A comprehensive review of extreme learning machine on medical imaging
The feedforward neural network based on randomization has been of great interest in the
scientific community, particularly extreme learning machines, due to its simplicity, training …
scientific community, particularly extreme learning machines, due to its simplicity, training …
Analysis of Extreme Learning Machines (ELMs) for intelligent intrusion detection systems: A survey
The ever-increasing interconnectedness of our world, fueled by technological
advancements across industries, has made network security a paramount concern. This …
advancements across industries, has made network security a paramount concern. This …
Novel L1 regularized extreme learning machine for soft-sensing of an industrial process
Extreme learning machine (ELM) is suitable for nonlinear soft sensor development. Yet it
faces an overfitting problem. To overcome it, this work integrates bound optimization theory …
faces an overfitting problem. To overcome it, this work integrates bound optimization theory …
Improving IRI-2016 global total electron content maps using ELM neural network
Abstract The International Reference Ionosphere (IRI) is the most widely used empirical
model for presenting ionosphere parameters like Vertical Total Electron Content (VTEC) or …
model for presenting ionosphere parameters like Vertical Total Electron Content (VTEC) or …
ENIC: Ensemble and nature inclined classification with sparse depiction based deep and transfer learning for biosignal classification
SK Prabhakar, SW Lee - Applied Soft Computing, 2022 - Elsevier
The electrical activities of the brain are recorded and measured with
Electroencephalography (EEG) by means of placing the electrodes on the scalp of the brain …
Electroencephalography (EEG) by means of placing the electrodes on the scalp of the brain …
k-Tournament grasshopper extreme learner for FMG-Based gesture recognition
The recognition of hand signs is essential for several applications. Due to the variation of
possible signals and the complexity of sensor-based systems for hand gesture recognition, a …
possible signals and the complexity of sensor-based systems for hand gesture recognition, a …
Hebbian Learning with Kernel-Based Embedding of Input Data
TA Ushikoshi, EJR Freitas, M Menezes… - Neural Processing …, 2024 - Springer
Although it requires simple computations, provides good performance on linear
classification tasks and offers a suitable environment for active learning strategies, the …
classification tasks and offers a suitable environment for active learning strategies, the …
An Enhanced Extreme Learning Machine Based on Square-Root Lasso Method
M Genç - Neural Processing Letters, 2024 - Springer
Extreme learning machine (ELM) is one of the most notable machine learning algorithms
with many advantages, especially its training speed. However, ELM has some drawbacks …
with many advantages, especially its training speed. However, ELM has some drawbacks …
Artificial neural networks for the prediction of the soil-water characteristic curve: An overview
SA dos Santos Pereira, G de FN Gitirana Jr… - Soil and Tillage …, 2025 - Elsevier
Several models have been developed for predicting the soil-water characteristic curve
(SWCC). Artificial Neural Networks (ANN), which are machine learning systems based on …
(SWCC). Artificial Neural Networks (ANN), which are machine learning systems based on …
Battery sizing optimization in power smoothing applications
A Zulueta, DA Ispas-Gil, E Zulueta, J Garcia-Ortega… - Energies, 2022 - mdpi.com
The main objective of this work was to determine the worth of installing an electrical battery
in order to reduce peak power consumption. The importance of this question resides in the …
in order to reduce peak power consumption. The importance of this question resides in the …