{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,12]],"date-time":"2025-07-12T01:23:52Z","timestamp":1752283432219,"version":"3.37.3"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"28","license":[{"start":{"date-parts":[[2023,8,3]],"date-time":"2023-08-03T00:00:00Z","timestamp":1691020800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,3]],"date-time":"2023-08-03T00:00:00Z","timestamp":1691020800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003141","name":"Consejo Nacional de Ciencia y Tecnolog\u00eda","doi-asserted-by":"publisher","award":["\u201cIDENTIFICATION OF AGGRESSIVE AND OFFENSIVE TEXT THROUGH SPECIALIZED BERT\u2019S ENSEMBLES\u201d"],"award-info":[{"award-number":["\u201cIDENTIFICATION OF AGGRESSIVE AND OFFENSIVE TEXT THROUGH SPECIALIZED BERT\u2019S ENSEMBLES\u201d"]}],"id":[{"id":"10.13039\/501100003141","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003141","name":"Consejo Nacional de Ciencia y Tecnolog\u00eda","doi-asserted-by":"crossref","award":["#300832"],"award-info":[{"award-number":["#300832"]}],"id":[{"id":"10.13039\/501100003141","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003140","name":"Consejo de Ciencia y Tecnolog\u00eda del Estado de Tabasco","doi-asserted-by":"publisher","award":["BP-FP- 20201015143044227-814705","ID. 11989, No. 1311","No. 617, Conv. 2020-01, ID. 314967"],"award-info":[{"award-number":["BP-FP- 20201015143044227-814705","ID. 11989, No. 1311","No. 617, Conv. 2020-01, ID. 314967"]}],"id":[{"id":"10.13039\/501100003140","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s00521-023-08632-8","type":"journal-article","created":{"date-parts":[[2023,8,3]],"date-time":"2023-08-03T13:01:41Z","timestamp":1691067701000},"page":"21129-21164","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Curriculum learning and evolutionary optimization into deep learning for text classification"],"prefix":"10.1007","volume":"35","author":[{"given":"Alfredo Arturo","family":"El\u00edas-Miranda","sequence":"first","affiliation":[]},{"given":"Daniel","family":"Vallejo-Aldana","sequence":"additional","affiliation":[]},{"given":"Fernando","family":"S\u00e1nchez-Vega","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1018-4221","authenticated-orcid":false,"given":"A. Pastor","family":"L\u00f3pez-Monroy","sequence":"additional","affiliation":[]},{"given":"Alejandro","family":"Rosales-P\u00e9rez","sequence":"additional","affiliation":[]},{"given":"Victor","family":"Mu\u00f1iz-Sanchez","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,3]]},"reference":[{"key":"8632_CR1","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511975752","volume-title":"Impoliteness: using language to cause offence","author":"Culpeper Jonathan","year":"2011","unstructured":"Jonathan Culpeper (2011) Impoliteness: using language to cause offence, vol 28. Cambridge University Press, Cambridge"},{"key":"8632_CR2","doi-asserted-by":"crossref","unstructured":"Losada David\u00a0E, Crestani F, Parapar J (2018) Overview of erisk 2018: early risk prediction on the internet (extended lab overview). In: Proceedings of the 9th international conference of the CLEF association, CLEF, pp 1\u201320","DOI":"10.1007\/978-3-319-98932-7_30"},{"key":"8632_CR3","doi-asserted-by":"crossref","unstructured":"Wolak J, Finkelhor D, Mitchell KJ, Ybarra ML (2010) Online \u201cpredators\u201d and their victims: myths, realities, and implications for prevention and treatment","DOI":"10.1037\/2152-0828.1.S.13"},{"key":"8632_CR4","doi-asserted-by":"crossref","unstructured":"Tu Z, Liu Y, Shang L, Liu X, Li H (2017) Neural machine translation with reconstruction. In: Thirty-first AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v31i1.10950"},{"key":"8632_CR5","doi-asserted-by":"crossref","unstructured":"Dong L, Xu S, Xu B (2018) Speech-transformer: a no-recurrence sequence-to-sequence model for speech