{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T17:32:39Z","timestamp":1743010359450,"version":"3.40.3"},"publisher-location":"Cham","reference-count":55,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031600111"},{"type":"electronic","value":"9783031600128"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-60012-8_18","type":"book-chapter","created":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T01:02:20Z","timestamp":1717203740000},"page":"288-306","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The Role of Automated Classification in Preserving Indonesian Folk and National Songs"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6653-2697","authenticated-orcid":false,"given":"Aji","family":"Prasetya Wibawa","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1279-6176","authenticated-orcid":false,"given":"AH.","family":"Rofi\u2019uddin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8607-3478","authenticated-orcid":false,"given":"Rafal","family":"Dre\u017cewski","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9665-8613","authenticated-orcid":false,"given":"Ilham Ari Elbaith","family":"Zaeni","sequence":"additional","affiliation":[]},{"given":"Irfan Zuhdi","family":"Abdillah","sequence":"additional","affiliation":[]},{"given":"Triyanti","family":"Simbolon","sequence":"additional","affiliation":[]},{"given":"Fabyan Raif","family":"Erlangga","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7016-7901","authenticated-orcid":false,"given":"Agung Bella Putra","family":"Utama","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,1]]},"reference":[{"key":"18_CR1","doi-asserted-by":"publisher","unstructured":"Fatmawati, E.: Strategies to grow a proud attitude towards Indonesian cultural diversity. Linguist. Cult. Rev. 5(S1), 810\u2013820 (2021). https:\/\/doi.org\/10.21744\/lingcure.v5nS1.1465","DOI":"10.21744\/lingcure.v5nS1.1465"},{"key":"18_CR2","doi-asserted-by":"publisher","unstructured":"Awerman, A., Sina, I., Yurisman, B.W., Hendri, Y.: The role of music arts in multicultural education. J. Sci. Res. Educ. Technol. 2(2), 769\u2013781 (2023). https:\/\/doi.org\/10.58526\/jsret.v2i2.161","DOI":"10.58526\/jsret.v2i2.161"},{"issue":"3","key":"18_CR3","doi-asserted-by":"publisher","first-page":"1990","DOI":"10.35335\/legal","volume":"11","author":"B Erlina","year":"2022","unstructured":"Erlina, B.: Implementation of protection of traditional cultural expression in west lampung regency. Leg. Br. 11(3), 1990\u20132004 (2022). https:\/\/doi.org\/10.35335\/legal","journal-title":"Leg. Br."},{"key":"18_CR4","unstructured":"Astuti, K.S., Langit, P.V.: The influence of Arabic, Chinese, Western, and Hindu Cultures on the Indonesian folk songs. In: Asia-Pacific Symposium for Music Education Research, pp. 272\u2013279 (2023)"},{"key":"18_CR5","doi-asserted-by":"publisher","unstructured":"Cohen, M.I.: Three eras of Indonesian arts diplomacy. Bijdr. tot taal-, land- en Volkenkd. \/ J. Humanit. Soc. Sci. Southeast Asia 175(2\u20133), 253\u2013283 (2019). https:\/\/doi.org\/10.1163\/22134379-17502022","DOI":"10.1163\/22134379-17502022"},{"issue":"1","key":"18_CR6","doi-asserted-by":"publisher","first-page":"58","DOI":"10.21512\/humaniora.v3i1.3234","volume":"3","author":"LA Suhardjono","year":"2012","unstructured":"Suhardjono, L.A.: Battling for shared culture between Indonesia and Malaysia in the social media era. Humaniora 3(1), 58 (2012). https:\/\/doi.org\/10.21512\/humaniora.v3i1.3234","journal-title":"Humaniora"},{"issue":"9","key":"18_CR7","doi-asserted-by":"publisher","first-page":"98","DOI":"10.3390\/bs9090098","volume":"9","author":"A Pot","year":"2019","unstructured":"Pot, A., Porkert, J., Keijzer, M.: The bidirectional in bilingual: cognitive, social and linguistic effects of and on third-age language learning. Behav. Sci. (Basel) 9(9), 98 (2019). https:\/\/doi.org\/10.3390\/bs9090098","journal-title":"Behav. Sci. (Basel)"},{"issue":"2","key":"18_CR8","doi-asserted-by":"publisher","first-page":"128","DOI":"10.17977\/um018v4i22021p128-137","volume":"4","author":"K Trang","year":"2022","unstructured":"Trang, K., Nguyen, A.H.: A comparative study of machine learning-based approach for network traffic classification. Knowl. Eng. Data Sci. 4(2), 128 (2022). https:\/\/doi.org\/10.17977\/um018v4i22021p128-137","journal-title":"Knowl. Eng. Data Sci."