A critical review on the state-of-the-art and future prospects of Machine Learning for Earth Observation Operations
Abstract The continuing Machine Learning (ML) revolution indubitably has had a significant
positive impact on the analysis of downlinked satellite data. Other aspects of the Earth …
positive impact on the analysis of downlinked satellite data. Other aspects of the Earth …
A survey on dendritic neuron model: Mechanisms, algorithms and practical applications
Research on dendrites has been conducted for decades, providing valuable information for
the development of dendritic computation. Creating an ideal neuron model is crucial for …
the development of dendritic computation. Creating an ideal neuron model is crucial for …
A seasonal-trend decomposition-based dendritic neuron model for financial time series prediction
Financial time series prediction is a hot topic in machine learning field, but existing works
barely catch the point of such data. In this study, we employ the most suitable preprocessing …
barely catch the point of such data. In this study, we employ the most suitable preprocessing …
Improving dendritic neuron model with dynamic scale-free network-based differential evolution
Some recent research reports that a dendritic neuron model (DNM) can achieve better
performance than traditional artificial neuron networks (ANNs) on classification, prediction …
performance than traditional artificial neuron networks (ANNs) on classification, prediction …
An aggregative learning gravitational search algorithm with self-adaptive gravitational constants
The gravitational search algorithm (GSA) is a meta-heuristic algorithm based on the theory
of Newtonian gravity. This algorithm uses the gravitational forces among individuals to move …
of Newtonian gravity. This algorithm uses the gravitational forces among individuals to move …
Integrated computational intelligent paradigm for nonlinear electric circuit models using neural networks, genetic algorithms and sequential quadratic programming
In this paper, a novel application of biologically inspired computing paradigm is presented
for solving initial value problem (IVP) of electric circuits based on nonlinear RL model by …
for solving initial value problem (IVP) of electric circuits based on nonlinear RL model by …
Decision-tree-initialized dendritic neuron model for fast and accurate data classification
This work proposes a decision tree (DT)-based method for initializing a dendritic neuron
model (DNM). Neural networks become larger and larger, thus consuming more and more …
model (DNM). Neural networks become larger and larger, thus consuming more and more …
Dendritic neuron model trained by information feedback-enhanced differential evolution algorithm for classification
As the well-known McCulloch–Pitts neuron model has long been criticized to be
oversimplified, different algebra to formulate a single neuron model has received increasing …
oversimplified, different algebra to formulate a single neuron model has received increasing …
A novel machine learning technique for computer-aided diagnosis
The primary motivation of this paper is twofold: first, to employ a heuristic optimization
algorithm to optimize the dendritic neuron model (DNM) and second, to design a tidy visual …
algorithm to optimize the dendritic neuron model (DNM) and second, to design a tidy visual …
Interpretability diversity for decision-tree-initialized dendritic neuron model ensemble
To construct a strong classifier ensemble, base classifiers should be accurate and diverse.
However, there is no uniform standard for the definition and measurement of diversity. This …
However, there is no uniform standard for the definition and measurement of diversity. This …