The primary objective of this research is to obtain an accurate forecasting model for the US presidential election. To identify a reliable model, artificial neural networks (ANN) and support vector regression (SVR) models are compared based on some specified performance measures. Moreover, six...

We introduce a multistep-ahead forecasting methodology that combines empirical mode decomposition (EMD) and support vector regression (SVR). This methodology is based on the idea that the forecasting task is simplified by using as input for SVR the time series decomposed with EMD. The outcomes...

The use of advanced predictive techniques and reasoning models has greatly assisted clinicians in improving the diagnosis, prognosis, and treatment of diabetes. Although numerous studies have focused on the relationship between abnormal blood glucose levels and diabetes, few have focused on the...

In this research, two estimation algorithms for extracting cross-lingual news pairs based on machine learning from financial news articles have been proposed. Every second, innumerable text data, including all kinds news, reports, messages, reviews, comments, and tweets are generated on the...

Electricity markets are considered to be, the most volatile amongst commodity markets. The non-storability of electricity and the need for instantaneous balancing of demand and supply can often cause extreme short-lived fluctuations in electricity prices. These fluctuations are termed price...