{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T06:56:22Z","timestamp":1774162582801,"version":"3.50.1"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030501426","type":"print"},{"value":"9783030501433","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-50143-3_49","type":"book-chapter","created":{"date-parts":[[2020,6,5]],"date-time":"2020-06-05T21:03:01Z","timestamp":1591390981000},"page":"621-634","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Hybrid Model for Parkinson\u2019s Disease Prediction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1314-3441","authenticated-orcid":false,"given":"Augusto Junio","family":"Guimar\u00e3es","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7343-5844","authenticated-orcid":false,"given":"Paulo Vitor","family":"de Campos Souza","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1560-5136","authenticated-orcid":false,"given":"Edwin","family":"Lughofer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,6,5]]},"reference":[{"key":"49_CR1","doi-asserted-by":"crossref","unstructured":"Aldous, D.: The continuum random tree. I. Ann. Probab. 1\u201328 (1991)","DOI":"10.1214\/aop\/1176990534"},{"key":"49_CR2","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.eswa.2019.06.052","volume":"137","author":"L Ali","year":"2019","unstructured":"Ali, L., Zhu, C., Zhou, M., Liu, Y.: Early diagnosis of Parkinson\u2019s disease from multiple voice recordings by simultaneous sample and feature selection. Expert Syst. Appl. 137, 22\u201328 (2019)","journal-title":"Expert Syst. Appl."},{"issue":"5","key":"49_CR3","doi-asserted-by":"publisher","first-page":"2871","DOI":"10.1121\/1.5100272","volume":"145","author":"S Arora","year":"2019","unstructured":"Arora, S., Baghai-Ravary, L., Tsanas, A.: Developing a large scale population screening tool for the assessment of Parkinson\u2019s disease using telephone-quality voice. J. Acoust. Soc. Am. 145(5), 2871\u20132884 (2019)","journal-title":"J. Acoust. Soc. Am."},{"key":"49_CR4","doi-asserted-by":"crossref","unstructured":"Bach, F.R.: Bolasso: model consistent lasso estimation through the bootstrap. In: Proceedings of the 25th International Conference on Machine Learning, pp. 33\u201340 (2008)","DOI":"10.1145\/1390156.1390161"},{"issue":"2","key":"49_CR5","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1109\/TFUZZ.2004.825075","volume":"12","author":"J Balasubramaniam","year":"2004","unstructured":"Balasubramaniam, J., Rao, C.J.M.: On the distributivity of implication operators over T and S norms. IEEE Trans. Fuzzy Syst. 12(2), 194\u2013198 (2004)","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"4","key":"49_CR6","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1002\/ana.410070408","volume":"7","author":"F Boller","year":"1980","unstructured":"Boller, F., Mizutani, T., Roessmann, U., Gambetti, P.: Parkinson disease, dementia, and Alzheimer disease: clinicopathological correlations. Ann. Neurol.: Off. J. Am. Neurol. Assoc. Child Neurol. Soc. 7(4), 329\u2013335 (1980)","journal-title":"Ann. Neurol.: Off. J. Am. Neurol. Assoc. Child Neurol. Soc."},{"issue":"3","key":"49_CR7","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s00702-017-1676-0","volume":"124","author":"L Brabenec","year":"2017","unstructured":"Brabenec, L., Mekyska, J., Galaz, Z., Rektorova, I.: Speech disorders in Parkinson\u2019s disease: early diagnostics and effects of medication and brain stimulation. J. Neural Transm. 124(3), 303\u2013334 (2017)","journal-title":"J. Neural Transm."},{"key":"49_CR8","doi-asserted-by":"crossref","unstructured":"de Campos Souza, P.V., Guimaraes, A.J., Araujo, V.S., Rezende, T.S., Araujo, V.J.S.: Incremental regularized data density-based clustering neural networks to aid in the construction of effort forecasting systems in software development. Appl. Intell. 