Models based on an artificial neural network (the multilayer perceptron) and binary logistic regression were compared in their ability to differentiate between disease-free subjects and those with impaired glucose tolerance or diabetes mellitus diagnosed by fasting plasma glucose. Demographic, anthropometric and clinical data were collected from 7222 participants aged 30-88 years in the Tehran ...

This paper proposes a multilevel logic approach to output coding using multilevel neurons in the output layer. Training convergence for a single multilevel perceptron is considered. It has been found that a multilevel neural network classifier with a reduced number of outputs is often able to learn faster and requires fewer weights. Concepts are illustrated with an example of a digit classifier.

This paper presents a mapping scheme for the proposed implementation of neural network models on systolic arrays. The mapping technique is illustrated on the multilayer perceptron with back-propagation learning. Dependency graphs have been given that represent the operations in the execution phases of the neural network model and later suitable algorithms are presented to realize the operations...

Hierarchical Bayesian inference in parameterised models offers an approach for controlling complexity. In this paper we utilise a novel prior for the leaning of a model’s structure. We call the prior node relevance determination. It is applicable in a range of models including sigmoid belief networks and Boltzmann machines. We demonstrate how the approach may be applied to determine structure i...

In today’s world, gigantic amount of data is available in science, industry, business and many other areas. This data can provide valuable information which can be used by management for making important decisions. But problem is that how can find valuable information. The answer is data mining. Data Mining is popular topic among researchers. There is lot of work that cannot be explored till no...

This letter proposes a new type of neurons called multithreshold quadratic sigmoidal neurons to improve the classification capability of multilayer neural networks. In cooperation with single-threshold quadratic sigmoidal neurons, the multithreshold quadratic sigmoidal neurons can be used to improve the classification capability of multilayer neural networks by a factor of four compared to comm...

In this paper, work has been performed to recognize Handwritten English Character using a multilayer perceptron with one hidden layer. The feature extracted from the handwritten character is Boundary tracing along with Fourier Descriptor. Character is identified by analyzing its shape and comparing its features that distinguishes each character. Also an analysis was carried out to determine the...

Rosenblatt's convergence theorem for the simple perceptron initiated much excitement about iterative weight modifying neural networks. However, this convergence only holds for the class of linearly separable functions, which is vanishingly small compared to arbitrary functions. With multilayer networks of nonlinear units it is possible, though not guaranteed, to solve arbitrary functions. Backp...

We discuss the dynamics of batch learning of multilayer neural networks in the asymptotic limit, where the number of trining data is much larger than the number of parameters, emphasizing on the parameterization redundancy in overrealizable cases. In addition to showing experimental results on overtraining in multilayer perceptrons and three-layer linear neural networks, we theoretically prove ...

In this paper we introduce a low-latency monaural source separation framework using a Convolutional Neural Network (CNN). We use a CNN to estimate time-frequency soft masks which are applied for source separation. We evaluate the performance of the neural network on a database comprising of musical mixtures of three instruments: voice, drums, bass as well as other instruments which vary from so...