{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T13:08:07Z","timestamp":1772716087178,"version":"3.50.1"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031342035","type":"print"},{"value":"9783031342042","type":"electronic"}],"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_25","type":"book-chapter","created":{"date-parts":[[2023,6,6]],"date-time":"2023-06-06T23:04:18Z","timestamp":1686092658000},"page":"291-302","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Discrimination of\u00a0Attention Deficit Hyperactivity Disorder Using Capsule Networks and\u00a0LSTM Networks on\u00a0fMRI Data"],"prefix":"10.1007","author":[{"given":"Arunav","family":"Dey","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jigya","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manaswini","family":"Rathore","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roshni","family":"Govind","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vandana M.","family":"Ladwani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,7]]},"reference":[{"key":"25_CR1","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.neuroimage.2016.06.034","volume":"144","author":"P Bellec","year":"2017","unstructured":"Bellec, P., Chu, C., Chouinard-Decorte, F., Benhajali, Y., Margulies, D.S., Craddock, R.C.: The neuro bureau ADHD-200 preprocessed repository. Neuroimage 144, 275\u2013286 (2017)","journal-title":"Neuroimage"},{"key":"25_CR2","doi-asserted-by":"crossref","unstructured":"Castellanos, F.X., et al.: Cingulate-precuneus interactions: A new locus of dysfunction in adult attention-deficit\/hyperactivity disorder. Biol. Psychiat. 63(3), 332\u2013337 (2008)","DOI":"10.1016\/j.biopsych.2007.06.025"},{"key":"25_CR3","doi-asserted-by":"crossref","unstructured":"Consortium, A.: The ADHD-200 consortium: A model to advance the translational potential of neuroimaging in clinical neuroscience. Front. Syst. Neurosci. 6, 62 (2012)","DOI":"10.3389\/fnsys.2012.00062"},{"key":"25_CR4","doi-asserted-by":"crossref","unstructured":"Hao, A.J., He, B.L., Yin, C.H.: Discrimination of ADHD children based on deep bayesian network. In: 2015 IET International Conference on Biomedical Image and Signal Processing (ICBISP 2015), pp. 1\u20136. IET (2015)","DOI":"10.1049\/cp.2015.0764"},{"key":"25_CR5","unstructured":"Kim, B., Park, J., Kim, T., Kwon, Y.: Finding essential parts of the brain in RS-FMRI can improve diagnosing ADHD by deep learning. arXiv preprint arXiv:2108.10137 (2021)"},{"issue":"6","key":"25_CR6","doi-asserted-by":"publisher","first-page":"904","DOI":"10.1002\/hbm.21058","volume":"31","author":"K Konrad","year":"2010","unstructured":"Konrad, K., Eickhoff, S.B.: Is the ADHD brain wired differently? A review on structural and functional connectivity in attention deficit hyperactivity disorder. Human Brain Map. 31(6), 904\u2013916 (2010)","journal-title":"Human Brain Map."},{"key":"25_CR7","doi-asserted-by":"publisher","unstructured":"Kuang, D., Guo, X., An, X., Zhao, Y., He, L.: Discrimination of ADHD based on fMRI data with deep belief network. In: Huang, D.-S., Han, K., Gromiha, M. (eds.) ICIC 2014. LNCS, vol. 8590, pp. 225\u2013232. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-09330-7_27","DOI":"10.1007\/978-3-319-09330-7_27"},{"key":"25_CR8","doi-asserted-by":"crossref","unstructured":"Liu, R., Huang, Z.A., Jiang, M., Tan, K.C.: Multi-LSTM networks for accurate classification of attention deficit hyperactivity disorder from resting-state fMRI data. In: 2020 2nd International Conference on Industrial Artificial Intelligence (IAI), pp. 1\u20136. IEEE (2020)","DOI":"10.1109\/IAI50351.2020.9262176"},{"key":"25_CR9","doi-asserted-by":"crossref","unstructured":"Mao, Z., et al.: Spatio-temporal deep learning method for ADHD FMRI classification. Inf. Sci. 499, 1\u201311 (2019)","DOI":"10.1016\/j.ins.2019.05.043"},{"key":"25_CR10","doi-asserted-by":"crossref","unstructured":"Riaz, A., et al.: Deep fMRI: An end-to-end deep network for classification of fMRI data. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 1419\u20131422. IEEE (2018)","DOI":"10.1109\/ISBI.2018.8363838"},{"key":"25_CR11","unstructured":"Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"25_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.physd.2019.132306","volume":"404","author":"A Sherstinsky","year":"2020","unstructured":"Sherstinsky, A.: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlin. Phenomena 404, 132306 (2020)","journal-title":"Physica D: Nonlin. Phenomena"},{"key":"25_CR13","doi-asserted-by":"crossref","unstructured":"Wang, D., Hong, D., Wu, Q.: Attention deficit hyperactivity disorder classification based on deep learning. IEEE\/ACM Trans. Comput. Biol. Bioinform. (2022)","DOI":"10.1109\/TCBB.2022.3170527"},{"key":"25_CR14","doi-asserted-by":"crossref","unstructured":"Zhang, T., et al.: Separated channel attention convolutional neural network (SC-CNN-attention) to identify ADHD in multi-site RS-FMRI dataset. Entropy 22(8), 893 (2020)","DOI":"10.3390\/e22080893"},{"key":"25_CR15","doi-asserted-by":"publisher","first-page":"23626","DOI":"10.1109\/ACCESS.2017.2762703","volume":"5","author":"L Zou","year":"2017","unstructured":"Zou, L., Zheng, J., Miao, C., Mckeown, M.J., Wang, Z.J.: 3D CNN based automatic diagnosis of attention deficit hyperactivity disorder using functional and structural MRI. IEEE Access 5, 23626\u201323636 (2017)","journal-title":"IEEE Access"}],"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_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,6]],"date-time":"2023-06-06T23:12:45Z","timestamp":1686093165000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-34204-2_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031342035","9783031342042"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-34204-2_25","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"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)"}}]}}