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Search Results - training error
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Railway wheel squeal as a result of unsteady longitudinal and spin creepage
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An analysis of the factors inhibiting ECDIS from effectually achieving its intended primary function of contributing to safe navigation
Published 2019“…Improved training methods are required to address these types of technologically-generated error pathways.…”
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Protection-based Distributed Generation Penetration Limits on MV feeders - Using Machine Learning
Published 2022“…A high variance problem was observed (cross-validation error was high and training error was low) for the models that used all the network protection features as inputs. …”
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Estimation of bare soil surface temperature from air temperature and soil depth temperature in a tropical station
Published 2011ArticleFull Text AvailableAccess Repository -
Adverse event registry analysis of an EMS system in a low resource setting: a descriptive study
Published 2019“…This created a greater need for individual and group training of EMS clinical providers. We recommend further research in order to adequate describe the reason for the increase individual medical error, as well as to find more effective means of detecting adverse events and near misses in this population.…”
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The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria
Published 2010ArticleFull Text AvailableAccess Repository -
ARIMA model and neural network: a comparative study of crime rate modelling
Published 2015Conference ProceedingFull Text AvailableAccess Repository -
Artificial neural network model for prediction of friction factor in pipe flow
Published 2009ArticleFull Text AvailableAccess Repository -
Performance ranking of artificial neural network learning algorithms in solar radiation forecast
Published 2010Conference ProceedingFull Text AvailableAccess Repository -
Prediction of friction losses in spark-ignition engines: an artificial neural networks approach
Published 2011Conference ProceedingFull Text AvailableAccess Repository -
Artificial neural network modeling of heat transfer in a staggered cross-flow tube type heat exchanger
Published 2008-11ArticleFull Text AvailableAccess Repository -
Reconstruction of three-dimensional facial geometric features related to fetal alcohol syndrome using adult surrogates
Published 2021“…The average intra-observer error was 0.26 mm and the average inter-observer error was 0.89 mm. …”
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Artificial Neural Networks in Stock Return Prediction: Testing Model Specification in a Global Context
Published 2021“…Specifically, the number of hidden neurons examined were three, nine, 13, 18 and 30. All five networks train significantly well, with each network's training error indicating a good model fit. …”
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Prediction of construction material prices using macroeconomic indicators: A neural networks model
Published 2019“…For prediction of Steel prices, the ANN model developed using Python programing language had the superior performance over other models with its ability to predict the month-to-month variations in Steel prices while having mean-absolute-percentage error of 9.0% and 10.1% for training and testing sets respectively. …”
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The development of an integrated management system in the personal care products industry.
Published 2008“…Employees should be motivated through fair pay, training and recognition as this will reduce human error and also improve the general feeling in the organisation. …”
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The effects of audio feedback on the L2 writing quality of Egyptian university students
Published 2014“…It also examines ESL student attitudes and perceptions towards audio feedback and audio feedback training. Audio feedback is an innovative error correction technique in which the teacher records comments on students’ writing and saves them as an audio file. …”
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Artificial Neural Network Modeling for Al-Zn-Sn sacrificial anode protection of low carbon steel in saline media
Published 2012ArticleFull Text AvailableAccess Repository -
Model Calibration with Machine Learning
Published 2019“…In the experiment, we show that the calibrated model parameters reprice a set of options to a mean relative implied volatility error of less than one per cent. The limitations and shortcomings of neural networks in model calibration are also investigated and discussed.…”
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