Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 3,231

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Artificial neural networks: fundamentals, computing, design, and application

Basheer, I.A, Hajmeer, M·2000·Journal of Microbiological Methods

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/s0167-7012(00)00201-3

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 3 references

Research works printed but not linked: 54

  1. 2000, Artificial neural networks in hydrology. I. Preliminary concepts, J. Hydro. Eng. ASCE, 5, 115, 10.1061/(ASCE)1084-0699(2000)5:2(115) · doi:10.1061/(asce)1084-0699(2000)5:2(115 · Paper page
  2. Attoh-Okine, N., Basheer, I., Chen, D.-H., 1999. Use of artificial neural networks in geomechanical and pavement systems. Trans. Res. Board, Circular E-C012.
  3. Barron, 1993, Universal approximation bounds for superpositions of a sigmoidal function, IEEE Trans. Inform. Theory, 39, 930, 10.1109/18.256500 · DOI · Paper page
  4. Basheer, I.A., 1998. Neuromechanistic-based modeling and simulation of constitutive behavior of fine-grained soils. PhD Dissertation, Kansas State University, 435 pp.
  5. Basheer, 2000, Selection of methodology for modeling hysteresis behavior of soils using neural networks, J. Comput.-aided Civil Infrastruct. Eng., 5, 445, 10.1111/0885-9507.00206 · DOI
  6. Baum, 1989, What size net gives valid generalization?, Neural Computation, 1, 151, 10.1162/neco.1989.1.1.151 · DOI · Paper page
  7. Carpenter, 1988, The ART of adaptive pattern recognition by a self-organizing neural network, Computer, March, 77, 10.1109/2.33 · DOI · Paper page
  8. Carpenter, 1987, ART2: self-organization of stable category recognition codes for analog input patterns, Appl. Opt., 26, 4914, 10.1364/AO.26.004919 · DOI · Paper page
  9. Carson, 1995, Escherichia coli O157:H7 restriction pattern recognition by artificial neural network, J. Clin. Microbiol., 33, 2894, 10.1128/JCM.33.11.2894-2898.1995 · doi:10.1128/jcm.33.11.2894-2898.1995
  10. Cheng, 1994, Neural networks: a review from a statistical perspective, Statist. Sci., 9, 2, 10.1214/ss/1177010638 · DOI · Paper page
  11. Chun, 1993, Rapid identification of Streptomyces by artificial neural network analysis of pyrolysis mass spectra, FEMS Microbiol. Lett., 114, 115, 10.1111/j.1574-6968.1993.tb06560.x · doi:10.1111/j.1574-6968.1993.tb06560.x
  12. Chun, 1993, Artificial neural network analysis of pyrolysis mass spectrometric data in the identification of Streptomyces strains, FEMS Microbiol. Lett., 107, 321, 10.1111/j.1574-6968.1993.tb06051.x · doi:10.1111/j.1574-6968.1993.tb06051.x
  13. Eaton, 1992, Learning coefficient dependence on training set size, Neural Networks, 5, 283, 10.1016/S0893-6080(05)80026-7 · doi:10.1016/s0893-6080(05)80026-7
  14. Garth, 1996, Evaluation of model discrimination techniques in artificial neural networks with application to grain drying, Vol. 6, 939
  15. Geeraerd, 1998, Application of artificial neural networks as a nonlinear modular modeling technique to describe the bacterial growth in chilled food products, Int. J. Food Microbiol., 44, 49, 10.1016/S0168-1605(98)00127-5 · doi:10.1016/s0168-1605(98)00127-5
  16. Giacomini, 1997, Artificial neural network identification of heterotrophic marine bacteria based on their fatty-acid composition, IEEE Trans. Biomed. Eng., 44, 1185, 10.1109/10.649990 · doi:10.1109/10.649990
  17. Goodacre, 1994, Sub-species discrimination using pyrolysis mass spectrometry and self-organizing neural networks of Propionibacterium acnes isolated from normal human skin, Zbl. Bakt., 22, 124
