Top

Neural Computing and Applications

Published in:

11-11-2020 | Original Article

Automated organization of interaction between modules of information systems based on neural network data channels

Authors: Artem D. Obukhov, Mikhail N. Krasnyanskiy

Published in: Neural Computing and Applications | Issue 12/2021

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

The automation of the process of information systems construction is an important and urgent problem, as it allows reducing the negative influence of a person during decision making and developing software, releasing additional time and material resources to solve more complex and creative problems. Most modern information systems are developed on a modular base; therefore, a significant design stage is the implementation of links between system components. The purpose of the study is to automate the organization of intermodular interaction in information systems, which will reduce the complexity, time and cost of this implementing process. In order to achieve the result, a method is proposed for the organization of interaction between modules of information systems based on neural network data channels, realized within the general concept of a neural network architecture. The structure of neural network channels, the principles of their functioning, theoretical substantiation, mathematical and algorithmic support and area of application are considered in detail. A classification of neural network channels is presented, based on two of their characteristics: categories and degrees. As a result of the conducted research, the practical implementation of two neural network data channels is realized (transmission and adaptation), the structure of the program code, the used tools and libraries are analyzed. Based on a set of metrics for the complexity of the program code (Halstead, Jilb), the estimation of the computational complexity of algorithms, time and material costs for implementation, a comparative analysis of neural network data transmission channels and adaptation with classical approaches in the form of a set of network data transmission protocol and the required algorithmic support for data processing is carried out. The obtained experimental results confirm the lower complexity of neural network channels (reduction by at least 20% according to Halstead metrics and cyclometric complexity), reduction in time (by 12–32%) and cost (by 36–63%) of implementation and increase in the accuracy of the problem solving (by 11.8–15.5%). This demonstrates the effectiveness of using neural network data channels to automate the organization of intermodular interaction in information systems.

previous article An efficient and secure feature location approach in source code using Jacobian matrix-based clustering

next article Online reliability optimization for URLLC in HetNets: a DQN approach

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Franco EF, Hirama K, Carvalho MM (2018) Applying system dynamics approach in software and information system projects: a mapping study. Inf Softw Technol 93:58–73. https://doi.org/10.1016/j.infsof.2017.08.013CrossRef

Dingsøyr T et al (2018) Exploring software development at the very large-scale: a revelatory case study and research agenda for agile method adaptation. Empir Softw Eng 23(1):490–520. https://doi.org/10.1007/s10664-017-9524-2CrossRef

Chaib A, Boussebough I, Chaoui AA (2018) Adaptive service composition in an ambient environment with a multi-agent system. J Ambient Intell Humaniz Comput 9(2):367–380. https://doi.org/10.1007/s12652-017-0510-8CrossRef

Sinha K, Suh ES (2018) Pareto-optimization of complex system architecture for structural complexity and modularity. Res Eng Des 29(1):123–141. https://doi.org/10.1007/s00163-017-0260-9CrossRef

Mohsin A, Janjua NK (2018) A review and future directions of SOA-based software architecture modeling approaches for system of systems. SOCA 12(3–4):183–200. https://doi.org/10.1007/s11761-018-0245-1CrossRef

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

Maurya S, Tiwari NK, Gupta SC (2018) Layered software defined networking. Optical and wireless technologies. Springer, Singapore, pp 351–362. https://doi.org/10.1007/978-981-10-7395-3_40CrossRef

Gill AQ, Chew E (2019) Configuration information system architecture: Insights from applied action design research. Inf Manag 56(4):507–525. https://doi.org/10.1016/j.im.2018.09.011CrossRef

Ajami S, Woodbridge Y, Feitelson DG (2019) Syntax, predicates, idioms—what really affects code complexity? Empir Softw Eng 24(1):287–328. https://doi.org/10.1007/s10664-018-9628-3CrossRef

10.

Abiodun OI et al (2018) State-of-the-art in artificial neural network applications: a survey. Heliyon 4(11):e00938. https://doi.org/10.1016/j.heliyon.2018.e00938CrossRef

11.

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.2019.2929266CrossRef

12.

Hernández G et al (2020) Hybrid neural networks for big data classification. Neurocomputing 390:327–340. https://doi.org/10.1109/ACCESS.2018.2846483CrossRef

13.