},{"issue":"1","key":"18_CR9","doi-asserted-by":"publisher","first-page":"41","DOI":"10.17977\/um018v5i12022p41-52","volume":"5","author":"MY Chuttur","year":"2022","unstructured":"Chuttur, M.Y., Parianen, Y.: A comparison of machine learning models to prioritise emails using emotion analysis for customer service excellence. Knowl. Eng. Data Sci. 5(1), 41 (2022). https:\/\/doi.org\/10.17977\/um018v5i12022p41-52","journal-title":"Knowl. Eng. Data Sci."},{"issue":"1","key":"18_CR10","doi-asserted-by":"publisher","first-page":"24","DOI":"10.17977\/um018v6i12023p24-40","volume":"6","author":"I Iddrisu","year":"2023","unstructured":"Iddrisu, I., Appiahene, P., Appiah, O., Fuseini, I.: Exploring the impact of students demographic attributes on performance prediction through binary classification in the KDP model. Knowl. Eng. Data Sci. 6(1), 24 (2023). https:\/\/doi.org\/10.17977\/um018v6i12023p24-40","journal-title":"Knowl. Eng. Data Sci."},{"key":"18_CR11","doi-asserted-by":"publisher","unstructured":"Pujianto, U., Setiawan, A.L., Rosyid, H.A., Salah, A.M.M.: Comparison of Na\u00efve Bayes Algorithm and Decision Tree C4.5 for Hospital Readmission Diabetes Patients using HbA1c Measurement. Knowl. Eng. Data Sci. 2(2), 58 (2019). https:\/\/doi.org\/10.17977\/um018v2i22019p58-71","DOI":"10.17977\/um018v2i22019p58-71"},{"key":"18_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107408","volume":"165","author":"X Yu","year":"2023","unstructured":"Yu, X., et al.: Synergizing the enhanced RIME with fuzzy K-nearest neighbor for diagnose of pulmonary hypertension. Comput. Biol. Med.. Biol. Med. 165, 107408 (2023). https:\/\/doi.org\/10.1016\/j.compbiomed.2023.107408","journal-title":"Comput. Biol. Med.. Biol. Med."},{"key":"18_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113176","volume":"146","author":"O Gokalp","year":"2020","unstructured":"Gokalp, O., Tasci, E., Ugur, A.: A novel wrapper feature selection algorithm based on iterated greedy metaheuristic for sentiment classification. Expert Syst. Appl. 146, 113176 (2020). https:\/\/doi.org\/10.1016\/j.eswa.2020.113176","journal-title":"Expert Syst. Appl."},{"issue":"13","key":"18_CR14","doi-asserted-by":"publisher","first-page":"12190","DOI":"10.1021\/acsanm.3c01919","volume":"6","author":"W Jin","year":"2023","unstructured":"Jin, W., Pei, J., Xie, P., Chen, J., Zhao, H.: Machine learning-based prediction of mechanical properties and performance of nickel-graphene nanocomposites using molecular dynamics simulation data. ACS Appl. Nano Mater. 6(13), 12190\u201312199 (2023). https:\/\/doi.org\/10.1021\/acsanm.3c01919","journal-title":"ACS Appl. Nano Mater."},{"issue":"28\u201329","key":"18_CR15","doi-asserted-by":"publisher","first-page":"35239","DOI":"10.1007\/s11042-020-10082-6","volume":"80","author":"U Naseem","year":"2021","unstructured":"Naseem, U., Razzak, I., Eklund, P.W.: A survey of pre-processing techniques to improve short-text quality: a case study on hate speech detection on twitter. Multimed. Tools Appl. 80(28\u201329), 35239\u201335266 (2021). https:\/\/doi.org\/10.1007\/s11042-020-10082-6","journal-title":"Multimed. Tools Appl."},{"key":"18_CR16","doi-asserted-by":"crossref","unstructured":"Budiarto, L., Rokhman, N.M., Uriu, W.: Bulletin of social informatics theory and application uncovering negative sentiments: a study of indonesian twitter users\u2019 health opinions on coffee consumption, vol. 7, no. 1, pp. 24\u201331 (2023)","DOI":"10.31763\/businta.v7i1.606"},{"key":"18_CR17","doi-asserted-by":"publisher","unstructured":"Ridzuan, F., Wan Zainon, W.M.N.: A review on data cleansing methods for big data. Procedia Comput. Sci. 161, 731\u2013738 (2019). https:\/\/doi.org\/10.1016\/j.procs.2019.11.177","DOI":"10.1016\/j.procs.2019.11.177"},{"key":"18_CR18","doi-asserted-by":"publisher","unstructured":"Omran, E., Al Tararwah, E., Al Qundus, J.: A comparative analysis of machine learning algorithms for hate speech detection in social media. Online J. Commun. Media Technol. 