Neuro Fuzzy Classification Systems
Mostrando 1-8 de 8 artigos, teses e dissertações.
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1. Sistema computacional para auxílio ao diagnóstico em exames de tuberculose animal
The results obtained in evaluating the efficiency of a Neuro-Fuzzy System NEFCLASS (Neuro-Fuzzy Classification) in image classification of cattle tuberculosis, based on its texture features extracted using the wavelet transform are presented. For testing, images of animal tissues diagnosed with tuberculosis were used, as provided by the Tuberculosis Laborato
Arq. Bras. Med. Vet. Zootec.. Publicado em: 2013-04
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2. Sistemas de detecção de intrusão com técnicas de inteligência artificial / Intrusion detection systems with artificial inteligence technics
Due the increase of the amount of important information on computer networks, security has became primordial to ensure the integrity, confidentiality and availability of data traffic. To improve security, there are useful tools such as Firewalls and Intrusion Detection Systems (IDS). Currently, methods of Artificial Intelligence (AI) are used to improve thes
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 25/02/2011
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3. DATA MINING APPLIED TO CUSTOMER RETENTION IN WIRELESS TELECOMMUNICATIONS / MINERAÇÃO DE DADOS NA RETENÇÃO DE CLIENTES EM TELEFONIA CELULAR
The goal of this work is to propose a complete data mining system for the solution of customer retention problems, commonly found in many industries. Such a solution encompasses the accurate identification among huge amounts of data of those consumers who would most likely end their relationship with the firm, based on their historical behavior and individua
Publicado em: 2005
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4. Sistema neural hÃbrido para reconhecimento de padrÃes em um nariz artificial / Hybrid neural system forpattern recognition in an artificial nose
This dissertation investigates the use of Hybrid Intelligent Systems in the pattern recognition system of an artificial nose. The work involves five main parts: (1) an evaluation of the odors database by a multivariate statistics technique; (2) a validation of the Time Delay Neural Networks in the odors recognition; (3) an evaluation of the Wavelet Transform
Publicado em: 2004
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5. HIBRID NEURO-FUZZY-GENETIC SYSTEM FOR AUTOMATIC DATA MINING / SISTEMA HÍBRIDO NEURO-FUZZY-GENÉTICO PARA MINERAÇÃO AUTOMÁTICA DE DADOS
This dissertation presents the proposal and the development of a totally automatic data mining system. The main objective is to create a system that is capable of extracting obscure information from complex databases, without demanding the presence of a technical specialist to configure it. The Hierarchical Neuro-Fuzzy Binary Space Partitioning model (NFHB)
Publicado em: 2004
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6. SISTEMAS INTELIGENTES NO ESTUDO DE PERDAS COMERCIAIS DO SETOR DE ENERGIA ELÉTRICA / INTELLIGENT SYSTEMS APPLIED TO FRAUD ANALYSIS IN THE ELECTRICAL POWER INDUSTRIES
Esta dissertação investiga uma nova metodologia, baseada em técnicas inteligentes, para a redução das perdas comerciais relativas ao fornecimento de energia elétrica. O objetivo deste trabalho é apresentar um modelo de inteligência computacional capaz de identificar irregularidades na medição de demanda e consumo de energia elétrica, considerando
Publicado em: 2003
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7. NEURO-FUZZY BSP HIERARCHICAL SYSTEM FOR TIME FORECASTING AND FUZZY RULE EXTRACTION DOR DATA MINING APPLICATONS / SISTEMA NEURO-FUZZY HIERÁRQUICO BSP PARA PREVISÃO E EXTRAÇÃO DE REGRAS FUZZY EM APLICAÇÕES DE DATA MINING
This dissertation investigates the use of a Neuro-Fuzzy Hierarchical system for time series forecasting and fuzzy rule extraction for Data Mining applications. The objective of this work was to extend the Neuro-Fuzzy BSP Hierarchical model for the classification of registers and time series forecasting. The process of classification of registers in the Data
Publicado em: 2000
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8. BAYESIAN LEARNING FOR NEURAL NETWORKS / APRENDIZADO BAYESIANO PARA REDES NEURAIS
This dissertation investigates the Bayesianan Neural Networks, which is a new approach that merges the potencial of the artificial neural networks with the robust analytical analysis of the Bayesian Statistic. Typically, theconventional neural networks such as backpropagation, have good performance but presents problems of convergence, when enough data for t
Publicado em: 1999