Skip to main content
SpringerLink
Log in

Neural network method for automatic data generation in adaptive information systems

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The paper discusses the development of a new method for automatic generation of information in adaptive information systems (AIS), based on the application of neural network technologies. The analysis of existing approaches for the solution of this problem is carried out, on the basis of which a conclusion is made about the prospects of using neural networks for information generation in AIS. Prevailing public methods (AutoKeras, DEvol, AutoSklearn, AutoGAN) do not allow solving the whole range of selected problems of generating information in AIS in automatic mode. The analysis showed that these methods do not satisfy the following conditions: support for multidimensional input and output data, formation of independent outputs, work with generative adversarial networks and autoencoders, quality assessment of generated output objects of arbitrary type. Therefore, a neural network method for automatic generation of information in AIS was developed; its formalization in the notation of set theory, theoretical grounding, algorithms for solving specific problems in accordance with the developed method are presented. During the experimental research, the practical implementation of the neural network method and its comparison with existing analogues were conducted. The scientific novelty of the method consists in the automatic determination of the class of data generation problems and operations for the preparation and processing of initial data, the use of an iterative approach for the automatic selection of the structure and parameters of the neural network, adaptation of Fréchet Inception Distance and Inception Score metrics for evaluating arbitrary data. The practical significance of the method is shown in the versatility of the approach, increasing the accuracy and reducing the search time for the optimal structure of neural networks, the possibility of integrating different architectures of neural networks (including generative adversarial and autoencoders) for automatic solution of information generation problems. Comparing the neural network method with existing approaches (AutoKeras, DEvol, AutoSklearn, AutoGAN), it was found that the developed method is more functional, exhibits more speed of the structure of neural networks search (3.2 times), and provides comparable accuracy (by 2.3%).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Aquino G, Rubio JDJ, Pacheco J, Gutierrez GJ, Ochoa G, Balcazar R, Zacarias A (2020) Novel nonlinear hypothesis for the delta parallel robot modeling. IEEE Access 8:46324–46334. https://doi.org/10.1109/ACCESS.2020.2979141

    Article  Google Scholar 

  2. de Jesús Rubio J (2009) SOFMLS: online self-organizing fuzzy modified least-squares network. IEEE Trans Fuzzy Syst 17(6):1296–1309. https://doi.org/10.1109/TFUZZ.2009.2029569

    Article  Google Scholar 

  3. Chiang HS, Chen MY, Huang YJ (2019) Wavelet-based EEG processing for epilepsy detection using fuzzy entropy and associative petri net. IEEE Access 7:103255–103262. https://doi.org/10.1109/ACCESS.2019.2929266

    Article  Google Scholar 

  4. de Rubio JJ (2020) Stability analysis of the modified Levenberg-Marquardt algorithm for the artificial neural network training. In: IEEE transactions on neural networks and learning systems, pp 1–15. https://doi.org/10.1109/TNNLS.2020.3015200

  5. Meda-Campaña JA (2018) On the estimation and control of nonlinear systems with parametric uncertainties and noisy outputs. IEEE Access 6:31968–31973. https://doi.org/10.1109/ACCESS.2018.2846483

    Article  Google Scholar 

  6. Hernández G, Zamora E, Sossa H, Téllez G, Furlán F (2020) Hybrid neural networks for big data classification. Neurocomputing 390:327–340. https://doi.org/10.1016/j.neucom.2019.08.095

    Article  Google Scholar 

  7. Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signals Syst 2(4):303–314. https://doi.org/10.1007/BF02551274

    Article  MathSciNet  MATH  Google Scholar 

  8. Hecht-Nielsen R (1995) Replicator neural networks for universal optimal source coding. Science 269(5232):1860–1863. https://doi.org/10.1126/science.269.5232.1860

    Article  Google Scholar 

  9. Ghosh J, Lambert D, Skillicorn D, Srivastava J (2006) Transform regression and the Kolmogorov superposition theorem. In: Proceedings of the 2006 SIAM international conference on data mining. Society for industrial and applied mathematics. https://doi.org/10.1137/1.9781611972764.4

