{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T02:16:35Z","timestamp":1777601795178,"version":"3.51.4"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T00:00:00Z","timestamp":1715040000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T00:00:00Z","timestamp":1715040000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s00521-024-09855-z","type":"journal-article","created":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T07:01:46Z","timestamp":1715065306000},"page":"14449-14464","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Software effort estimation using convolutional neural network and fuzzy clustering"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0323-6452","authenticated-orcid":false,"given":"Mohammad","family":"Azzeh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abedalrhman","family":"Alkhateeb","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Bou Nassif","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,7]]},"reference":[{"key":"9855_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/J.SCICO.2021.102744","volume":"214","author":"Y Alqasrawi","year":"2022","unstructured":"Alqasrawi Y, Azzeh M, Elsheikh Y (2022) Locally weighted regression with different kernel smoothers for software effort estimation. Sci Comput Program 214:102744. https:\/\/doi.org\/10.1016\/J.SCICO.2021.102744","journal-title":"Sci Comput Program"},{"issue":"8","key":"9855_CR2","doi-asserted-by":"publisher","first-page":"3185","DOI":"10.1109\/TSE.2021.3083360","volume":"48","author":"V Tawosi","year":"2022","unstructured":"Tawosi V, Sarro F, Petrozziello A, Harman M (2022) Multi-objective software effort estimation: a replication study. IEEE Trans Softw Eng 48(8):3185\u20133205. https:\/\/doi.org\/10.1109\/TSE.2021.3083360","journal-title":"IEEE Trans Softw Eng"},{"issue":"5","key":"9855_CR3","doi-asserted-by":"publisher","first-page":"3153","DOI":"10.1007\/S10664-019-09686-W\/TABLES\/9","volume":"24","author":"LL Minku","year":"2019","unstructured":"Minku LL (2019) A novel online supervised hyperparameter tuning procedure applied to cross-company software effort estimation. Empir Softw Eng 24(5):3153\u20133204. https:\/\/doi.org\/10.1007\/S10664-019-09686-W\/TABLES\/9","journal-title":"Empir Softw Eng"},{"issue":"6","key":"9855_CR4","doi-asserted-by":"publisher","first-page":"1994","DOI":"10.1109\/TSE.2020.3047072","volume":"48","author":"T Xia","year":"2022","unstructured":"Xia T, Shu R, Shen X, Menzies T (2022) Sequential model optimization for software effort estimation. IEEE Trans Softw Eng 48(6):1994\u20132009. https:\/\/doi.org\/10.1109\/TSE.2020.3047072","journal-title":"IEEE Trans Softw Eng"},{"key":"9855_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/J.INFSOF.2020.106330","volume":"125","author":"P Phannachitta","year":"2020","unstructured":"Phannachitta P (2020) On an optimal analogy-based software effort estimation. Inf Softw Technol 125:106330. https:\/\/doi.org\/10.1016\/J.INFSOF.2020.106330","journal-title":"Inf Softw Technol"},{"key":"9855_CR6","doi-asserted-by":"publisher","DOI":"10.1002\/smr.1882","author":"M Azzeh","year":"2017","unstructured":"Azzeh M, Nassif AB (2017) Analyzing the relationship between project productivity and environment factors in the use case points method. J Softw: Evolut Process. https:\/\/doi.org\/10.1002\/smr.1882","journal-title":"J Softw: Evolut Process"},{"issue":"4","key":"9855_CR7","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1007\/s10515-012-0108-5","volume":"20","author":"J Keung","year":"2013","unstructured":"Keung J, Kocaguneli E, Menzies T (2013) Finding conclusion stability for selecting the best effort predictor in software effort estimation. Autom Softw Eng 20(4):543\u2013567. https:\/\/doi.org\/10.1007\/s10515-012-0108-5","journal-title":"Autom Softw Eng"},{"issue":"6","key":"9855_CR8","doi-asserted-by":"publisher","first-page":"1403","DOI":"10.1109\/TSE.2011.111","volume":"38","author":"E Kocaguneli","year":"2012","unstructured":"Kocaguneli E, Menzies T, Keung JW (2012) On the value of ensemble effort estimation. IEEE Trans Software Eng 38(6):1403\u20131416. https:\/\/doi.org\/10.1109\/TSE.2011.111","journal-title":"IEEE Trans Software