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RETRACTED ARTICLE: Determination of the most influential factors for number of patents prediction by adaptive neuro-fuzzy technique

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This article was retracted on 12 September 2019

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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.

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Change history

  • 12 September 2019

    The Editor-in-Chief has retracted this article (Milovancevic et al. 2016) because validity of the content of this article cannot be verified.

  • 12 September 2019

    The Editor-in-Chief has retracted this article (Milovan��evi�� et al. 2016) because validity of the content of this article cannot be verified.

References

  • Al-Ghandoor, A., Samhouri, M.: Electricity consumption in the industrial sector of jordan: application of multivariate linear regression and adaptive neuro-fuzzy techniques. Jordan J. Mech. Ind. Eng. 3(1), 69–76 (2009)

    Google Scholar 

  • Altuntas, S., Dereli, T., Kusiak, A.: Forecasting technology success based on patent data. Technol. Forecast. Soc. Change 96, 202–214 (2015)

    Article  Google Scholar 

  • Caviggioli, F.: Foreign applications at the Japan Patent Office: an empirical analysis of selected growth factors. World Patent Inf. 33, 157–167 (2011)

    Article  Google Scholar 

  • Dannegger, F., Hingley, P.: Predictive accuracy of survey-based forecasts for numbers of filings at the European Patent Office. World Patent Inf. 35, 187–200 (2013)

    Article  Google Scholar 

  • Ekici, B.B., Aksoy, U.T.: Prediction of building energy needs in early stage of design by using ANFIS. Expert Syst. Appl. 38, 5352 (2011)

    Article  Google Scholar 

  • Hidalgo, A., Gabaly, S.: Use of prediction methods for patent and trademark applications in Spain. World Patent Inf. 34, 19–29 (2012)

    Article  Google Scholar 

  • Hidalgo, A., Gabaly, S.: Optimization of prediction methods for patents and trademarks in Spain through the use of exogenous variables. World Patent Inf. 35, 130–140 (2013)

    Article  Google Scholar 

  • Hingley, P., Bas, S.: Numbers and sizes of applicants at the European Patent Office. World Patent Inf. 31, 285–298 (2009)

    Article  Google Scholar 

  • Hingley, P., Park, W.: A dynamic log-linear regression model to forecast numbers of future filings at the European Patent Office. World Patent Inf. 42, 19–27 (2015)

    Article  Google Scholar 

  • Inal, M.: Determination of dielectric properties of insulator materials by means of ANFIS: a comparative study. Expert Syst. Appl. 195, 34 (2008)

    Google Scholar 

  • Jang, J.-S.R.: ANFIS: adaptive-network-based fuzzy inference systems. IEEE Trans. Syst. Man Cybern. 23, 665–685 (1993)

    Article  Google Scholar 

  • Khajeh, A., Modarress, H., Rezaee, B.: Application of adaptive neuro-fuzzy inference system for solubility prediction of carbon dioxide in polymers. Expert Syst. Appl. 36, 5728 (2009)

    Article  Google Scholar 

  • Kurnaz, S., Cetin, O., Kaynak, O.: Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles. Expert Syst. Appl. 37, 1229–1234 (2010)

    Article  Google Scholar 

  • Lai, Y.-H., Che, H.-C.: Modeling patent legal value by Extension Neural Network. Expert Syst. Appl. 36, 10520–10528 (2009)

    Article  Google Scholar 

  • Leamer, E.: Sources of Comparative Advantage: Theory and Evidence. MIT Press, Cambridge (1984)

    Google Scholar 

  • Lo, S.P., Lin, Y.Y.: The prediction of wafer surface non-uniformity using FEM and ANFIS in the chemical mechanical polishing process. J. Mater. Process. Technol. 168, 250 (2005)

    Article  Google Scholar 

  • Petković, D.: Adaptive neuro-fuzzy fusion of sensor data. Infrared Phys. Technol. 67, 222–228 (2014). doi:10.1016/j.infrared.2014.07.031

    Article  Google Scholar 

  • Petković, D.: Adaptive neuro-fuzzy approach for estimation of wind speed distribution. Electr. Power Energy Syst. 73, 389–392 (2015a). doi:10.1016/j.ijepes.2015.05.039

    Article  Google Scholar 

  • Petković, D.: Adaptive neuro-fuzzy optimization of the net present value and internal rate of return of a wind farm project under wake effect. Bus. Econ. Res. J. 8, 11–28 (2015b). doi:10.7835/jcc-berj-2015-0102

    Article  Google Scholar 

  • Petković, D., Ćojbašić, Ž.: Adaptive neuro-fuzzy estimation of automatic nervous system parameters effect on heart rate variability. Neural Comput. Appl. 21(8), 2065–2070 (2012)

