{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T19:21:36Z","timestamp":1769196096670,"version":"3.49.0"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2020,5,25]],"date-time":"2020-05-25T00:00:00Z","timestamp":1590364800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,5,25]],"date-time":"2020-05-25T00:00:00Z","timestamp":1590364800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2021,3]]},"DOI":"10.1007\/s00521-020-05029-9","type":"journal-article","created":{"date-parts":[[2020,5,25]],"date-time":"2020-05-25T13:02:41Z","timestamp":1590411761000},"page":"1479-1496","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Design of backtracking search heuristics for parameter estimation of power signals"],"prefix":"10.1007","volume":"33","author":[{"given":"Ammara","family":"Mehmood","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad Asif Zahoor","family":"Raja","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aneela","family":"Zameer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naveed Ishtiaq","family":"Chaudhary","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,5,25]]},"reference":[{"key":"5029_CR1","volume-title":"Voltage stability of electric power systems","author":"T Van Cutsem","year":"2007","unstructured":"Van Cutsem T, Vournas C (2007) Voltage stability of electric power systems. Springer, Berlin"},{"key":"5029_CR2","volume-title":"Electrical power systems quality","author":"RC Dugan","year":"1996","unstructured":"Dugan RC, McGranaghan MF, Beaty HW, Santoso S (1996) Electrical power systems quality, vol 2. Mcgraw-Hill, New York"},{"issue":"1","key":"5029_CR3","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1007\/s12530-017-9177-9","volume":"9","author":"AT Phan","year":"2018","unstructured":"Phan AT, Wira P, Hermann G (2018) A dedicated state space for power system modeling and frequency and unbalance estimation. Evol Syst 9(1):57\u201369","journal-title":"Evol Syst"},{"key":"5029_CR4","volume-title":"Power systems harmonics","author":"E Acha","year":"2001","unstructured":"Acha E, Madrigal M (2001) Power systems harmonics. Wiley, New York"},{"key":"5029_CR5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00034-017-0737-9","volume":"37","author":"L Xu","year":"2018","unstructured":"Xu L, Ding F (2018) Iterative parameter estimation for signal models based on measured data. Circuits Syst Signal Process 37:1\u201324","journal-title":"Circuits Syst Signal Process"},{"issue":"4","key":"5029_CR6","doi-asserted-by":"crossref","first-page":"1756","DOI":"10.1007\/s12555-017-0482-7","volume":"16","author":"L Xu","year":"2018","unstructured":"Xu L, Xiong W, Alsaedi A, Hayat T (2018) Hierarchical parameter estimation for the frequency response based on the dynamical window data. Int J Control Autom Syst 16(4):1756\u20131764","journal-title":"Int J Control Autom Syst"},{"issue":"2","key":"5029_CR7","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1007\/s11045-017-0495-7","volume":"29","author":"H Li","year":"2018","unstructured":"Li H, Zhou M, Wu R, Zhang Z, Zheng J (2018) Parameter estimation of air maneuvering target for multi-antenna system via reconstructing time samples and signal. Multidimens Syst Signal Process 29(2):621\u2013641","journal-title":"Multidimens Syst Signal Process"},{"issue":"5\u20136","key":"5029_CR8","doi-asserted-by":"crossref","first-page":"854","DOI":"10.1016\/j.mcm.2010.05.015","volume":"52","author":"Y Cao","year":"2010","unstructured":"Cao Y, Liu Z (2010) Signal frequency and parameter estimation for power systems using the hierarchical identification principle. Math Comput Model 52(5\u20136):854\u2013861","journal-title":"Math Comput Model"},{"issue":"2","key":"5029_CR9","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1109\/TPWRD.2007.893187","volume":"22","author":"H Qian","year":"2007","unstructured":"Qian H, Zhao R, Chen T (2007) Interharmonics analysis based on interpolating windowed FFT algorithm. IEEE Trans Power Deliv 22(2):1064\u20131069","journal-title":"IEEE Trans Power Deliv"},{"issue":"4","key":"5029_CR10","doi-asserted-by":"crossref","first-page":"1728","DOI":"10.1109\/TPWRD.2008.919032","volume":"23","author":"Q Zhang","year":"2008","unstructured":"Zhang Q, Liu H, Chen