49(9), 3221\u20133234 (2019)","DOI":"10.1007\/s10489-019-01449-w"},{"key":"49_CR9","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/978-3-319-95312-0_2","volume-title":"Fuzzy Information Processing","author":"PV de Campos Souza","year":"2018","unstructured":"de Campos Souza, P.V., Torres, L.C.B.: Regularized fuzzy neural network based on or neuron for time series forecasting. In: Barreto, G.A., Coelho, R. (eds.) NAFIPS 2018. CCIS, vol. 831, pp. 13\u201323. Springer, Cham (2018). \nhttps:\/\/doi.org\/10.1007\/978-3-319-95312-0_2"},{"key":"49_CR10","doi-asserted-by":"crossref","unstructured":"de Campos Souza, P.V., Torres, L.C.B., Guimar\u00e3es, A.J., Araujo, V.S.: Pulsar detection for wavelets soda and regularized fuzzy neural networks based on and neuron and robust activation function. Int. J. Artif. Intell. Tools 28(01), 1950003 (2019)","DOI":"10.1142\/S0218213019500039"},{"issue":"2","key":"49_CR11","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1214\/009053604000000067","volume":"32","author":"B Efron","year":"2004","unstructured":"Efron, B., Hastie, T., Johnstone, I., Tibshirani, R., et al.: Least angle regression. Ann. Stat. 32(2), 407\u2013499 (2004)","journal-title":"Ann. Stat."},{"issue":"1","key":"49_CR12","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1001\/archneur.56.1.33","volume":"56","author":"DJ Gelb","year":"1999","unstructured":"Gelb, D.J., Oliver, E., Gilman, S.: Diagnostic criteria for Parkinson disease. Arch. Neurol. 56(1), 33\u201339 (1999)","journal-title":"Arch. Neurol."},{"key":"49_CR13","series-title":"IFIP Advances in Information and Communication Technology","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1007\/978-3-030-19823-7_34","volume-title":"Artificial Intelligence Applications and Innovations","author":"AJ Guimar\u00e3es","year":"2019","unstructured":"Guimar\u00e3es, A.J., Araujo, V.J.S., Araujo, V.S., Batista, L.O., de Campos Souza, P.V.: A hybrid model based on fuzzy rules to act on the diagnosed of Autism in adults. In: MacIntyre, J., Maglogiannis, I., Iliadis, L., Pimenidis, E. (eds.) AIAI 2019. IAICT, vol. 559, pp. 401\u2013412. Springer, Cham (2019). \nhttps:\/\/doi.org\/10.1007\/978-3-030-19823-7_34"},{"issue":"1","key":"49_CR14","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1145\/1656274.1656278","volume":"11","author":"M Hall","year":"2009","unstructured":"Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newslett. 11(1), 10\u201318 (2009)","journal-title":"ACM SIGKDD Explor. Newslett."},{"key":"49_CR15","series-title":"Springer Series in Statistics","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The Elements of Statistical Learning: Data Mining, Inference, And Prediction","author":"T Hastie","year":"2009","unstructured":"Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, And Prediction. SSS. Springer, New York (2009). \nhttps:\/\/doi.org\/10.1007\/978-0-387-84858-7"},{"issue":"1\u20133","key":"49_CR16","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"GB Huang","year":"2006","unstructured":"Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1\u20133), 489\u2013501 (2006)","journal-title":"Neurocomputing"},{"key":"49_CR17","doi-asserted-by":"crossref","unstructured":"Hyde, R., Angelov, P.: Data density based clustering. In: 2014 14th UK Workshop on Computational Intelligence (UKCI), pp. 1\u20137. IEEE (2014)","DOI":"10.1109\/UKCI.2014.6930157"},{"issue":"4","key":"49_CR18","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1111\/j.1600-0404.2006.00579.x","volume":"113","author":"L Ishihara","year":"2006","unstructured":"Ishihara, L., Brayne, C.: A systematic review of depression and mental illness preceding Parkinson\u2019s disease. Acta Neurol. Scand. 113(4), 211\u2013220 (2006)","journal-title":"Acta Neurol. Scand."},{"key":"49_CR19","unstructured":"John, G.H., Langley, P.: Estimating continuous distributions in Bayesian classifiers. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 338\u2013345. Morgan Kaufmann Publishers Inc. (1995)"},{"key":"49_CR20","doi-asserted-by":"publisher","unstructured":"Junio\u00a0Guimar\u00e3es, A., Vitor\u00a0de Campos\u00a0Souza, P., Jonathan Silva\u00a0Ara\u00fajo, V., Silva\u00a0Rezende, T., Souza\u00a0Ara\u00fajo, V.: Pruning fuzzy neural network applied to the construction of expert systems to aid in the diagnosis of the treatment of cryotherapy and immunotherapy. Big Data Cogn. Comput. 