  18. Goodacre, 1997, Use of pyrolysis mass spectrometry with supervised learning for the assessment of the adulteration of milk of different species, Appl. Spectrosc., 51, 1144, 10.1366/0003702971941665 · DOI
  19. Goodacre, 1993, Rapid assessment of olive oil adulteration using pyrolysis mass spectrometry and artificial neural networks, J. Sci. Food Agric., 63, 297, 10.1002/jsfa.2740630306 · doi:10.1002/jsfa.2740630306
  20. Hajmeer, 1997, Computational neural networks for predictive microbiology. II. Application to microbial growth, Int. J. Food Microbiol., 34, 51, 10.1016/S0168-1605(96)01169-5 · doi:10.1016/s0168-1605(96)01169-5
  21. Hajmeer, 1998, A nonlinear response surface model based on artificial neural networks for growth of Saccharomyces cerevisiae, J. Rapid Methods Automation Microbiol., 6, 103, 10.1111/j.1745-4581.1998.tb00191.x · doi:10.1111/j.1745-4581.1998.tb00191.x
  22. Hajmeer, M.N., Basheer, I.A., Fung, D.Y.C., Marsden, J.L., 2000. New approach for modeling generalized microbial growth curves using artificial neural networks (under review). · doi:10.1111/j.1745-4581.2000.tb00328.x
  23. Han, 1996, Optimization of feedforward neural networks, Eng. Appl. Artif. Intell., 9, 109, 10.1016/0952-1976(95)00001-1 · DOI
  24. Hecht-Nielsen, 1988, Applications of counterpropagation networks, Neural Networks, 1, 131, 10.1016/0893-6080(88)90015-9 · DOI · Paper page
  25. Hoffman, 1987, The problem of extracting the knowledge of experts from the perspective of experimental psychology, AI Mag., 8, 53
  26. Hopfield, 1986, Computing with neural circuits: a model, Science, 233, 625, 10.1126/science.3755256 · DOI · Paper page
  27. Horimoto, 1997, Classification of microbial defects in milk using a dynamic headspace gas chromatograph and computer-aided data processing. 2. Artificial neural networks, partial least-squares regression analysis, and principal component regression analysis, J. Agric. Food Chem., 45, 743, 10.1021/jf960660g · doi:10.1021/jf960660g
  28. Jadid, 1996, Predicting moment-curvature parameters from experimental data, Eng. Appl. Artif. Intell., 9, 309, 10.1016/0952-1976(96)00021-8 · doi:10.1016/0952-1976(96)00021-8
  29. Jain, 1996, Artificial neural networks: a tutorial, Comput. IEEE, March, 31, 10.1109/2.485891 · DOI · Paper page
  30. Jonsson, 1997, Electronic nose for microbial classification of grains, Int. J. Food Microbiol., 35, 187, 10.1016/S0168-1605(96)01218-4 · doi:10.1016/s0168-1605(96)01218-4
  31. Lachtermacher, 1995, Backpropagation in time-series forecasting, J. Forecasting, 14, 381, 10.1002/for.3980140405 · DOI · Paper page
  32. Lakshmanan, 1997, Detecting rare signatures, Vol. 7, 521
  33. Lippmann, 1987, An introduction to computing with neural nets, IEEE ASSP Mag., 4, 4, 10.1109/MASSP.1987.1165576 · DOI · Paper page
  34. Looney, 1996, Advances in feedforward neural networks: demystifying knowledge acquiring black boxes, IEEE Trans. Knowledge Data Eng., 8, 211, 10.1109/69.494162 · DOI · Paper page
  35. Moody, 1992, Networks with learned unit response functions, Vol. 4, 1048
  36. Najjar, 1997, Computational neural networks for predictive microbiology. I. Methodology, Int. J. Food Microbiol., 34, 27, 10.1016/S0168-1605(96)01168-3 · doi:10.1016/s0168-1605(96)01168-3
  37. Ni, 1998, Food quality prediction with neural networks, Food Technol., 5, 60
  38. Pal, 1996, Neurocomputing: motivation, models, and hybridization, Computer, March, 24, 10.1109/MC.1996.485890 · doi:10.1109/mc.1996.485890