    Article  Google Scholar 

  • Petković, D., Petković, N.D.: Applications and adaptive neuro-fuzzy estimation of conductive silicone rubber properties. Strojarstvo: časopis za teoriju i praksu u strojarstvu 54(3), 197–203 (2013)

    Google Scholar 

  • Petković, D., Issa, M., Pavlović, N.D., Pavlović, N.T., Zentner, L.: Adaptive neuro-fuzzy estimation of conductive silicone rubber mechanical properties. Expert Syst. Appl. 39, 9477–9482 (2012a)

    Article  Google Scholar 

  • Petković, D., Issa, M., Pavlović, N.D., Zentner, L., Ćojbašić, Ž.: Adaptive neuro fuzzy controller for adaptive compliant robotic gripper. Expert Syst. Appl. 39, 13295–13304 (2012b)

    Article  Google Scholar 

  • Petković, D., Mirna, I., Pavlović D, N.D., Pavlović D, N.T., Zentner, L.: Adaptive neuro-fuzzy estimation of conductive silicone rubber mechanical properties. Expert Syst. Appl. 39(10), 9477–9482 (2012c)

    Article  Google Scholar 

  • Petković, D., Mirna, I., Pavlović, N.D., Zentner, L., Ćojbašić, Ž.: Adaptive neuro fuzzy controller for adaptive compliant robotic gripper. Expert Syst. Appl. 39(18), 13295–13304 (2012d)

    Article  Google Scholar 

  • Petković, D., Pavlović, N.D., Ćojbašić, Ž., Pavlović, N.T.: Adaptive neuro fuzzy estimation of underactuated robotic gripper contact forces. Expert Syst. Appl. 40(1), 281–286 (2013a)

    Article  Google Scholar 

  • Petković, D., Ćojbašić, Ž., Lukić, S.: Adaptive neuro fuzzy selection of heart rate variability parameters affected by autonomic nervous system. Expert Syst. Appl. 40(11), 4490–4495 (2013b)

    Article  Google Scholar 

  • Petković, D., Ćojbašić, Ž., Nikolić, V.: Adaptive neuro-fuzzy approach for wind turbine power coefficient estimation. Renew. Sustain. Energy Rev. 28, 191–195 (2013c)

    Article  Google Scholar 

  • Petković, D., Pavlović, N.T., Shamshirband, S., Kiah, M.L.M., Anuar, N.B., Idris, M.Y.I.: Adaptive neuro-fuzzy estimation of optimal lens system parameters. Opt. Lasers Eng. 55, 84–93 (2014a)

    Article  Google Scholar 

  • Petković, D., Ćojbašić, Ž., Nikolić, V., Shamshirband, S., Kiah, M.L.M., Anuar, N.B., Wahab, A.W.A.: Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission. Energy 64, 868–874 (2014b)

    Article  Google Scholar 

  • Petković, D., Shamshirband, S., Petković, N.T., Anuar, N.B., Kiah, M.L.M.: Modulation transfer function estimation of optical lens system by adaptive neuro-fuzzy methodology. Opt. Spectrosc. 117(1), 121–131 (2014c). doi:10.7868/S0030403414070046

    Article  Google Scholar 

  • Petković, D., Shamshirband, S., Anuar, N.B., Nasir, M.H.N.M., Petković, N.T., Akib, S.: Adaptive neuro-fuzzy prediction of modulation transfer function of optical lens system. Infrared Phys. Technol. 65, 54–60 (2014d). doi:10.7868/S0030403414070046

    Article  Google Scholar 

  • Petković, D., Shamshirband, S., Ćojbašić, Ž., Nikolić, V., Anuar, N.B., Sabri, A.Q.M., Akib, S.: Adaptive neuro-fuzzy estimation of building augmentation of wind turbine power. Comput. Fluids 97(25), 188–194 (2014e)

    Article  Google Scholar 

  • Petković, D., Shamshirband, S., Iqbal, J., Anuar, N.B., Petković, N.D., Kiah, M.L.M.: Adaptive neuro-fuzzy prediction of grasping object weight for passively compliant gripper. Appl. Soft Comput. 22, 424–431 (2014f)

    Article  Google Scholar 

  • Petković, D., Mirna, I., Petković, N.D., Zentner, L., Nor Ridzuan Daud, M., Shamshirband, S.: Contact positions estimation of sensing structure using adaptive neuro-fuzzy inference system. Kybernetes 43(5), 783–796 (2014g)