recognition. In: 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 5884\u20135888","DOI":"10.1109\/ICASSP.2018.8462506"},{"key":"8632_CR6","unstructured":"Jocher G, Stoken A, Borovec J (2020) NanoCode012, ChristopherSTAN, Liu Changyu, Laughing, tkianai, Adam Hogan, lorenzomammana, yxNONG, AlexWang1900, Laurentiu Diaconu, Marc, wanghaoyang0106, ml5ah, Doug, Francisco Ingham, Frederik, Guilhen, Hatovix, Jake Poznanski, Jiacong Fang, Lijun Yu, changyu98, Mingyu Wang, Naman Gupta, Osama Akhtar, PetrDvoracek, and Prashant Rai. ultralytics\/yolov5: v3.1\u2014bug fixes and performance improvements"},{"key":"8632_CR7","first-page":"05","volume":"25","author":"L\u00f3pez-Monroy Adri\u00e1n","year":"2021","unstructured":"Adri\u00e1n L\u00f3pez-Monroy, Alfredo Miranda, Daniel Aldana, Juan Carmona, Humberto Espinosa (2021) Deep learning for language and vision tasks in surveillance applications. Computaci\u00f3n y Sistemas 25:05","journal-title":"Computaci\u00f3n y Sistemas"},{"issue":"02","key":"8632_CR8","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1142\/S0218488598000094","volume":"6","author":"Hochreiter Sepp","year":"1998","unstructured":"Sepp Hochreiter (1998) The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int J Uncertain Fuzziness Knowl-Based Syst 6(02):107\u2013116","journal-title":"Int J Uncertain Fuzziness Knowl-Based Syst"},{"issue":"1","key":"8632_CR9","first-page":"1929","volume":"15","author":"Srivastava Nitish","year":"2014","unstructured":"Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"8632_CR10","unstructured":"Larochelle H, Bengio Y, Louradour J, Lamblin P (2009) Exploring strategies for training deep neural networks. J Mach Learn Res 10(1):1\u201340. https:\/\/dl.acm.org\/doi\/10.5555\/1577069.1577070"},{"key":"8632_CR11","doi-asserted-by":"publisher","unstructured":"Bengio Y, Louradour J, Collobert R, Weston J (2009) Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, Montreal, Quebec, Canada, 41\u201348. https:\/\/doi.org\/10.1145\/1553374.1553380","DOI":"10.1145\/1553374.1553380"},{"key":"8632_CR12","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers), Minneapolis, Minnesota. Association for Computational Linguistics, pp 4171\u20134186"},{"issue":"1","key":"8632_CR13","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/0010-0277(93)90058-4","volume":"48","author":"L Elman Jeffrey","year":"1993","unstructured":"Elman Jeffrey L (1993) Learning and development in neural networks: the importance of starting small. Cognition 48(1):71\u201399","journal-title":"Cognition"},{"issue":"1","key":"8632_CR14","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1137\/1015003","volume":"15","author":"Wasserstrom Eliahu","year":"1973","unstructured":"Eliahu Wasserstrom (1973) Numerical solutions by the continuation method. SIAM Rev 15(1):89\u2013119","journal-title":"SIAM Rev"},{"key":"8632_CR15","unstructured":"Spitkovsky VI, Alshawi H, Jurafsky D (2010) From baby steps to leapfrog: How \u201cless is more\u201d in unsupervised dependency parsing. Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, Los Angeles, California, USA, 751\u2013759. https:\/\/aclanthology.org\/N10-1116"},{"key":"8632_CR16","doi-asserted-by":"crossref","unstructured":"Klein D, Manning CD (2004) Corpus-based induction of syntactic structure: Models of dependency and constituency. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, Barcelona, Spain, pp. 478\u2013485. https:\/\/aclanthology.org\/P04-1061","DOI":"10.3115\/1218955.1219016"},{"key":"8632_CR17","first-page":"1189","volume":"23","author":"M Kumar","year":"2010","unstructured":"Kumar M, Benjamin Packer, Daphne Koller (2010) Self-paced learning for latent variable models. Adv Neural Inf Process Syst 23:1189\u20131197","journal-title":"Adv Neural Inf Process Syst"},{"key":"8632_CR18","doi-asserted-by":"crossref","unstructured":"Soviany P, Ionescu RT, Rota P, Sebe N (2021) Curriculum learning: a survey. arXiv:2101.10382","DOI":"10.1007\/s11263-022-01611-x"},{"key":"8632_CR19","doi-asserted-by":"crossref","unstructured":"Zhang D, Meng D, Li C,\u00a0Jiang L, Zhao Q, Han J (2015) A self-paced multiple-instance learning framework for co-saliency detection. In: Proceedings of the IEEE international conference on computer vision (ICCV)","DOI":"10.1109\/ICCV.2015.75"},{"issue":"8","key":"8632_CR20","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter, J\u00fcrgen Schmidhuber (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"8632_CR21","unstructured":"Cirik V, Hovy E, Morency L-P (2016) Visualizing and understanding curriculum learning for long short-term memory networks. arXiv:1611.06204"},{"key":"8632_CR22","doi-asserted-by":"crossref","unstructured":"Robbins H, Monro S (1951) A stochastic approximation method. Ann Math Stat 22(3):400\u2013407","DOI":"10.1214\/aoms\/1177729586"},{"key":"8632_CR23","unstructured":"Duchi J, Hazan E, Singer  Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12(7):2121\u20132159. https:\/\/dl.acm.org\/doi\/10.5555\/1953048.2021068"},{"key":"8632_CR24","unstructured":"Hinton G, Srivastava N,\u00a0Swersky K,\u00a0Tieleman T,\u00a0Mohamed A (2012) Coursera: neural networks for machine learning. Lecture 9c: using noise as a regularizer"},{"key":"8632_CR25","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980"},{"key":"8632_CR26","doi-asserted-by":"crossref","unstructured":"Aly A, Guadagni G, Dugan JB (2019) Derivative-free optimization of neural networks using local search. In: 2019 IEEE 10th annual ubiquitous computing, electronics mobile communication conference (UEMCON), pp 0293\u20130299","DOI":"10.1109\/UEMCON47517.2019.8993007"},{"key":"8632_CR27","unstructured":"Davis L (1991) Handbook of genetic algorithms Chapman & Hall, London, Thomson Publishing Group; First Ed., January 1"},{"key":"8632_CR28","doi-asserted-by":"crossref","unstructured":"Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: MHS\u201995. Proceedings of the sixth international symposium on micro machine and human science. IEEE, pp 39\u201343","DOI":"10.1109\/MHS.1995.494215"},{"issue":"2","key":"8632_CR29","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1109\/TEVC.2003.819944","volume":"8","author":"Ong Yew Soon","year":"2004","unstructured":"Soon Ong Yew, Keane Andy J (2004) Meta-lamarckian learning in memetic algorithms. IEEE Trans Evol Comput 8(2):99\u2013110","journal-title":"IEEE Trans Evol Comput"},{"issue":"1","key":"8632_CR30","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1109\/59.651620","volume":"13","author":"Y Lee Kwang","year":"1998","unstructured":"Lee Kwang Y, Yang Frank F (1998) Optimal reactive power planning using evolutionary algorithms: a comparative study for evolutionary programming, evolutionary strategy, genetic algorithm, and linear programming. IEEE Trans Power Syst 13(1):101\u2013108","journal-title":"IEEE Trans Power Syst"},{"issue":"3","key":"8632_CR31","doi-asserted-by":"publisher","first-page":"889","DOI":"10.1137\/S1052623400378742","volume":"13","author":"Audet Charles","year":"2002","unstructured":"Charles Audet, Dennis Jr John E (2002) Analysis of generalized pattern searches. SIAM J Optim 13(3):889\u2013903","journal-title":"SIAM J Optim"},{"issue":"1","key":"8632_CR32","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/s11590-008-0089-2","volume":"3","author":"A Abramson Mark","year":"2009","unstructured":"Abramson Mark A, Charles Audet, Chrissis James W, Walston Jennifer G (2009) Mesh adaptive direct search algorithms for mixed variable optimization. Optim Lett 3(1):35\u201347","journal-title":"Optim Lett"},{"key":"8632_CR33","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/874\/1\/012062","volume":"874","author":"N Neveu","year":"2017","unstructured":"Neveu N, Larson J, Power JG, Spentzouris L (2017) Photoinjector optimization using a derivative-free, model-based trust-region algorithm for the argonne wakefield accelerator. J Phys Conf Ser 874:012062","journal-title":"J Phys Conf Ser"},{"issue":"2","key":"8632_CR34","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1162\/106365601750190398","volume":"9","author":"Hansen Nikolaus","year":"2001","unstructured":"Nikolaus Hansen, Andreas Ostermeier (2001) Completely derandomized self-adaptation in evolution strategies. Evol Comput 9(2):159\u2013195","journal-title":"Evol Comput"},{"key":"8632_CR35","unstructured":"Loshchilov I, Hutter F (2016) Cma-es for hyperparameter optimization of deep neural networks. arXiv:1604.07269"},{"key":"8632_CR36","doi-asserted-by":"crossref","unstructured":"Heidrich-Meisner V, Igel C (2008) Evolution strategies for direct policy search. In: International conference on parallel problem solving from nature. Springer, pp 428\u2013437","DOI":"10.1007\/978-3-540-87700-4_43"},{"key":"8632_CR37","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.swevo.2019.05.010","volume":"49","author":"Junior Francisco Erivaldo Fernandes","year":"2019","unstructured":"Fernandes Junior Francisco Erivaldo, Yen Gary G (2019) Particle swarm optimization of deep neural networks architectures for image classification. Swarm Evol Comput 49:62\u201374","journal-title":"Swarm Evol Comput"},{"key":"8632_CR38","doi-asserted-by":"publisher","first-page":"18895","DOI":"10.1109\/ACCESS.2017.2752901","volume":"5","author":"Sheng Weiguo","year":"2017","unstructured":"Weiguo Sheng, Pengxiao Shan, Jiafa Mao, Yujun Zheng, Shengyong Chen, Zidong Wang (2017) An adaptive memetic algorithm with rank-based mutation for artificial neural network architecture optimization. IEEE Access 5:18895\u201318908","journal-title":"IEEE Access"},{"key":"8632_CR39","unstructured":"Such FP, Madhavan V, Conti E, Lehman J, Stanley KO, Clune J (2017) Deep neuroevolution: Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning. arXiv:1712.06567"},{"key":"8632_CR40","unstructured":"Michel P, Hashimoto T, Neubig G (2021) Modeling the second player in distributionally robust optimization. arXiv:2103.10282"},{"key":"8632_CR41","doi-asserted-by":"crossref","unstructured":"Dablain D, Krawczyk B, Chawla NV (2021) Deepsmote: fusing deep learning and smote for imbalanced data. arXiv:2105.02340","DOI":"10.1109\/TNNLS.2021.3136503"},{"issue":"4","key":"8632_CR42","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1109\/TEVC.2016.2641477","volume":"21","author":"Segura Carlos","year":"2016","unstructured":"Carlos Segura, Arturo Hern\u00e1ndez-Aguirre, Francisco Luna, Enrique Alba (2016) Improving diversity in evolutionary algorithms: new best solutions for frequency assignment. IEEE Trans Evol Comput 21(4):539\u2013553","journal-title":"IEEE Trans Evol Comput"},{"key":"8632_CR43","doi-asserted-by":"crossref","unstructured":"Tai KS, Socher R, Manning CD (2015) Improved semantic representations from tree-structured long short-term memory networks. arXiv:1503.00075","DOI":"10.3115\/v1\/P15-1150"},{"issue":"3","key":"8632_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3052770","volume":"35","author":"Huang Minlie","year":"2017","unstructured":"Minlie Huang, Qiao Qian, Xiaoyan Zhu (2017) Encoding syntactic knowledge in neural networks for sentiment classification. ACM Trans Inf Syst (TOIS) 35(3):1\u201327","journal-title":"ACM Trans Inf Syst (TOIS)"},{"issue":"4","key":"8632_CR45","first-page":"377","volume":"3","author":"M Zulqarnain","year":"2019","unstructured":"Zulqarnain M, Ghazali R, Ghouse MG, Mushtaq MF (2019) Efficient processing of gru based on word embedding for text classification. JOIV Int J Inform Visual 3(4):377\u2013383","journal-title":"JOIV Int J Inform Visual"},{"key":"8632_CR46","unstructured":"Lin Z, Feng M, dos Nogueira SC,\u00a0Yu M, Xiang B, Zhou B, Bengio Y (2017) A structured