{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T14:17:21Z","timestamp":1774275441516,"version":"3.50.1"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"30","license":[{"start":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T00:00:00Z","timestamp":1707955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T00:00:00Z","timestamp":1707955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100002591","name":"Korea National Sport University","doi-asserted-by":"publisher","award":["NRF-2021R1A2C2006895"],"award-info":[{"award-number":["NRF-2021R1A2C2006895"]}],"id":[{"id":"10.13039\/501100002591","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-18246-4","type":"journal-article","created":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T08:02:47Z","timestamp":1707984167000},"page":"74141-74158","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["GlocalEmoNet: An optimized neural network for music emotion classification and segmentation using timbre and chroma features"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9842-8704","authenticated-orcid":false,"given":"Yagya Raj","family":"Pandeya","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joonwhoan","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,15]]},"reference":[{"issue":"1","key":"18246_CR1","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1080\/21674086.1950.11925787","volume":"19","author":"H Kohut","year":"1950","unstructured":"Kohut H, Levarie S (1950) On the enjoyment of listening to music. Psychoanal Q 19(1):64\u201387","journal-title":"Psychoanal Q"},{"key":"18246_CR2","doi-asserted-by":"publisher","first-page":"511","DOI":"10.3389\/fpsyg.2013.00511","volume":"4","author":"T Sch\u00e4fer","year":"2013","unstructured":"Sch\u00e4fer T, Sedlmeier P, St\u00e4dtler C, Huron D (2013) The psychological functions of music listening. Front Psychol 4:511","journal-title":"Front Psychol"},{"issue":"2","key":"18246_CR3","first-page":"45","volume":"11","author":"CL Krumhansl","year":"2002","unstructured":"Krumhansl CL (2002) Music: A link between cognition and emotion. Am Psychol Soc 11(2):45\u201350","journal-title":"Am Psychol Soc"},{"key":"18246_CR4","doi-asserted-by":"crossref","unstructured":"Inskip C, Macfarlane A, Rafferty P (2012) Towards the disintermediation of creative music search: Analyzing queries to determine important facets. Int J Digit Libr 12(2):137\u00b1147","DOI":"10.1007\/s00799-012-0084-1"},{"key":"18246_CR5","unstructured":"Berardinis J, Cangelosi A, Coutinho E (2020) The multiple voices of music emotions: Source separation for improving music emotion recognition models and their interpretability. In Proceedings of the 21st International Society for Music Information Retrieval Conference (ISMIR 2020), Virtual Conference, pp 2\u201319"},{"key":"18246_CR6","unstructured":"Chaki A, Doshi P, Bhattacharya S, Patnaik P (2020) Explaining perceived emotions in music: An attentive approach\u201d. In Proceedings of the 21st International Society for Music Information Retrieval Conference (ISMIR 2020), Virtual Conference, pp 1\u201318"},{"key":"18246_CR7","doi-asserted-by":"crossref","unstructured":"Zhou J, Chen X, Yang D (2019) Multimodel music emotion recognition using unsupervised deep neural networks. In: Li W, Li S, Shao S, Li Z (eds) Proceedings of the 6th Conference on Sound and Music Technology (CSMT), Lecture notes in electrical engineering, vol. 568. Springer, Singapore","DOI":"10.1007\/978-981-13-8707-4_3"},{"key":"18246_CR8","doi-asserted-by":"publisher","first-page":"19834","DOI":"10.1038\/s41598-021-98856-2","volume":"11","author":"YR Pandeya","year":"2021","unstructured":"Pandeya YR, Bhattarai B, Lee J (2021) Music video emotion classification using slow\u2013fast audio\u2013video network and unsupervised feature representation. Sci Rep 11:19834","journal-title":"Sci Rep"},{"issue":"14","key":"18246_CR9","doi-asserted-by":"publisher","first-page":"4927","DOI":"10.3390\/s21144927","volume":"21","author":"YR Pandeya","year":"2021","unstructured":"Pandeya YR, Bhattarai B, Lee J (2021) Deep-learning-based multimodal emotion classification for music videos. Sensors 21(14):4927","journal-title":"Sensors"},{"key":"18246_CR10","doi-asserted-by":"publisher","first-page":"2887","DOI":"10.1007\/s11042-020-08836-3","volume":"80","author":"YR Pandeya","year":"2020","unstructured":"Pandeya YR, Lee J (2020) Deep learning-based late fusion of multimodal information for emotion classification of music video. Multimed Tools Appl 80:2887\u20132905","journal-title":"Multimed Tools Appl"},{"key":"18246_CR11","unstructured":"Choi K, Fazekas G, Sandler MB, Cho K (2017) Transfer learning for music classification and regression tasks. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR 2017), Suzhou, China, pp 141\u2013149"},{"key":"18246_CR12","unstructured":"Delbouys R, Hennequin R, Piccoli F, Royo-Letelier J, Moussallam M (2018) Music mood detection based on audio and lyrics with deep neural net. In Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR 2018), Paris, France, pp 370\u2013375"},{"key":"18246_CR13","doi-asserted-by":"publisher","first-page":"166335","DOI":"10.1007\/s11704-021-0569-4","volume":"16","author":"H Donghong","year":"2022","unstructured":"Donghong H, Yanru K, Jiayi H, Guoren W (2022) A survey of music emotion recognition. Front Comput Sci 16:166335","journal-title":"Front Comput Sci"},{"key":"18246_CR14","doi-asserted-by":"crossref","unstructured":"Ekman P (1999) Basic emotions in handbook of cognition and emotion. Wiley, Hoboken, pp. 45\u201360","DOI":"10.1002\/0470013494.ch3"},{"key":"18246_CR15","unstructured":"Kim YE, Schmidt EM, Migneco R, Morton BG, Richardson P, Scott J, Speck JA, Turnbull D (2010) Music emotion recognition: A state of the art review. In Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR 2010), Utrecht, Netherlands"},{"key":"18246_CR16","doi-asserted-by":"publisher","unstructured":"Panda R, Malheiro R, Paiva RP (2020) Novel audio features for music emotion recognition. IEEE Trans Affect Comput 11(4):614\u2013626. https:\/\/doi.org\/10.1109\/TAFFC.2018.2820691","DOI":"10.1109\/TAFFC.2018.2820691"},{"key":"18246_CR17","doi-asserted-by":"publisher","first-page":"5787","DOI":"10.3390\/app12125787","volume":"12","author":"X Wang","year":"2022","unstructured":"Wang X, Wang L, Xie L (2022) Comparison and analysis of acoustic features of Western and chinese classical music emotion recognition based on V-A model. Appl Sci 12:5787","journal-title":"Appl Sci"},{"key":"18246_CR18","unstructured":"Chowdhury A, Portabella AV, Haunschmid V, Widmer G (2019) Towards explainable music emotion recognition: The route via mid-level features. In Proceedings of the 20th international society for music information retrieval conference (ISMIR 2019), Delft, The Netherlands, pp, 237\u2013243"},{"key":"18246_CR19","unstructured":"Cuesta H, McFee B, G\u00f3mez E (2020) Multiple f0 estimation in vocal ensembles using convolutional neural networks. In Proceedings of the 21st International Society for Music Information Retrieval Conference (ISMIR 2020), Virtual Conference"},{"key":"18246_CR20","unstructured":"Bittner RM, McFee B, Salamon J, Li P, Bello JP (2017) Deep salience representations for f0 estimation in Polyphonic Music. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR 2017), Suzhou, China"},{"issue":"1","key":"18246_CR21","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1038\/scientificamerican0792-66","volume":"267","author":"JH Holland","year":"1992","unstructured":"Holland JH (1992) Genetic algorithms. Sci Am 267(1):66\u201373","journal-title":"Sci Am"},{"issue":"4","key":"18246_CR22","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1007\/s10462-016-9486-6","volume":"47","author":"S Akyol","year":"2017","unstructured":"Akyol S, Alatas B (2017) Plant intelligence based metaheuristic optimization algorithms. Artif Intell Rev 47(4):417\u2013462","journal-title":"Artif Intell Rev"},{"issue":"5","key":"18246_CR23","first-page":"476","volume":"19","author":"S Aalaei","year":"2016","unstructured":"Aalaei S, Shahraki H, Rowhanimanesh A, Eslami S (2016) Feature selection using genetic algorithm for breast cancer diagnosis: Experiment on three different datasets. Iran J Basic Med Sci 19(5):476","journal-title":"Iran J Basic Med