The defuzzifier maps the fuzzy sets to a single number as the output. In other words, in a fuzzy classification approach, a collection of n data objects (x(1), x(2),… , x(n) is represented by a set of m attributes x1(n), x2(n),… , xm(n). Each attribute in x(n) ...
As a linear regression method, LR allows for the output of probabilities for binary classification problems. Therefore, we chose LR as the classifier to derive the radscore and proceeded to the next step of model construction. Previously, radiomics based on CT and MRI have been used to predict...
This paper proposes a machine learning (ML) based approach that predicts the fault type for a given textual bug report. We built a dataset from 70+ projects for training and evaluation of our approach. Further, we performed a user study to establish a baseline for non-expert human ...
Variability in single-subject whole-brain functional volumes was determined using the software program Artifact Recognition Toolbox http://www.nitrc.org/projects/artifact_detect. Individual whole-brain BOLD fMRI volumes meeting at least one of the following two criteria were excluded from determination ...
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this approach explores the design space using an optimal policy that balances exploitation and exploration to identify the global optimum. In active learning, a probability surrogate model, Gaussian process (GP), is constructed using initial input-output data obtained from costly experiments or simulatio...
MTGIpick was written in Matlab and Java, compiled in Windows,Linux and Mac, and run on those platforms. We have supplied a version of MTGIpick. The output of MTGIpick consists of the genomic signatures, score of each region and predicted genomic islands. ...
A method of simulating an information technology (IT) system to produce parametric data includes translating specifications of the IT system from a first modeling language to a second modeling languag
Although COVID-19 has accelerated the development of the home workout industry, there are few studies evaluating the use of machine learning in this field. Therefore, we adopted a machine learning approach to predict whether home workouts will continue to be used based on the following reasons....
Although COVID-19 has accelerated the development of the home workout industry, there are few studies evaluating the use of machine learning in this field. Therefore, we adopted a machine learning approach to predict whether home workouts will continue to be used based on the following reasons....