Brain-Computer Interfaces (BCIs) measure brain signals activity, intentionally and unintentionally induced by users, and provides a communication channel without depending on the brain’s normal peripheral nerves and muscles output pathway. Feature Selection (FS) is a global optimization machine learning problem that reduces features, removes irrelevant and noisy data resulting in acceptable rec...

In recent years, Educational Data Mining has put on a massive appreciation within the research realm and it has become a vital need for the academic institutions to improve the quality of education. The quality of education is measured by the academic performance of students and the results produced. In higher education institutions a substantial amount of knowledge is hidden and need to be ext...

An integrated Neural Network and Gravitational Search Algorithm (HNNGSA) are used to solve Blasius differential equation. To aim this purpose, GSA technique is applied to train a multi-layer perceptron neural network, which is used as approximation solution of the Blasius differential equation. A trial solution of the differential equation is written as sum of two parts. The first part satisfie...

This paper presents transient stability assessment of electrical power system using probabilistic neural network (PNN) and principle component analysis. Transient stability of a power system is first determined based on the generator relative rotor angles obtained from time domain simulation outputs. Simulations were carried out on the IEEE 9-bus test system considering three phase faults on th...

This paper presents a comparative study of different methods for the identification of multiword expressions, applied to a Brazilian Portuguese corpus. First, we selected the candidates based on the frequency of bigrams. Second, we used the linguistic information based on the grammatical classes of the words forming the bigrams, together with the frequency information in order to compare the pe...

Bankruptcy prediction is an important classification problem for a business, and has become a major concern of managers. In this paper, two well-known backpropagation neural network models serving as data mining tools for classification problems are employed to perform bankruptcy forecasting: one is the backpropagation multi-layer perceptron, and the other is the radial basis function network. ...

Many algorithms have been recently reported for the training of analog multi-layer perceptron. Most of these algorithms were evaluated either from a computational or simulation view point. This paper applies several of these algorithms to the training of an analog multi-layer perceptron chip. The advantages and shortcomings of these algorithms in terms of training and generalisation performance...

Artificial Neural Network (ANN) is one of the modern computational methods proposed to solve increasingly complex problems in the real world (Xie et al., 2006 and Chau, 2007). ANN is characterized by its pattern of connections between the neurons (called its architecture), its method of determining the weights on the connections (called its training, or learning, algorithm), and its activation ...

| We investigate an energy function for MLP called superposed energy. Applying to autoassociative learning of a sandglass-type MLP, it can adaptively adjust the e ective number of the bottlenecklayer units to the intrinsic dimensionality of nonlinear data, and the optimal dimensionality reduced representation can be extracted after learning.

Handwritten numeral recognition is in general a benchmark problem of Pattern Recognition and Artificial Intelligence. Compared to the problem of printed numeral recognition, the problem of handwritten numeral recognition is compounded due to variations in shapes and sizes of handwritten characters. Considering all these, the problem of handwritten numeral recognition is addressed under the pres...