96.3% and 89% for gender and age classification, respectively. Moreover, the ability to predict human gender and age during the mood of different emotional states is practically approved.

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Convolutional Neural Networks for Leaf Image-Based Plant Disease Classification

IAES International Journal of Artificial Intelligence (IJ-AI) ◽

10.11591/ijai.v8.i4.pp328-341 ◽

2019 ◽

Vol 8 (4) ◽

pp. 328

Author(s):

Sachin B. Jadhav

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Plant Diseases ◽

Experimental Results ◽

Disease Classification ◽

Soybean Leaves ◽

Soybean Diseases ◽

Validation Strategy

<span lang="EN-US">Plant pathologists desire soft computing technology for accurate and reliable diagnosis of plant diseases. In this study, we propose an efficient soybean disease identification method based on a transfer learning approach by using a pre-trained convolutional neural network (CNN’s) such as AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201. The proposed convolutional neural networks were trained using 1200 plant village image dataset of diseased and healthy soybean leaves, to identify three soybean diseases out of healthy leaves. Pre-trained CNN used to enable a fast and easy system implementation in practice. We used the five-fold cross-validation strategy to analyze the performance of networks. In this study, we used a pre-trained convolutional neural network as feature extractors and classifiers. The experimental results based on the proposed approach using pre-trained AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201 networks achieve an accuracy of 95%, 96.4 %, 96.4 %, 92.1%, 93.6% respectively. The experimental results for the identification of soybean diseases indicated that the proposed networks model achieves the highest accuracy</span>

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Validation strategy for satellite observations of tropospheric reactive gases

Annals of Geophysics ◽

10.4401/ag-6335 ◽

2014 ◽

Author(s):

Andreas Richter ◽

Mark Weber ◽

John P. Burrows ◽

Jean-Christopher Lambert ◽

Anne van Gijsel

Keyword(s):

Spectral Range ◽

Thermal Infrared ◽

Satellite Observations ◽

Infrared Spectral Range ◽

Infrared Spectral ◽

Uv Visible ◽

Reactive Gases ◽

Validation Strategy

Over the last twodecades, satellite observations of tropospheric composition have becomepossible using nadir viewing spectrometers operating in the UV, visible, nearinfrared, and thermal infrared spectral range. [...]

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Pitfalls and Remedies for Cross Validation with Multi-trait Genomic Prediction Methods

10.1101/595397 ◽

2019 ◽

Author(s):

Daniel Runcie ◽

Hao Cheng

Keyword(s):

Genomic Prediction ◽

Prediction Accuracy ◽

Cross Validation ◽

Prediction Models ◽

Selection Index ◽

Parametric Method ◽

Multiple Traits ◽

Gold Standard Method ◽

Secondary Traits ◽

Validation Strategy

ABSTRACTIncorporating measurements on correlated traits into genomic prediction models can increase prediction accuracy and selection gain. However, multi-trait genomic prediction models are complex and prone to overfitting which may result in a loss of prediction accuracy relative to single-trait genomic prediction. Cross-validation is considered the gold standard method for selecting and tuning models for genomic prediction in both plant and animal breeding. When used appropriately, cross-validation gives an accurate estimate of the prediction accuracy of a genomic prediction model, and can effectively choose among disparate models based on their expected performance in real data. However, we show that a naive cross-validation strategy applied to the multi-trait prediction problem can be severely biased and lead to sub-optimal choices between single and multi-trait models when secondary traits are used to aid in the prediction of focal traits and these secondary traits are measured on the individuals to be tested. We use simulations to demonstrate the extent of the problem and propose three partial solutions: 1) a parametric solution from selection index theory, 2) a semi-parametric method for correcting the cross-validation estimates of prediction accuracy, and 3) a fully non-parametric method which we call CV2*: validating model predictions against focal trait measurements from genetically related individuals. The current excitement over high-throughput phenotyping suggests that more comprehensive phenotype measurements will be useful for accelerating breeding programs. Using an appropriate cross-validation strategy should more reliably determine if and when combining information across multiple traits is useful.

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