Eng"},{"key":"9855_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/S10586-023-03979-Y\/METRICS","author":"S Kassaymeh","year":"2023","unstructured":"Kassaymeh S, Alweshah M, Al-Betar MA, Hammouri AI, M. A. Al-Ma\u2019aitah, (2023) Software effort estimation modeling and fully connected artificial neural network optimization using soft computing techniques. Cluster Comput. https:\/\/doi.org\/10.1007\/S10586-023-03979-Y\/METRICS","journal-title":"Cluster Comput"},{"key":"9855_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/S10586-023-03967-2\/METRICS","author":"N Pal","year":"2023","unstructured":"Pal N, Yadav MP, Yadav DK (2023) Appropriate number of analogues in analogy based software effort estimation using quality datasets. Cluster Comput. https:\/\/doi.org\/10.1007\/S10586-023-03967-2\/METRICS","journal-title":"Cluster Comput"},{"issue":"2","key":"9855_CR11","first-page":"84","volume":"7","author":"RP Pantoni","year":"2008","unstructured":"Pantoni RP, Mossin EA, Brand\u00e3o D (2008) Task effort fuzzy estimator for software development. INFOCOMP J Comput Sci 7(2):84\u201389","journal-title":"INFOCOMP J Comput Sci"},{"key":"9855_CR12","doi-asserted-by":"publisher","unstructured":"Minku LL (2021) Multi-stream online transfer learning for software effort estimation: Is it necessary?. In: Proceedings of the 17th international conference on predictive models and data analytics in software engineeringhttps:\/\/doi.org\/10.1145\/3475960.3475988.","DOI":"10.1145\/3475960.3475988"},{"key":"9855_CR13","doi-asserted-by":"publisher","unstructured":"Azzeh M, Nassif AB (2013) Fuzzy Model Tree for early effort estimation. In: 2013 12th international conference on machine learning and applications. https:\/\/doi.org\/10.1109\/ICMLA.2013.115.","DOI":"10.1109\/ICMLA.2013.115"},{"issue":"8","key":"9855_CR14","doi-asserted-by":"publisher","first-page":"1040","DOI":"10.1109\/TSE.2012.88","volume":"39","author":"E Kocaguneli","year":"2013","unstructured":"Kocaguneli E, Menzies T, Keung J, Cok D, Madachy R (2013) Active Learning and effort estimation: finding the essential content of software effort estimation data. IEEE Trans Softw Eng 39(8):1040\u20131053. https:\/\/doi.org\/10.1109\/TSE.2012.88","journal-title":"IEEE Trans Softw Eng"},{"key":"9855_CR15","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/s12065-020-00360-5","volume":"14","author":"KE Rao","year":"2020","unstructured":"Rao KE, Rao GA, Rao E (2020) \u201cRetraction Note: ensemble learning with recursive feature elimination integrated software effort estimation: a novel approach. Evolut Intell 14:151\u2013162. https:\/\/doi.org\/10.1007\/s12065-020-00360-5","journal-title":"Evolut Intell"},{"issue":"2","key":"9855_CR16","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1109\/TSE.2011.27","volume":"38","author":"E Kocaguneli","year":"2012","unstructured":"Kocaguneli E, Menzies T, Bener AB, Keung JW (2012) Exploiting the essential assumptions of analogy-based effort estimation. IEEE Trans Software Eng 38(2):425\u2013438. https:\/\/doi.org\/10.1109\/TSE.2011.27","journal-title":"IEEE Trans Software Eng"},{"key":"9855_CR17","doi-asserted-by":"publisher","unstructured":"de Morais RA (2021) Deep learning based models for software effort estimation using story points in agile environments. https:\/\/doi.org\/10.7939\/R3-JCF5-8X08","DOI":"10.7939\/R3-JCF5-8X08"},{"key":"9855_CR18","unstructured":"Phan H, Jannesari A (2022) Story point effort estimation by text level graph neural network. Accessed 31 Mar 2023. [Online]. Available: https:\/\/arxiv.org\/abs\/2203.03062v2"},{"issue":"1","key":"9855_CR19","doi-asserted-by":"publisher","first-page":"230104","DOI":"10.37190\/e-Inf230104","volume":"17","author":"H Kassem","year":"2023","unstructured":"Kassem H, Mahar K, Saad AA (2023) Story point estimation using issue reports with deep attention neural network. e-Inf Softw Eng J 17(1):230104. https:\/\/doi.org\/10.37190\/e-Inf230104","journal-title":"e-Inf Softw Eng J"},{"key":"9855_CR20","doi-asserted-by":"publisher","unstructured":"Marapelli B, Carie A, Islam SM ((2020) RNN-CNN model: A bi-directional long short-term memory deep learning network for story point estimation. In: CITISIA 2020 5th international conference on innovative technologies