The learning process and hyper-parameter optimization of artificial neural networks (ANNs)
and deep learning (DL) architectures is considered one of the most challenging machine …

Adam is an adaptive gradient descent approach that is commonly used in back-propagation
(BP) algorithms for training feed-forward neural networks (FFNNs). However, it has the …

Soil moisture is an integral quantity parameter in hydrology and agriculture practices.
Satellite remote sensing has been widely applied to estimate surface soil moisture …

Abstract The Multiple Sequence Alignment (MSA) is a key task in bioinformatics, because it
is used in different important biological analysis, such as function and structure prediction of …

Research has been carried out with several training functions using standard
backpropagation methods, One-Step Secant (OSS), and Bayesian regulation. The purpose …

The optimal parameter values in a feed-forward neural network model play an important role
in determining the efficiency and significance of the trained model. In this paper, we propose …

Back propagation (BP) is widely used for parameter search of fully-connected layers in many
neural networks. Although BP has the potential of quickly converging to a solution, due to its …

Abstract Convolutional Neural Networks for text categorization allows the extraction of
features from the text represented through word embedding. The high dimensionality of the …

This study aims to overcome the problems in predicting student achievement at the
Polytechnic Business Indonesia Pematangsiantar. To predict student achievement is done …

The gravitational search algorithm is a global optimization algorithm that has the advantages
of a swarm intelligence algorithm. Compared with traditional algorithms, the performance in …