Statistical analysis with missing data
Statistical analysis with missing data
Neural Networks
Learning and evolution in neural networks
Adaptive Behavior
Improving the accuracy of Institute for Scientific Information's journal impact factors
Journal of the American Society for Information Science
On the Internal Representations of Product Units
Neural Processing Letters
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
InfoGrid: providing information integration for knowledge discovery
Information Sciences—Informatics and Computer Science: An International Journal - special issue: Knowledge discovery from distributed information sources
Web page feature selection and classification using neural networks
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
Biostatistical Analysis (5th Edition)
Biostatistical Analysis (5th Edition)
Information Sciences: an International Journal
Journal of the American Society for Information Science and Technology
Differences in impact factor across fields and over time
Journal of the American Society for Information Science and Technology
Journal of the American Society for Information Science and Technology
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Aggregation functions: Construction methods, conjunctive, disjunctive and mixed classes
Information Sciences: an International Journal
A trigram hidden Markov model for metadata extraction from heterogeneous references
Information Sciences: an International Journal
An Internet measure of the value of citations
Information Sciences: an International Journal
Hi-index | 0.07 |
The purpose of this study is to define a bibliometric indicator of the scientific impact of a journal, which combines objectivity with the ability to bridge many different bibliometric factors and in particular the side factors presented along with celebrated ISI impact factor. The particular goal is to determine a standard threshold value in which an independent self-organizing system will decide the correlation between this value and the impact factor of a journal. We name this factor ''Cited Distance Factor (CDF)'' and it is extracted via a well-fitted, recurrent Elman neural network. For a case study of this implementation we used a dataset of all journals of cell biology, ranking them according to the impact factor from the Web of Science Database and then comparing the rank according to the cited distance. For clarity reasons we also compare the cited distance factor with already known measures and especially with the recently introduced eigenfactor of the institute of scientific information (ISI).