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Automatic classification of fish germ cells through optimum-path forest
(2011-12-26)
The spermatogenesis is crucial to the species reproduction, and its monitoring may shed light over some important information of such process. Thus, the germ cells quantification can provide useful tools to improve the ...
Machine learning techniques to predict overweight or obesity
(2021-01-01)
Overweight and obesity are considered a public health problem, as they are related to the risk of various diseases, and also to the risk of increased morbidity and mortality. The main objective of this work was to apply ...
Machine learning approaches outperform distance- and tree-based methods for DNA barcoding of Pterocarpus wood
(Springer, 2019-05-01)
Main conclusion Machine-learning approaches (MLAs) for DNA barcoding outperform distance- and tree-based methods on identification accuracy and cost-effectiveness to arrive at species-level identification of wood. DNA ...
A method for evaluating spindle rotation errors of machine tools using a laser interferometer
(2008)
This paper presents a method for assessing radial and axial error motions of spindles. It uses the Hewlett Packard
5529A laser interferometer. The measurement is made using reflection directly from a high-precision sphere. ...
Fast robot voice interface through optimum-path forest
(2012-10-01)
Voice-based user interfaces have been actively pursued aiming to help individuals with motor impairments, providing natural interfaces to communicate with machines. In this work, we have introduced a recent machine learning ...
Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study
(ELSEVIER SCIENCE BV, 2011)
Objective: We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. Methods and ...
A Diversity-Accuracy Measure for Homogenous Ensemble Selection
Several selection methods in the literature are essentially based on an evaluation function that determines whether a model M contributes positively to boost the performances of the whole ensemble. In this paper, we propose ...