{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T11:17:23Z","timestamp":1784114243425,"version":"3.55.0"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T00:00:00Z","timestamp":1771372800000},"content-version":"vor","delay-in-days":17,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001779","name":"Monash University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001779","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Advances in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are enabling machines to emulate human-like behaviors. In the context of social computing, lifelike characters are crucial as they facilitate natural and intuitive interactions between humans and computers. Chatbots, a key application of such technologies, are computer programs that use Natural Language Processing (NLP) to engage in text-based conversations. They are widely used in customer service and other domains, but the challenge lies in designing chatbots that feel more human to enhance user engagement. Research has shown that incorporating emotions into chatbots is critical for achieving this goal. Effective emotion recognition systems must be able to process real-time text interactions, understand users\u2019 sentiments on various topics, address their concerns, and respond appropriately based on the detected emotions. This paper proposes a meta-learning ensemble approach for text-based emotion detection in conversational data. The proposed method combines the outputs of multiple well-established machine learning algorithms to improve accuracy in recognizing emotions in text. A comparative analysis was conducted on two conversational datasets, demonstrating that the meta-learning ensemble method outperforms individual machine learning algorithms on both datasets. The proposed approach achieved 73% classification accuracy on the Empathetic Dialogues dataset, while on the EmoContext dataset, it achieved 95.1% classification accuracy, significantly outperforming results over individual machine learning algorithms. The conclusions demonstrate that utilizing a meta-learner for model fusion successfully leverages the advantages of separate algorithms while alleviating their intrinsic shortcomings, resulting in enhanced overall performance.<\/jats:p>","DOI":"10.1007\/s00521-025-11805-2","type":"journal-article","created":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T06:02:45Z","timestamp":1771394565000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Meta-learning ensemble for emotion detection in conversational text"],"prefix":"10.1007","volume":"38","author":[{"given":"Sheetal","family":"Kusal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shruti","family":"Patil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aasheer","family":"Peerbhai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ketan","family":"Kotecha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7161-2109","authenticated-orcid":false,"given":"Ganeshsree","family":"Selvachandran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ajith","family":"Abraham","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,18]]},"reference":[{"key":"11805_CR1","doi-asserted-by":"publisher","unstructured":"Gupta J, Ahuja M, Garg N (2022) A review on human-computer interaction (HCI). In: 2022 2nd international conference on innovative sustainable computational technologies (CISCT), Dehradun, India, pp 1\u20136. https:\/\/doi.org\/10.1109\/CISCT55310.2022.10046656","DOI":"10.1109\/CISCT55310.2022.10046656"},{"key":"11805_CR2","unstructured":"Chatbot: English meaning \u2013 Cambridge dictionary. Retrieved from: https:\/\/dictionary.cambridge.org\/dictionary\/english\/chatbot"},{"key":"11805_CR3","doi-asserted-by":"crossref","unstructured":"Cui L, Wei F, Huang S, Tan C, Duan C, Zhou M (2017) SuperAgent: a customer service chatbot for e-commerce websites. In: Proceedings of ACL 2017, system demonstrations, Vancouver, Canada. Association for Computational Linguistics. pp 97\u2013102. https:\/\/aclanthology.org\/P17-4017","DOI":"10.18653\/v1\/P17-4017"},{"key":"11805_CR4","doi-asserted-by":"publisher","unstructured":"Hu T, Xu A, Liu Z, You Q, Guo Y, Sinha V, Luo J, Akkiraju R (2018) Touch your heart: a tone-aware chatbot for customer care on social media. In: Proceedings of the 2018 CHI conference on human factors in computing systems (CHI \u201818), paper 415, Association for Computing Machinery, New York, NY, USA. pp 1\u201312. https:\/\/doi.org\/10.1145\/3173574.3173989","DOI":"10.1145\/3173574.3173989"},{"issue":"1","key":"11805_CR5","doi-asserted-by":"publisher","first-page":"29","DOI":"10.21248\/jlcl.22.2007.88","volume":"22","author":"EA B. Abu Shawar","year":"2007","unstructured":"B. Abu Shawar EA (2007) Chatbots: Are they useful? J Lang Technol Comput Linguist 22(1):29\u201349. https:\/\/doi.org\/10.21248\/jlcl.22.2007.88","journal-title":"J Lang Technol Comput Linguist"},{"key":"11805_CR6","unstructured":"Kothari A, Zyane R, Hoover J (2017) Chatbots for eCommerce: learn how to build a virtual shopping assistant. Bleeding Edge Press (1st Edition). Accessed via: https:\/\/www.amazon.com\/Chatbots-eCommerce-virtual-shopping-assistant-ebook\/dp\/B0714LD3H2"},{"key":"11805_CR7","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1016\/j.chb.2019.01.020","volume":"97","author":"E Go","year":"2019","unstructured":"Go E, Sundar SS (2019) Humanizing chatbots: the effects of visual, identity and conversational cues on humanness perceptions. Comput Hum Behav 97:304\u2013316. https:\/\/doi.org\/10.1016\/j.chb.2019.01.020","journal-title":"Comput Hum Behav"},{"key":"11805_CR8","doi-asserted-by":"publisher","unstructured":"Al-Zubaide H, Issa AA (2011) OntBot: Ontology based chatbot.In: International symposium on innovations in information and communications technology, Amman, Jordan, pp 7\u201312. https:\/\/doi.org\/10.1109\/ISIICT.2011.6149594","DOI":"10.1109\/ISIICT.2011.6149594"},{"key":"11805_CR9","unstructured":"Ritter A, Cherry C, Dolan WB (2011) Data-driven response generation in social media. In: Proceedings of the 2011 conference on empirical methods in natural language processing, Edinburgh, Scotland, UK. Association for Computational Linguistics. pp 583\u2013593. https:\/\/aclanthology.org\/D11-1054"},{"key":"11805_CR10","doi-asserted-by":"publisher","unstructured":"Serban IV, Sankar C, Germain M, Zhang S, Lin Z, Subramanian S, Kim T, Pieper M, Chandar S, Ke NR, Rajeshwar S, de Brebisson A, Sotelo JMR, Suhubdy D, Michalski V, Nguyen A, Pineau J, Bengio Y (2017) A deep reinforcement learning chatbot. arXiv:1709.02349. https:\/\/doi.org\/10.48550\/arXiv.1709.02349","DOI":"10.48550\/arXiv.1709.02349"},{"key":"11805_CR11","doi-asserted-by":"publisher","unstructured":"Qiu M, Li FL, Wang S, Gao X, Chen Y, Zhao W, Chen H, Huang J, Chu W (2017). AliMe chat: a sequence to sequence and rerank based chatbot engine. In: Proceedings of the 55th annual meeting of the association for computational linguistics (Volume 2: Short Papers), Vancouver, Canada. Association for Computational Linguistics. pp 