scholarly journals Training Simulation with Nothing but Training Data - Simulating Performance based on Training Data Without the Help of Performance Diagnostics in a Laboratory

2016 ◽  
Author(s):  
Melanie Ludwig ◽  
David Schaefer ◽  
Alexander Asteroth
Keyword(s):  
Training Data ◽  
Training Simulation ◽  
Performance Diagnostics

Making Evidence-Based Practice User Friendly: A Curriculum for Training "Data-Proficient" Clinicians

2009 ◽  
Author(s):  
Christopher Layne ◽  
Virginia Strand ◽  
Robert Abramovitz ◽  
Glenn Saxe
Keyword(s):  
Evidence Based Practice ◽  
Training Data ◽  
Evidence Based ◽  
User Friendly

Efektifitas Penggunaan Metode Pembelajaran Simulasi Dengan Latihan Terhadap Keterampilan Pengisian Partograf Pada Mahasiswa Semester II Di Prodi Kebidanan (D-III) Universitas Kadiri Tahun 2015

2017 ◽  
Vol 4 (1) ◽  
pp. 12
Author(s):  
Dessy Lutfiasari ◽  
Mahmudah Mahmudah
Keyword(s):  
Current Method ◽  
Skills Training ◽  
Health Sciences ◽  
Control Group ◽  
Learning Methods ◽  
Control Group Design ◽  
Significance Level ◽  
Whitney Test ◽  
Mann Whitney Test ◽  
Training Simulation

The use of the current method of learning very big influence on the growth and development of students' creativity and interest for all subjects to be taught, especially in the use of partograf. From interviews to the 10 students of IV semester Prodi Midwifery (D-III) Kadiri University is known that 4 (40%) of students said it was understood, 4 (40%) of other students say they are confused and 2 (20%) of them said that he was a student not familiar with partograf. This shows the lack of understanding of students in filling partograph. The research objective is to determine the effectiveness of the use of learning methods for skills training simulation with filling partograph the second semester students in Midwifery (D-III) Kadiri University Faculty of Health Sciences in 2015. The research design used is pre experiment with design Static Group Comparison/Posttest Only Control Group Design. The population studied were all students of the second semester in Midwifery (D-III) Faculty of Health Sciences University of Kadiri numbered 50 students and sampling techniques Federer totaled 32 students. This is a research instrument partograph sheet. Results of the study were analyzed using the Mann Whitney test with a significance level of 0.05 were used.The results showed 7 respondents (46.7%) are adept at using partograf with simulation teaching methods and 6 respondents (40.0%) are adept at using partograph with practice learning methods. Data were analyzed by Mann Whitney test obtained ρ = 0.965; α = 0.05 means that H0 is accepted and H1 rejected. This means there is no difference in the effective use of learning methods for skills training simulation with partograph filling. Based on the results of this study are expected to choose the method of learning as a learning method in charging partograph because both methods equally effective.; Keywords: simulation methods, drilling methods, partograph filling

A Note on Document Classification with Small Training Data

2011 ◽  
Vol 131 (8) ◽  
pp. 1459-1466
Author(s):  
Yasunari Maeda ◽  
Hideki Yoshida ◽  
Masakiyo Suzuki ◽  
Toshiyasu Matsushima
Keyword(s):  
Document Classification ◽  
Training Data

Local and Regional Hour-Ahead Forecasts of Solar Irradiance with Training Data Selection and Support Vector Regression

2016 ◽  
Vol 136 (12) ◽  
pp. 898-907 ◽  
Author(s):  
Joao Gari da Silva Fonseca Junior ◽  
Hideaki Ohtake ◽  
Takashi Oozeki ◽  
Kazuhiko Ogimoto
Keyword(s):  
Support Vector Regression ◽  
Solar Irradiance ◽  
Training Data ◽  
Data Selection ◽  
Support Vector ◽  
Training Data Selection

Cardiology Fellowship Education in the Era of High-density Training, Data Tracking, and Quality Measures

2011 ◽  
Vol 9 (2) ◽  
pp. 99
Author(s):  
Alex J Auseon ◽  
Albert J Kolibash ◽  
◽  
Keyword(s):  
Ohio State University ◽  
Training Data ◽  
State University ◽  
Training Requirements ◽  
Healthcare Environment ◽  
Medical Centers ◽  
Work Hour ◽  
The Ohio State University ◽  
Data Tracking

