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The objective of the current work is to develop an automatic tool to identify microbiological data types using computer vision and pattern recognition. Current systems rely on the subjective reading of profiles by a human expert. This process is time-consuming and prone to errors. Bacteriophage (phage) typing & Fluorescent imaging methods are used to extract representative feature profiles and...
Support Vector Machines (SVMs) ensembles have been widely used to improve classification accuracy in complicated pattern recognition tasks. In this work we propose to apply an ensemble of SVMs coupled with feature-subset selection methods to aleviate the curse of dimensionality associated with expression-based classification of DNA microarray data. We compare the single SVM classifier to SVM ensembles...
Base-calling is one of many problems that can be solved using pattern recognition, the act of classifying raw data based on prior or statistical information extracted from the data into various classes. In this paper, we propose a new framework using polynomial classifiers to model electropherogram traces obtained from ABI sequencing machines to perform base-calling. Initially, pre-processing, which...
The Gene Ontology (GO) is a controlled vocabulary of terms to describe protein functions. It also includes a hierarchical description of the relationships among the terms in the form of a directed acyclic graph (DAG). Several systems have been developed that employ pattern recognition to assign gene function, using a variety of features, including sequence similarity, presence of protein functional...
This work presents an infant cry automatic recognizer development, with the objective of classifying two kinds of infant cries, normal and pathological, from recently born babies. Extraction of acoustic features is used such as MFCC (Mel Frequency Cepstral Coefficients), obtained from Infant Cry Units sound waves, and a genetic feature selection system combined with a feed forward input delay neural...
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