This work select the features in high dimensional data using eye movements of reading and writing by ElectroOculoGraph (EOG) signals. EOG measures the changes in the electric potential field caused by eye movements. This work has three phases; the first phase identifies and removes noise from the signal. The second phase involves analysis of EOG signals by CBFS Feature Selection method and the ...

Recognition and classification of Power Quality Distorted Signals (PQDSs) in power systems is an essential duty. One of the noteworthy issues in Power Quality Analysis (PQA) is identification of distorted signals using an efficient scheme. This paper recommends a Time–Frequency Analysis (TFA), for extracting features, so-called "hybrid approach", using incorporation of Multi Resolution Analysis...

It is often difficult to come up with a well-principled approach to the selection of low-level features for characterizing images for content-based retrieval. This is particularly true for medical imagery, where gross characterizations on the basis of color and other global properties do not work. An alternative for medical imagery consists of the “scattershot” approach that first extracts a la...

Feature selection is an important issue, especially for classification problems where artificial neural networks are involved. It is known that using large number of inputs can make the network overspecific and require significantly longer time to learn the characteristics of the training data. Such over-specificity also reduces the generalisation capabilities of a neural network, so the networ...