A Note on Estimating The Number of Super Imposed Exponential Signals by the Cross-Validation Approach

In this paper, a computer-aided training method for hyperparameter selection of limited data X-ray computed tomography (XCT) reconstruction was proposed. The proposed method employed the ant colony optimisation (ACO) approach to assist in hyperparameter selection for the adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm, which is a total-variation (TV) based regularisation algorithm. During the implementation, there was a colony of artificial ants that swarm through the AwPCSD algorithm. Each ant chose a set of hyperparameters required for its iterative CT reconstruction and the correlation coefficient (CC) score was given for reconstructed images compared to the reference image. A colony of ants in one generation left a pheromone through its chosen path representing a choice of hyperparameters. Higher score means stronger pheromones/probabilities to attract more ants in the next generations. At the end of the implementation, the hyperparameter configuration with the highest score was chosen as an optimal set of hyperparameters. In the experimental results section, the reconstruction using hyperparameters from the proposed method was compared with results from three other cases: the conjugate gradient least square (CGLS), the AwPCSD algorithm using the set of arbitrary hyperparameters and the cross-validation method.The experiments showed that the results from the proposed method were superior to those of the CGLS algorithm and the AwPCSD algorithm using the set of arbitrary hyperparameters. Although the results of the ACO algorithm were slightly inferior to those of the cross-validation method as measured by the quantitative metrics, the ACO algorithm was over 10 times faster than cross—Validation. The optimal set of hyperparameters from the proposed method was also robust against an increase of noise in the data and can be applicable to different imaging samples with similar context. The ACO approach in the proposed method was able to identify optimal values of hyperparameters for a dataset and, as a result, produced a good quality reconstructed image from limited number of projection data. The proposed method in this work successfully solves a problem of hyperparameters selection, which is a major challenge in an implementation of TV based reconstruction algorithms.

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Comparative Analysis of Different Spatial Interpolation Methods Applied to Monthly Rainfall as Support for Landscape Management

Applied Sciences ◽

10.3390/app11209566 ◽

2021 ◽

Vol 11 (20) ◽

pp. 9566

Author(s):

Tommaso Caloiero ◽

Gaetano Pellicone ◽

Giuseppe Modica ◽

Ilaria Guagliardi

Keyword(s):

New Zealand ◽

Spatial Interpolation ◽

Cross Validation ◽

Landscape Management ◽

Monthly Rainfall ◽

Rainfall Data ◽

The Other ◽

Geostatistical Methods ◽

The Cross ◽

Almost All

Landscape management requires spatially interpolated data, whose outcomes are strictly related to models and geostatistical parameters adopted. This paper aimed to implement and compare different spatial interpolation algorithms, both geostatistical and deterministic, of rainfall data in New Zealand. The spatial interpolation techniques used to produce finer-scale monthly rainfall maps were inverse distance weighting (IDW), ordinary kriging (OK), kriging with external drift (KED), and ordinary cokriging (COK). Their performance was assessed by the cross-validation and visual examination of the produced maps. The results of the cross-validation clearly evidenced the usefulness of kriging in the spatial interpolation of rainfall data, with geostatistical methods outperforming IDW. Results from the application of different algorithms provided some insights in terms of strengths and weaknesses and the applicability of the deterministic and geostatistical methods to monthly rainfall. Based on the RMSE values, the KED showed the highest values only in April, whereas COK was the most accurate interpolator for the other 11 months. By contrast, considering the MAE, the KED showed the highest values in April, May, June and July, while the highest values have been detected for the COK in the other months. According to these results, COK has been identified as the best method for interpolating rainfall distribution in New Zealand for almost all months. Moreover, the cross-validation highlights how the COK was the interpolator with the best least bias and scatter in the cross-validation test, with the smallest errors.

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Artificial Intelligence–Assisted Prediction of Late-Onset Cardiomyopathy Among Childhood Cancer Survivors

JCO Clinical Cancer Informatics ◽

10.1200/cci.20.00176 ◽

2021 ◽

pp. 459-468

Author(s):

Fatma Güntürkün ◽

Oguz Akbilgic ◽

Robert L. Davis ◽

Gregory T. Armstrong ◽

Rebecca M. Howell ◽

...