3(2) (2019). \nhttps:\/\/doi.org\/10.3390\/bdcc3020022\n\n, \nhttps:\/\/www.mdpi.com\/2504-2289\/3\/2\/22","DOI":"10.3390\/bdcc3020022"},{"issue":"1","key":"49_CR21","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.bbe.2019.05.005","volume":"40","author":"B Karan","year":"2020","unstructured":"Karan, B., Sahu, S.S., Mahto, K.: Parkinson disease prediction using intrinsic mode function based features from speech signal. Biocybern. Biomed. Eng. 40(1), 249\u2013264 (2020)","journal-title":"Biocybern. Biomed. Eng."},{"key":"49_CR22","doi-asserted-by":"crossref","unstructured":"Lemos, A., Caminhas, W., Gomide, F.: New uninorm-based neuron model and fuzzy neural networks. In: 2010 Annual Meeting of the North American Fuzzy Information Processing Society, pp. 1\u20136. IEEE (2010)","DOI":"10.1109\/NAFIPS.2010.5548195"},{"issue":"11","key":"49_CR23","doi-asserted-by":"publisher","first-page":"2469","DOI":"10.1109\/TCSI.2006.884408","volume":"53","author":"CT Lin","year":"2006","unstructured":"Lin, C.T., et al.: Adaptive EEG-based alertness estimation system by using ICA-based fuzzy neural networks. IEEE Trans. Circuits Syst. I Regul. Pap. 53(11), 2469\u20132476 (2006)","journal-title":"IEEE Trans. Circuits Syst. I Regul. Pap."},{"key":"49_CR24","unstructured":"Lin, C.T., Lee, C.: Neural Fuzzy Systems: A Neuro-fuzzy Synergism to Intelligent Systems. Prentice-Hall, Inc. (1996)"},{"key":"49_CR25","unstructured":"Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of ICML, vol. 30, p. 3 (2013)"},{"key":"49_CR26","volume-title":"Parallel Distributed Processing","author":"McClelland, J.L., Rumelhart, D.E., Group, P.R., et al.","year":"1987","unstructured":"McClelland, J.L., Rumelhart, D.E., Group, P.R., et al.: Parallel Distributed Processing, vol. 2. MIT Press, Cambridge (1987)"},{"key":"49_CR27","unstructured":"Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 807\u2013814 (2010)"},{"key":"49_CR28","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1016\/j.eswa.2015.10.034","volume":"46","author":"L Naranjo","year":"2016","unstructured":"Naranjo, L., P\u00e9rez, C.J., Campos-Roca, Y., Mart\u00edn, J.: Addressing voice recording replications for Parkinson\u2019s disease detection. Expert Syst. Appl. 46, 286\u2013292 (2016)","journal-title":"Expert Syst. Appl."},{"key":"49_CR29","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.cmpb.2017.02.019","volume":"142","author":"L Naranjo","year":"2017","unstructured":"Naranjo, L., P\u00e9rez, C.J., Mart\u00edn, J., Campos-Roca, Y.: A two-stage variable selection and classification approach for Parkinson\u2019s disease detection by using voice recording replications. Comput. Methods Programs Biomed. 142, 147\u2013156 (2017)","journal-title":"Comput. Methods Programs Biomed."},{"key":"49_CR30","unstructured":"Nilashi, M., bin Ibrahim, O., Ahmadi, H., Shahmoradi, L.: An analytical method for diseases prediction using machine learning techniques. Comput. Chem. Eng. 106, 212\u2013223 (2017)"},{"key":"49_CR31","unstructured":"Quinlan, J.R.: C4. 5: programs for machine learning. Elsevier (2014)"},{"key":"49_CR32","doi-asserted-by":"publisher","unstructured":"Silva Ara\u00fajo, V.J., Guimar\u00e3es, A.J., de Campos Souza, P.V., Rezende, T.S., Ara\u00fajo, V.S.: Using resistin, glucose, age and BMI and pruning fuzzy neural network for the construction of expert systems in the prediction of breast cancer. Mach. Learn. Knowl. Extr. 1(1), 466\u2013482 (2019). \nhttps:\/\/doi.org\/10.3390\/make1010028\n\n, \nhttps:\/\/www.mdpi.com\/2504-4990\/1\/1\/28","DOI":"10.3390\/make1010028"},{"issue":"4","key":"49_CR33","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1016\/S0892-1997(05)80210-3","volume":"9","author":"ME Smith","year":"1995","unstructured":"Smith, M.E., Ramig, L.O., Dromey, C., Perez, K.S., Samandari, R.: Intensive voice treatment in Parkinson disease: laryngostroboscopic findings. J. Voice 9(4), 453\u2013459 (1995)","journal-title":"J. Voice"},{"key":"49_CR34","doi-asserted-by":"publisher","unstructured":"Soares, E., Costa, P., Costa, B., Leite, D.: Ensemble of evolving data clouds and fuzzy models for weather time series prediction. Appl. Soft Comput. 