{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T19:57:10Z","timestamp":1743019030683,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031342035"},{"type":"electronic","value":"9783031342042"}],"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\/978-3-031-34204-2_44","type":"book-chapter","created":{"date-parts":[[2023,6,6]],"date-time":"2023-06-06T23:04:18Z","timestamp":1686092658000},"page":"548-557","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Subsampled Dataset Challenges and\u00a0Machine Learning Techniques in\u00a0Table Tennis"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4311-3449","authenticated-orcid":false,"given":"Dimitrios","family":"Simopoulos","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6130-3900","authenticated-orcid":false,"given":"Andreas","family":"Nikolakakis","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6604-1110","authenticated-orcid":false,"given":"George","family":"Anastassopoulos","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,7]]},"reference":[{"key":"44_CR1","doi-asserted-by":"crossref","unstructured":"Kulkarni, K., Shenoy, S.: Table tennis stroke recognition using two-dimensional human pose estimation. In: Proceedings Of The IEEE\/CVF Conference On Computer Vision And Pattern Recognition, pp. 4576\u20134584 (2021)","DOI":"10.1109\/CVPRW53098.2021.00515"},{"key":"44_CR2","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1109\/TVCG.2021.3114832","volume":"28","author":"J Wu","year":"2021","unstructured":"Wu, J., Liu, D., Guo, Z., Xu, Q., Wu, Y.: TacticFlow: Visual analytics of ever-changing tactics in racket sports. IEEE Trans. Visual. Comput. Graph. 28, 835\u2013845 (2021)","journal-title":"IEEE Trans. Visual. Comput. Graph."},{"key":"44_CR3","first-page":"e2","volume":"2","author":"L Draschkowitz","year":"2015","unstructured":"Draschkowitz, L., Draschkowitz, C., Hlavacs, H.: Using video analysis and machine learning for predicting shot success in table tennis. EAI Endorsed Trans. Creative Technol. 2, e2\u2013e2 (2015)","journal-title":"EAI Endorsed Trans. Creative Technol."},{"key":"44_CR4","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1080\/21642583.2018.1450167","volume":"6","author":"Y Ji","year":"2018","unstructured":"Ji, Y., Zhang, J., Shi, Z., Liu, M., Ren, J.: Research on real-time tracking of table tennis ball based on machine learning with low-speed camera. Systems Sci. Control Eng. 6, 71\u201379 (2018)","journal-title":"Systems Sci. Control Eng."},{"key":"44_CR5","doi-asserted-by":"crossref","unstructured":"Zhang, H., et al.: Application of intelligent sensor network in the assessment of table tennis teaching and training intensity, training volume, and physical fitness. J. Sensors 2022 (2022)","DOI":"10.1155\/2022\/4553644"},{"key":"44_CR6","doi-asserted-by":"crossref","unstructured":"Djoki\u0107, Z., Malagoli Lanzoni, I., Katsikadelis, M., Straub, G., et al.: Others Serve analyses of elite European table tennis matches, Universidad de Granada (2020)","DOI":"10.30827\/Digibug.63715"},{"key":"44_CR7","doi-asserted-by":"publisher","first-page":"228","DOI":"10.9734\/BJMCS\/2011\/623","volume":"1","author":"P Wong","year":"2011","unstructured":"Wong, P., Dooley, L.: Tracking table tennis balls in real match scenes for umpiring applications. British J. Math. Comput. Sci. 1, 228\u2013241 (2011)","journal-title":"British J. Math. Comput. Sci."},{"key":"44_CR8","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1016\/j.proeng.2011.05.088","volume":"13","author":"R Gastinger","year":"2011","unstructured":"Gastinger, R., Litzenberger, S., Sabo, A.: Design, development and construction of a monitoring table tennis net. Proc. Eng. 13, 297\u2013303 (2011)","journal-title":"Proc. Eng."