Keyword(s):

Artificial Intelligence ◽

Cancer Survivors ◽

Childhood Cancer ◽

Clinical Features ◽

Cross Validation ◽

Childhood Cancer Survivors ◽

Model Based ◽

Extreme Gradient Boosting ◽

The Cross

PURPOSE Early identification of childhood cancer survivors at high risk for treatment-related cardiomyopathy may improve outcomes by enabling intervention before development of heart failure. We implemented artificial intelligence (AI) methods using the Children's Oncology Group guideline–recommended baseline ECG to predict cardiomyopathy. MATERIAL AND METHODS Seven AI and signal processing methods were applied to 10-second 12-lead ECGs obtained on 1,217 adult survivors of childhood cancer prospectively followed in the St Jude Lifetime Cohort (SJLIFE) study. Clinical and echocardiographic assessment of cardiac function was performed at initial and follow-up SJLIFE visits. Cardiomyopathy was defined as an ejection fraction < 50% or an absolute drop from baseline ≥ 10%. Genetic algorithm was used for feature selection, and extreme gradient boosting was applied to predict cardiomyopathy during the follow-up period. Model performance was evaluated by five-fold stratified cross-validation. RESULTS The median age at baseline SJLIFE evaluation was 31.7 years (range 18.4-66.4), and the time between baseline and follow-up evaluations was 5.2 years (0.5-9.5). Two thirds (67.1%) of patients were exposed to chest radiation, and 76.6% to anthracycline chemotherapy. One hundred seventeen (9.6%) patients developed cardiomyopathy during follow-up. In the model based solely on ECG features, the cross-validation area under the curve (AUC) was 0.87 (95% CI, 0.83 to 0.90), whereas the model based on clinical features had an AUC of 0.69 (95% CI, 0.64 to 0.74). In the model based on ECG and clinical features, the cross-validation AUC was 0.89 (95% CI, 0.86 to 0.91), with a sensitivity of 78% and a specificity of 81%. CONCLUSION AI using ECG data may assist in the identification of childhood cancer survivors at increased risk for developing future cardiomyopathy.

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Development of a dual-energy X-ray absorptiometry-derived body volume equation in Hispanic adults for administering a four-compartment model

British Journal Of Nutrition ◽

10.1017/s0007114520000598 ◽

2020 ◽

Vol 123 (12) ◽

pp. 1373-1381 ◽

Cited By ~ 1

Author(s):

Brett S. Nickerson ◽

Michael V. Fedewa ◽

Cherilyn N. McLester ◽

John R. McLester ◽

Michael R. Esco

Keyword(s):

Cross Validation ◽

Dual Energy ◽

Body Volume ◽

Main Body ◽

Validation Sample ◽

Body Composition Assessment ◽

X Ray ◽

Hispanic Females ◽

Hispanic Adults ◽

The Cross

AbstractThe purpose of the present study was: (1) to develop a new dual-energy X-ray absorptiometry (DXA)-derived body volume (BV) equation with the GE-Lunar prodigy while utilising underwater weighing (UWW) as a criterion and (2) to cross-validate the novel DXA-derived BV equation (4C-DXANickerson), Wilson DXA-derived BV equation (4C-DXAWilson) and air displacement plethysmography (ADP)-derived BV (4C-ADP) in Hispanic adults. A total of 191 Hispanic adults (18–45 years) participated in the present study. The development sample consisted of 120 females and males (50 % females), whereas the cross-validation sample comprised of forty-one females and thirty males (n 71). Criterion body fat percentage (BF %) and fat-free mass (FFM) were determined using a four-compartment (4C) model with UWW as a criterion for BV (4C-UWW). 4C-DXANickerson, 4C-DXAWilson and 4C-ADP were compared against 4C-UWW in the cross-validation sample. 4C-DXANickerson, 4C-DXAWilson and 4C-ADP all produced similar validity statistics when compared with 4C-UWW in Hispanic males (all P > 0·05). 4C-DXANickerson also yielded similar BF % and FFM values as 4C-UWW when evaluating the mean differences (constant error (CE)) in Hispanic females (CE = –0·79 % and 0·38 kg; P = 0·060 and 0·174, respectively). However, 4C-DXAWilson produced significantly different BF % and FFM values (CE = 3·22 % and –2·20 kg, respectively; both P < 0·001). Additionally, 4C-DXAWilson yielded significant proportional bias when estimating BF % (P < 0·001), whereas 4C-ADP produced significant proportional bias for BF % and FFM (both P < 0·05) when evaluated in Hispanic females. The present study findings demonstrate that 4C-DXANickerson is a valid measure of BV in Hispanics and is recommended for use in clinics, where DXA is the main body composition assessment technique.

