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 …

Over the past two decades, the feedforward neural network (FNN) optimization has been a
key interest among the researchers and practitioners of multiple disciplines. The FNN …

A well-performed deep learning model in image segmentation relies on a large number of
labeled data. However, it is hard to obtain sufficient high-quality raw data in industrial …

Time series forecasting of meteorological variables such as daily temperature has recently
drawn considerable attention from researchers to address the limitations of traditional …

Artificial neural networks (ANN) provide a range of powerful new techniques for solving
problems in sensor data analysis, fault detection, process identification, and control and …

This article reviews meta-learning also known as learning-to-learn which seeks rapid and
accurate model adaptation to unseen tasks with applications in highly automated AI, few …

Properly designing a wavelet neural network (WNN) is crucial for achieving the optimal
generalization performance. In this paper, in order to improve the predictive capability of …

The use of artificial neural networks for various problems has provided many benefits in
various fields of research and engineering. Yet, depending on the problem, different …

Forensic anthropology has developed classification techniques for sex estimation of
unknown skeletal remains, for example population-specific discriminant function analyses …

Few-shot relation extraction (few-shot RE) aims to recognize relations between the entity
pair in a given text by utilizing very few annotated instances. As a simple yet efficient …