    Article  Google Scholar 

  • Petković, D., Shamshirband, S., Petković, N.D., Saboohi, H., Altameem, T.A., Gani, A.: Determining the joints most strained in an underactuated robotic finger by adaptive neuro-fuzzy methodology. Adv. Eng. Softw. 77, 28–34 (2014h)

    Article  Google Scholar 

  • Petković, D., Shamshirband, S., Anuar, N.B., Naji, S., Kiah, M.L.M., Gani, A.: Adaptive neuro-fuzzy evaluation of wind farm power production as function of wind speed and direction. Stoch. Env. Res. Risk Assess. 29(3), 793–802 (2015a). doi:10.1007/s00477-014-0901-8

    Article  Google Scholar 

  • Petković, D., Mirna, I., Petković, N.D., Zentner, L.: Potential of adaptive neuro-fuzzy inference system for contact positions detection of sensing structure. Measurement 61, 234–242 (2015b). doi:10.1016/j.measurement.2014.10.040

    Article  Google Scholar 

  • Petković, D., Shamshirband, S., Anuar, N.B., Sabri, A.Q.M., Rahman, Z.B.A., Petković, N.D.: Input displacement neuro-fuzzy control and object recognition by compliant multi-fingered passively adaptive robotic gripper. J. Intell. Robot. Syst. (2015c). doi:10.1007/s10846-015-0182-6

    Article  Google Scholar 

  • Petković, D., Shamshirband, S., Tong, C.W., Al-Shammari, E.T.: Generalized adaptive neuro-fuzzy based method for wind speed distribution prediction. Flow Meas. Instrum. 43, 47–52 (2015d). doi:10.1016/j.flowmeasinst.2015.03.003

    Article  Google Scholar 

  • Petković, D., Gocic, M., Trajković, S., Shamshirband, S., Motamedi, S., Hashim, R., Bonakdari, H.: Determination of the most influential weather parameters on reference evapotranspiration by adaptive neuro-fuzzy methodology. Comput. Electron. Agric. 114, 277–284 (2015e). doi:10.1016/j.compag.2015.04.012

    Article  Google Scholar 

  • Petković, D., Shamshirband, S., Abbasi, A., Kiani, K., Al-Shammari, E.T.: Prediction of contact forces of underactuated finger by adaptive neuro fuzzy approach. Mech. Syst. Signal Process. 64–65, 520–527 (2015f). doi:10.1016/j.ymssp.2015.03.013

    Article  Google Scholar 

  • Singh, R., Kianthola, A., Singh, T.N.: Estimation of elastic constant of rocks using an ANFIS approach. Appl. Soft Comput. 12, 40–45 (2012)

    Article  Google Scholar 

  • Tian, L., Collins, C.: Adaptive neuro-fuzzy control of a flexible manipulator. Mechatronics 15, 1305–1320 (2005)

    Article  Google Scholar 

  • Yamauchi, I., Nagaoka, S.: Does the outsourcing of prior art search increase the efficiency of patent examination? Evid. Jpn. Res. Policy 44, 1601–1614 (2015)

    Article  Google Scholar 

  • Zhang, S., Yuan, C.-C., Chang, K-Chiun, Ken, Y.: Exploring the nonlinear effects of patent H index, patent citations, and essential technological strength on corporate performance by using artificial neural network. J. Informetr. 6, 485–495 (2012)

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Mechanical Engineering, University of Niš, Aleksandra Medvedeva 14, 18000, Niš, Serbia

    Miloš Milovančević, Dušan Marković & Vlastimir Nikolić

  2. Faculty of Economics, University of Niš, Trg kralja Aleksandra 11, 18000, Niš, Serbia

    Igor Mladenović

Authors
  1. Miloš Milovančević
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  2. Dušan Marković
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  3. Vlastimir Nikolić
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  4. Igor Mladenović
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Correspondence to Dušan Marković.

Additional information

The Editor-in-Chief has retracted this article because validity of the content of this article cannot be verified. This article showed evidence of substantial text overlap (most notably with the article cited), peer review and authorship manipulation. Author Dusan Markovic stated he was not aware of being included as an author of this article. Authors Miloš Milovancevic, Vlastimir Nikolic and Igor Mladenovic have not responded to any correspondence from the editor or publisher about this retraction.

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Milovančević, M., Marković, D., Nikolić, V. et al. RETRACTED ARTICLE: Determination of the most influential factors for number of patents prediction by adaptive neuro-fuzzy technique. Qual Quant 51, 1207–1216 (2017). https://doi.org/10.1007/s11135-016-0326-1

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  • DOI: https://doi.org/10.1007/s11135-016-0326-1

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