The Introduction of the Special Issue The data mining techniques, based on the genetic algorithm have been largely used for classifying and processing of biomedical signals in software in several areas of expertise.The data preparation, mathematical modeling and computational cost in the execution of these algorithms may influence the rapid interpretation of the results, when the processing of these algorithms is executed in software. The implementation of genetic algorithms (GAs) inspired by Holland model in hardware to filter signals aims to speed up convergence time of these algorithms by implementing the modules considered a bottleneck for a software,implementation. However, these modules have the same problems with the representation of the chromosome, dependence on genetic operators, representation adopted for the chromosome and population, and the loss of chromosomes with relevant features for the solution of the problem to which the AG has being applied. This work presents an adaptive filter that takes a genetic algorithm based on abstract data types (GAADT) for processing biomedical signals, called CGAADT, the GPU /CUDA plataform. The compact genetic algorithm based on abstract data types (CGAADT) developed presents a solution for high performance of genetic algorithms based on abstract data types. The choice of this genetic algorithm model is justified by the fact that the GAADT have been define with the purpose of avoid the problems of models AG until then found of evolutionary computation literature. The result obtained by GAADT has better quality than others AG models, since this works the definition of dominant gene, which are the information provided in the relevant chromosomes to solve the problem.
The Research Scope of the Special Issue ·Processing Signal ·Genetic Algorithm ·Hight Performance ·Compute Unified Device Architecture ·Genetic Algorithm Based on Abstract Data Types
The Article Title of the Special Issue 1 . Biometric signal processing using the GPU / CUDA platform. 2.Data Mining of biomedical signals using genetic algorithm 3.Hybrid model using Genetic Algorithms and Neural Networks in the CUDA platform in the Classification of Biomedical Signals. 4.High Performance Systems in the Processing of Biomedical Signals using Genetic Algorithms based on Abstract Data Types. 5.Compact Genetic Algorithm based on Abstract Data Types on the CUDA Platform. 6. Kohonen’s Self-Organizing Maps in biomedical signal classification using the CUDA/ GPU platform. 7. A Hybrid Model of Kohonen’s self-organizing maps and Genetic Algorithms based on Abstract Data Types in the Classification of Biomedical Signals. 8. High Performance Data Mining on the GPU/ CUDA Platform in the Classification of Biomedical Signals.
Submission guidelines All papers should be submitted via the Probe-Computer science and information technology submission system: http://probe.usp-pl.com/index.php/CSIT/index Submitted articles should not be published or under review elsewhere. All submissions will be subject to the journal’s standard peer review process. Criteria for acceptance include originality, contribution, scientific merit and relevance to the field of interest of the Special Issue.
Important Dates Paper Submission Due: From July 24 to July 28 , 2019
The Lead Guest Editor Andrilene MacIe PhD in Computer Science and Master in Computational Modeling of Knowledge at the Federal University of Alagoas, holds a Latu-sensu Post-Graduation in Information Management with emphasis on Information Systems in the Production Engineering Department of the Federal University of Pernambuco and a degree in Technology of Data Processing with emphasis on Information Systems. Has experience in the area of Computer Science, with emphasis on Information Systems.
Guest Editor 1.Ulisses Martins Dias: 2.Danielle Furtado dos Santos Dias: 3.Valter Wellington Ramos Junior: 4.Bruno Raphael Pastor de Melo: 5.Andréa Marques Vanderlei Ferreira: 6.Paulo César do Nascimento Cunha: 7.ROBERTA VILHENA VIEIRA LOPES: |
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