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 …
T Xiong, C Li, Y Bao - Neurocomputing, 2018 - Elsevier
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 …
X Bi, X Zhao, H Huang, D Chen, Y Ma - Cognitive Computation, 2020 - Springer
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 …
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 …
X Bi, Z Liu, Y He, X Zhao, Y Sun, H Liu - Complexity, 2020 - Wiley Online Library
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 …
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 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 …