13(4), e202348 (2023). https:\/\/doi.org\/10.30935\/ojcmt\/13603","DOI":"10.30935\/ojcmt\/13603"},{"key":"18_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssci.2023.106138","volume":"163","author":"X Luo","year":"2023","unstructured":"Luo, X., Li, X., Goh, Y.M., Song, X., Liu, Q.: Application of machine learning technology for occupational accident severity prediction in the case of construction collapse accidents. Saf. Sci.. Sci. 163, 106138 (2023). https:\/\/doi.org\/10.1016\/j.ssci.2023.106138","journal-title":"Saf. Sci.. Sci."},{"key":"18_CR20","doi-asserted-by":"publisher","unstructured":"Bhawna, A., Gurunath, G., Shashwat, V., Yogesh, S.: Natural Language Processing Based Two-Stage Machine Learning Model for Automatic Mapping of Activity Codes Using Drilling Descriptions, May 2023. https:\/\/doi.org\/10.2118\/214522-MS","DOI":"10.2118\/214522-MS"},{"key":"18_CR21","doi-asserted-by":"publisher","unstructured":"Zhao, S., Zhu, L., Wang, X., Yang, Y.: CenterCLIP: token clustering for efficient text-video retrieval. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 970\u2013981, July 2022. https:\/\/doi.org\/10.1145\/3477495.3531950","DOI":"10.1145\/3477495.3531950"},{"key":"18_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/1883698","volume":"2022","author":"V Dogra","year":"2022","unstructured":"Dogra, V., et al.: A complete process of text classification system using state-of-the-Art NLP models. Comput. Intell. Neurosci.. Intell. Neurosci. 2022, 1\u201326 (2022). https:\/\/doi.org\/10.1155\/2022\/1883698","journal-title":"Comput. Intell. Neurosci.. Intell. Neurosci."},{"key":"18_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/7923262","volume":"2022","author":"F Lan","year":"2022","unstructured":"Lan, F.: Research on text similarity measurement hybrid algorithm with term semantic information and TF-IDF method. Adv. Multimed. 2022, 1\u201311 (2022). https:\/\/doi.org\/10.1155\/2022\/7923262","journal-title":"Adv. Multimed."},{"key":"18_CR24","doi-asserted-by":"publisher","DOI":"10.1007\/s10639-023-12007-w","author":"H Sahlaoui","year":"2023","unstructured":"Sahlaoui, H., Alaoui, E.A.A., Agoujil, S., Nayyar, A.: An empirical assessment of smote variants techniques and interpretation methods in improving the accuracy and the interpretability of student performance models. Educ. Inf. Technol. (2023). https:\/\/doi.org\/10.1007\/s10639-023-12007-w","journal-title":"Educ. Inf. Technol."},{"issue":"11","key":"18_CR25","doi-asserted-by":"publisher","first-page":"9076","DOI":"10.3390\/su15119076","volume":"15","author":"R Asadi","year":"2023","unstructured":"Asadi, R., et al.: Self-paced ensemble-SHAP approach for the classification and interpretation of crash severity in work zone areas. Sustainability 15(11), 9076 (2023). https:\/\/doi.org\/10.3390\/su15119076","journal-title":"Sustainability"},{"issue":"23","key":"18_CR26","doi-asserted-by":"publisher","first-page":"4003","DOI":"10.3390\/electronics11234003","volume":"11","author":"M Alamri","year":"2022","unstructured":"Alamri, M., Ykhlef, M.: Survey of credit card anomaly and fraud detection using sampling techniques. Electronics 11(23), 4003 (2022). https:\/\/doi.org\/10.3390\/electronics11234003","journal-title":"Electronics"},{"key":"18_CR27","doi-asserted-by":"publisher","unstructured":"Reddy, B.A.C., Chandra, G.K., Sisodia, D.S., Anuragi, A.: Balancing techniques for improving automated detection of hate speech and offensive language on social media. In: 2023 2nd International Conference for Innovation in Technology (INOCON), pp. 1\u20138, March 2023. https:\/\/doi.org\/10.1109\/INOCON57975.2023.10101157","DOI":"10.1109\/INOCON57975.2023.10101157"},{"key":"18_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.triboint.2023.108464","volume":"184","author":"J Prost","year":"2023","unstructured":"Prost, J., Boidi, G., Puhwein, A.M., Varga, M., Vorlaufer, G.: Classification of operational states in porous journal bearings using a semi-supervised multi-sensor Machine Learning approach. Tribol. Int.. Int. 184, 108464 (2023). https:\/\/doi.org\/10.1016\/j.triboint.2023.108464","journal-title":"Tribol. Int.. Int."