in intelligent systems and industrial applications. https:\/\/doi.org\/10.1109\/CITISIA50690.2020.9371770.","DOI":"10.1109\/CITISIA50690.2020.9371770"},{"issue":"7","key":"9855_CR21","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1109\/TSE.2018.2792473","volume":"45","author":"M Choetkiertikul","year":"2019","unstructured":"Choetkiertikul M, Dam HK, Tran T, Pham T, Ghose A, Menzies T (2019) A deep learning model for estimating story points. IEEE Trans Softw Eng 45(7):637\u2013656. https:\/\/doi.org\/10.1109\/TSE.2018.2792473","journal-title":"IEEE Trans Softw Eng"},{"issue":"2","key":"9855_CR22","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/S0165-0114(97)00077-8","volume":"90","author":"LA Zadeh","year":"1997","unstructured":"Zadeh LA (1997) Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst 90(2):111\u2013127. https:\/\/doi.org\/10.1016\/S0165-0114(97)00077-8","journal-title":"Fuzzy Sets Syst"},{"issue":"2\u20133","key":"9855_CR23","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/0098-3004(84)90020-7","volume":"10","author":"JC Bezdek","year":"1984","unstructured":"Bezdek JC, Ehrlich R, Full W (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10(2\u20133):191\u2013203. https:\/\/doi.org\/10.1016\/0098-3004(84)90020-7","journal-title":"Comput Geosci"},{"issue":"1","key":"9855_CR24","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1109\/TVCG.2016.2598831","volume":"23","author":"M Liu","year":"2017","unstructured":"Liu M, Shi J, Li Z, Li C, Zhu J, Liu S (2017) Towards better analysis of deep convolutional neural networks. IEEE Trans Vis Comput Graph 23(1):91\u2013100. https:\/\/doi.org\/10.1109\/TVCG.2016.2598831","journal-title":"IEEE Trans Vis Comput Graph"},{"issue":"8","key":"9855_CR25","doi-asserted-by":"publisher","first-page":"5455","DOI":"10.1007\/s10462-020-09825-6","volume":"53","author":"A Khan","year":"2020","unstructured":"Khan A, Sohail A, Zahoora U, Qureshi AS (2020) A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev 53(8):5455\u20135516. https:\/\/doi.org\/10.1007\/s10462-020-09825-6","journal-title":"Artif Intell Rev"},{"key":"9855_CR26","doi-asserted-by":"publisher","first-page":"100288","DOI":"10.1016\/j.cosrev.2020.100288","volume":"38","author":"PS Kumar","year":"2020","unstructured":"Kumar PS, Behera HS, Kumari A, Nayak J, Naik B (2020) Advancement from neural networks to deep learning in software effort estimation: perspective of two decades. Comput Sci Rev 38:100288","journal-title":"Comput Sci Rev"},{"issue":"1","key":"9855_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10664-011-9189-1","volume":"18","author":"E Kocaguneli","year":"2013","unstructured":"Kocaguneli E, Menzies T, Keung JW (2013) Kernel methods for software effort estimation: effects of different kernel functions and bandwidths on estimation accuracy. Empir Softw Eng 18(1):1\u201324. https:\/\/doi.org\/10.1007\/s10664-011-9189-1","journal-title":"Empir Softw Eng"},{"key":"9855_CR28","doi-asserted-by":"publisher","DOI":"10.1049\/iet-sen.2013.0165","author":"M Azzeh","year":"2015","unstructured":"Azzeh M, Nassi AB (2015) Analogy-based effort estimation: a new method to discover set of analogies from dataset characteristics. IET Softw. https:\/\/doi.org\/10.1049\/iet-sen.2013.0165","journal-title":"IET Softw"},{"key":"9855_CR29","doi-asserted-by":"publisher","first-page":"110904","DOI":"10.1016\/J.JSS.2021.110904","volume":"175","author":"JTH Jose Thiago","year":"2021","unstructured":"Jose Thiago JTH, Oliveira ALI (2021) Ensemble effort estimation using dynamic selection. J Syst Softw 175:110904. https:\/\/doi.org\/10.1016\/J.JSS.2021.110904","journal-title":"J Syst Softw"},{"key":"9855_CR30","doi-asserted-by":"publisher","unstructured":"Nassif AB, Azzeh M, Capretz LF, Ho D (2013) A comparison between decision trees and decision tree forest models for software development effort estimation. In: 2013 Third international conference on communications and information technology (ICCIT). https:\/\/doi.org\/10.1109\/ICCITechnology.2013.6579553.","DOI":"10.1109\/ICCITechnology.2013.6579553"},{"issue":"sup2","key":"9855_CR31","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1080\/24699322.2019.1649074","volume":"24","author":"J