H, Li Q, Zhang Z (2008) A precise and adaptive algorithm for interharmonics measurement based on iterative DFT. IEEE Trans Power Deliv 23(4):1728\u20131735","journal-title":"IEEE Trans Power Deliv"},{"key":"5029_CR11","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.sigpro.2017.08.009","volume":"142","author":"S Zubair","year":"2018","unstructured":"Zubair S, Chaudhary NI, Khan ZA, Wang W (2018) Momentum fractional LMS for power signal parameter estimation. Signal Process 142:441\u2013449","journal-title":"Signal Process"},{"key":"5029_CR12","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.isatra.2017.03.011","volume":"68","author":"NI Chaudhary","year":"2017","unstructured":"Chaudhary NI et al (2017) A new computing approach for power signal modeling using fractional adaptive algorithms. ISA Trans 68:189\u2013202","journal-title":"ISA Trans"},{"key":"5029_CR13","doi-asserted-by":"publisher","first-page":"467276","DOI":"10.1155\/2013\/467276","volume":"2013","author":"NI Chaudhary","year":"2013","unstructured":"Chaudhary NI et al (2013) Identification of input nonlinear control autoregressive systems using fractional signal processing approach. Sci World J 2013:467276. https:\/\/doi.org\/10.1155\/2013\/467276","journal-title":"Sci World J"},{"key":"5029_CR14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00034-018-0985-3","volume":"38","author":"L Wan","year":"2019","unstructured":"Wan L, Ding F (2019) Decomposition-and gradient-based iterative identification algorithms for multivariable systems using the multi-innovation theory. Circuits Syst Signal Process 38:1\u201321","journal-title":"Circuits Syst Signal Process"},{"issue":"10","key":"5029_CR15","doi-asserted-by":"crossref","first-page":"10070","DOI":"10.1109\/TVT.2018.2857480","volume":"67","author":"C Huang","year":"2018","unstructured":"Huang C, Liu L, Yuen C (2018) Asymptotically optimal estimation algorithm for the sparse signal with arbitrary distributions. IEEE Trans Veh Technol 67(10):10070\u201310075","journal-title":"IEEE Trans Veh Technol"},{"key":"5029_CR16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00034-018-0985-3","volume":"38","author":"H Xia","year":"2019","unstructured":"Xia H, Ji Y, Xu L, Hayat T (2019) Maximum likelihood-based recursive least-squares algorithm for multivariable systems with colored noises using the decomposition technique. Circuits Syst Signal Process 38:1\u201319","journal-title":"Circuits Syst Signal Process"},{"key":"5029_CR17","doi-asserted-by":"crossref","first-page":"1554","DOI":"10.1109\/TII.2018.2844191","volume":"15","author":"Z Shuai","year":"2018","unstructured":"Shuai Z, Zhang J, Tang L, Teng Z, Wen H (2018) Frequency shifting and filtering algorithm for power system harmonic estimation. IEEE Trans Ind Inform 15:1554\u20131565","journal-title":"IEEE Trans Ind Inform"},{"key":"5029_CR18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00034-018-0985-3","volume":"38","author":"S Liu","year":"2019","unstructured":"Liu S, Ding F, Xu L, Hayat T (2019) Hierarchical principle-based iterative parameter estimation algorithm for dual-frequency signals. Circuits Syst Signal Process 38:1\u201318","journal-title":"Circuits Syst Signal Process"},{"key":"5029_CR19","doi-asserted-by":"crossref","first-page":"2882","DOI":"10.1109\/TSG.2018.2813881","volume":"10","author":"B Hu","year":"2018","unstructured":"Hu B, Gharavi H (2018) A fast recursive algorithm for spectrum tracking in power grid systems. IEEE Trans Smart Grid 10:2882\u20132891","journal-title":"IEEE Trans Smart Grid"},{"key":"5029_CR20","first-page":"1","volume":"38","author":"Y Hu","year":"2018","unstructured":"Hu Y, Zhou Q, Yu H, Zhou Z, Ding F (2018) Two-stage generalized projection identification algorithms for stochastic systems. Circuits Syst Signal Process 38:1\u201317","journal-title":"Circuits Syst Signal Process"},{"key":"5029_CR21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00158-018-2056-6","volume":"59","author":"R El Maani","year":"2019","unstructured":"El Maani R, Radi B, El Hami A (2019) Multiobjective backtracking search algorithm: application to FSI. Struct Multidiscip Optim 59:1\u201321","journal-title":"Struct Multidiscip