Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward
neural network (SLFN), which converges much faster than traditional methods and yields …

The explosive growth of data in volume, velocity and diversity that are produced by medical
applications has contributed to abundance of big data. Current solutions for efficient data …

In view of the importance of seasonal forecasting of agricultural commodity price, particularly
vegetable prices, and the limited research attention paid to it previously, this study proposes …

The human brain can be inherently modeled as a brain network, where nodes denote
billions of neurons and edges denote massive connections between neurons. Analysis on …

In a two-class classification task, if the number of examples of one class (majority) is much
greater than that of another class (minority), then the classification is said to be class …

This paper presents a novel library for Extreme Learning Machines (ELM) called Scikit-ELM
(https://github. com/akusok/scikit-elm, https://scikit-elm. readthedocs. io). Usability and …

A hybridized image classification strategy is proposed based on discrete wavelet transform,
artificial bee colony (ABC) and extreme learning machine (ELM). The proposed …

Brain networks provide essential insights into the diagnosis of functional brain disorders,
such as Alzheimer's disease (AD). Many machine learning methods have been applied to …

This paper presents a two‐dimensional wavelet based decomposition algorithm for
classification of biomedical images. The two‐dimensional wavelet decomposition is done up …

A feedforward neural network with random weights (RW-FFNN) uses a randomized feature
map layer. This randomization enables the optimization problem to be replaced by a …