},{"key":"18_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.118271","volume":"239","author":"M Shahbazi","year":"2021","unstructured":"Shahbazi, M., Shirali, A., Aghajan, H., Nili, H.: Using distance on the Riemannian manifold to compare representations in brain and in models. Neuroimage 239, 118271 (2021). https:\/\/doi.org\/10.1016\/j.neuroimage.2021.118271","journal-title":"Neuroimage"},{"issue":"2","key":"18_CR30","doi-asserted-by":"publisher","first-page":"158","DOI":"10.31763\/businta.v6i2.601","volume":"6","author":"FMD Mandagi","year":"2022","unstructured":"Mandagi, F.M.D., Paat, F.J., Tooy, D., Pakasi, S.E., Wantasen, S.: Web-based system for medicinal plants identification using convolutional neural network. Bull. Soc. Informatics Theory Appl. 6(2), 158\u2013167 (2022)","journal-title":"Bull. Soc. Informatics Theory Appl."},{"issue":"8","key":"18_CR31","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1016\/j.bpsc.2019.11.001","volume":"5","author":"SW Yip","year":"2020","unstructured":"Yip, S.W., Kiluk, B., Scheinost, D.: Toward addiction prediction: an overview of cross-validated predictive modeling findings and considerations for future neuroimaging research. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 5(8), 748\u2013758 (2020). https:\/\/doi.org\/10.1016\/j.bpsc.2019.11.001","journal-title":"Biol. Psychiatry Cogn. Neurosci. Neuroimaging"},{"issue":"1","key":"18_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-10358-x","volume":"12","author":"S Uddin","year":"2022","unstructured":"Uddin, S., Haque, I., Lu, H., Moni, M.A., Gide, E.: Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction. Sci. Rep. 12(1), 1\u201311 (2022). https:\/\/doi.org\/10.1038\/s41598-022-10358-x","journal-title":"Sci. Rep."},{"issue":"5","key":"18_CR33","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1080\/08839514.2020.1723868","volume":"34","author":"K Park","year":"2020","unstructured":"Park, K., Hong, J.S., Kim, W.: A methodology combining cosine similarity with classifier for text classification. Appl. Artif. Intell.Artif. Intell. 34(5), 396\u2013411 (2020). https:\/\/doi.org\/10.1080\/08839514.2020.1723868","journal-title":"Appl. Artif. Intell.Artif. Intell."},{"key":"18_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-15459-x","author":"B Parlak","year":"2023","unstructured":"Parlak, B.: A novel feature and class-based globalization technique for text classification. Multimed. Tools Appl. (2023). https:\/\/doi.org\/10.1007\/s11042-023-15459-x","journal-title":"Multimed. Tools Appl."},{"issue":"1","key":"18_CR35","doi-asserted-by":"publisher","first-page":"2976","DOI":"10.1038\/s41598-023-30176-z","volume":"13","author":"L-X Chen","year":"2023","unstructured":"Chen, L.-X., Su, S.-W., Liao, C.-H., Wong, K.-S., Yuan, S.-M.: An open automation system for predatory journal detection. Sci. Rep. 13(1), 2976 (2023). https:\/\/doi.org\/10.1038\/s41598-023-30176-z","journal-title":"Sci. Rep."},{"key":"18_CR36","doi-asserted-by":"publisher","unstructured":"Hasan, M., Ullah, S., Khan, M.J., Khurshid, K.: Comparative analysis of SVM, ann and cnn for classifying vegetation species using hyperspectral thermal infrared data. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch., vol. 42, no. 2\/W13, 1861\u20131868 (2019). https:\/\/doi.org\/10.5194\/isprs-archives-XLII-2-W13-1861-2019","DOI":"10.5194\/isprs-archives-XLII-2-W13-1861-2019"},{"issue":"4","key":"18_CR37","doi-asserted-by":"publisher","first-page":"901","DOI":"10.1007\/s11517-018-1930-0","volume":"57","author":"D Zhao","year":"2019","unstructured":"Zhao, D., Liu, H., Zheng, Y., He, Y., Lu, D., Lyu, C.: A reliable method for colorectal cancer prediction based on feature selection and support vector machine. Med. Biol. Eng. Comput.Comput. 