Sci"},{"key":"18246_CR24","doi-asserted-by":"publisher","first-page":"14637","DOI":"10.1109\/ACCESS.2019.2892852","volume":"7","author":"F Iqbal","year":"2019","unstructured":"Iqbal F, Hashmi JM, Fung BCM, Batool R, Khattak AM, Aleem S, Hung PCK (2019) A hybrid framework for sentiment analysis using genetic algorithm based feature reduction. IEEE Access 7:14637\u201314652","journal-title":"IEEE Access"},{"key":"18246_CR25","doi-asserted-by":"publisher","first-page":"69951","DOI":"10.1109\/ACCESS.2021.3077295","volume":"9","author":"D Javaheri","year":"2021","unstructured":"Javaheri D, Lalbakhsh P, Hosseinzadeh M (2021) A novel method for detecting future generations of targeted and metamorphic malware based on genetic algorithm. IEEE Access 9:69951\u201369970. https:\/\/doi.org\/10.1109\/ACCESS.2021.3077295","journal-title":"IEEE Access"},{"key":"18246_CR26","doi-asserted-by":"crossref","unstructured":"Jeyaranjani J, Devaraj D (2022) Improved genetic algorithm for optimal demand response in smart grid. Sustain Comput Informat Syst 35:100710","DOI":"10.1016\/j.suscom.2022.100710"},{"issue":"2","key":"18246_CR27","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1109\/TCBB.2019.2921961","volume":"18","author":"X Zhang","year":"2021","unstructured":"Zhang X, Zhang Y, Li Y (2021) MGRFE: Multilayer recursive feature elimination based on an embedded genetic algorithm for cancer classification. IEEE\/ACM Trans Comput Biol Bioinf 18(2):621\u2013632","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"18246_CR28","doi-asserted-by":"crossref","unstructured":"Karkavitsas GV, Tsihrintzis GA (2011) Automatic music genre classification using hybrid genetic algorithms. In: Tsihrintzis GA, Virvou M, Jain LC, Howlett RJ (eds.) Intelligent interactive multimedia systems and services, Smart Innovation, systems and technologies, vol 11. Springer, Berlin, Heidelberg","DOI":"10.1007\/978-3-642-22158-3_32"},{"key":"18246_CR29","doi-asserted-by":"publisher","unstructured":"Wicaksono AS, Supianto AA (2018) Hyper parameter optimization using genetic algorithm on machine learning methods for online news popularity prediction. Int J Adv Comput Sci Applic (IJACSA) 9(12). https:\/\/doi.org\/10.14569\/IJACSA.2018.091238","DOI":"10.14569\/IJACSA.2018.091238"},{"key":"18246_CR30","doi-asserted-by":"publisher","first-page":"84365","DOI":"10.1109\/ACCESS.2022.3196905","volume":"10","author":"N Ghatasheh","year":"2022","unstructured":"Ghatasheh N, Altaharwa I, Aldebei K (2022) Modified genetic algorithm for feature selection and hyper parameter optimization: Case of XGBoost in spam prediction. IEEE Access 10:84365\u201384383. https:\/\/doi.org\/10.1109\/ACCESS.2022.3196905","journal-title":"IEEE Access"},{"key":"18246_CR31","doi-asserted-by":"publisher","unstructured":"Pannakkong W, Thiwa-Anont K, Singthong K, Parthanadee P, Buddhakulsomsiri J (2022) Hyperparameter tuning of machine learning algorithms using response surface methodology: A case study of ANN, SVM, and DBN. Math Probl Eng 17. https:\/\/doi.org\/10.1155\/2022\/8513719","DOI":"10.1155\/2022\/8513719"},{"key":"18246_CR32","doi-asserted-by":"crossref","unstructured":"Syarif, Prugel-Bennett A, Wills G (2016) SVM parameter optimization using grid search and genetic algorithm to improve classification performance. Telkomnika 14\u20134, 1502\u20131509","DOI":"10.12928\/telkomnika.v14i4.3956"},{"key":"18246_CR33","doi-asserted-by":"publisher","first-page":"1186","DOI":"10.3390\/app12031186","volume":"12","author":"ID Raji","year":"2022","unstructured":"Raji ID, Bello-Salau H, Umoh IJ, Onumanyi AJ, Adegboye MA, Salawudeen AT (2022) Simple deterministic selection-based genetic algorithm for hyperparameter tuning of machine learning models. Appl Sci 12:1186","journal-title":"Appl Sci"},{"key":"18246_CR34","unstructured":"Xiao X, Yan M, Basodi S, Ji C, Pan Y (2006) Efficient hyperparameter optimization in deep learning using a variable length genetic algorithm, arXiv:2006.12703"},{"key":"18246_CR35","doi-asserted-by":"publisher","first-page":"79","DOI":"10.3390\/informatics8040079","volume":"8","author":"E Elgeldawi","year":"2021","unstructured":"Elgeldawi E, Sayed A, Galal AR, Zaki AM (2021) Hyperparameter tuning for machine learning algorithms used for arabic sentiment analysis. Informatics 8:79. https:\/\/doi.org\/10.3390\/informatics8040079","journal-title":"Informatics"},{"key":"18246_CR36","doi-asserted-by":"publisher","unstructured":"Li C, Jiang JZ, Zhao YQ, Li RG, Wang ED, Zhang X, Zhao K (2021) Genetic algorithm based hyper-parameters optimization for transfer Convolutional Neural Network. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2103.03875","DOI":"10.48550\/arXiv.2103.03875"},{"key":"18246_CR37","doi-asserted-by":"publisher","unstructured":"Huang M, Rong W, Arjannikov T, Jiang N, Xiong Z (2016) Bi-modal deep boltzmann machine based musical emotion classification. In: Villa A, Masulli P, Pons Rivero A (eds) Artificial Neural Networks and Machine Learning \u2013 ICANN 2016. ICANN 2016. Lecture Notes in Computer Science, vol 9887. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-319-44781-0_24","DOI":"10.1007\/978-3-319-44781-0_24"},{"key":"18246_CR38","doi-asserted-by":"publisher","unstructured":"Pandeya YR, You J, Bhattarai B, Lee J (2021) Multi-modal, multi-task and multi-label for music genre classification and emotion regression. In: Proceedings of the International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Korea, Republic of, pp. 1042-1045. https:\/\/doi.org\/10.1109\/ICTC52510.2021.9620826","DOI":"10.1109\/ICTC52510.2021.9620826"},{"issue":"108637","key":"18246_CR39","first-page":"0003","volume":"190","author":"D Tanko","year":"2022","unstructured":"Tanko D, Dogan S, Demir FB, Baygin M, Sahin SE, Tuncer T (2022) Shoelace pattern-based speech emotion recognition of the lecturers in distance education: ShoePat23. Appl Acoust 190(108637):0003-682X","journal-title":"Appl Acoust"},{"issue":"102210","key":"18246_CR40","first-page":"0933","volume":"123","author":"T Tuncer","year":"2022","unstructured":"Tuncer T, Dogan S, Baygin M, Acharya UR (2022) Tetromino pattern based accurate EEG emotion classification model. Artif Intell Med 123(102210):0933\u20133657","journal-title":"Artif Intell Med"},{"key":"18246_CR41","doi-asserted-by":"crossref","unstructured":"Dogan, M. Akay, P. D. Barua, M. Baygin, S. Dogan, T. Tuncer, A. H. Dogru, and U. R. Acharya (2021) PrimePatNet87: Prime pattern and tunable q-factor wavelet transform techniques for automated accurate EEG emotion recognition. Comput Biol Med 138:104867, 0010\u20134825","DOI":"10.1016\/j.compbiomed.2021.104867"},{"issue":"6","key":"18246_CR42","doi-asserted-by":"publisher","first-page":"166335","DOI":"10.1007\/s11704-021-0569-4","volume":"16","author":"D Han","year":"2022","unstructured":"Han D, Kong Y, Han J, Wang G (2022) A survey of music emotion recognition. Front Comput Sci 16(6):166335","journal-title":"Front Comput Sci"},{"issue":"3","key":"18246_CR43","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1080\/0929821042000317813","volume":"33","author":"PN Juslin","year":"2004","unstructured":"Juslin PN, Laukka P (2004) Expression, perception, and induction of musical emotions: a review and a questionnaire study of everyday listening. J New Music Res 33(3):217\u2013238. https:\/\/doi.org\/10.1080\/0929821042000317813","journal-title":"J New Music Res"},{"issue":"3","key":"18246_CR44","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1177\/0305735617707354","volume":"46","author":"P Elvers","year":"2018","unstructured":"Elvers P, Fischinger T, Steffens J (2018) Music listening as self-enhancement: effects of empowering music on momentary explicit and implicit self-esteem. Psychol Music 46(3):307\u2013325","journal-title":"Psychol Music"},{"key":"18246_CR45","doi-asserted-by":"publisher","unstructured":"Raglio, L. Attardo, G. Gontero, S. Rollino, E. Groppo, and E. Granieri (2015) Effects of music and music therapy on mood in neurological patients. World J Psychiatry, vol. 5(1):68\u201378, https:\/\/doi.org\/10.5498\/wjp.v5.i1.68","DOI":"10.5498\/wjp.v5.i1.68"},{"key":"18246_CR46","unstructured":"E.B. Patricia, \u201cMusic as a Mood Modulator\u201d, Retrospective Theses and Dissertations, 1992, 17311."