Predicting citation counts based on deep neural network learning techniquesUnknown author2019-03-11articleJournal of Informetrics117 citationsgreenNo readable copy stored yet — this record is metadata only.scientometrics and bibliometrics researchPsychology Research and BibliometricsWeb visibility and informetricsAbstractNo abstract was published for this record.Citations in this libraryStored only when both papers are in the corpus — 117 citations exist upstream.References2005An index to quantify an individual's scientific research output2006The History and Meaning of the Journal Impact Factor2013Quantifying Long-Term Scientific Impact2004Sleeping Beauties in science2015Defining and identifying Sleeping Beauties in science2012Predicting scientific success2014A review of the characteristics of 108 author-level bibliometric indicators2014Predicting scientific success based on coauthorship networksCited by2021Detecting research topic trends by author-defined keyword frequency2020Early detection of valuable patents using a deep learning model: Case of semiconductor industry2021Early indicators of scientific impact: Predicting citations with altmetrics2021Combining deep neural network and bibliometric indicator for emerging research topic prediction2020Identification of highly-cited papers using topic-model-based and bibliometric features: the consideration of keyword popularity2023Factors associating with or predicting more cited or higher quality journal articles: An Annual Review of Information Science and Technology (ARIST) paper2021Utilizing citation network structure to predict paper citation counts: A Deep learning approach2024Unleashing the power of AI: a systematic review of cutting-edge techniques in AI-enhanced scientometrics, webometrics and bibliometrics