{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T13:53:50Z","timestamp":1743083630720,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031425356"},{"type":"electronic","value":"9783031425363"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-42536-3_11","type":"book-chapter","created":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T18:02:30Z","timestamp":1693418550000},"page":"111-120","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Generating Synthetic Fetal Cardiotocography Data with Conditional Generative Adversarial Networks"],"prefix":"10.1007","author":[{"given":"Halal Abdulrahman","family":"Ahmed","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan A.","family":"Nepomuceno","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bel\u00e9n","family":"Vega-M\u00e1rquez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Isabel A.","family":"Nepomuceno-Chamorro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,31]]},"reference":[{"key":"11_CR1","unstructured":"Turing. Synthetic Data Generation: Definition, Types, Techniques, and Tools (2022). www.turing.com. https:\/\/www.turing.com\/kb\/synthetic-data-generation-techniques. Accessed 14 June 2023"},{"issue":"3","key":"11_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3446374","volume":"54","author":"D Saxena","year":"2021","unstructured":"Saxena, D., Cao, J.: Generative adversarial networks (GANs) challenges, solutions, and future directions. ACM Comput. Surv. (CSUR) 54(3), 1\u201342 (2021)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"11_CR3","unstructured":"Tewari, A.: Types of generative adversarial networks (GANs). OpenGenus IQ: Computing Expertise & Legacy (2022). https:\/\/iq.opengenus.org\/types-of-gans\/"},{"key":"11_CR4","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/978-3-030-20055-8_22","volume-title":"14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019)","author":"B Vega-M\u00e1rquez","year":"2020","unstructured":"Vega-M\u00e1rquez, B., Rubio-Escudero, C., Riquelme, J.C., Nepomuceno-Chamorro, I.: Creation of synthetic data with Conditional Generative Adversarial Networks. In: Mart\u00ednez \u00c1lvarez, F., Troncoso Lora, A., S\u00e1ez Mu\u00f1oz, J.A., Quinti\u00e1n, H., Corchado, E. (eds.) SOCO 2019. AISC, vol. 950, pp. 231\u2013240. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-20055-8_22"},{"key":"11_CR5","unstructured":"Martin, A., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning (2017)"},{"key":"11_CR6","doi-asserted-by":"publisher","unstructured":"Park, N., Mohammadi, M., Gorde, K., Jajodia, S., Park, H., Kim, Y.: Data synthesis based on generative adversarial networks. Proc. VLDB Endow. 11(10), 1071\u20131083 (2018). ISSN 21508097. https:\/\/doi.org\/10.14778\/3231751.3231757","DOI":"10.14778\/3231751.3231757"},{"key":"11_CR7","unstructured":"Xu, L., Skoularidou, M., Cuesta-Infante, A., Veeramachaneni, K.: Modeling tabular data using conditional GAN. In: Advances in Neural Information Processing Systems, vol. 32, pp. 7335\u20137345. Curran Associates, Inc., (2019). URL: https:\/\/proceedings.neurips.cc\/paper\/2019\/file\/254ed7d2de3b23ab10936522dd547b78Paper.pdf"},{"issue":"2","key":"11_CR8","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1093\/jigpal\/jzaa059","volume":"30","author":"B Vega-M\u00e1rquez","year":"2022","unstructured":"Vega-M\u00e1rquez, B., Rubio-Escudero, C., Nepomuceno-Chamorro, I.: Generation of synthetic data with Conditional Generative Adversarial Networks. Logic J. IGPL 30(2), 252\u2013262 (2022)","journal-title":"Logic J. IGPL"},{"issue":"15","key":"11_CR9","doi-asserted-by":"publisher","first-page":"2733","DOI":"10.3390\/math10152733","volume":"10","author":"A Figueira","year":"2022","unstructured":"Figueira, A., Vaz, B.: Survey on synthetic data generation, evaluation methods and GANs. Mathematics 10(15), 2733 (2022)","journal-title":"Mathematics"},{"key":"11_CR10","unstructured":"Pedregosa, F.: Scikit-learn: machine Learning in Python (2011). https:\/\/www.jmlr.org\/papers\/v12\/pedregosa11a.html. Accessed 11 2022"},{"key":"11_CR11","unstructured":"Guest_Blog. Introduction to XGBoost algorithm in machine learning. Analytics Vidhya (2023). https:\/\/www.analyticsvidhya.com\/blog\/2018\/09\/an-end-to-end-guide-to-understand-the-math-behind-xgboost\/. Accessed 11 2022"},{"key":"11_CR12","doi-asserted-by":"publisher","unstructured":"Campos, D., Bernardes, J.: Cardiotocography. UCI Machine Learning Repository (2010). https:\/\/doi.org\/10.24432\/C51S4N. Accessed 20 Apr 2023","DOI":"10.24432\/C51S4N"},{"key":"11_CR13","unstructured":"Sinha, S., Zhang, H., Goyal, A., Bengio, Y., Larochelle, H., Odena, A.: Small-GAN: speeding up GAN training using core-sets. In: International Conference on Machine Learning, pp. 9005\u20139015. PMLR (2020)"},{"key":"11_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1007\/978-3-030-92238-2_46","volume-title":"Neural Information Processing","author":"M Padala","year":"2021","unstructured":"Padala, M., Das, D., Gujar, S.: Effect of input noise dimension in GANs. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds.) ICONIP 2021. LNCS, vol. 13110, pp. 558\u2013569. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-92238-2_46"},{"key":"11_CR15","unstructured":"Sharma, S.: Epoch vs batch size vs iterations - towards data science. Medium (2019). https:\/\/towardsdatascience.com\/epoch-vs-iterations-vs-batch-size-4dfb9c7ce9c9. Accessed 03 2023"},{"key":"11_CR16","unstructured":"Brownlee, J.: How to configure the learning rate when training deep learning neural networks. MachineLearningMastery.com (2019a). https:\/\/machinelearningmastery.com\/learning-rate-for-deep-learning-neural-networks\/. Accessed 02 2023"},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Widodo, S., Brawijaya, H., Samudi, S.: Stratified K-fold cross validation optimization on machine learning for prediction.\u00a0Sinkron: jurnal dan penelitian teknik informatika 7(4), 2407\u20132414 (2022)","DOI":"10.33395\/sinkron.v7i4.11792"},{"key":"11_CR18","unstructured":"Explain stratified K fold cross validation in ML in python. ProjectPro (2022). https:\/\/www.projectpro.io\/recipes\/explain-stratified-k-fold-cross-validation"},{"key":"11_CR19","doi-asserted-by":"publisher","unstructured":"Szeghalmy, S., Fazekas, A.: A comparative study of the use of stratified cross-validation and distribution-balanced stratified cross-validation in imbalanced learning. Sensors 23, 2333 (2023). https:\/\/doi.org\/10.3390\/s23042333. Accessed 01 2023","DOI":"10.3390\/s23042333"}],"container-title":["Lecture Notes in Networks and Systems","18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-42536-3_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T18:04:51Z","timestamp":1693418691000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-42536-3_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031425356","9783031425363"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-42536-3_11","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"31 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SOCO","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Soft Computing Models in Industrial and Environmental Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Salamanca","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icscmiea2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2023.sococonference.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}