Optim"},{"issue":"4","key":"5029_CR22","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1080\/0952813X.2015.1042533","volume":"28","author":"S Mallick","year":"2016","unstructured":"Mallick S, Kar R, Mandal D, Ghoshal SP (2016) CMOS analogue amplifier circuits optimisation using hybrid backtracking search algorithm with differential evolution. J Exp Theor Artif Intell 28(4):719\u2013749","journal-title":"J Exp Theor Artif Intell"},{"issue":"4","key":"5029_CR23","doi-asserted-by":"crossref","first-page":"1801","DOI":"10.12928\/telkomnika.v16i4.9058","volume":"16","author":"NN Nazri","year":"2018","unstructured":"Nazri NN, Malik NN, Idoumghar L, Latiff NM, Ali S (2018) Backtracking search optimization for collaborative beamforming in wireless sensor networks. Telkomnika 16(4):1801\u20131808","journal-title":"Telkomnika"},{"issue":"21","key":"5029_CR24","doi-asserted-by":"crossref","first-page":"11165","DOI":"10.1016\/j.ijhydene.2014.05.052","volume":"39","author":"A Askarzadeh","year":"2014","unstructured":"Askarzadeh A, dos Santos Coelho L (2014) A backtracking search algorithm combined with Burger\u2019s chaotic map for parameter estimation of PEMFC electrochemical model. Int J Hydrog Energy 39(21):11165\u201311174","journal-title":"Int J Hydrog Energy"},{"issue":"2","key":"5029_CR25","doi-asserted-by":"crossref","first-page":"023503","DOI":"10.1063\/1.5018573","volume":"10","author":"H Yang","year":"2018","unstructured":"Yang H, Yu J, Qiu Y, Li Q, Chen W (2018) A coordinated optimization method considering time-delay effect of islanded photovoltaic microgrid based on modified backtracking search algorithm. J Renew Sustain Energy 10(2):023503","journal-title":"J Renew Sustain Energy"},{"issue":"7","key":"5029_CR26","doi-asserted-by":"crossref","first-page":"1668","DOI":"10.3390\/en11071668","volume":"11","author":"J Zhou","year":"2018","unstructured":"Zhou J, Zhang C, Peng T, Xu Y (2018) Parameter identification of pump turbine governing system using an improved backtracking search algorithm. Energies 11(7):1668","journal-title":"Energies"},{"key":"5029_CR27","doi-asserted-by":"crossref","first-page":"2066","DOI":"10.1016\/j.neucom.2017.10.036","volume":"275","author":"S Yan","year":"2018","unstructured":"Yan S, Zhou J, Zheng Y, Li C (2018) An improved hybrid backtracking search algorithm based T\u2013S fuzzy model and its implementation to hydroelectric generating units. Neurocomputing 275:2066\u20132079","journal-title":"Neurocomputing"},{"key":"5029_CR28","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1016\/j.energy.2016.09.140","volume":"116","author":"M Modiri-Delshad","year":"2016","unstructured":"Modiri-Delshad M, Kaboli SHA, Taslimi-Renani E, Rahim NA (2016) Backtracking search algorithm for solving economic dispatch problems with valve-point effects and multiple fuel options. Energy 116:637\u2013649","journal-title":"Energy"},{"key":"5029_CR29","doi-asserted-by":"crossref","first-page":"10069","DOI":"10.1109\/ACCESS.2018.2797976","volume":"6","author":"MA Hannan","year":"2018","unstructured":"Hannan MA, Lipu MSH, Hussain A, Saad MH, Ayob A (2018) Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm. IEEE Access 6:10069\u201310079","journal-title":"IEEE Access"},{"key":"5029_CR30","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1016\/j.asoc.2018.08.027","volume":"73","author":"WU Khan","year":"2018","unstructured":"Khan WU et al (2018) Backtracking search integrated with sequential quadratic programming for nonlinear active noise control systems. Appl Soft Comput 73:666\u2013683","journal-title":"Appl Soft Comput"},{"key":"5029_CR31","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.neucom.2015.03.041","volume":"166","author":"SK Agarwal","year":"2015","unstructured":"Agarwal SK, Shah S, Kumar R (2015) Classification of mental tasks from EEG data using backtracking search optimization based neural classifier. Neurocomputing 166:397\u2013403","journal-title":"Neurocomputing"},{"key":"5029_CR32","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.eswa.2017.07.034","volume":"91","author":"MZ bin Mohd Zain","year":"2018","unstructured":"bin Mohd Zain MZ, Kanesan J, Kendall G, Chuah JH (2018) Optimization of fed-batch fermentation processes using the backtracking search algorithm. Expert