Globalization processes and market deregulation policies are rapidly changing the
competitive environments of many economic sectors. The appearance of new competitors …

Data of different levels of complexity and of ever growing diversity of characteristics are the
raw materials that machine learning practitioners try to model using their wide palette of …

The motivation for this paper is to introduce a hybrid neural network architecture of Particle
Swarm Optimization and Adaptive Radial Basis Function (ARBF–PSO), a time varying …

With the development of Internet techniques, data volumes are doubling every two years,
faster than predicted by Moore's Law. Big Data Analytics becomes particularly important for …

This study aims to forecast oil prices using evolutionary techniques such as gene expression
programming (GEP) and artificial neural network (NN) models to predict oil prices over the …

Abstract Knowledge-based information systems are advanced tools in the hands of the
marketer, enabling him to take evidence-based decisions in complex situations. In this …

Classification problem is a decision making task where many researchers have been
working on. There are a number of techniques proposed to perform classification. Neural …

The book focuses on forecasting foreign exchange rates via artificial neural networks. It
creates and applies the highly useful computational techniques of Artificial Neural Networks …

This research examines and analyses the use of Neural Network Regression (NNR) models
in foreign exchange (FX) forecasting and trading models. The NNR models are …

In this study, we generate 50 Multi-layer Perceptons, 50 Radial Basis Functions, 50 Higher
Order Neural Networks and 50 Recurrent Neural Network and we explore their utility in …