A non-linear index to evaluate a journal's scientific impact

Information Sciences

Volumn 180, Issue 11, 2010, Pages 2156-2175

  Papavlasopoulos, Sozon ^a   Poulos, Marios ^a   Korfiatis, Nikolaos ^b   Bokos, George ^a  

^a Dept of Archives   (Greece)

^b UNIVERSITY OF COPENHAGEN   (Denmark)

Author keywords

Bibliometrics; Elman neural network; Impact factor; Semantic classification

Indexed keywords

BIBLIOMETRICS; CELL BIOLOGY; DATA SETS; ELMAN NEURAL NETWORK; IMPACT FACTOR; INSTITUTE OF SCIENTIFIC INFORMATIONS; NONLINEAR INDEX; SELF-ORGANIZING SYSTEMS; SEMANTIC CLASSIFICATION; WEB OF SCIENCE;

CABLE STAYED BRIDGES; CYTOLOGY; INDEXING (OF INFORMATION); INTERSYMBOL INTERFERENCE; SEMANTICS; TECHNICAL WRITING;

NEURAL NETWORKS;

EID: 77649336412     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2010.01.018     Document Type: Article

References (47)

1. Adam D. The counting house. Nature 415 6873 (2002) 726
2. Althouse B.M., West J.D., Bergstrom C.T., and Bergstrom T. Differences in impact factor across fields and over time. Journal of the American Society for Information Science and Technology 60 1 (2009) 27-34
3. Bergstrom C.T., and West J.D. Assessing citations with the Eigenfactor™ Metrics. Neurology 71 (2008) 1850-1851
4. Bollen J., Rodriquez M.A., and Van De Sompel H. Journal status. Scientometrics 69 3 (2006) 669-687
5. Bourlard H., and Kamp Y. Auto-association by multilayer perceptrons and singular value decomposition. Biological Cybernetics 59 4 (1988) 291-294
6. T.J. Cholewo, J.M. Zurada, Sequential network construction for time series prediction, International Conference on Neural Networks, 1997.
7. F. Codeca, F. Casella, Neural network library in modelica, Proceedings of the 5th International Modelica Conference, 2006.
8. H. Demuth, M. Beale, Neural Network Toolbox User's Guide, The MathWorks Inc., 1998.
9. Elman J.L. Learning and development in neural networks: the importance of starting small. Cognition 48 1 (1993) 71-99
10. Elman J.L. Learning and development in neural networks: the importance of starting small. Cognition 48 1 (1993) 71-99
11. A.P. Engelbrecht, I. Cloete, Selective learning using sensitivity analysis, in: The 1998 IEEE International Joint Conference onNeural Networks Proceedings, IEEE World Congress on Computational Intelligence, 1998.
12. Garfield E. Citation analysis as a tool in journal evaluation journals can be ranked by frequency and impact of citations for science policy studies. Science 178 4060 (1972) 471-479
13. Garfield E. The history and meaning of the journal impact factor. Journal of the American Medical Association 295 (2006) 90-93
14. Garfield E., and Merton R.K. Citation Indexing. Its Theory and Application in Science, Technology, and Humanities (1979), Wiley, New York
15. Giannadakis N., Rowe A., Ghanem M., and Guo Y. InfoGrid: Providing information integration for knowledge discovery. Information Sciences 155 3-4 (2003) 199-226
16. Glänzel W., and Moed H.F. Journal impact measures in bibliometric research. Scientometrics 53 2 (2002) 171-193
17. Guo G.M. A computer-aided bibliometric system to generate core article ranked lists in interdisciplinary subjects. Information Sciences 177 17 (2007) 3539-3556
18. Habibzadeh F., and Yadollahie M. Journal weighted impact factor: a proposal. Journal of Informetrics 2 2 (2008) 164-172
19. Harzing A.W., and van der Wal R. A Google Scholar h-index for journals: an alternative metric to measure journal impact in economics and business. Journal of the American Society for Information Science and Technology 60 (2009) 1
20. Hirsch J.E. An index to quantify an individual's scientific research output. Proceedings of the National Academy of Sciences 102 46 (2005) 16569-16572