  10. Obukhov AD, Krasnyansky MN (2019) Neural network architecture of information systems. Vestnik Udmurtskogo Universiteta. Matematika. Mekhanika. Komp'yuternye Nauki 29(3):438–455. https://doi.org/10.20537/vm190312

  11. Obukhov A, Krasnyanskiy M, Nikolyukin M (2020) Algorithm of adaptation of electronic document management system based on machine learning technology. Prog Artif Intell 9:287–303. https://doi.org/10.1007/s13748-020-00214-2

    Article  Google Scholar 

  12. Obukhov AD, Krasnyanskiy MN (2020) Automated organization of interaction between modules of information systems based on neural network data channels. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05491-5

    Article  Google Scholar 

  13. Tian T, Gong D, Kuo FC, Liu H (2019) Genetic algorithm based test data generation for MPI parallel programs with blocking communication. J Syst Softw 155:130–144. https://doi.org/10.1016/j.jss.2019.04.049

    Article  Google Scholar 

  14. Matejka J, Fitzmaurice G (2017) Same stats, different graphs: generating datasets with varied appearance and identical statistics through simulated annealing. In: Proceedings of the 2017 CHI conference on human factors in computing systems, pp 1290–1294. https://doi.org/10.1145/3025453.3025912

  15. Jain N, Porwal R (2019) Automated test data generation applying heuristic approaches—a survey. In: Software engineering. Springer, Singapore. https://doi.org/10.1007/978-981-10-8848-3_68

  16. Romli R et al (2017) A review on meta-heuristic search techniques for automated test data generation: applicability towards improving automatic programming assessment. In: International conference of reliable information and communication technology. Springer, Cham, pp 896–906. https://doi.org/10.1007/978-3-319-59427-9_92

  17. Boopathi M, Sujatha R, Kumar CS, Narasimman S (2017) Quantification of software code coverage using artificial bee colony optimization based on Markov approach. Arab J Sci Eng 42(8):3503–3519. https://doi.org/10.1007/978-3-319-59427-9_92

    Article  MATH  Google Scholar 

  18. Shu D, Cunningham J, Stump G, Miller SW, Yukish MA, Simpson TW, Tucker CS (2020) 3D Design using generative adversarial networks and physics-based validation. J Mech Des 142(7):071701. https://doi.org/10.1115/1.4045419

    Article  Google Scholar 

  19. Pouyanfar S, Sadiq S, Yan Y, Tian H, Tao Y, Reyes MP, Iyengar SS (2018) A survey on deep learning: algorithms, techniques, and applications. ACM Comput Surv (CSUR) 51(5):1–36. https://doi.org/10.1145/3234150

    Article  Google Scholar 

  20. Xie Y, Yu J, Chen X, Ding Q, Wang E (2019) Low-element image restoration based on an out-of-order elimination algorithm. Entropy 21(12):1192. https://doi.org/10.3390/e21121192

    Article  MathSciNet  Google Scholar 

  21. Fan G, Li J, Hao H (2019) Lost data recovery for structural health monitoring based on convolutional neural networks. Struct Control Health Monit 26(10):e2433. https://doi.org/10.1002/stc.2433

    Article  Google Scholar 

  22. Gheyas IA, Smith LS (2010) A neural network-based framework for the reconstruction of incomplete data sets. Neurocomputing 73(16–18):3039–3065. https://doi.org/10.1016/j.neucom.2010.06.021

    Article  Google Scholar 

  23. Choudhury SJ, Pal NR (2019) Imputation of missing data with neural networks for classification. Knowl Based Syst 182:104838. https://doi.org/10.1016/j.knosys.2019.07.009

    Article  Google Scholar 

  24. Popova O, Popov B, Karandey V, Evseeva M (2016) Analysis of forecasting methods as a tool for information structuring in science research. Curr J Appl Sci Technol. https://doi.org/10.9734/BJAST/2016/26353

    Article  Google Scholar 

  25. Sysoev YS, Sal’nikov AA, Beketov VG, Chernov AV (2016) Predicting the state of engineering objects based on current monitoring of their parameters. Meas Tech 59(4):345–350. https://doi.org/10.1007/s11018-016-0969-2