Elias I et al (2020) Genetic algorithm with radial basis mapping network for the electricity consumption modeling. Appl Sci 10(12):4239. https://doi.org/10.3390/app10124239CrossRef

14.

Aquino G et al (2020) Novel nonlinear hypothesis for the delta parallel robot modeling. IEEE Access 8:46324–46334. https://doi.org/10.1109/ACCESS.2020.2979141CrossRef

15.

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.2018.2846483CrossRef

16.

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

17.

Dhir S, Kumar D, Singh VB (2019) Success and failure factors that impact on project implementation using agile software development methodology. Software engineering. Springer, Singapore, pp 647–654. https://doi.org/10.1007/978-981-10-8848-3_62CrossRef

18.

Fernández-García AJ et al (2019) A microservice-based architecture for enhancing the user experience in cross-device distributed mashup UIs with multiple forms of interaction. Univ Access Inf Soc 18(4):747–770. https://doi.org/10.1007/s10209-017-0606-0CrossRef

19.

Tazi K, Abbou FM, Abdi F (2020) Multi-agent system for microgrids: design, optimization and performance. Artif Intell Rev 53(2):1233–1292. https://doi.org/10.1007/s10462-019-09695-7CrossRef

20.

Azeroual O, Saake G, Schallehn E (2018) Analyzing data quality issues in research information systems via data profiling. Int J Inf Manag 41:50–56. https://doi.org/10.1016/j.ijinfomgt.2018.02.007CrossRef

21.

Fong R, Vedaldi A (2019) Explanations for attributing deep neural network predictions. Explainable AI: interpreting explaining and visualizing deep learning. Springer, Cham, pp 149–167. https://doi.org/10.1007/978-3-030-28954-6_8CrossRef

22.

Kosvyra A, Maramis C, Chouvarda I (2019) Developing an integrated genomic profile for cancer patients with the use of NGS data. Emerg Sci J 3(3):157–167. https://doi.org/10.28991/esj-2019-01178CrossRef

23.

Doa’ei Y, Jahan AM (2018) Application of artificial intelligence and meta-heuristic algorithms in civil health monitoring systems. Civil Eng J 4(7):1653–1666. https://doi.org/10.28991/cej-03091102CrossRef

24.

Ghorbani S, Barari M, Hosseini M (2017) A modern method to improve of detecting and categorizing mechanism for micro seismic events data using boost learning system. Civil Eng J 3(9):715–726. https://doi.org/10.21859/cej-03098CrossRef

25.

Diao Y et al (2020) CRHASum: extractive text summarization with contextualized-representation hierarchical-attention summarization network. Neural Comput Appl 32:1–13. https://doi.org/10.1007/s00521-019-04638-3CrossRef

26.

Singh GAP, Gupta PK (2019) Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans. Neural Comput Appl 31(10):6863–6877. https://doi.org/10.1007/s00521-018-3518-xCrossRef

27.

Lai X, Wu X, Zhang L, Lu W, Zhong C (2019) Imputations of missing values using a tracking-removed autoencoder trained with incomplete data. Neurocomputing 366:54–65. https://doi.org/10.1016/j.neucom.2019.07.066CrossRef

28.

del Campo I et al (2019) A versatile hardware/software platform for personalized driver assistance based on online sequential extreme learning machines. Neural Comput Appl 31(12):8871–8886. https://doi.org/10.1007/s00521-019-04386-4CrossRef

29.

Kowsari K et al (2019) Text classification algorithms: a survey. Information 10(4):150. https://doi.org/10.3390/info10040150CrossRef

30.

Ahmed F, Qin YJ, Martínez L (2019) Sustainable change management through employee readiness: decision support system adoption in technology-intensive British E-businesses. Sustainability 11(11):2998. https://doi.org/10.3390/su11112998CrossRef

31.

Diefenbach D, Lopez V, Singh K, Maret P (2018) Core techniques of question answering systems over knowledge bases: a survey. Knowl Inf Syst 55(3):529–569. https://doi.org/10.1007/s10115-017-1100-yCrossRef

32.