},{"key":"44_CR9","doi-asserted-by":"crossref","unstructured":"Blank, P., Groh, B., Eskofier, B.: Ball speed and spin estimation in table tennis using a racket-mounted inertial sensor. In: Proceedings of the 2017 ACM International Symposium on Wearable Computers, pp. 2\u20139 (2017)","DOI":"10.1145\/3123021.3123040"},{"key":"44_CR10","doi-asserted-by":"crossref","unstructured":"Nonaka, Y., Irie, K., Ando, S., Yamada, Y. Application of IMU-based motion measurement methods to table tennis coaching. In: Proceedings of the Symposium On Sports and Human Dynamics. Japan Society Of Mechanical Engineers, Tokyo, Japan (2018)","DOI":"10.1299\/jsmeshd.2018.B-2"},{"key":"44_CR11","doi-asserted-by":"crossref","unstructured":"Blank, P., Ho\u00dfbach, J., Schuldhaus, D., Eskofier, B.: Sensor-based stroke detection and stroke type classification in table tennis. Proceedings of the 2015 ACM International Symposium on Wearable Computers, pp. 93\u2013100 (2015)","DOI":"10.1145\/2802083.2802087"},{"key":"44_CR12","doi-asserted-by":"crossref","unstructured":"Ba\u0144kosz, Z., Winiarski, S.: Using wearable inertial sensors to estimate kinematic parameters and variability in the table tennis topspin forehand stroke. Applied Bion. Biomech. (2020)","DOI":"10.1155\/2020\/8413948"},{"key":"44_CR13","doi-asserted-by":"crossref","unstructured":"Li, H., et al.: Others Video-based table tennis tracking and trajectory prediction using convolutional neural networks. Fractals 30 (2022)","DOI":"10.1142\/S0218348X22401569"},{"key":"44_CR14","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1080\/17461391.2013.819382","volume":"14","author":"I Malagoli Lanzoni","year":"2014","unstructured":"Malagoli Lanzoni, I., Di Michele, R., Merni, F.: A notational analysis of shot characteristics in top-level table tennis players. Europ. J. Sport Sci. 14, 309\u2013317 (2014)","journal-title":"Europ. J. Sport Sci."},{"key":"44_CR15","doi-asserted-by":"publisher","first-page":"2627","DOI":"10.1016\/S1352-2310(97)00447-0","volume":"32","author":"M Gardner","year":"1998","unstructured":"Gardner, M., Dorling, S.: Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences. Atmos. Environ. 32, 2627\u20132636 (1998)","journal-title":"Atmos. Environ."},{"key":"44_CR16","unstructured":"Ali, J., Khan, R., Ahmad, N., Maqsood, I.: Random forests and decision trees. Int. J. Comput. Sci. Issues (IJCSI) 9, 272 (2012)"},{"key":"44_CR17","unstructured":"Si, S., Zhang, H., Keerthi, S., Mahajan, D., Dhillon, I., Hsieh, C.: Gradient boosted decision trees for high dimensional sparse output. In: International Conference On Machine Learning, pp. 3182\u20133190 (2017)"},{"key":"44_CR18","unstructured":"Abadi, M., et al.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. (2015). https:\/\/www.tensorflow.org\/, Software available from tensorflow.org"},{"key":"44_CR19","unstructured":"Chollet, F., et al.: Keras (2015). https:\/\/keras.io"},{"key":"44_CR20","doi-asserted-by":"crossref","unstructured":"Zhang, Z.: Improved adam optimizer for deep neural networks. In: 2018 IEEE\/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1\u20132 (2018)","DOI":"10.1109\/IWQoS.2018.8624183"},{"key":"44_CR21","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1093\/biomet\/85.2.363","volume":"85","author":"D Denison","year":"1998","unstructured":"Denison, D., Mallick, B., Smith, A.: A bayesian cart algorithm. Biometrika 85, 363\u2013377 (1998)","journal-title":"Biometrika"}],"container-title":["Communications in Computer and Information Science","Engineering Applications of Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-34204-2_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,6]],"date-time":"2023-06-06T23:16:52Z","timestamp":1686093412000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-34204-2_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031342035","9783031342042"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-34204-2_44","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"7 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Engineering Applications of Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Le\u00f3n","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":"14 June 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 June 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eann2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eannconf.org\/2023\/","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":"Easyacademia","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"125","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":"41","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":"8","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":"33% - 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":"2.4","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":"3.2","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}