{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T21:40:05Z","timestamp":1773524405018,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":19,"publisher":"Springer Singapore","isbn-type":[{"value":"9789811651878","type":"print"},{"value":"9789811651885","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-981-16-5188-5_18","type":"book-chapter","created":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T23:04:52Z","timestamp":1629414292000},"page":"242-254","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Data Enhancement Method for Gene Expression Profile Based on Improved WGAN-GP"],"prefix":"10.1007","author":[{"given":"Shaojun","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Fei","family":"Han","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,8,20]]},"reference":[{"key":"18_CR1","doi-asserted-by":"crossref","unstructured":"Shah, S.H., Iqbal, M.J., Ahmad, I., Khan, S., Rodrigues, J.J.P.C.: Optimized gene selection and classification of cancer from microarray gene expression data using deep learning. Neural Comput. Appl. 1\u201312 (2020)","DOI":"10.1007\/s00521-020-05367-8"},{"key":"18_CR2","doi-asserted-by":"crossref","unstructured":"Aduviri, R., Matos, D., Villanueva, E.: Feature selection algorithm recommendation for gene expression data through gradient boosting and neural network metamodels. In: Proceedings of 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2726\u20132728 (2018)","DOI":"10.1109\/BIBM.2018.8621397"},{"key":"18_CR3","doi-asserted-by":"crossref","unstructured":"Yuan, J., Li, K.: The fault diagnosis model for railway system based on an improved feature selection method. In: Proceedings of 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication (ICEIEC), pp. 1\u20134 (2019)","DOI":"10.1109\/ICEIEC.2019.8784619"},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"Fang, F., Lv, Q.Q., Wang, M.S., Yang, X.H., Zhou, Q.G., Zhou, R.: A hybrid feature selection algorithm applied to high-dimensional imbalanced small-sample data classification. In: Proceedings of 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 41\u201346 (2019)","DOI":"10.1109\/APSIPAASC47483.2019.9023210"},{"key":"18_CR5","doi-asserted-by":"crossref","unstructured":"Roth, H.R., et al.: Anatomy-specific classification of medical images using deep convolutional nets. In: Proceedings of 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp. 101\u2013104 (2015)","DOI":"10.1109\/ISBI.2015.7163826"},{"key":"18_CR6","unstructured":"Omer, D.: Classification of heart sounds with re-sampled energy method. In: Proceedings of 2018 26th Signal Processing and Communications Applications Conference (SIU), pp. 1\u20134 (2018)"},{"key":"18_CR7","unstructured":"Goodfellow, I.J., et al.: Generative adversarial nets. In: Proceedings of Advances in Neural Information Processing Systems (NIPS), pp. 2672\u20132680 (2014)"},{"key":"18_CR8","unstructured":"Mirza, M., Simon, O.: Conditional generative adversarial nets. arXiv e-prints, arXiv:1411.1784 (2014)"},{"issue":"4","key":"18_CR9","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1109\/TMI.2020.3048975","volume":"40","author":"M Wang","year":"2021","unstructured":"Wang, M., et al.: Semi-supervised capsule cGAN for speckle noise reduction in retinal OCT images. IEEE Trans. Med. Imaging 40(4), 1168\u20131183 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"22","key":"18_CR10","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.neucom.2020.08.040","volume":"418","author":"LY Chen","year":"2020","unstructured":"Chen, L.Y., Liu, Y.F., Xiao, W.D., Wang, Y.X., Xie, H.Y.: SpeakerGAN: speaker identification with conditional generative adversarial network. Neurocomputing 418(22), 211\u2013220 (2020)","journal-title":"Neurocomputing"},{"key":"18_CR11","unstructured":"Martin, A., Soumith, C., L\u00e9on, B.: Wasserstein generative adversarial networks. In: Proceedings of Proceedings of the 34th International Conference on Machine Learning (ICML), pp. 214\u2013223 (2017)"},{"key":"18_CR12","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.: Improved training of wasserstein GANs. In: Proceedings of Advances in Neural Information Processing Systems (NIPS), pp. 5767\u20135777 (2017)"},{"key":"18_CR13","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1016\/j.neucom.2018.10.109","volume":"396","author":"X Gao","year":"2020","unstructured":"Gao, X., Deng, F., Yue, X.H.: Data augmentation in fault diagnosis based on the Wasserstein generative adversarial network with gradient penalty. Neurocomputing 396, 487\u2013494 (2020)","journal-title":"Neurocomputing"},{"key":"18_CR14","doi-asserted-by":"crossref","unstructured":"Luo, Y.Y., Lu, H.G., Jia, N.: Super-resolution algorithm of satellite cloud image based on WGAN-GP. In: Proceedings of 2019 International Conference on Meteorology Observations (ICMO), pp. 1\u20134 (2019)","DOI":"10.1109\/ICMO49322.2019.9026112"},{"issue":"7","key":"18_CR15","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.neucom.2020.10.077","volume":"428","author":"ZX Huang","year":"2021","unstructured":"Huang, Z.X., et al.: Considering anatomical prior information for low-dose CT image enhancement using attribute-augmented wasserstein generative adversarial networks. Neurocomputing 428(7), 104\u2013115 (2021)","journal-title":"Neurocomputing"},{"issue":"4","key":"18_CR16","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.ins.2020.07.073","volume":"545","author":"DY Liu","year":"2021","unstructured":"Liu, D.Y., Huang, X.P., Zhan, W.F., Ai, L.F., Zheng, X., Cheng, S.L.: View synthesis-based light field image compression using a generative adversarial network. Inf. Sci. 545(4), 118\u2013131 (2021)","journal-title":"Inf. Sci."},{"issue":"2","key":"18_CR17","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1109\/JBHI.2020.3042523","volume":"25","author":"YF Jiang","year":"2021","unstructured":"Jiang, Y.F., Chen, H., Loew, M., Ko, H.: COVID-19 CT image synthesis with a conditional generative adversarial network. IEEE J. Biomed. Health Inform. 25(2), 441\u2013452 (2021)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"11","key":"18_CR18","doi-asserted-by":"publisher","first-page":"3236","DOI":"10.1016\/j.patcog.2007.02.007","volume":"40","author":"ZX Zhu","year":"2007","unstructured":"Zhu, Z.X., Ong, Y.S., Dash, M.: Markov blanket-embedded genetic algorithm for gene selection. Pattern Recogn. 40(11), 3236\u20133248 (2007)","journal-title":"Pattern Recogn."},{"issue":"14","key":"18_CR19","doi-asserted-by":"publisher","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","volume":"31","author":"R Genuer","year":"2010","unstructured":"Genuer, R., Poggi, J.M., Tuleau-Malot, C.: Variable selection using random forests. Pattern Recogn. Lett. 31(14), 2225\u20132236 (2010)","journal-title":"Pattern Recogn. Lett."}],"container-title":["Communications in Computer and Information Science","Neural Computing for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-5188-5_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T22:44:19Z","timestamp":1725662659000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-5188-5_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9789811651878","9789811651885"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-5188-5_18","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"20 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NCAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Computing for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 August 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ncaa2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/dl2link.com\/ncaa2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"144","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":"54","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":"0","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":"38% - 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":"3.07","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":"3.62","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)"}}]}}