self-attentive sentence embedding. arXiv:1703.03130"},{"key":"8632_CR47","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning C (2014) GloVe: Global vectors for word representation. In: Proceedings of the 2014 conference on Empirical Methods in Natural Language Processing (EMNLP), 1532\u20131543, Doha, Qatar. Association for Computational Linguistics","DOI":"10.3115\/v1\/D14-1162"},{"key":"8632_CR48","unstructured":"G\u00f3mez-Adorno H, Reyes-Maga\u00f1a J, Bel-Enguix G, Sierra G (2019) Spanish word embeddings learned on word association norms. In: Proceedings of the 13th Alberto Mendelzon International Workshop on Foundations of Data Management, Asuncion, Paraguay, June 3-7, 2019, CEUR Workshop Proceedings, Vol. 2369, 2019, https:\/\/ceur-ws.org\/Vol-2369\/paper03.pdf"},{"key":"8632_CR49","unstructured":"Arag\u00f3n ME, Jarqu\u00edn-V\u00e1squez HJ, Montes-Y-G\u00f3mez M, Escalante HJ, Pineda LV, G\u00f3mez-Adorno H, Posadas-Dur\u00e1n JP, Bel-Enguix G (2020) Overview of mex-a3t at iberlef 2020: fake news and aggressiveness analysis in Mexican Spanish. In: IberLEF@ SEPLN, pp 222\u2013235"},{"key":"8632_CR50","unstructured":"Inches G, Crestani F (2012) Overview of the international sexual predator identification competition at pan-2012. In: CLEF (Online working notes\/labs\/workshop), volume\u00a030"},{"key":"8632_CR51","doi-asserted-by":"crossref","unstructured":"Pang B, Lee L (2005) Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. arXiv preprint cs\/0506075","DOI":"10.3115\/1219840.1219855"},{"key":"8632_CR52","doi-asserted-by":"crossref","unstructured":"Zhang T, Huang M,\u00a0Zhao L (2018) Learning structured representation for text classification via reinforcement learning. In: Thirty-second AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.12047"},{"key":"8632_CR53","unstructured":"Guzman-Silverio M, Balderas-Paredes \u00c1, L\u00f3pez-Monroy AP (2020) Transformers and data augmentation for aggressiveness detection in Mexican Spanish. In: IberLEF@ SEPLN, pp 293\u2013302"},{"key":"8632_CR54","first-page":"7659","volume":"34","author":"Dev Sunipa","year":"2020","unstructured":"Sunipa Dev, Tao Li, Phillips Jeff M, Vivek Srikumar (2020) On measuring and mitigating biased inferences of word embeddings. Proc AAAI Conf Artif Intell 34:7659\u20137666","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"8632_CR55","unstructured":"Anders CJ, Neumann D, Samek W, M\u00fcller K-R, Lapuschkin S (2021) Software for dataset-wide XAI: from local explanations to global insights with Zennit, CoRelAy, and ViRelAy. In: CoRR arXiv:abs\/2106.13200"},{"key":"8632_CR56","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.ins.2020.08.040","volume":"547","author":"G D\u2019Angelo","year":"2021","unstructured":"D\u2019Angelo G, Palmieri F (2021) GGA: a modified genetic algorithm with gradient-based local search for solving constrained optimization problems. Inf Sci 547:136\u2013162","journal-title":"Inf Sci"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08632-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08632-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08632-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T00:31:04Z","timestamp":1693355464000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08632-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,3]]},"references-count":56,"journal-issue":{"issue":"28","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["8632"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08632-8","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2023,8,3]]},"assertion":[{"value":"18 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 August 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"However, there is no conflict of interest for the financing and for the results obtained in this research work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}