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

Optimizing the structure of neural networks is an essential step for the discovery of
knowledge from data. This paper deals with a new approach which determines the …

Industry 4.0 involves the digital transformation of industrial sectors. Given the current climate
change scenario and the scarcity of water in semi-arid regions, this digital transformation …

Many real scenarios in machine learning are of dynamic nature. Learning in these types of
environments represents an important challenge for learning systems. In this context, the …

Noise impact on surrounding areas of airports has become an important issue with direct
consequence on their potential of development. Accurate predictions of the noise levels …

The motivation behind this paper is to explore an algorithm that has the ability to optimize
the free parameters required to design a neural network without being diligent in …

This paper presents the analysis of the feed-forward, multilayer feedforward network and its
structure and parameters on pattern recognition effectiveness. The detailed, experimental …

Protein-nanoparticle conjugation provides unique interactions be-tween biological systems
and synthetic materials used for analytical, diagnostic and therapeutic applications. This …

The focus of this article is to select the best architecture for a Neural Network Energy
Prediction Model (NNEPM). A few network architecture is simulated and modeled; Multilayer …

It is well known that the structure of neural network and the amount of available training data
influence the accuracy of developed models; however, the exact character of this relation …