    Article  Google Scholar 

  26. Silva ES, Hassani H, Heravi S, Huang X (2019) Forecasting tourism demand with denoised neural networks. Ann Tour Res 74:134–154. https://doi.org/10.1016/j.annals.2018.11.006

    Article  Google Scholar 

  27. Liu H, Mi XW, Li YF (2018) Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network. Energy Convers Manag 156:498–514. https://doi.org/10.1016/j.enconman.2017.11.053

    Article  Google Scholar 

  28. Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B (2019) A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol 110:12–22. https://doi.org/10.1016/j.jclinepi.2019.02.004

    Article  Google Scholar 

  29. Kang F, Liu J, Li J, Li S (2017) Concrete dam deformation prediction model for health monitoring based on extreme learning machine. Struct Control Health Monit 24(10):e1997. https://doi.org/10.1002/stc.1997

    Article  Google Scholar 

  30. Li H, Yuan D, Ma X, Cui D, Cao L (2017) Genetic algorithm for the optimization of features and neural networks in ECG signals classification. Sci Rep 7(1):1–12. https://doi.org/10.1038/srep41011

    Article  Google Scholar 

  31. Ye F (2017) Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data. PLoS ONE 12(12):e0188746. https://doi.org/10.1371/journal.pone.0188746

    Article  Google Scholar 

  32. Jin H, Song Q, Hu X (2019) Auto-keras: an efficient neural architecture search system. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. pp 1946–1956. https://doi.org/10.1145/3292500.3330648

  33. Real E, Aggarwal A, Huang Y, Le QV (2019) Regularized evolution for image classifier architecture search. In: Proceedings of the aaai conference on artificial intelligence, vol 33, pp 4780–4789. https://doi.org/10.1609/aaai.v33i01.33014780

  34. Budjač R, Nikmon M, Schreiber P, Zahradníková B, Janáčová D (2019) Automated machine learning overview. Res Pap Fac Mater Sci Technol Slovak Univ Technol 27(45):107–112. https://doi.org/10.2478/rput-2019-0033

    Article  Google Scholar 

  35. Feurer M, Klein A, Eggensperger K, Springenberg JT, Blum M, Hutter F (2019) Auto-sklearn: efficient and robust automated machine learning. In: Automated machine learning. Springer, Cham, pp 113–134. https://doi.org/10.1007/978-3-030-05318-5_6

  36. Le Q, Zoph B (2017) Using machine learning to explore neural network architecture. Google AI Blog. https://research.googleblog.com/2017/05/using-machine-learning-to-explore.html

  37. Gong X, Chang S, Jiang Y, Wang Z (2019) Autogan: neural architecture search for generative adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp 3224–3234. https://doi.org/10.1109/ICCV.2019.00332

  38. Liu C, Zoph B, Neumann M, Shlens J, Hua W, Li LJ, Murphy K (2018) Progressive neural architecture search. In: Proceedings of the European conference on computer vision (ECCV), pp 19–34. https://doi.org/10.1007/978-3-030-01246-5_2

  39. Cai H, Gan C, Wang T, Zhang Z, Han S (2019) Once-for-all: train one network and specialize it for efficient deployment. In: International conference on learning representations.

  40. Laredo D, Ma SF, Leylaz G et al (2020) Automatic model selection for fully connected neural networks. Int J Dyn Control 8:1063–1079. https://doi.org/10.1007/s40435-020-00708-w

    Article  Google Scholar 

  41. Dai B, Zhang Y, Lin D (2017) Detecting visual relationships with deep relational networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3076–3086. https://doi.org/10.1109/CVPR.2017.352

  42. Nagarajaiah S, Yang Y (2017) Modeling and harnessing sparse and low-rank data structure: a new paradigm for structural dynamics, identification, damage detection, and health monitoring. Struct Control Health Monit 24(1):e1851. https://doi.org/10.1002/stc.1851

    Article  Google Scholar 

  43. Alqahtani H, Kavakli-Thorne M, Kumar G (2019) Applications of generative adversarial networks (GANs): an updated review. Arch Comput Methods Eng. https://doi.org/10.1007/s11831-019-09388-y