498\u2013503. https:\/\/doi.org\/10.18653\/v1\/P17-2079","DOI":"10.18653\/v1\/P17-2079"},{"key":"11805_CR12","doi-asserted-by":"publisher","unstructured":"Brandtzaeg PB, F\u00f8lstad A (2017)Why people use chatbots. In: Kompatsiaris I et al. Internet Science. INSCI 2017. Lecture notes in computer science, Springer, Cham. vol 10673, pp 377\u2013392. https:\/\/doi.org\/10.1007\/978-3-319-70284-1_30","DOI":"10.1007\/978-3-319-70284-1_30"},{"key":"11805_CR13","doi-asserted-by":"publisher","unstructured":"Xu A, Liu Z, Guo Y, Sinha V, Akkiraju R (2017) A new chatbot for customer service on social media. In: Proceedings of the 2017 CHI conference on human factors in computing systems (CHI \u201817). Association for Computing Machinery, New York, NY, USA. pp 3506\u20133510. https:\/\/doi.org\/10.1145\/3025453.3025496","DOI":"10.1145\/3025453.3025496"},{"issue":"3","key":"11805_CR14","doi-asserted-by":"publisher","DOI":"10.3390\/sym15030645","volume":"15","author":"K Ahmed","year":"2023","unstructured":"Ahmed K, Nadeem MI, Li D, Zheng Z, Al-Kahtani N, Alkahtani HK, Mostafa SM, Mamyrbayev O (2023) Contextually enriched meta-learning ensemble model for Urdu sentiment analysis. Symmetry 15(3):645. https:\/\/doi.org\/10.3390\/sym15030645","journal-title":"Symmetry"},{"key":"11805_CR15","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-023-01043-6","volume":"13","author":"R Kora","year":"2023","unstructured":"Kora R, Mohammed A (2023) An enhanced approach for sentiment analysis based on meta-ensemble deep learning. Soc Netw Anal Min 13:8. https:\/\/doi.org\/10.1007\/s13278-023-01043-6","journal-title":"Soc Netw Anal Min"},{"key":"11805_CR16","doi-asserted-by":"publisher","unstructured":"Troussas C, Krouska A, Virvou M (2016) Evaluation of ensemble-based sentiment classifiers for twitter data. In: 2016 7th international conference on information, intelligence, systems & applications (IISA), Chalkidiki, Greece, pp 1\u20136. https:\/\/doi.org\/10.1109\/IISA.2016.7785380","DOI":"10.1109\/IISA.2016.7785380"},{"issue":"4","key":"11805_CR17","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1016\/j.jksues.2016.04.002","volume":"30","author":"DME-DM Hussein","year":"2018","unstructured":"Hussein DME-DM (2018) A survey on sentiment analysis challenges. J King Saud Univ-Eng Sci 30(4):330\u2013338. https:\/\/doi.org\/10.1016\/j.jksues.2016.04.002","journal-title":"J King Saud Univ-Eng Sci"},{"issue":"3","key":"11805_CR18","first-page":"176","volume":"5","author":"A Ali","year":"2015","unstructured":"Ali A, Shamsuddin SM, Ralescu AL (2015) Classification with class imbalance problem: a review. Int J Adv Soft Comput Appl 5(3):176\u2013204","journal-title":"Int J Adv Soft Comput Appl"},{"issue":"9","key":"11805_CR19","doi-asserted-by":"publisher","first-page":"7271","DOI":"10.1016\/j.jksuci.2021.06.012","volume":"34","author":"S Kuruvayil","year":"2022","unstructured":"Kuruvayil S, Palaniswamy S (2022) Emotion recognition from facial images with simultaneous occlusion, pose and illumination variations using meta-learning. J King Saud Univ-Comput Inf Sci 34(9):7271\u20137282. https:\/\/doi.org\/10.1016\/j.jksuci.2021.06.012","journal-title":"J King Saud Univ-Comput Inf Sci"},{"key":"11805_CR20","doi-asserted-by":"publisher","unstructured":"Wang W, Zhang J, Lin Z, Cui L, Zhang X (2023) Meta-learning improves emotion recognition. In: Kountchev R, Nakamatsu K, Wang W, Kountcheva R (eds.) Proceedings of the world conference on intelligent and 3-D technologies (WCI3DT 2022). Smart innovation, systems and technologies, Springer, Singapore. vol 323, pp 