IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v51y2017i3d10.1007_s11135-016-0326-1.html
   My bibliography  Save this article

RETRACTED ARTICLE: Determination of the most influential factors for number of patents prediction by adaptive neuro-fuzzy technique

Author

Listed:
  • Miloš Milovančević

    (University of Niš)

  • Dušan Marković

    (University of Niš)

  • Vlastimir Nikolić

    (University of Niš)

  • Igor Mladenović

    (University of Niš)

Abstract

Number of patents may be developed on the basis on different natural and science and technological factors. Number of patents prediction based on the different factors in many countries is analyzed in this investigation. These factors represent natural and science resources. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data in order to select the most influential factors for the number of patents prediction. Five inputs are considered: research and development (R&D) resources, natural resources, quality of academic institutions, quality of collaboration with the private sector and quality of education. As the ANFIS output, number of patents is considered. The ANFIS process for variable selection is also implemented in order to detect the predominant factors affecting the prediction of number of patents. Results show that the R&D is the most influential factor for the number of patents prediction.

Suggested Citation

  • Miloš Milovančević & Dušan Marković & Vlastimir Nikolić & Igor Mladenović, 2017. "RETRACTED ARTICLE: Determination of the most influential factors for number of patents prediction by adaptive neuro-fuzzy technique," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(3), pages 1207-1216, May.
    • Handle: RePEc:spr:qualqt:v:51:y:2017:i:3:d:10.1007_s11135-016-0326-1
    DOI: 10.1007/s11135-016-0326-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11135-016-0326-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11135-016-0326-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hingley, Peter & Bas, Serpil, 2009. "Numbers and sizes of applicants at the European Patent Office," World Patent Information, Elsevier, vol. 31(4), pages 285-298, December.
    2. Yamauchi, Isamu & Nagaoka, Sadao, 2015. "Does the outsourcing of prior art search increase the efficiency of patent examination? Evidence from Japan," Research Policy, Elsevier, vol. 44(8), pages 1601-1614.
    3. Caviggioli, Federico, 2011. "Foreign applications at the Japan Patent Office - An empirical analysis of selected growth factors," World Patent Information, Elsevier, vol. 33(2), pages 157-167, June.
    4. Zhang, Sifei & Yuan, Chien-Chung & Chang, Ke-Chiun & Ken, Yun, 2012. "Exploring the nonlinear effects of patent H index, patent citations, and essential technological strength on corporate performance by using artificial neural network," Journal of Informetrics, Elsevier, vol. 6(4), pages 485-495.
    5. Petković, Dalibor & Ćojbašić, Žarko & Nikolić, Vlastimir & Shamshirband, Shahaboddin & Mat Kiah, Miss Laiha & Anuar, Nor Badrul & Abdul Wahab, Ainuddin Wahid, 2014. "Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission," Energy, Elsevier, vol. 64(C), pages 868-874.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Pieter E. Stek, 2020. "Mapping high R&D city-regions worldwide: a patent heat map approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(1), pages 279-296, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Krzysztof Klincewicz & Szymon Szumiał, 2022. "Successful patenting—not only how, but with whom: the importance of patent attorneys," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5111-5137, September.
    2. Derafshian, Mehdi & Amjady, Nima, 2015. "Optimal design of power system stabilizer for power systems including doubly fed induction generator wind turbines," Energy, Elsevier, vol. 84(C), pages 1-14.
    3. Ma, Ding & Yu, Qian & Li, Jing & Ge, Mengni, 2021. "Innovation diffusion enabler or barrier: An investigation of international patenting based on temporal exponential random graph models," Technology in Society, Elsevier, vol. 64(C).