Background:Educating trainees during cardiology fellowship is a process in constant evolution, with program directors regularly adapting to increasing demands and regulations as they strive to prepare graduates for practice in today’s healthcare environment.Methods and Results:In a 10-year follow-up to a previous manuscript regarding fellowship education, we reviewed the literature regarding the most topical issues facing training programs in 2010, describing our approach at The Ohio State University.Conclusion:In the midst of challenges posed by the increasing complexity of training requirements and documentation, work hour restrictions, and the new definitions of quality and safety, we propose methods of curricula revision and collaboration that may serve as an example to other medical centers.

CNN performance dependence on linear image processing

2020 ◽  
Vol 2020 (10) ◽  
pp. 310-1-310-7
Author(s):  
Khalid Omer ◽  
Luca Caucci ◽  
Meredith Kupinski
Keyword(s):  
Image Processing ◽  
Texture Classification ◽  
Full Rank ◽  
Detection Performance ◽  
Ideal Observer ◽  
Training Data ◽  
Image Texture ◽  
Training Images ◽  
Analytic Expressions ◽  
Linear Compression

This work reports on convolutional neural network (CNN) performance on an image texture classification task as a function of linear image processing and number of training images. Detection performance of single and multi-layer CNNs (sCNN/mCNN) are compared to optimal observers. Performance is quantified by the area under the receiver operating characteristic (ROC) curve, also known as the AUC. For perfect detection AUC = 1.0 and AUC = 0.5 for guessing. The Ideal Observer (IO) maximizes AUC but is prohibitive in practice because it depends on high-dimensional image likelihoods. The IO performance is invariant to any fullrank, invertible linear image processing. This work demonstrates the existence of full-rank, invertible linear transforms that can degrade both sCNN and mCNN even in the limit of large quantities of training data. A subsequent invertible linear transform changes the images’ correlation structure again and can improve this AUC. Stationary textures sampled from zero mean and unequal covariance Gaussian distributions allow closed-form analytic expressions for the IO and optimal linear compression. Linear compression is a mitigation technique for high-dimension low sample size (HDLSS) applications. By definition, compression strictly decreases or maintains IO detection performance. For small quantities of training data, linear image compression prior to the sCNN architecture can increase AUC from 0.56 to 0.93. Results indicate an optimal compression ratio for CNN based on task difficulty, compression method, and number of training images.

Semi-supervised Multi-task Network For Image Aesthetic Assessment

2020 ◽  
Vol 2020 (8) ◽  
pp. 188-1-188-7
Author(s):  
Xiaoyu Xiang ◽  
Yang Cheng ◽  
Jianhang Chen ◽  
Qian Lin ◽  
Jan Allebach
Keyword(s):  
Neural Network ◽  
Semantic Content ◽  
Training Data ◽  
Score Distribution ◽  
Generalization Ability ◽  
Subjective Preference ◽  
Ranking Tasks

Image aesthetic assessment has always been regarded as a challenging task because of the variability of subjective preference. Besides, the assessment of a photo is also related to its style, semantic content, etc. Conventionally, the estimations of aesthetic score and style for an image are treated as separate problems. In this paper, we explore the inter-relatedness between the aesthetics and image style, and design a neural network that can jointly categorize image by styles and give an aesthetic score distribution. To this end, we propose a multi-task network (MTNet) with an aesthetic column serving as a score predictor and a style column serving as a style classifier. The angular-softmax loss is applied in training primary style classifiers to maximize the margin among classes in single-label training data; the semi-supervised method is applied to improve the network’s generalization ability iteratively. We combine the regression loss and classification loss in training aesthetic score. Experiments on the AVA dataset show the superiority of our network in both image attributes classification and aesthetic ranking tasks.

Evaluation of detailed training data to identify risk factors for retirement from tendon injuries in Thoroughbred racehorses

2007 ◽  
Vol 231 (9) ◽  
pp. 1385-1385
Author(s):  
Kenneth K. H. Lam ◽  
Tim D. H. Parkin ◽  
Christopher M. Riggs ◽  
Kenton L. Morgan
Keyword(s):  
Risk Factors ◽  
Tendon Injuries ◽  
Training Data ◽  
Thoroughbred Racehorses
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