A New Method for Response Integration in Modular Neural Networks using Type-2 Fuzzy Logic for Biometric Systems

Urias,; Hidalgo, Jacqueline M.; Melín, Patricia; Castillo, Oscar

doi:10.1109/ijcnn.2007.4370974

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…Now let us see some work carried after 1990s. J. Urias, D. Hidalgo, P. Melin, O. Castillo proposed a new method for response integration in modular neural networks using type-2 fuzzy logic [14]. Biometric authentication is used to achieve person recognition.…”

Section: Fig 2: the Neural Network Architecture To Classify Grayscalementioning

confidence: 99%

The Study of Adoption of Neural Network Approach in Fingerprint Recognition

Meva¹,

Kumbharana²,

Kothari³

2012

IJCA

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Fingerprint identification and verification are one of the well established methods for implementing security aspects. This technique has become mature due to lots of research in this area. One more feature of artificial intelligence is clubbed with image processing for fingerprint that is artificial neural network. ANN is used at different levels in fingerprint recognition. In this paper we will study the use of neural network approach in fingerprint recognition at different stages.

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Section: Fig 2: the Neural Network Architecture To Classify Grayscalementioning

confidence: 99%

The Study of Adoption of Neural Network Approach in Fingerprint Recognition

Meva¹,

Kumbharana²,

Kothari³

2012

IJCA

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“…A new method [5] of response integration is used for the modular neural network by using type-2 fuzzy logic. Face, fingerprint and voice are the three biometric characteristics which are used.…”

Section: Introductionmentioning

confidence: 99%

Use of Modular Neural Network for Heart Disease

Vazirani

Kala

Shukla

et al. 2010

IJCCT

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The medical field is very versatile field and one of the interested research areas for the scientist. It deals with many medical disease problems starting with the diagnosis of the disease, preventing from the disease and treatment for the disease. There are various types of medical disease and accordingly various types of treatment methods. In this paper we mostly concern about the diagnosis of the heart disease. Mainly two types of the diagnosis method are used one is manual and other is automatic diagnosis which consists of diagnosis of disease with the help of intelligent expert system. In this paper the modular neural network is used to diagnosis the heart disease. The attributes are divided and given to the two neural network models Backpropagation Neural Network (BPNN) and Radial Basis Function Neural Network (RBFNN) for training and testing. The two integration techniques are used two integrate the results and provide the final training accuracy and testing accuracy. The modular neural network with probabilistic product method gave an accuracy of 87.02% over training data and 85.88% over testing accuracy and with probabilistic product method gave an accuracy of 89.72% over training data and 84.70% over testing accuracy, which was experimentally determined to be better than monolithic neural networks.

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