Syst Appl 91:286\u2013297","journal-title":"Expert Syst Appl"},{"key":"5029_CR33","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.engappai.2018.10.008","volume":"77","author":"J Lin","year":"2019","unstructured":"Lin J (2019) Backtracking search based hyper-heuristic for the flexible job-shop scheduling problem with fuzzy processing time. Eng Appl Artif Intell 77:186\u2013196","journal-title":"Eng Appl Artif Intell"},{"key":"5029_CR34","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.dsp.2015.09.014","volume":"48","author":"J Bian","year":"2016","unstructured":"Bian J, Xing J, Liu J, Li Z, Li H (2016) An adaptive and computationally efficient algorithm for parameters estimation of superimposed exponential signals with observations missing randomly. Digit Signal Process 48:148\u2013162","journal-title":"Digit Signal Process"},{"issue":"8","key":"5029_CR35","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1016\/j.aml.2013.02.012","volume":"26","author":"X Li","year":"2013","unstructured":"Li X, Ding F (2013) Signal modeling using the gradient search. Appl Math Lett 26(8):807\u2013813","journal-title":"Appl Math Lett"},{"key":"5029_CR36","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04133-9","author":"A Mehmood","year":"2019","unstructured":"Mehmood A, Chaudhary NI, Zameer A et al (2020) Novel computing paradigms for parameter estimation in power signal models. Neural Comput Appl 32:6253\u20136282","journal-title":"Neural Comput Appl"},{"issue":"7","key":"5029_CR37","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1049\/iet-spr.2015.0280","volume":"10","author":"L Zhou","year":"2016","unstructured":"Zhou L, Li X, Xu H, Zhu P (2016) Multi-innovation stochastic gradient method for harmonic modelling of power signals. IET Signal Process 10(7):737\u2013742","journal-title":"IET Signal Process"},{"key":"5029_CR38","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.1016\/j.neucom.2015.09.034","volume":"173","author":"J Pan","year":"2016","unstructured":"Pan J, Yang X, Cai H, Mu B (2016) Image noise smoothing using a modified Kalman filter. Neurocomputing 173:1625\u20131629","journal-title":"Neurocomputing"},{"issue":"15","key":"5029_CR39","first-page":"8121","volume":"219","author":"P Civicioglu","year":"2013","unstructured":"Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219(15):8121\u20138144","journal-title":"Appl Math Comput"},{"issue":"6","key":"5029_CR40","doi-asserted-by":"crossref","first-page":"1747","DOI":"10.1007\/s00521-018-3809-2","volume":"31","author":"A Chatzipavlis","year":"2019","unstructured":"Chatzipavlis A, Tsekouras GE, Trygonis V, Velegrakis AF, Tsimikas J, Rigos A, Hasiotis T, Salmas C (2019) Modeling beach realignment using a neuro-fuzzy network optimized by a novel backtracking search algorithm. Neural Comput Appl 31(6):1747\u20131763","journal-title":"Neural Comput Appl"},{"issue":"8","key":"5029_CR41","doi-asserted-by":"crossref","first-page":"4157","DOI":"10.1007\/s00521-017-3329-5","volume":"31","author":"H Wang","year":"2019","unstructured":"Wang H, Hu Z, Sun Y, Su Q, Xia X (2019) A novel modified BSA inspired by species evolution rule and simulated annealing principle for constrained engineering optimization problems. Neural Comput Appl 31(8):4157\u20134184","journal-title":"Neural Comput Appl"},{"key":"5029_CR42","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.isatra.2019.01.042","volume":"91","author":"A Mehmood","year":"2019","unstructured":"Mehmood A et al (2019) Backtracking search optimization heuristics for nonlinear Hammerstein controlled auto regressive auto regressive systems. ISA Trans 91:99\u2013113","journal-title":"ISA Trans"},{"issue":"2","key":"5029_CR43","first-page":"39","volume":"7","author":"K Bhattacharjee","year":"2018","unstructured":"Bhattacharjee K (2018) Economic dispatch problems using backtracking search optimization. Int J Energy Optim Eng (IJEOE) 7(2):39\u201360","journal-title":"Int J Energy Optim Eng (IJEOE)"},{"key":"5029_CR44","doi-asserted-by":"publisher","first-page":"9046409","DOI":"10.1155\/2019\/9046409","volume":"2019","author":"F Zaman","year":"2019","unstructured":"Zaman F et al (2019) Backtracking search optimization paradigm for pattern correction of faulty