This paper reviews advanced Neural Network (NN) techniques available to process
hyperspectral data, with a special emphasis on plant disease detection. Firstly, we provide a …

The primary goal of pattern recognition is supervised or unsupervised classification. Among
the various frameworks in which pattern recognition has been traditionally formulated, the …

Classification is one of the most active research and application areas of neural networks.
The literature is vast and growing. This paper summarizes some of the most important …

This paper presents a brief survey on Automatic Speech Recognition and discusses the
major themes and advances made in the past 60 years of research, so as to provide a …

Motivation: Two practical realities constrain the analysis of microarray data, mass spectra
from proteomics, and biomedical infrared or magnetic resonance spectra. One is the 'curse …

Pattern classification represents a challenging problem in machine learning and data
science research domains, especially when there is a limited availability of training samples …

Convolutional neural network (CNN) has evolved as a new algorithm that has demonstrated
its effectiveness in real-time issue solving over many other machine learning (ML) …

Automatic (machine) recognition, description, classification, and groupings of patterns are
important problems in a variety of engineering and scientific disciplines such as biology …

The pseudo-Fisher linear classifier is considered as the “diagonal” Fisher linear classifier
applied to the principal components corresponding to non-zero eigenvalues of the sample …

Artificial neural networks (ANNs) have gained extensive popularity in recent years.
Research activities are considerable, and the literature is growing. Yet, there is a large …