{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T09:19:01Z","timestamp":1743067141200,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031790287"},{"type":"electronic","value":"9783031790294"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-79029-4_19","type":"book-chapter","created":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T22:25:40Z","timestamp":1738189540000},"page":"270-280","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Classifying Graphs of\u00a0Elementary Mathematical Functions Using Convolutional Neural Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-0755-6411","authenticated-orcid":false,"given":"Joaquim","family":"Viana","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5632-7948","authenticated-orcid":false,"given":"Helder","family":"Matos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9226-9020","authenticated-orcid":false,"given":"Marcelle","family":"Mota","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0456-8547","authenticated-orcid":false,"given":"Reginaldo","family":"Santos","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,30]]},"reference":[{"key":"19_CR1","doi-asserted-by":"publisher","unstructured":"Algan, G., Ulusoy, I.: Image classification with deep learning in the presence of noisy labels: a survey. Knowl.-Based Syst. 215, 106771 (2021). https:\/\/doi.org\/10.48550\/arXiv.1912.05170","DOI":"10.48550\/arXiv.1912.05170"},{"key":"19_CR2","doi-asserted-by":"publisher","unstructured":"Bakhtiarnia, A., Zhang, Q., Iosifidis, A.: Efficient high-resolution deep learning: a survey. ACM Comput. Surv. 56 (2024). https:\/\/doi.org\/10.1145\/3645107","DOI":"10.1145\/3645107"},{"key":"19_CR3","doi-asserted-by":"publisher","unstructured":"Bhatt, D., et al.: CNN variants for computer vision: history, architecture, application, challenges and future scope. Electronics 10(20) (2021). https:\/\/doi.org\/10.3390\/electronics10202470","DOI":"10.3390\/electronics10202470"},{"issue":"16","key":"19_CR4","doi-asserted-by":"publisher","first-page":"13387","DOI":"10.1007\/s00521-022-07368-1","volume":"34","author":"P Cao","year":"2022","unstructured":"Cao, P., Zhu, Z., Wang, Z., Zhu, Y., Niu, Q.: Applications of graph convolutional networks in computer vision. Neural Comput. Appl. 34(16), 13387\u201313405 (2022). https:\/\/doi.org\/10.1007\/s00521-022-07368-1","journal-title":"Neural Comput. Appl."},{"key":"19_CR5","doi-asserted-by":"crossref","unstructured":"Chen, J., Nagaya, R., Takagi, N.: Development of a method for extracting graph elements from mathematical graphs. In: 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2921\u20132926. IEEE (2012)","DOI":"10.1109\/ICSMC.2012.6378237"},{"key":"19_CR6","unstructured":"Chollet, F., et\u00a0al.: Keras (2015). https:\/\/keras.io"},{"key":"19_CR7","unstructured":"Dai, J., Lin, S.: Image recognition: current challenges and emerging opportunities (2018). https:\/\/www.microsoft.com\/en-us\/research\/lab\/microsoft-research-asia\/articles\/image-recognition-current-challenges-and-emerging-opportunities\/. Accessed 20 Jun 2024"},{"key":"19_CR8","doi-asserted-by":"publisher","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009).https:\/\/doi.org\/10.1109\/cvpr.2009.5206848","DOI":"10.1109\/cvpr.2009.5206848"},{"key":"19_CR9","doi-asserted-by":"publisher","unstructured":"Fuda, T., Omachi, S., Aso, H.: Recognition of line graph images in documents by tracing connected components. Syst. Comput. Japan 38(14), 103\u2013114 (2007). https:\/\/doi.org\/10.1002\/scj.10615","DOI":"10.1002\/scj.10615"},{"key":"19_CR10","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1007\/978-3-642-31223-6_5","volume-title":"Diagrammatic Representation and Inference","author":"C Goncu","year":"2012","unstructured":"Goncu, C., Marriott, K.: Accessible graphics: graphics for vision impaired people. In: Cox, P., Plimmer, B., Rodgers, P. (eds.) Diagrams 2012. LNCS (LNAI), vol. 7352, pp. 6\u20136. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-31223-6_5"},{"key":"19_CR11","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"19_CR12","doi-asserted-by":"publisher","unstructured":"Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. Comput. Res. Repository (CoRR) (2017). https:\/\/doi.org\/10.48550\/arXiv.1704.04861","DOI":"10.48550\/arXiv.1704.04861"},{"key":"19_CR13","doi-asserted-by":"publisher","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv (2014). https:\/\/doi.org\/10.48550\/arXiv.1412.6980","DOI":"10.48550\/arXiv.1412.6980"},{"key":"19_CR14","unstructured":"Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Tech. Rep.\u00a00, University of Toronto, Toronto, Ontario (2009). https:\/\/www.cs.toronto.edu\/~kriz\/learning-features-2009-TR.pdf"},{"key":"19_CR15","doi-asserted-by":"publisher","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84\u201390 (2017). https:\/\/doi.org\/10.1145\/3065386","DOI":"10.1145\/3065386"},{"key":"19_CR16","doi-asserted-by":"publisher","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015). https:\/\/doi.org\/10.1038\/nature14539","DOI":"10.1038\/nature14539"},{"key":"19_CR17","doi-asserted-by":"publisher","unstructured":"Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., Talwalkar, A.: Hyperband: a novel bandit-based approach to hyperparameter optimization. J. Mach. Learn. Res. (2016). https:\/\/doi.org\/10.48550\/ARXIV.1603.06560","DOI":"10.48550\/ARXIV.1603.06560"},{"key":"19_CR18","doi-asserted-by":"publisher","unstructured":"Nazemi, A., Fernando, C., Murray, I., A.\u00a0McMeekin, D.: Accessible and navigable representation of mathematical function graphs to the vision-impaired. Comput. Inf. Sci. 9(1), 31 (2015). https:\/\/doi.org\/10.5539\/cis.v9n1p31","DOI":"10.5539\/cis.v9n1p31"},{"key":"19_CR19","unstructured":"Phyo, Y.K.: Graphical functions made from an effortless sketch. MS paint that returns equations. https:\/\/towardsdatascience.com\/graphical-functions-made-from-an-effortless-sketch-266ccf95c46d (2020). Accessed 8 Apr 2024"},{"key":"19_CR20","unstructured":"Souza, S.D.A.: Usando o winplot. http:\/\/www.mat.ufpb.br\/~sergio\/winplot\/winplot.html (2004). Accessed 08 Apr 2024"},{"key":"19_CR21","doi-asserted-by":"publisher","unstructured":"Takagi, N.: Mathematical figure recognition for automating production of tactile graphics. In: 2009 IEEE International Conference on Systems, Man and Cybernetics, pp. 4651\u20134656 (2009). https:\/\/doi.org\/10.1109\/ICSMC.2009.5346749","DOI":"10.1109\/ICSMC.2009.5346749"},{"key":"19_CR22","doi-asserted-by":"publisher","unstructured":"Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. Computing Research Repository (CoRR) (2019). https:\/\/doi.org\/10.48550\/ARXIV.1905.11946","DOI":"10.48550\/ARXIV.1905.11946"}],"container-title":["Lecture Notes in Computer Science","Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-79029-4_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T22:25:46Z","timestamp":1738189546000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-79029-4_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031790287","9783031790294"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-79029-4_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"30 January 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"The dataset for this project is publicly available to facilitate transparency, reproducibility, and further research through the following link: .The script used is available here:","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Dataset Availability"}},{"value":"BRACIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazilian Conference on Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bel\u00e9m do Par\u00e1","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazil","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":"17 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"34","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bracis2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}