antenna array in wireless mobile communications. Wirel Commun Mob Comput 2019:9046409. https:\/\/doi.org\/10.1155\/2019\/9046409","journal-title":"Wirel Commun Mob Comput"},{"key":"5029_CR45","doi-asserted-by":"crossref","first-page":"106977","DOI":"10.1016\/j.measurement.2019.106977","volume":"149","author":"MAZ Raja","year":"2020","unstructured":"Raja MAZ, Akhtar R, Chaudhary NI, Khan WU, Zhiyu Z, Jamil A, Zaman F (2020) Design of backtracking search optimization paradigm for joint amplitude-angle measurement of sources lying in fraunhofer zone. Measurement 149:106977","journal-title":"Measurement"},{"key":"5029_CR46","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.apenergy.2018.06.010","volume":"226","author":"K Yu","year":"2018","unstructured":"Yu K, Liang JJ, Qu BY, Cheng Z, Wang H (2018) Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models. Appl Energy 226:408\u2013422","journal-title":"Appl Energy"},{"key":"5029_CR47","doi-asserted-by":"crossref","first-page":"105705","DOI":"10.1016\/j.asoc.2019.105705","volume":"24","author":"A Mehmood","year":"2019","unstructured":"Mehmood A, Zameer A, Chaudhary NI, Raja MAZ (2019) Backtracking search heuristics for identification of electrical muscle stimulation models using Hammerstein structure. Appl Soft Comput 24:105705","journal-title":"Appl Soft Comput"},{"issue":"8","key":"5029_CR48","doi-asserted-by":"crossref","first-page":"3573","DOI":"10.1007\/s00521-017-3271-6","volume":"31","author":"I Kayri","year":"2019","unstructured":"Kayri I, Gencoglu MT (2019) Predicting power production from a photovoltaic panel through artificial neural networks using atmospheric indicators. Neural Comput Appl 31(8):3573\u20133586","journal-title":"Neural Comput Appl"},{"key":"5029_CR49","doi-asserted-by":"publisher","first-page":"8787","DOI":"10.1007\/s00521-019-04194-w","volume":"31","author":"SA El-Sattar","year":"2019","unstructured":"El-Sattar SA, Kamel S, El Sehiemy RA et al (2019) Single- and multi-objective optimal power flow frameworks using Jaya optimization technique. Neural Comput Appl 31:8787\u20138806. https:\/\/doi.org\/10.1007\/s00521-019-04194-w","journal-title":"Neural Comput Appl"},{"key":"5029_CR50","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04205-w","author":"MA Mohamed","year":"2019","unstructured":"Mohamed MA, Diab AAZ, Rezk H et al (2019) A novel adaptive model predictive controller for load frequency control of power systems integrated with DFIG wind turbines. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-019-04205-w","journal-title":"Neural Comput Appl"},{"issue":"7","key":"5029_CR51","doi-asserted-by":"crossref","first-page":"2243","DOI":"10.1007\/s00521-017-3185-3","volume":"31","author":"SP Mishra","year":"2019","unstructured":"Mishra SP, Dash PK (2019) Short-term prediction of wind power using a hybrid pseudo-inverse Legendre neural network and adaptive firefly algorithm. Neural Comput Appl 31(7):2243\u20132268","journal-title":"Neural Comput Appl"},{"issue":"7","key":"5029_CR52","doi-asserted-by":"crossref","first-page":"2635","DOI":"10.1007\/s00521-017-3219-x","volume":"31","author":"T Wang","year":"2019","unstructured":"Wang T, He X, Deng T (2019) Neural networks for power management optimal strategy in hybrid microgrid. Neural Comput Appl 31(7):2635\u20132647","journal-title":"Neural Comput Appl"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05029-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05029-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05029-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T23:40:39Z","timestamp":1621899639000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05029-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,25]]},"references-count":52,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,3]]}},"alternative-id":["5029"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05029-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,25]]},"assertion":[{"value":"4 October 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 May 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"All the authors of the manuscript declare that there is no potential conflict of interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}