  With the growing number of published scientific papers world-wide, the need
to evaluation and quality assessment methods for research papers is increasing.
Scientific fields such as scientometrics, informetrics and bibliometrics
establish quantified analysis methods and measurements for scientific papers.
In this area, an important problem is to predict the future influence of a
published paper. Particularly, early discrimination between influential papers
and insignificant papers may find important applications. In this regard, one
of the most important metrics is the number of citations to the paper, since
this metric is widely utilized in the evaluation of scientific publications and
moreover, it serves as the basis for many other metrics such as h-index. In
this paper, we propose a novel method for predicting long-term citations of a
paper based on the number of its citations in the first few years after
publication. In order to train a citations prediction model, we employed
artificial neural networks which is a powerful machine learning tool with
recently growing applications in many domains including image and text
processing. The empirical experiments show that our proposed method
out-performs state-of-the-art methods with respect to the prediction accuracy
in both yearly and total prediction of the number of citations.