    4. Yadegaridehkordi, Elaheh & Hourmand, Mehdi & Nilashi, Mehrbakhsh & Shuib, Liyana & Ahani, Ali & Ibrahim, Othman, 2018. "Influence of big data adoption on manufacturing companies' performance: An integrated DEMATEL-ANFIS approach," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 199-210.
    5. Sayyaadi, Hoseyn & Baghsheikhi, Mostafa, 2019. "Retrofit of a steam power plant using the adaptive neuro-fuzzy inference system in response to the load variation," Energy, Elsevier, vol. 175(C), pages 1164-1173.
    6. Xu, Lei & Hou, Lei & Zhu, Zhenyu & Li, Yu & Liu, Jiaquan & Lei, Ting & Wu, Xingguang, 2021. "Mid-term prediction of electrical energy consumption for crude oil pipelines using a hybrid algorithm of support vector machine and genetic algorithm," Energy, Elsevier, vol. 222(C).
    7. Anicic, Obrad & Jovic, Srdjan, 2016. "Adaptive neuro-fuzzy approach for ducted tidal turbine performance estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1111-1116.
    8. Shamshirband, Shahaboddin & Petković, Dalibor & Amini, Amineh & Anuar, Nor Badrul & Nikolić, Vlastimir & Ćojbašić, Žarko & Mat Kiah, Miss Laiha & Gani, Abdullah, 2014. "Support vector regression methodology for wind turbine reaction torque prediction with power-split hydrostatic continuous variable transmission," Energy, Elsevier, vol. 67(C), pages 623-630.
    9. Nagaoka, Sadao & Yamauchi, Isamu, 2022. "Information constraints and examination quality in patent offices: The effect of initiation lags," International Journal of Industrial Organization, Elsevier, vol. 82(C).
    10. Rajamohan Jayabal & K. Vijayarekha & S. Rakesh Kumar, 2018. "Design of ANFIS for Hydrophobicity Classification of Polymeric Insulators with Two-Stage Feature Reduction Technique and Its Field Deployment," Energies, MDPI, vol. 11(12), pages 1-16, December.
    11. Kim, Dongwoo & Song, Kang Sub & Lim, Junyub & Kim, Yongchan, 2018. "Analysis of two-phase injection heat pump using artificial neural network considering APF and LCCP under various weather conditions," Energy, Elsevier, vol. 155(C), pages 117-127.
    12. Yonghe Lu & Xinyu Tong & Xin Xiong & Hou Zhu, 2024. "Knowledge graph enhanced citation recommendation model for patent examiners," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(4), pages 2181-2203, April.
    13. Seixas, M. & Melício, R. & Mendes, V.M.F., 2014. "Offshore wind turbine simulation: Multibody drive train. Back-to-back NPC (neutral point clamped) converters. Fractional-order control," Energy, Elsevier, vol. 69(C), pages 357-369.
    14. Igor Mladenović & Miloš Milovančević & Svetlana Sokolov-Mladenović, 2017. "RETRACTED ARTICLE: Analyzing of innovations influence on economic growth by fuzzy system," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(3), pages 1297-1304, May.
    15. Aghbashlo, Mortaza & Hosseinpour, Soleiman & Tabatabaei, Meisam & Younesi, Habibollah & Najafpour, Ghasem, 2016. "On the exergetic optimization of continuous photobiological hydrogen production using hybrid ANFIS–NSGA-II (adaptive neuro-fuzzy inference system–non-dominated sorting genetic algorithm-II)," Energy, Elsevier, vol. 96(C), pages 507-520.
    16. Lee-Yong Sung & Jonghoon Ahn, 2020. "Comparative Analyses of Energy Efficiency between on-Demand and Predictive Controls for Buildings’ Indoor Thermal Environment," Energies, MDPI, vol. 13(5), pages 1-15, March.
    17. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    18. Al-Shammari, Eiman Tamah & Shamshirband, Shahaboddin & Petković, Dalibor & Zalnezhad, Erfan & Yee, Por Lip & Taher, Ros Suraya & Ćojbašić, Žarko, 2016. "Comparative study of clustering methods for wake effect analysis in wind farm," Energy, Elsevier, vol. 95(C), pages 573-579.
    19. Shamshirband, Shahaboddin & Petković, Dalibor & Anuar, Nor Badrul & Gani, Abdullah, 2014. "Adaptive neuro-fuzzy generalization of wind turbine wake added turbulence models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 36(C), pages 270-276.
    20. Yang, Jian & Song, Dongran & Dong, Mi & Chen, Sifan & Zou, Libing & Guerrero, Josep M., 2016. "Comparative studies on control systems for a two-blade variable-speed wind turbine with a speed exclusion zone," Energy, Elsevier, vol. 109(C), pages 294-309.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:qualqt:v:51:y:2017:i:3:d:10.1007_s11135-016-0326-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.