  39. Ramos-Nino, 1997, A comparison of quantitative structure–activity relationships for the effect of benzoic and cinnamic acids on Listeria monocytogenes using multiple linear regression, artificial neural network and fuzzy systems, J. Appl. Microbiol., 82, 168, 10.1111/j.1365-2672.1997.tb02847.x · doi:10.1111/j.1365-2672.1997.tb02847.x
  40. Rizzo, 1994, Characterization of aquifer properties using artificial neural networks: neural kriging, Water Res., 30, 483, 10.1029/93WR02477 · DOI
  41. Rumelhart, 1986, Learning internal representation by error propagation, Vol. 1
  42. Sietsma, 1988, Neural net pruning — why and how, IEEE Int. Conf. Neural Networks, 1, 325, 10.1109/ICNN.1988.23864 · DOI · Paper page
  43. Specht, 1991, A general regression neural network, IEEE Trans. Neural Networks, 2, 568, 10.1109/72.97934 · DOI · Paper page
  44. Stein, 1993, Selecting data for neural networks, AI Expert, 2, 42
  45. Timmins, 1997, Rapid quantitative analysis of binary mixtures of Escherichia coli strains using pyrolysis mass spectrometry with multivariate calibration and artificial neural networks, J. Appl. Microbiol., 83, 208, 10.1046/j.1365-2672.1997.00218.x · doi:10.1046/j.1365-2672.1997.00218.x
  46. Upadhaya, 1992, Application of neural network for sensory validation and plant monitoring, Neural Technol., 97, 170
  47. Vallejo-Cordoba, 1995, Predicting milk shelf-life based on artificial neural networks and headspace gas chromatographic data, J. Food Sci., 60, 885, 10.1111/j.1365-2621.1995.tb06253.x · doi:10.1111/j.1365-2621.1995.tb06253.x
  48. Werbos, P.J., 1974. Beyond regression: new tools for prediction and analysis in the behavioral sciences. PhD Thesis, Harvard University.
  49. White, 1989, Learning in artificial neural networks: a statistical perspective, Neural Computation, 1, 425, 10.1162/neco.1989.1.4.425 · DOI · Paper page
  50. White, 1990, Connectionist nonparametric regression — multilayer feedforward networks can learn arbitrary mappings, Neural Networks, 3, 535, 10.1016/0893-6080(90)90004-5 · DOI · Paper page
  51. Widrow, 1990, 30 years of adaptive neural networks: perceptron, Madaline, and backpropagation, Proc. IEEE, 78, 1415, 10.1109/5.58323 · DOI · Paper page
  52. Wythoff, 1993, Backpropagation neural networks: a tutorial, Chemometr. Intell. Lab. Syst., 18, 115, 10.1016/0169-7439(93)80052-J · DOI · Paper page
  53. Zaika, 1994, Model for the combined effects of temperature, initial pH, sodium chloride and sodium nitrite concentrations on anaerobic growth of Shigella flexneri, Int. J. Food Microbiol., 23, 345, 10.1016/0168-1605(94)90162-7 · DOI
  54. Zupan, 1991, Neural networks: a new method for solving chemical problems or just a passing phase?, Anal. Chim. Acta, 248, 1, 10.1016/S0003-2670(00)80865-X · DOI · Paper page

Cited by

See all 3,231

Metadata

Authors
Basheer, I. A., & Hajmeer, M.
Venue
Journal of Microbiological Methods
Publisher
Elsevier BV
Published
2000-12
Volume
43
Issue
1
Pages
3-31
ISSN
0167-7012 (ISSN-L)
OpenAlex
W2028070629
Cited by
3,231
References
3
FWCI
14.58
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Artificial neural networks: fundamentals, computing, design, and application. https://app.cochat.ai/papers/10.1016/s0167-7012(00)00201-3
The paper
Basheer, I. A., & Hajmeer, M. (2000). Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods. https://doi.org/10.1016/s0167-7012(00)00201-3

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.