Zhang","year":"2019","unstructured":"Zhang J, Chen L (2019) Clustering-based undersampling with random over sampling examples and support vector machine for imbalanced classification of breast cancer diagnosis. Comput Assist Surg 24(sup2):62\u201372. https:\/\/doi.org\/10.1080\/24699322.2019.1649074","journal-title":"Comput Assist Surg"},{"key":"9855_CR32","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1016\/j.asoc.2016.05.008","volume":"49","author":"M Azzeh","year":"2016","unstructured":"Azzeh M, Nassif AB (2016) A hybrid model for estimating software project effort from use case points. Appl Soft Comput J 49:981\u2013989. https:\/\/doi.org\/10.1016\/j.asoc.2016.05.008","journal-title":"Appl Soft Comput J"},{"key":"9855_CR33","doi-asserted-by":"crossref","unstructured":"Sarno R, Sidabutar J (2015) Improving the accuracy of COCOMO's effort estimation based on neural networks and fuzzy logic model. In: 2015 International Conference on Information & Communication Technology and Systems (ICTS) Accessed 07 Sep 2023. [Online]. Available: https:\/\/ieeexplore.ieee.org\/abstract\/document\/7379898\/","DOI":"10.1109\/ICTS.2015.7379898"},{"issue":"11","key":"9855_CR34","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1109\/TSE.2006.114","volume":"32","author":"T Menzies","year":"2006","unstructured":"Menzies T, Chen Z, Hihn J, Lum K (2006) Selecting best practices for effort estimation. IEEE Trans Softw Eng 32(11):883\u2013895. https:\/\/doi.org\/10.1109\/TSE.2006.114","journal-title":"IEEE Trans Softw Eng"},{"key":"9855_CR35","doi-asserted-by":"publisher","DOI":"10.1002\/smr.2110","author":"M Azzeh","year":"2018","unstructured":"Azzeh M, Nassif AB (2018) Project productivity evaluation in early software effort estimation. J Softw: Evolut Process. https:\/\/doi.org\/10.1002\/smr.2110","journal-title":"J Softw: Evolut Process"},{"issue":"3","key":"9855_CR36","doi-asserted-by":"publisher","first-page":"1197","DOI":"10.1007\/S00500-021-06564-W\/FIGURES\/9","volume":"26","author":"A Kaushik","year":"2022","unstructured":"Kaushik A, Kaur P, Choudhary N (2022) Stacking regularization in analogy-based software effort estimation. Soft comput 26(3):1197\u20131216. https:\/\/doi.org\/10.1007\/S00500-021-06564-W\/FIGURES\/9","journal-title":"Soft comput"},{"key":"9855_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2019.110448","volume":"159","author":"M J\u00f8rgensen","year":"2020","unstructured":"J\u00f8rgensen M, Halkjelsvik T (2020) Sequence effects in the estimation of software development effort. J Syst Softw 159:110448. https:\/\/doi.org\/10.1016\/j.jss.2019.110448","journal-title":"J Syst Softw"},{"key":"9855_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2015.01.028","author":"M Azzeh","year":"2015","unstructured":"Azzeh M, Nassif AB, Minku LL (2015) An empirical evaluation of ensemble adjustment methods for analogy-based effort estimation. J Syst Softw. https:\/\/doi.org\/10.1016\/j.jss.2015.01.028","journal-title":"J Syst Softw"},{"issue":"8","key":"9855_CR39","doi-asserted-by":"publisher","first-page":"1512","DOI":"10.1016\/j.infsof.2012.09.012","volume":"55","author":"LL Minku","year":"2013","unstructured":"Minku LL, Yao X (2013) Ensembles and locality: insight on improving software effort estimation. Inf Softw Technol 55(8):1512\u20131528. https:\/\/doi.org\/10.1016\/j.infsof.2012.09.012","journal-title":"Inf Softw Technol"},{"issue":"3","key":"9855_CR40","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1007\/s11390-007-9043-5","volume":"22","author":"JJC Gallego","year":"2007","unstructured":"Gallego JJC, Rodr\u00edguez D, Sicilia M\u00c1, Rubio MG, Crespo AG (2007) Software project effort estimation based on multiple parametric models generated through data clustering. J Comput Sci Technol 22(3):371\u2013378. https:\/\/doi.org\/10.1007\/s11390-007-9043-5","journal-title":"J Comput Sci Technol"},{"issue":"2","key":"9855_CR41","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1007\/s11219-021-09547-0","volume":"29","author":"M Azzeh","year":"2021","unstructured":"Azzeh M, Nassif AB, Mart\u00edn CL (2021) Empirical analysis on productivity prediction and locality for use case points method. Softw Qual J 