21. Holden G., Rosenberg G., Barker K., and Onghena P. An assessment of the predictive validity of impact factor scores: implications for academic employment decisions in social work. Research on Social Work Practice 16 6 (2006) 613
22. Hood W.W., and Wilson C.S. Informetric studies using databases: opportunities and challenges. Scientometrics 58 3 (2003) 587-608
23. Ingwersen P. The calculation of web impact factors. Journal of Documentation 54 2 (1998) 236-243
24. Kohonen T., and Somervuo P. Self-organizing maps of symbol strings. Neurocomputing 21 1-3 (1998) 19-30
25. Little R.J.A., and Rubin D.B. Statistical Analysis with Missing Data (2003), Wiley, New York
26. Lounis H., Boukadoum M., and Siveton V. Assessing hydro power system relevant variables: a comparison between a neural network and different machine learning approaches. Proceedings of the International Conference on Neuro-Fuzzy Technology (2002) 45-51
27. Marton J. Obsolescence or immediacy? evidence supporting Price's hypothesis. Scientometrics 7 3 (1985) 145-153
28. Moed H.F., and Van Leeuwen T.N. Improving the accuracy of Institute for Scientific Information's journal impact factors. Journal of the American Society for Information Science 46 6 (1995) 461-467
29. Nolfi S., Parisi D., and Elman J.L. Learning and evolution in neural networks. Adaptive Behavior 3 1 (1994) 5-28
30. O'Reilly R.C., and Munakata Y. Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain (2000), MIT Press
31. Otte E., and Rousseau R. Social network analysis: a powerful strategy, also for the information sciences. Journal of Information Science 28 6 (2002) 441-452
32. Pham D.T., and Liu X. Training of Elman networks and dynamic system modeling. International Journal of Systems Science 27 2 (1996) 221-226
33. Pinski G., and Narin F. Citation influence for journal aggregates of scientific publications: theory, with application to the literature of physics. Information Processing and Management 12 (1976) 297
34. Price D.D.S. Little Science, Big Science...and Beyond (1986), Columbia University Press
35. Ripley B.D. Pattern Recognition and Neural Networks (1996), Cambridge University Press
36. Rodriguez P., Wiles J., and Elman J.L. A recurrent neural network that learns to count. Connection Science 11 1 (1999) 5-40
37. Rousseau R. Median and percentile impact factors: a set of new indicators. Scientometrics 63 3 (2005) 431-441
38. Seglen P.O. Why the impact factor of journals should not be used for evaluating research. British Medical Journal 314 (1997)
39. Selamat A., and Omatu S. Web page feature selection and classification using neural networks. Information Sciences 158 (2004) 69-88
40. Specht D.F. Probabilistic neural networks. Neural Networks 3 1 (1990) 109-118
41. Valle-Lisboa J.C., Reali F., Anastasi{dotless}́a H., and Mizraji E. Elman topology with sigma-pi units: an application to the modeling of verbal hallucinations in schizophrenia. Neural Networks 18 7 (2005) 863-877
42. Van Raan A.F.J. Scaling rules in the science system: influence of field-specific citation characteristics on the impact of research groups. Journal of the American Society for Information Science and Technology 59 4 (2008) 565-576
43. W.J. Vankan, W.F. Lammen, J. Kos, R. Maas, Complementary approximate modeling in Matlab and Modelica, in: Proceedings of the EuroSim 2004 Conference, 2004.
44. Wang J.H., Yu Y.W., and Tsai J.H. On the internal representations of product units. Neural Processing Letters 12 3 (2000) 247-254
45. Wilcoxon F. Individual comparisons by ranking methods. Biometrics Bulletin (1945) 80-83
46. F. Wong, K.H. Park, Z. Bien, A skin color model parameter prediction by using Elman network, in: Proceedings of the 6th Symposium on Advanced Intelligent Systems (ISIS 2005), Korean Institute of Intelligent Systems, 2005, pp. 704-706.
47. Zar J.H. Biostatistical Analysis (2007), Prentice-Hall, Inc., Upper Saddle River, NJ, USA