Köksal Ö, Tekinerdogan B (2019) Architecture design approach for IoT-based farm management information systems. Precis Agric 20(5):926–958. https://doi.org/10.1007/s11119-018-09624-8CrossRef

33.

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

34.

Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signals Syst 2(4):303–314. https://doi.org/10.1007/BF02551274MathSciNetCrossRefMATH

35.

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

36.

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, pp 35–46. https://doi.org/10.1137/1.9781611972764.4

37.

Kiseľák J et al (2020) “SPOCU”: scaled polynomial constant unit activation function. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05182-1CrossRef

38.

Stephens R (2019) Essential algorithms: a practical approach to computer algorithms using Python and C. Wiley, Hoboken (ISBN: 978-1-119-57599-3)CrossRef

39.

Arcos-Garcia A, Alvarez-Garcia JA, Soria-Morillo LM (2018) Evaluation of deep neural networks for traffic sign detection systems. Neurocomputing 316:332–344. https://doi.org/10.1016/j.neucom.2018.08.009CrossRef

40.

Stroud RO, Ertas A, Mengel S (2019) Application of cyclomatic complexity in enterprise architecture frameworks. IEEE Syst J 13(3):2166–2176. https://doi.org/10.1109/JSYST.2019.2897592CrossRef

41.

Pan W, Chai C (2019) Measuring software stability based on complex networks in software. Clust Comput 22(2):2589–2598. https://doi.org/10.1007/s10586-017-1353-yCrossRef

42.

Stasiak A, Chudzikiewicz J, Zieliński Z (2018) Software metrics for similarity determination of complex software systems. In: KKIO software engineering conference. Springer, Cham, pp 175–191. https://doi.org/10.1007/978-3-319-99617-2_12

43.

Savchenko D, Hynninen T, Taipale O (2018) Code quality measurement: Case study. In: 2018 IEEE 41st international convention on information and communication technology, electronics and microelectronics (MIPRO), pp 1455–1459. https://doi.org/10.23919/MIPRO.2018.8400262

44.

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

45.

Manikavelan D, Ponnusamy R (2020) Software quality analysis based on cost and error using fuzzy combined COCOMO model. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-01783-9CrossRef

46.

Fadhil AA, Alsarraj RGH, Altaie AM (2020) Software cost estimation based on Dolphin algorithm. IEEE Access 8:75279–75287. https://doi.org/10.1109/ACCESS.2020.2988867CrossRef

47.

Miller J et al (2018) Applicability of the software cost model COCOMO II to HPC projects. Int J Comput Sci Eng 17(3):283–296. https://doi.org/10.1504/IJCSE.2018.095849CrossRef

48.

Du KL, Swamy MNS (2019) Elements of computational learning theory. Neural networks and statistical learning. Springer, London, pp 65–79. https://doi.org/10.1007/978-1-4471-7452-3_3CrossRefMATH

49.

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

50.

Alwosheel A, van Cranenburgh S, Chorus CG (2018) Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis. J Choice Model 28:167–182. https://doi.org/10.1016/j.jocm.2018.07.002CrossRef

51.

Bucolo M et al (2019) Turing patterns in the simplest MCNN. Nonlinear Theory Appl IEICE 10(4):390–398. https://doi.org/10.1587/nolta.10.390CrossRef

52.

Holzinger A et al (2019) Interactive machine learning: experimental evidence for the human in the algorithmic loop. Appl Intell 49(7):2401–2414. https://doi.org/10.1007/s10489-018-1361-5CrossRef

Title: Automated organization of interaction between modules of information systems based on neural network data channels
Authors: Artem D. Obukhov
Mikhail N. Krasnyanskiy
Publication date: 11-11-2020
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 12/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05491-5

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 12/2021

The Mode-Fisher pooling for time complexity optimization in deep convolutional neural networks

Applications of metaheuristic algorithms for optimal operation of cascaded hydropower plants

Periodically intermittent control for finite-time synchronization of delayed quaternion-valued neural networks

A new hybrid model to foretell thermal power efficiency from energy performance certificates at residential dwellings applying a Gaussian process regression

Improved coral reefs optimization with adaptive -hill climbing for feature selection

Hybrid classification of Android malware based on fuzzy clustering and the gradient boosting machine

Premium Partner