29(2):309\u2013336. https:\/\/doi.org\/10.1007\/s11219-021-09547-0","journal-title":"Softw Qual J"},{"issue":"10","key":"9855_CR42","doi-asserted-by":"publisher","first-page":"73","DOI":"10.14257\/ijhit.2015.8.10.07","volume":"8","author":"P Kaur","year":"2015","unstructured":"Kaur P, Singh R (2015) A proposed framework for software effort estimation using the combinational approach of fuzzy logic and neural networks. Int J Hybrid Inf Technol 8(10):73\u201380. https:\/\/doi.org\/10.14257\/ijhit.2015.8.10.07","journal-title":"Int J Hybrid Inf Technol"},{"key":"9855_CR43","doi-asserted-by":"publisher","DOI":"10.1002\/smr.2324","author":"FA Amazal","year":"2020","unstructured":"Amazal FA, Idri A (2020) Estimating software development effort using fuzzy clustering-based analogy. J Softw: Evolut Process. https:\/\/doi.org\/10.1002\/smr.2324","journal-title":"J Softw: Evolut Process"},{"key":"9855_CR44","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/8367214","author":"AB Nassif","year":"2019","unstructured":"Nassif AB, Azzeh M, Idri A, Abran A (2019) Software development effort estimation using regression fuzzy models. Comput Intell Neurosci. https:\/\/doi.org\/10.1155\/2019\/8367214","journal-title":"Comput Intell Neurosci"},{"key":"9855_CR45","unstructured":"Azzeh M, Neagu D, Cowling P (2008) Adjusting analogy software effort estimation based on fuzzy logic. In: ICSOFT 2008-Proceedings of the 3rd international conference on software and data technologies"},{"issue":"08","key":"9855_CR46","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1109\/34.85677","volume":"13","author":"XL Xie","year":"1991","unstructured":"Xie XL, Beni G (1991) A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 13(08):841\u2013847. https:\/\/doi.org\/10.1109\/34.85677","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9855_CR47","unstructured":"Menzies B, Caglayan B, Kocaguneli E, Krall J, Peters F. The PROMISE repository of empirical software engineering. PROMISE. https:\/\/scholar.google.com\/scholar?hl=en&as_sdt=0%2C5&q=The+PROMISE+Repository+of+empirical+software+engineering+data+&btnG= (Accessed 27 Dec 2020)"},{"key":"9855_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/J.SCICO.2022.102819","volume":"219","author":"M Azzeh","year":"2022","unstructured":"Azzeh M, Nassif AB, Elsheikh Y, Angelis L (2022) On the value of project productivity for early effort estimation. Sci Comput Program 219:102819. https:\/\/doi.org\/10.1016\/J.SCICO.2022.102819","journal-title":"Sci Comput Program"},{"issue":"7","key":"9855_CR49","doi-asserted-by":"publisher","first-page":"1879","DOI":"10.1016\/j.jss.2013.02.053","volume":"86","author":"E Kocaguneli","year":"2013","unstructured":"Kocaguneli E, Menzies T (2013) Software effort models should be assessed via leave-one-out validation. J Syst Softw 86(7):1879\u20131890. https:\/\/doi.org\/10.1016\/j.jss.2013.02.053","journal-title":"J Syst Softw"}],"updated-by":[{"DOI":"10.1007\/s00521-024-10906-8","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2024,12,27]],"date-time":"2024-12-27T00:00:00Z","timestamp":1735257600000}}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09855-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09855-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09855-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,27]],"date-time":"2024-12-27T17:02:55Z","timestamp":1735318975000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09855-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,7]]},"references-count":49,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["9855"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09855-z","relation":{"correction":[{"id-type":"doi","id":"10.1007\/s00521-024-10906-8","asserted-by":"object"}]},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,7]]},"assertion":[{"value":"3 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 December 2024","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s00521-024-10906-8","URL":"https:\/\/doi.org\/10.1007\/s00521-024-10906-8","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known conflict interest or personal relationships that could have appeared to influence the work reported in this paper.\u201d","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}