102,684 research outputs found
A geostatistical model based on Brownian motion to Krige regions in R2 with irregular boundaries and holes
Master's Project (M.S.) University of Alaska Fairbanks, 2019Kriging is a geostatistical interpolation method that produces predictions and prediction intervals. Classical
kriging models use Euclidean (straight line) distance when modeling spatial autocorrelation. However, for estuaries,
inlets, and bays, shortest-in-water distance may capture the system’s proximity dependencies better than Euclidean
distance when boundary constraints are present. Shortest-in-water distance has been used to krige such regions (Little
et al., 1997; Rathbun, 1998); however, the variance-covariance matrices used in these models have not been shown to
be mathematically valid. In this project, a new kriging model is developed for irregularly shaped regions in R
2
. This
model incorporates the notion of flow connected distance into a valid variance-covariance matrix through the use of a
random walk on a lattice, process convolutions, and the non-stationary kriging equations. The model developed in this
paper is compared to existing methods of spatial prediction over irregularly shaped regions using water quality data
from Puget Sound
Bayesian Hierarchical Modelling for Tailoring Metric Thresholds
Software is highly contextual. While there are cross-cutting `global'
lessons, individual software projects exhibit many `local' properties. This
data heterogeneity makes drawing local conclusions from global data dangerous.
A key research challenge is to construct locally accurate prediction models
that are informed by global characteristics and data volumes. Previous work has
tackled this problem using clustering and transfer learning approaches, which
identify locally similar characteristics. This paper applies a simpler approach
known as Bayesian hierarchical modeling. We show that hierarchical modeling
supports cross-project comparisons, while preserving local context. To
demonstrate the approach, we conduct a conceptual replication of an existing
study on setting software metrics thresholds. Our emerging results show our
hierarchical model reduces model prediction error compared to a global approach
by up to 50%.Comment: Short paper, published at MSR '18: 15th International Conference on
Mining Software Repositories May 28--29, 2018, Gothenburg, Swede
Stable Electromyographic Sequence Prediction During Movement Transitions using Temporal Convolutional Networks
Transient muscle movements influence the temporal structure of myoelectric
signal patterns, often leading to unstable prediction behavior from
movement-pattern classification methods. We show that temporal convolutional
network sequential models leverage the myoelectric signal's history to discover
contextual temporal features that aid in correctly predicting movement
intentions, especially during interclass transitions. We demonstrate
myoelectric classification using temporal convolutional networks to effect 3
simultaneous hand and wrist degrees-of-freedom in an experiment involving nine
human-subjects. Temporal convolutional networks yield significant
performance improvements over other state-of-the-art methods in terms of both
classification accuracy and stability.Comment: 4 pages, 5 figures, accepted for Neural Engineering (NER) 2019
Conferenc
The efficacy of using data mining techniques in predicting academic performance of architecture students.
In recent years, there has been a tremendous increase in the number of applicants seeking placement in the undergraduate architecture programme. It is important to identify new intakes who possess the capability to succeed during the selection phase of admission at universities. Admission variable (i.e. prior academic achievement) is one of the most important criteria considered during selection process. The present study investigates the efficacy of using data mining techniques to predict academic performance of architecture student based on information contained in prior academic achievement.
The input variables, i.e. prior academic achievement, were extracted from students' academic records. Logistic regression and support vector machine (SVM) are the data mining techniques adopted in this study. The collected data was divided into two parts. The first part was used for training the model, while the other part was used to evaluate the predictive accuracy of the developed models.
The results revealed that SVM model outperformed the logistic regression model in terms of accuracy. Taken together, it is evident that prior academic achievement are good predictors of academic performance of architecture students.
Although the factors affecting academic performance of students are numerous, the present study focuses on the effect of prior academic achievement on academic performance of architecture students.
The developed SVM model can be used a decision-making tool for selecting new intakes into the architecture program at Nigerian universities
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