64, 445\u2013453 (2018). \nhttps:\/\/doi.org\/10.1016\/j.asoc.2017.12.032\n\n, \nhttp:\/\/www.sciencedirect.com\/science\/article\/pii\/S1568494617307573","DOI":"10.1016\/j.asoc.2017.12.032"},{"key":"49_CR35","doi-asserted-by":"publisher","unstructured":"Souza, P.V.C., et al.: Using hybrid systems in the construction of expert systems in the identification of cognitive and motor problems in children and young people. In: 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1\u20136, June 2019. \nhttps:\/\/doi.org\/10.1109\/FUZZ-IEEE.2019.8858906","DOI":"10.1109\/FUZZ-IEEE.2019.8858906"},{"key":"49_CR36","doi-asserted-by":"crossref","unstructured":"Vaiciukynas, E., Verikas, A., Gelzinis, A., Bacauskiene, M.: Detecting Parkinson\u2019s disease from sustained phonation and speech signals. PloS One 12(10) (2017)","DOI":"10.1371\/journal.pone.0185613"},{"key":"49_CR37","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3264-1","volume-title":"The Nature of Statistical Learning Theory","author":"V Vapnik","year":"2013","unstructured":"Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (2013). \nhttps:\/\/doi.org\/10.1007\/978-1-4757-3264-1"},{"issue":"1","key":"49_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/bios10010001","volume":"10","author":"R Viswanathan","year":"2020","unstructured":"Viswanathan, R., et al.: Complexity measures of voice recordings as a discriminative tool for Parkinson\u2019s disease. Biosensors 10(1), 1 (2020)","journal-title":"Biosensors"},{"issue":"11","key":"49_CR39","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1016\/j.molmed.2006.09.007","volume":"12","author":"A Wood-Kaczmar","year":"2006","unstructured":"Wood-Kaczmar, A., Gandhi, S., Wood, N.: Understanding the molecular causes of Parkinson\u2019s disease. Trends Mole. Med. 12(11), 521\u2013528 (2006)","journal-title":"Trends Mole. Med."},{"issue":"1","key":"49_CR40","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/0165-0114(95)00133-6","volume":"80","author":"RR Yager","year":"1996","unstructured":"Yager, R.R., Rybalov, A.: Uninorm aggregation operators. Fuzzy Sets Syst. 80(1), 111\u2013120 (1996)","journal-title":"Fuzzy Sets Syst."},{"issue":"1","key":"49_CR41","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1186\/s12938-016-0242-6","volume":"15","author":"HH Zhang","year":"2016","unstructured":"Zhang, H.H., et al.: Classification of Parkinson\u2019s disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples. Biomed. Eng. Online 15(1), 122 (2016)","journal-title":"Biomed. Eng. Online"}],"container-title":["Communications in Computer and Information Science","Information Processing and Management of Uncertainty in Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-50143-3_49","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,6,5]],"date-time":"2020-06-05T23:38:47Z","timestamp":1591400327000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-50143-3_49"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030501426","9783030501433"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-50143-3_49","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"5 June 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IPMU","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lisbon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 June 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 June 2020","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":"ipmu2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ipmu2020.inesc-id.pt\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"213","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"146","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"69% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The IPMU 2020 was held virtually due to the coronavirus pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}