Non-iterative and fast deep learning: Multilayer extreme learning machines
In the past decade, deep learning techniques have powered many aspects of our daily life,
and drawn ever-increasing research interests. However, conventional deep learning …
and drawn ever-increasing research interests. However, conventional deep learning …
EEG-based emotion recognition using hierarchical network with subnetwork nodes
Emotions play a crucial role in decision-making, brain activity, human cognition, and social
intercourse. This paper proposes a hierarchical network structure with subnetwork nodes to …
intercourse. This paper proposes a hierarchical network structure with subnetwork nodes to …
[PDF][PDF] Coverless information hiding based on the molecular structure images of material.
The traditional information hiding methods embed the secret information by modifying the
carrier, which will inevitably leave traces of modification on the carrier. In this way, it is hard …
carrier, which will inevitably leave traces of modification on the carrier. In this way, it is hard …
Domain space transfer extreme learning machine for domain adaptation
Extreme learning machine (ELM) has been applied in a wide range of classification and
regression problems due to its high accuracy and efficiency. However, ELM can only deal …
regression problems due to its high accuracy and efficiency. However, ELM can only deal …
Breast cancer diagnosis by different machine learning methods using blood analysis data
Today, one of the most common types of cancer is breast cancer. It is crucial to prevent the
propagation of malign cells to reduce the rate of cancer induced mortality. Cancer detection …
propagation of malign cells to reduce the rate of cancer induced mortality. Cancer detection …
A review of advances in extreme learning machine techniques and its applications
Feedforward neural networks (FFNN) has been used for machine learning researches, and
it really has a wide acceptance. It was noted in the recent time that feedforward neural …
it really has a wide acceptance. It was noted in the recent time that feedforward neural …
Robust adaptive rescaled lncosh neural network regression toward time-series forecasting
In time series forecasting with outliers and random noise, parameter estimation in a neural
network via minimizing the loss is unreliable. Therefore, an adaptive rescaled lncosh loss …
network via minimizing the loss is unreliable. Therefore, an adaptive rescaled lncosh loss …
A novel AdaBoost framework with robust threshold and structural optimization
The AdaBoost algorithm is a popular ensemble method that combines several weak
learners to boost generalization performance. However, conventional AdaBoost. RT …
learners to boost generalization performance. However, conventional AdaBoost. RT …
A regression method with subnetwork neurons for vigilance estimation using EOG and EEG
In recent years, it has been observed that there is an increasing rate of road accidents due to
the low vigilance of drivers. Thus, the estimation of drivers' vigilance state plays a significant …
the low vigilance of drivers. Thus, the estimation of drivers' vigilance state plays a significant …
Wind power prediction of kernel extreme learning machine based on differential evolution algorithm and cross validation algorithm
As fossil fuel is being depleted, the percentage of wind power capacity in total electricity
generation is increasing. In order to improve the absorption capacity of wind power, wind …
generation is increasing. In order to improve the absorption capacity of wind power, wind …