    Article  Google Scholar 

  44. Mei M, Zhong Y, He F, Xu C (2020) An innovative multi-label learning based algorithm for city data computing. GeoInformatica 24(1):221–245. https://doi.org/10.1007/s10707-019-00383-w

    Article  Google Scholar 

  45. Tay CK, Shim KJ (2018) A cloud-based data gathering and processing system for intelligent demand forecasting. In: 2018 IEEE international conference on big data (big data). IEEE, pp 5451–5453. https://doi.org/10.1109/BigData.2018.8622546

  46. Goswami S, Chakraborty S, Ghosh S, Chakrabarti A, Chakraborty B (2018) A review on application of data mining techniques to combat natural disasters. Ain Shams Eng J 9(3):365–378. https://doi.org/10.1016/j.asej.2016.01.012

    Article  Google Scholar 

  47. Moen E et al (2019) Deep learning for cellular image analysis. Nat Methods 16:1–14. https://doi.org/10.1038/s41592-019-0403-1

    Article  Google Scholar 

  48. Chang CC, Keisler HJ (2014) Continuous model theory. In: The theory of models. North-Holland, pp 25–38. https://doi.org/10.1016/B978-0-7204-2233-7.50011-3

  49. Deb C, Zhang F, Yang J, Lee SE, Shah KW (2017) A review on time series forecasting techniques for building energy consumption. Renew Sustain Energy Rev 74:902–924. https://doi.org/10.1016/j.rser.2017.02.085

    Article  Google Scholar 

  50. Narodytska N, Shrotri A, Meel KS, Ignatiev A, Marques-Silva J (2019). Assessing heuristic machine learning explanations with model counting. In: International conference on theory and applications of satisfiability testing. Springer, Cham, pp 267–278. https://doi.org/10.1007/978-3-030-24258-9_19

  51. Ferrari A, Micucci D, Mobilio M, Napoletano P (2019). Human activities recognition using accelerometer and gyroscope. In: European conference on ambient intelligence. Springer, Cham, pp 357–362. https://doi.org/10.1007/978-3-030-34255-5_28

  52. Seo D, Ha Y, Ha S, Jo KH, Kang HD (2020) Study of GANs using a few images for sealer inspection systems. In: International workshop on frontiers of computer vision. Springer, Singapore, pp 223–235. https://doi.org/10.1007/978-981-15-4818-5_17

  53. Wang C, Chen Z, Shang K, Wu H (2019) Label-removed generative adversarial networks incorporating with K-means. Neurocomputing 361:126–136. https://doi.org/10.1016/j.neucom.2019.06.041

    Article  Google Scholar 

  54. Chong MJ, Forsyth D (2020) Effectively unbiased FID and inception score and where to find them. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 6070–6079

  55. Fronza I, Corral L, Pahl C (2020) An approach to evaluate the complexity of block-based software product. Inform Educ 19(1):15–32. https://doi.org/10.15388/infedu.2020.02

  56. Hovorushchenko T, Pavlova O, Medzatyi D (2019) Ontology-based intelligent agent for determination of sufficiency of metric information in the software requirements. In: International scientific conference “intellectual systems of decision making and problem of computational intelligence”. Springer, Cham, pp 447–460. https://doi.org/10.1007/978-3-030-26474-1_32

Download references

Acknowledgment

The study was supported by the Ministry of Education and Science of the Russian Federation under the grant of the President of the Russian Federation, MК-74.2020.9.

Author information

Authors and Affiliations

  1. Tambov State Technical University, Tambov, Russia

    Artem D. Obukhov & Mikhail N. Krasnyanskiy

Authors
  1. Artem D. Obukhov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mikhail N. Krasnyanskiy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Artem D. Obukhov.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Obukhov, A.D., Krasnyanskiy, M.N. Neural network method for automatic data generation in adaptive information systems. Neural Comput & Applic 33, 15457–15479 (2021). https://doi.org/10.1007/s00521-021-06169-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06169-2

Keywords

Search

Navigation