Titlebook: Biological and Artificial Intelligence Environments; Bruno Apolloni,Maria Marinaro,Roberto Tagliaferri Conference proceedings 2005 Springe

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P. Kitslaar,M. Lemson,C. Schreurs,H. Bergsa very flexible manner with reusability prospective. The project is implemented through a “digital core” constituted of a FPGA, a microcontroller and several memory blocks which co-operate to the computation. The FPGA is programmed in VHDL to implement the data mining process. The data mining system

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Genetic Design of Linear Block Error-Correcting Codes codes. We offer a detailed description of the algorithm, with particular regard to the genetic operators (selection, mutation and crossover) which have been specifically adapted to the problem. Preliminary experimental results indicate that the method can be very effective, especially in terms of fast production of good sub-optimal codes.

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Overview: A fresh look on the state of the art of the research in Neural networks and related fields on the part of the computational intelligence.A special flavoured perspective of the above research from a 15978-90-481-6863-7978-1-4020-3432-9

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https://doi.org/10.1007/978-90-313-8396-2tivity ratio, and extracted from PA chest radiographs by a fully automatized method. The methods are a rule based system and a feed-forward neural network trained by back-propagation. Both the systems allow to recognize almost the 75% of false positives without losing any true positives.

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https://doi.org/10.1007/978-3-7091-9911-4itrarily complex patterns within these data, neural networks play a unique, exciting and pivotal role in areas as diverse as protein structure and function prediction. This paper presents a critical overview of recent advances in bioinformatics which have utilised neural network methods.