A hybrid model based on neural networks for biomedical relation extraction

Kernel-based learning for biomedical relation extraction

forecasting crude oil prices: a hybrid model based on wavelet transforms and neural networks

in general, energy prices, such as those of crude oil, are affected by deterministic events such as seasonal changes as well as non-deterministic events such as geopolitical events. it is the non-deterministic events which cause the prices to vary randomly and makes price prediction a difficult task. one could argue that these random changes act like noise which effects the deterministic variat...

A Hybrid model based on neural network and Data Envelopment Analysis model for Evaluation of unit Performance

Efficiency and evaluation is one of the main and most important demands of organizations, companies and institutions. As these organizations deal with a large amount of data, therefore, it is necessary to evaluate them on the basis of scientific methods to improve their efficiency. Data envelopment analysis is a suitable method for measuring the efficiency and performance of organizations. This...

mortality forecasting based on lee-carter model

over the past decades a number of approaches have been applied for forecasting mortality. in 1992, a new method for long-run forecast of the level and age pattern of mortality was published by lee and carter. this method was welcomed by many authors so it was extended through a wider class of generalized, parametric and nonlinear model. this model represents one of the most influential recent d...

A Hybrid approach for biomedical event extraction

In this paper we propose a system which uses hybrid methods that combine both rule-based and machine learning (ML)-based approaches to solve GENIA Event Extraction of BioNLP Shared Task 2013. We apply UIMA1 Framework to support coding. There are three main stages in model: Pre-processing, trigger detection and biomedical event detection. We use dictionary and support vector machine classifier t...