Deep Learning (DL) is a subfield of machine learning that significantly impacts extracting
new knowledge. By using DL, the extraction of advanced data representations and …

In real applications learning algorithms have to address several issues such as, huge
amount of data, samples which arrive continuously and underlying data generation …

In recent years, there has been an increasing interest in the application of machine learning
for the prediction of pavement performance. Prediction models are used to predict the future …

Recent research has devoted significant efforts on the utilization of WiFi signals to recognize
various human activities. An individual's limb motions in the WiFi coverage area could …

In order to minimize the generalization error in neural networks, a novel technique to identify
overfitting phenomena when training the learner is formally introduced. This enables support …

This paper proposes a novel neural network (NN) training method that employs the hybrid
exponential smoothing method and the Levenberg-Marquardt (LM) algorithm, which aims to …

The improved computation presented in this paper is aimed to optimize the neural networks
learning process using Levenberg-Marquardt (LM) algorithm. Quasi-Hessian matrix and …

Discovering genes with similar functions across diverse biomedical contexts poses a
significant challenge in gene representation learning due to data heterogeneity. In this …

The application of drones has recently revolutionised the map** of wetlands due to their
high spatial resolution and the flexibility in capturing images. In this study, the drone imagery …

Artificial neural networks (ANNs) becomes very popular tool in hydrology, especially in
rainfall–runoff modelling. However, a number of issues should be addressed to apply this …