57(4), 901\u2013912 (2019). https:\/\/doi.org\/10.1007\/s11517-018-1930-0","journal-title":"Med. Biol. Eng. Comput.Comput."},{"key":"18_CR38","doi-asserted-by":"publisher","unstructured":"Mohan, L., Pant, J., Suyal, P., Kumar, A.: Support vector machine accuracy improvement with classification. In: 2020 12th International Conference on Computational Intelligence and Communication Networks (CICN), pp. 477\u2013481, September 2020. https:\/\/doi.org\/10.1109\/CICN49253.2020.9242572","DOI":"10.1109\/CICN49253.2020.9242572"},{"issue":"4","key":"18_CR39","doi-asserted-by":"publisher","first-page":"978","DOI":"10.1109\/TBME.2019.2926104","volume":"67","author":"X Tang","year":"2020","unstructured":"Tang, X., Ma, Z., Hu, Q., Tang, W.: A real-time arrhythmia heartbeats classification algorithm using parallel delta modulations and rotated linear-kernel support vector machines. IEEE Trans. Biomed. Eng. 67(4), 978\u2013986 (2020). https:\/\/doi.org\/10.1109\/TBME.2019.2926104","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"2","key":"18_CR40","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1007\/s41870-019-00409-4","volume":"15","author":"AP Gopi","year":"2023","unstructured":"Gopi, A.P., Jyothi, R.N.S., Narayana, V.L., Sandeep, K.S.: Classification of tweets data based on polarity using improved RBF kernel of SVM. Int. J. Inf. Technol. 15(2), 965\u2013980 (2023). https:\/\/doi.org\/10.1007\/s41870-019-00409-4","journal-title":"Int. J. Inf. Technol."},{"key":"18_CR41","doi-asserted-by":"publisher","unstructured":"Vinge, R., McKelvey, T.: Understanding support vector machines with polynomial kernels. In: 2019 27th European Signal Processing Conference (EUSIPCO), pp. 1\u20135, September 2019. https:\/\/doi.org\/10.23919\/EUSIPCO.2019.8903042","DOI":"10.23919\/EUSIPCO.2019.8903042"},{"key":"18_CR42","doi-asserted-by":"publisher","unstructured":"Kalcheva, N., Karova, M., Penev, I.: Comparison of the accuracy of SVM kemel functions in text classification. In: 2020 International Conference on Biomedical Innovations and Applications (BIA), pp. 141\u2013145, , September 2020. https:\/\/doi.org\/10.1109\/BIA50171.2020.9244278","DOI":"10.1109\/BIA50171.2020.9244278"},{"issue":"6","key":"18_CR43","doi-asserted-by":"publisher","first-page":"1417","DOI":"10.1007\/s10796-021-10135-7","volume":"23","author":"H Kaur","year":"2021","unstructured":"Kaur, H., Ahsaan, S.U., Alankar, B., Chang, V.: A proposed sentiment analysis deep learning algorithm for analyzing COVID-19 tweets. Inf. Syst. Front. 23(6), 1417\u20131429 (2021). https:\/\/doi.org\/10.1007\/s10796-021-10135-7","journal-title":"Inf. Syst. Front."},{"issue":"2","key":"18_CR44","doi-asserted-by":"publisher","first-page":"182","DOI":"10.3390\/agriengineering3020012","volume":"3","author":"KK Paidipati","year":"2021","unstructured":"Paidipati, K.K., Chesneau, C., Nayana, B.M., Kumar, K.R., Polisetty, K., Kurangi, C.: Prediction of rice cultivation in india\u2014support vector regression approach with various kernels for non-linear patterns. AgriEngineering 3(2), 182\u2013198 (2021). https:\/\/doi.org\/10.3390\/agriengineering3020012","journal-title":"AgriEngineering"},{"key":"18_CR45","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.neucom.2019.10.051","volume":"401","author":"F Nie","year":"2020","unstructured":"Nie, F., Zhu, W., Li, X.: Decision tree SVM: an extension of linear SVM for non-linear classification. Neurocomputing 401, 153\u2013159 (2020). https:\/\/doi.org\/10.1016\/j.neucom.2019.10.051","journal-title":"Neurocomputing"},{"key":"18_CR46","doi-asserted-by":"publisher","unstructured":"Ghosh, S., Dasgupta, A., Swetapadma, A.: A study on support vector machine based linear and non-linear pattern classification. In: 2019 International Conference on Intelligent Sustainable Systems (ICISS), pp. 24\u201328, February 