},{"issue":"6","key":"18246_CR47","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1037\/h0077714","volume":"39","author":"JA Russell","year":"1980","unstructured":"Russell JA (1980) A circumplex model of affect. J Pers Soc Psychol 39(6):1161\u20131178","journal-title":"J Pers Soc Psychol"},{"key":"18246_CR48","doi-asserted-by":"publisher","unstructured":"Ekman P (1999) Basic emotions in handbook of cognition and emotion. Wiley, Hoboken 45\u201360. https:\/\/doi.org\/10.1002\/0470013494.ch3","DOI":"10.1002\/0470013494.ch3"},{"key":"18246_CR49","doi-asserted-by":"crossref","unstructured":"Santana MA, Lima CL, Torcate AS, Fonseca FS, Santos WP (2021) Affective computing in the context of music therapy: A systematic review. Res Soc Dev 10(15):e392101522844","DOI":"10.33448\/rsd-v10i15.22844"},{"key":"18246_CR50","doi-asserted-by":"publisher","first-page":"27096","DOI":"10.1007\/s10489-023-04967-w","volume":"53","author":"MJ Lucia-Mulas","year":"2023","unstructured":"Lucia-Mulas MJ, Revuelta-Sanz P, Ruiz-Mezcua B, Gonzalez-Carrasco I (2023) Automaticmusic emotion classification model for movie soundtrack subtitling based on neuroscientific premises. Appl Intell 53:27096\u201327109","journal-title":"Appl Intell"},{"issue":"1","key":"18246_CR51","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1080\/27706710.2022.2075241","volume":"1","author":"W Qian","year":"2022","unstructured":"Qian W, Tan J, Jiang Y, Tian Y (2022) \u201cDeep learning with convolutional neural networks for EEG-based music emotion decoding and visualization. Brain-Apparatus Commun 1(1):38\u201349","journal-title":"Brain-Apparatus Commun"},{"key":"18246_CR52","doi-asserted-by":"publisher","unstructured":"Yang J (2021) A novel music emotion recognition model using neural network technology. Front Psychol 12:760060. https:\/\/doi.org\/10.3389\/fpsyg.2021.760060","DOI":"10.3389\/fpsyg.2021.760060"},{"key":"18246_CR53","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1007\/s13735-022-00230-z","volume":"11","author":"N He","year":"2022","unstructured":"He N, Ferguson S (2022) Music emotion recognition based on segment-level two-stage learning. Int J Multimed Inf Retr 11:383\u2013394","journal-title":"Int J Multimed Inf Retr"},{"key":"18246_CR54","first-page":"5181899","volume":"2022","author":"X Jia","year":"2022","unstructured":"Jia X (2022) Music emotion classification method based on deep learning and improved attention mechanism. Comput Intell Neurosci 2022:5181899","journal-title":"Comput Intell Neurosci"},{"key":"18246_CR55","doi-asserted-by":"crossref","unstructured":"Cerri R, Barros RC, de Carvalho ACPLF (2014) Hierarchical multi-label classification using local neural networks. J Comput Syst Sci 80:39\u201356","DOI":"10.1016\/j.jcss.2013.03.007"},{"key":"18246_CR56","unstructured":"Parmezan RS, Silva DF, Batista GEAPA (2020) A combination of local approaches for hierarchical music genre classification. In: Proc. of the 21st Int. society for music information retrieval conf., Montr\u00e9al, Canada."},{"key":"18246_CR57","doi-asserted-by":"publisher","unstructured":"Zhong Z, Hirano M, Shimada K, Tateishi K, Takahashi S, Mitsufuji Y (2023) An attention-based approach to hierarchical multi-label music instrument classification. ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, pp. 1\u20135. https:\/\/doi.org\/10.1109\/ICASSP49357.2023.10095162","DOI":"10.1109\/ICASSP49357.2023.10095162"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18246-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-18246-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18246-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T02:14:54Z","timestamp":1725329694000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-18246-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,15]]},"references-count":57,"journal-issue":{"issue":"30","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["18246"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-18246-4","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,15]]},"assertion":[{"value":"14 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 February 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interests\/Competing interests"}}]}}