13\u201322. https:\/\/doi.org\/10.1007\/978-981-19-7184-6_2","DOI":"10.1007\/978-981-19-7184-6_2"},{"key":"11805_CR21","doi-asserted-by":"publisher","unstructured":"Vaishnavi C, Palaniswamy S (2022) Emotion recognition at real-time applications using meta-learning. In: 2022 IEEE 3rd global conference for advancement in technology (GCAT), Bangalore, India, pp 1\u20136. https:\/\/doi.org\/10.1109\/GCAT55367.2022.9971841","DOI":"10.1109\/GCAT55367.2022.9971841"},{"key":"11805_CR22","doi-asserted-by":"publisher","unstructured":"Lee H, Li SW, Vu NT (2022) Meta-learning for natural language processing: a survey. In: Proceedings of the 2022 conference of the north american chapter of the association for computational linguistics: human language technologies, Seattle, United States. Association for Computational Linguistics. pp 666\u2013684. https:\/\/doi.org\/10.18653\/v1\/2022.naacl-main.49","DOI":"10.18653\/v1\/2022.naacl-main.49"},{"key":"11805_CR23","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.procs.2019.05.008","volume":"152","author":"R Ahuja","year":"2019","unstructured":"Ahuja R, Chug A, Kohli S, Gupta S, Ahuja P (2019) The impact of features extraction on the sentiment analysis,\u201d. Proced Comput Sci 152:341\u2013348. https:\/\/doi.org\/10.1016\/j.procs.2019.05.008","journal-title":"Proced Comput Sci"},{"key":"11805_CR24","doi-asserted-by":"publisher","first-page":"15129","DOI":"10.1007\/s10462-023-10509-0","volume":"56","author":"S Kusal","year":"2023","unstructured":"Kusal S, Patil S, Choudrie J, Kotecha K, Vora D, Pappas I (2023) A systematic review of applications of natural language processing and future challenges with special emphasis in text-based emotion detection. Artif Intell Rev 56:15129\u201315215. https:\/\/doi.org\/10.1007\/s10462-023-10509-0","journal-title":"Artif Intell Rev"},{"key":"11805_CR25","doi-asserted-by":"publisher","unstructured":"Plutchik R (1980) Chapter 1\u2014a general psychoevolutionary theory of emotion. In: Plutchik R, Kellerman H (eds.) Theories of emotion, Academic Press, pp 3\u201333. https:\/\/doi.org\/10.1016\/B978-0-12-558701-3.50007-7","DOI":"10.1016\/B978-0-12-558701-3.50007-7"},{"key":"11805_CR26","doi-asserted-by":"publisher","unstructured":"Ekman P (1999) Basic emotions. In: Handbook of cognition and emotion. Dalgleish T, Power MJ (eds) Wiley. https:\/\/doi.org\/10.1002\/0470013494.ch3","DOI":"10.1002\/0470013494.ch3"},{"key":"11805_CR27","doi-asserted-by":"publisher","unstructured":"Ortony A, Clore GL, Collins A (1988) The cognitive structure of emotions. Cambridge University Press, 1988. https:\/\/doi.org\/10.1017\/CBO9780511571299","DOI":"10.1017\/CBO9780511571299"},{"issue":"3","key":"11805_CR28","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/0092-6566(77)90037-X","volume":"11","author":"JA Russell","year":"1977","unstructured":"Russell JA, Mehrabian A (1977) Evidence for a three-factor theory of emotions. J Res Pers 11(3):273\u2013294. https:\/\/doi.org\/10.1016\/0092-6566(77)90037-X","journal-title":"J Res Pers"},{"key":"11805_CR29","unstructured":"Ravi S, Larochelle H (2017) Optimization as a model for few-shot learning. In: International conference on learning representations (ICLR 2017). https:\/\/openreview.net\/forum?id=rJY0-Kcll"},{"key":"11805_CR30","doi-asserted-by":"publisher","unstructured":"Zoph B, Le QV (2017) Neural architecture search with reinforcement learning. arXiv:1611.01578. https:\/\/doi.org\/10.48550\/arXiv.1611.01578","DOI":"10.48550\/arXiv.1611.01578"},{"issue":"04","key":"11805_CR31","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1142\/S0219622006002258","volume":"5","author":"Q