2019. https:\/\/doi.org\/10.1109\/ISS1.2019.8908018","DOI":"10.1109\/ISS1.2019.8908018"},{"key":"18_CR47","doi-asserted-by":"publisher","unstructured":"S. N., S. Wagle, Ghosh, P., Kishore, K.: Sentiment classification of English and Hindi music lyrics using supervised machine learning algorithms. In: 2022 2nd Asian Conference on Innovation in Technology (ASIANCON), pp. 1\u20136, August 2022. https:\/\/doi.org\/10.1109\/ASIANCON55314.2022.9908688","DOI":"10.1109\/ASIANCON55314.2022.9908688"},{"key":"18_CR48","doi-asserted-by":"publisher","unstructured":"Wang, N., Zhao, X., Wang, L., Zou, Z.: Novel system for rapid investigation and damage detection in cultural heritage conservation based on deep learning. J. Infrastruct. Syst., vol. 25, no. 3, September 2019. https:\/\/doi.org\/10.1061\/(ASCE)IS.1943-555X.0000499","DOI":"10.1061\/(ASCE)IS.1943-555X.0000499"},{"key":"18_CR49","unstructured":"Miller, S.J.: Metadata for digital collections. American Library Association (2022)"},{"key":"18_CR50","unstructured":"Schr\u00f6der, A.M., Ghajargar, M.: Unboxing the algorithm: designing an understandable algorithmic experience in music recommender systems (2021)"},{"issue":"9","key":"18_CR51","doi-asserted-by":"publisher","first-page":"5831","DOI":"10.1007\/s11227-019-02862-1","volume":"75","author":"H Gao","year":"2019","unstructured":"Gao, H., Zeng, X., Yao, C.: Application of improved distributed naive Bayesian algorithms in text classification. J. Supercomput.Supercomput. 75(9), 5831\u20135847 (2019). https:\/\/doi.org\/10.1007\/s11227-019-02862-1","journal-title":"J. Supercomput.Supercomput."},{"issue":"2","key":"18_CR52","doi-asserted-by":"publisher","first-page":"103","DOI":"10.36040\/mnemonic.v5i2.4748","volume":"5","author":"G Angeline","year":"2022","unstructured":"Angeline, G., Wibawa, A.P., Pujianto, U.: Klasifikasi Dialek Bahasa Jawa Menggunakan Metode Naives Bayes. J. Mnemon. 5(2), 103\u2013110 (2022). https:\/\/doi.org\/10.36040\/mnemonic.v5i2.4748","journal-title":"J. Mnemon."},{"key":"18_CR53","doi-asserted-by":"publisher","unstructured":"Dedes, K., Putra Utama, A.B., Wibawa, A.P., Afandi, A.N., Handayani, A.N., Hernandez, L.: Neural machine translation of Spanish-English food recipes using LSTM. JOIV Int. J. Informatics Vis. 6(2), 290 (2022). https:\/\/doi.org\/10.30630\/joiv.6.2.804","DOI":"10.30630\/joiv.6.2.804"},{"issue":"3","key":"18_CR54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3439726","volume":"54","author":"S Minaee","year":"2022","unstructured":"Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., Gao, J.: Deep learning\u2013based text classification. ACM Comput. Surv.Comput. Surv. 54(3), 1\u201340 (2022). https:\/\/doi.org\/10.1145\/3439726","journal-title":"ACM Comput. Surv.Comput. Surv."},{"key":"18_CR55","doi-asserted-by":"publisher","unstructured":"Krishnan, A., Vincent, A., Jos, G., Rajan, R.: Multimodal fusion for segment classification in folk music. In: 2021 IEEE 18th India Council International Conference (INDICON), pp. 1\u20137, December 2021. https:\/\/doi.org\/10.1109\/INDICON52576.2021.9691751","DOI":"10.1109\/INDICON52576.2021.9691751"}],"container-title":["Lecture Notes in Computer Science","Distributed, Ambient and Pervasive Interactions"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-60012-8_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T23:09:50Z","timestamp":1718406590000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-60012-8_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031600111","9783031600128"],"references-count":55,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-60012-8_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"1 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HCII","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human-Computer Interaction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Washington DC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hcii2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.hci.international\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}