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Now showing items 391-400 of 2136
Comparing the Effect Size of School Level Support on Teachers’ Technology Integration
(Springer, 2020)
Teachers are expected to lead the innovative use of Information Communication and Technology (ICT) at the classroom level of context. However, research literature shows that a number of factors influence their ICT pedagogical ...
Comparison between the shifted-Laplacian preconditioning and the controllability methods for computational acoustics
(Elsevier, 2010)
Processes that can be modelled with numerical calculations of acoustic pressure fields include medical and industrial ultrasound, echo sounding, and environmental noise. We present two methods for making these calculations ...
Comparison of cluster validation indices with missing data
(ESANN, 2018)
Clustering is an unsupervised machine learning technique, which aims to divide a given set of data into subsets. The number of hidden groups in cluster analysis is not always obvious and, for this purpose, various cluster ...
Comparison of Deep Neural Networks in the Classification of Bark Beetle-Induced Spruce Damage Using UAS Images
(MDPI AG, 2023)
The widespread tree mortality caused by the European spruce bark beetle (Ips typographus L.) is a significant concern for Norway spruce-dominated (Picea abies H. Karst) forests in Europe and there is evidence of increases ...
Comparison of different revenue models in SaaS
(GSTF, 2012)
Cloud computing brings new possibilities for software firms to sell their products within a Software-as-a-Service (SaaS) model. However, although SaaS provides new revenue models, it may not easily achieve a profitable ...
Comparison of different solution algorithms for sparse linear equations arising from flowsheeting problems
(Elsevier, 1989)
Computational efficiencies of seven different algorithms for the solution of sparse linear equation systems have been compared. The comparison was made both by using randomly generated equation sets and by linking the ...
Comparison of feature importance measures as explanations for classification models
(Springer, 2021)
Explainable artificial intelligence is an emerging research direction helping the user or developer of machine learning models understand why models behave the way they do. The most popular explanation technique is feature ...
Comparison of Internal Clustering Validation Indices for Prototype-Based Clustering
(MDPI, 2017)
Clustering is an unsupervised machine learning and pattern recognition method. In general,
in addition to revealing hidden groups of similar observations and clusters, their number needs to
be determined. Internal ...
Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia : A retrospective study
(Frontiers Media SA, 2022)
Introduction: Preeclampsia, one of the leading causes of maternal and fetal morbidity and mortality, demands accurate predictive models for the lack of effective treatment. Predictive models based on machine learning ...
Comparison of Machine Learning Methods in Stochastic Skin Optical Model Inversion
(MDPI AG, 2020)
In this study, we compare six different machine learning methods in the inversion of a stochastic model for light propagation in layered media, and use the inverse models to estimate four parameters of the skin from the ...