Yang","year":"2006","unstructured":"Yang Q, Wu X (2006) 10 challenging problems in data mining research. Int J Inf Technol Decis Making 5(04):597\u2013604. https:\/\/doi.org\/10.1142\/S0219622006002258","journal-title":"Int J Inf Technol Decis Making"},{"key":"11805_CR32","doi-asserted-by":"publisher","unstructured":"Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: ICML\u201917: proceedings of the 34th international conference on machine learning, Sydney, Australia, PMLR 70, pp 1126\u20131135. https:\/\/dl.acm.org\/doi\/https:\/\/doi.org\/10.5555\/3305381.3305498","DOI":"10.5555\/3305381.3305498"},{"key":"11805_CR33","doi-asserted-by":"crossref","unstructured":"Kusal S, Pirbhai A, Patil S (2024) Meta-learning in textual sentiment and emotion analysis: a comprehensive review. In: 2024 MIT art, design and technology school of computing international conference (MITADTSoCiCon). IEEE. pp 1\u20138","DOI":"10.1109\/MITADTSoCiCon60330.2024.10574942"},{"key":"11805_CR34","unstructured":"Chen Y (2015) Convolutional neural network for sentence classification (Master\u2019s thesis, University of Waterloo)"},{"key":"11805_CR35","doi-asserted-by":"crossref","unstructured":"Oussous A, Lahcen AA, Belfkih S (2018) Improving sentiment analysis of moroccan tweets using ensemble learning. In: Big data, cloud and applications: third international conference, BDCA 2018, Kenitra, Morocco, April 4\u20135, 2018, Revised Selected Papers 3. Springer International Publishing. pp 91\u2013104","DOI":"10.1007\/978-3-319-96292-4_8"},{"key":"11805_CR36","doi-asserted-by":"crossref","unstructured":"Li X, Long X, Xia Y, Li S (2022) Low resource style transfer via domain adaptive meta learning. arXiv preprint arXiv:2205.12475","DOI":"10.18653\/v1\/2022.naacl-main.220"},{"key":"11805_CR37","doi-asserted-by":"crossref","unstructured":"Mazumder P, Singh P, Rai P (2021) Few-shot lifelong learning. In Proceedings of the AAAI conference on artificial intelligence, vol 35, No. 3, pp 2337\u20132345","DOI":"10.1609\/aaai.v35i3.16334"},{"issue":"3","key":"11805_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3386252","volume":"53","author":"Y Wang","year":"2020","unstructured":"Wang Y, Yao Q, Kwok JT, Ni LM (2020) Generalizing from a few examples: a survey on few-shot learning. ACM Comput Surveys (csur) 53(3):1\u201334","journal-title":"ACM Comput Surveys (csur)"},{"key":"11805_CR39","unstructured":"Vanschoren J (2018) Meta-learning: A survey. arXiv preprint arXiv:1810.03548"},{"key":"11805_CR40","doi-asserted-by":"publisher","unstructured":"Suttles J, Ide N (2013) Distant supervision for emotion classification with discrete binary values. In: Gelbukh A (eds) Computational linguistics and intelligent text processing. CICLing 2013. Lecture Notes in Computer Science, Springer, Berlin, Heidelberg. vol 7817, pp 121\u2013136. https:\/\/doi.org\/10.1007\/978-3-642-37256-8_11","DOI":"10.1007\/978-3-642-37256-8_11"},{"issue":"4","key":"11805_CR41","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1016\/j.dss.2012.05.024","volume":"53","author":"A Balahur","year":"2012","unstructured":"Balahur A, Hermida JM, Montoyo A (2012) Detecting implicit expressions of emotion in text: a comparative analysis. Decis Support Syst 53(4):742\u2013753. https:\/\/doi.org\/10.1016\/j.dss.2012.05.024","journal-title":"Decis Support Syst"},{"key":"11805_CR42","doi-asserted-by":"publisher","first-page":"10535","DOI":"10.1007\/s00521-023-08248-y","volume":"35","author":"D Nguyen","year":"2023","unstructured":"Nguyen D, Nguyen DT, Sridharan S, Denman S, Nguyen TT, Dean D, Fookes C (2023) Meta-transfer learning for emotion recognition. Neural Comput Appl 35:10535\u201310549. https:\/\/doi.org\/10.1007\/s00521-023-08248-y","journal-title":"Neural Comput Appl"},{"key":"11805_CR43","doi-asserted-by":"publisher","unstructured":"Huang C, Trabelsi A, Za\u00efane OR (2019) ANA at SemEval-2019 Task 3: contextual emotion detection in conversations through hierarchical LSTMs and BERT. In: Proceedings of the 13th international workshop on semantic evaluation, Minneapolis, Minnesota, USA. Association for Computational Linguistics. pp 49\u201353. https:\/\/doi.org\/10.18653\/v1\/S19-2006","DOI":"10.18653\/v1\/S19-2006"},{"issue":"10","key":"11805_CR44","doi-asserted-by":"publisher","first-page":"8825","DOI":"10.1016\/j.jksuci.2021.11.001","volume":"34","author":"A Mohammed","year":"2022","unstructured":"Mohammed A, Kora R (2022) An effective ensemble deep learning framework for text classification. J King Saud Univ-Comput Inf Sci 34(10):8825\u20138837. https:\/\/doi.org\/10.1016\/j.jksuci.2021.11.001","journal-title":"J King Saud Univ-Comput Inf Sci"},{"key":"11805_CR45","unstructured":"Gopalakrishnan K, Hedayatnia B, Chen Q, Gottardi A, Kwatra S, Venkatesh A, Hakkani-Tur D (2023) Topical-chat: Towards knowledge-grounded open-domain conversations. arXiv preprint arXiv:2308.11995"},{"key":"11805_CR46","doi-asserted-by":"publisher","unstructured":"Rashkin H, Smith EM, Li M, Boureau YL (2019) Towards empathetic open-domain conversation models: a new benchmark and dataset. In: Proceedings of the 57th annual meeting of the association for computational linguistics, Florence, Italy. Association for Computational Linguistics. pp 5370\u20135381. https:\/\/doi.org\/10.18653\/v1\/P19-1534","DOI":"10.18653\/v1\/P19-1534"},{"key":"11805_CR47","unstructured":"Li Y, Su H, Shen X, Li W, Cao Z, Niu S (2017) Dailydialog: a manually labelled multi-turn dialogue dataset. arXiv preprint arXiv:1710.03957"},{"key":"11805_CR48","doi-asserted-by":"publisher","unstructured":"Chatterjee A, Narahari KN, Joshi M, Agrawal P (2019) SemEval-2019 Task 3: EmoContext contextual emotion detection in text. In: Proceedings of the 13th international workshop on semantic evaluation, Minneapolis, Minnesota, USA. Association for Computational Linguistics. pp 39\u201348. https:\/\/doi.org\/10.18653\/v1\/S19-2005","DOI":"10.18653\/v1\/S19-2005"},{"issue":"1","key":"11805_CR49","doi-asserted-by":"publisher","first-page":"140","DOI":"10.24382\/swfr-wr17","volume":"7","author":"R Dunford","year":"2014","unstructured":"Dunford R, Su Q, Tamang E, Wintour A (2014) The Pareto principle. Plymouth Stud Sci 7(1):140\u2013148. https:\/\/doi.org\/10.24382\/swfr-wr17","journal-title":"Plymouth Stud Sci"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11805-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11805-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11805-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T03:40:49Z","timestamp":1773718849000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11805-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":49,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["11805"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11805-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2]]},"assertion":[{"value":"2 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Authors\u2019 declaration: This manuscript is the authors\u2019 original work and has not been published elsewhere. All authors have checked the manuscript and have agreed to this submission. Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"54"}}