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Prediction of Maize Single Cross Hybrids Using the Total Effects of Associated Markers Approach Assessed by Cross-Validation and Regional Trials

The Scientific World JOURNAL ◽

10.1155/2014/924348 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Wagner Mateus Costa Melo ◽

Renzo Garcia Von Pinho ◽

Marcio Balestre

Keyword(s):

Training Trial ◽

Cross Validation ◽

Predictive Accuracy ◽

Field Performance ◽

Training Population ◽

Maize Hybrids ◽

Single Cross ◽

The Cross ◽

Predictive Validation ◽

Single Cross Hybrids

The present study aimed to predict the performance of maize hybrids and assess whether the total effects of associated markers (TEAM) method can correctly predict hybrids using cross-validation and regional trials. The training was performed in 7 locations of Southern Brazil during the 2010/11 harvest. The regional assays were conducted in 6 different South Brazilian locations during the 2011/12 harvest. In the training trial, 51 lines from different backgrounds were used to create 58 single cross hybrids. Seventy-nine microsatellite markers were used to genotype these 51 lines. In the cross-validation method the predictive accuracy ranged from 0.10 to 0.96, depending on the sample size. Furthermore, the accuracy was 0.30 when the values of hybrids that were not used in the training population (119) were predicted for the regional assays. Regarding selective loss, the TEAM method correctly predicted 50% of the hybrids selected in the regional assays. There was also loss in only 33% of cases; that is, only 33% of the materials predicted to be good in training trial were considered to be bad in regional assays. Our results show that the predictive validation of different crop conditions is possible, and the cross-validation results strikingly represented the field performance.

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Physiological Biorhythm as a Correlate of Pilot Error Accidents and Incidents

Proceedings of the Human Factors Society Annual Meeting ◽

10.1177/1071181378022001130 ◽

1978 ◽

Vol 22 (1) ◽

pp. 498-501

Author(s):

Hal W. Hendrick ◽

Harlan E. Jones

Keyword(s):

Cross Validation ◽

Validation Group ◽

Aircraft Accidents ◽

Pilot Error ◽

Critical Phase ◽

Incident Reports ◽

Military Unit ◽

The Cross ◽

Critical Phases

Aircraft accidents and incidents attributed to pilot error were hypothesized to have occurred while the pilot was in a critical phase for one or more biorhythms. From screening accident and incident reports for a large military unit, two groups of 25 pilots who had been involved in pilot error accidents and one group of 50 pilots who had been involved in pilot error incidents were identified. 13 of the accident validation group and 12 of the cross validation group were found to have been in a critical physiological phase at the time of accident, or twice the number expected by chance. For the incident group, 20 of the 50 pilots were in a critical physiological phase at the time of incident. Results for all three groups exceeded chance at the .025 level. Results for emotional and intellectual biorhythms, and for double critical phases were found not significant.

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Checking the Efficacy of Two Basic Descriptors With a Set of Properties of Alkanes

International Journal of Quantitative Structure-Property Relationships ◽

10.4018/ijqspr.2019010105 ◽

2019 ◽

Vol 4 (1) ◽

pp. 67-87

Author(s):

Emili Besalú ◽

Riccardo Zanni ◽

Lionello Pogliani ◽

Jesus Vicente de Julian-Ortiz

Keyword(s):

In Silico ◽

Cross Validation ◽

Model Building ◽

Quantitative Structure ◽

Structure Property ◽

Structure Property Relationships ◽

Building Activity ◽

Quantitative Structure Property Relationships ◽

The Cross ◽

Residual Plots

Several experimental properties of alkanes are described by means of multilinear models at the cross-validation level. The models have been obtained considering two main sets of descriptors: mathematically-based and experimental ones. The best models are obtained normally involving one of the two sets. The main goal of this work is to show how the theoretical descriptors are able to perform a competitive role against the experimental ones. This constitutes an important topic in the quantitative structure-property relationships field because the use of mathematical and in silico descriptors is validated as a proper tool for model building. Activity distributions of the properties and indices employed are discussed, along with the shape of the obtained residual plots.

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Research on Recognition of Pathological Voice by AD Tree

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.658.647 ◽

2013 ◽

Vol 658 ◽

pp. 647-651 ◽

Cited By ~ 1

Author(s):

Jun Jie Zhu ◽

Xiao Jun Zhang ◽

Ji Hua Gu ◽

He Ming Zhao ◽

Qiang Zhou ◽

...

Keyword(s):

Cross Validation ◽

Recognition Rate ◽

Experimental Results ◽

Acoustic Features ◽

Recognition Process ◽

Normal Voice ◽

The Cross ◽

Fold Cross Validation ◽

Pathological Voice

This paper mainly studies on the classification of pathological voice from normal voice based on the sustained vowel /a/. Firstly, the original 18 acoustic features are extracted. Then on the basis of the extracted parameters, this paper recognizes the pathological voice using AD Tree. During the classification stage, the cross-validation of features is also as references in the process. This method is validated with a sound database provided by the Massachusetts Eye and Ear Infirmary (MEEI). After the 10 fold cross-validation, comparing with 7 other kinds of classifiers, the experimental results show that AD Tree can get the highest recognition rate of 95.2%. The method in this paper shows that all the extracted parameters are reasonable in the following recognition process and AD tree is a good recognition way in pathological voice research.

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