Neural Classifiers
Mostrando 1-12 de 35 artigos, teses e dissertações.
-
1. Breast Cancer Prediction Using Dominance-based Feature Filtering Approach: A Comparative Investigation in Machine Learning Archetype
Abstract Breast cancer is the most commonly witnessed cancer amongst women around the world. Computer aided diagnosis (CAD) have been playing a significant role in early detection of breast tumors hence to curb the overall mortality rate. This work presents an enhanced empirical study of impact of dominance-based filtering approach on performances of variou
Braz. arch. biol. technol.. Publicado em: 25/11/2019
-
2. Predico do Câncer de Mama com Aplicação de Modelos de Inteligência Computacional
RESUMO O uso de modelos para diagnóstico auxiliado por computador (CAD) tem sido proposto para auxiliar na detecção e classificação do câncer de mama. Neste trabalho, avaliou-se o desempenho dos modelos de rede neural de perceptrons de múltiplas camadas e máquina de vetores de suporte não linear para classificar nódulos de câncer de mama. Dez cara
TEMA (São Carlos). Publicado em: 16/09/2019
-
3. Breast cancer diagnosis based on mammary thermography and extreme learning machines
Abstract Introduction Breast cancer is the most common cancer in women and one of the major causes of death from cancer among female around the world. The early detection and treatment are the major way to healing. The use of mammary thermography in Mastology is increasing as a complementary imaging technique to early detect lesions. Its use as a screening
Res. Biomed. Eng.. Publicado em: 05/03/2018
-
4. Development of a skateboarding trick classifier using accelerometry and machine learning
Abstract Introduction Skateboarding is one of the most popular cultures in Brazil, with more than 8.5 million skateboarders. Nowadays, the discipline of street skating has gained recognition among other more classical sports and awaits its debut at the Tokyo 2020 Summer Olympic Games. This study aimed to explore the state-of-the-art for inertial measurement
Res. Biomed. Eng.. Publicado em: 2017-10
-
5. Breast tumor classification in ultrasound images using support vector machines and neural networks
Abstract Introduction The use of tools for computer-aided diagnosis (CAD) has been proposed for detection and classification of breast cancer. Concerning breast cancer image diagnosing with ultrasound, some results found in literature show that morphological features perform better than texture features for lesions differentiation, and indicate that a reduc
Res. Biomed. Eng.. Publicado em: 10/10/2016
-
6. Seleção de componentes em ensembles de clasificadores multirrótulo / Component Selection in Ensembles of Multi-label Classifiers
The selection of components in ensembles of classifiers is a very common activity in the field of Machine Learning with several studies showing its effectiveness in obtaining significant gains in accuracy. However, the most studied classification task involves mutually exclusive labels (classes). The objective of this work is to present a study on the select
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 27/07/2012
-
7. Abordagens multi-objetivo para o treinamento de redes neurais e seleção de características
Artificial neural networks have been successfully applied in solving problems such as functions approximation and patterns classification, where the extraction of a model can be difficult to see. The search for a model that best represents the problem makes the generalization ability the main concern in the training of artificial neural networks, a task that
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 02/03/2012
-
8. ComitÃs de Classificadores Baseados nas Redes SOM e Fuzzy ART com Sintonia de ParÃmetros e SeleÃÃo de Atributos via MetaheurÃsticas EvolucionÃrias / Ensembles of classifiers based on SOM and Fuzzy ART networks with parameter tuning and feature selection through evolutionary metaheuristics.
O paradigma de classificaÃÃo baseada em comitÃs tem recebido considerÃvel atenÃÃo na literatura cientÃfica em anos recentes. Neste contexto, redes neurais supervisionadas tÃm sido a escolha mais comum para compor os classificadores base dos comitÃs. Esta dissertaÃÃo tem a intenÃÃo de projetar e avaliar comitÃs de classificadores obtidos atravÃ
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 28/11/2011
-
9. Análise de imagem polarimétrica TerraSAR-X para classificação de uso e cobertura da terra na porção sudoeste da Amazônia Brasileira / TerraSAR-X image for classification the land use and land cover in the southwest portion of the Brazilian Amazon
The objective of this study is to analyze the potential use of images from satellite TerraSAR-X, at StripMap acquisition mode, to map and identify the land use and land cover (LULC) in the southwestern Brazilian Amazon. Classifiers based on statistical functions for maximum likelihood (ML) method and based on frequency-based contextual and neural network cla
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 24/02/2011
-
10. Identificação de bovinos através de reconhecimento de padrões do espelho nasal utilizando redes neurais artificiais / Identification of bovines through recognition of images patterns of the muzzle using artificial neural nets
Artificial Neural Networks (ANN) are mathematical models associated with artificial intelligence that can learn and generalize information, therefore they can be used as images classifiers. This paper aims to analyze the cattle muzzle in order to prove that it is a unique and permanent characteristic of the animal thus, being used as its unique identificatio
Publicado em: 2011
-
11. Reconhecimento de padrões em imagens por descritores de forma / Pattern recognition in images via shape descriptors
A idéia de capacitar uma máquina a reconhecer o ambiente em que atua tem motivado pesquisadores a investir esforços no estudo do mais complexo dos sentidos humanos, a visão. A visão é, antes de tudo, uma tarefa de representação e processamento de informações, sendo portanto adequada ao tratamento computacional. Visto que ainda não se possuem méto
Publicado em: 2011
-
12. Evaluation of machine learning classifiers in keratoconus detection from orbscan II examinations
PURPOSE: To evaluate the performance of support vector machine, multi-layer perceptron and radial basis function neural network as auxiliary tools to identify keratoconus from Orbscan II maps. METHODS: A total of 318 maps were selected and classified into four categories: normal (n = 172), astigmatism (n = 89), keratoconus (n = 46) and photorefractive kerate
Clinics. Publicado em: 2010