{"links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00220665","sets":["6504:11035:11037"]},"path":["11037"],"owner":"44499","recid":"220665","title":["顔面可視画像に基づく高血圧検出のための深層学習パラメータの最適化"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-02-17"},"_buckets":{"deposit":"5c440ade-9d7d-43ae-b692-3d40d25268bb"},"_deposit":{"id":"220665","pid":{"type":"depid","value":"220665","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"顔面可視画像に基づく高血圧検出のための深層学習パラメータの最適化","author_link":["577361","577363","577360","577362"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"顔面可視画像に基づく高血圧検出のための深層学習パラメータの最適化"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ソフトウェア科学・工学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2022-02-17","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"青学大"},{"subitem_text_value":"青学大"},{"subitem_text_value":"青学大"},{"subitem_text_value":"青学大"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/220665/files/IPSJ-Z84-5M-01.pdf","label":"IPSJ-Z84-5M-01.pdf"},"date":[{"dateType":"Available","dateValue":"2022-10-22"}],"format":"application/pdf","filename":"IPSJ-Z84-5M-01.pdf","filesize":[{"value":"355.5 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"bfefd131-7dc3-48ce-a0ed-9ece51896ccb","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"椎名, 大智"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"南雲, 健人"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"大岩, 孝輔"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"野澤, 昭雄"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"高血圧症の予防には,日常的な血圧モニタリングが重要であり,その実現には遠隔的な血圧センシング技術の確立が望まれる.我々は,血行動態の情報が含まれる顔面可視画像に基づいて血圧を推定する研究を行っている.先行研究では,深層学習アルゴリズムの一つであるCNNを用いた顔面可視画像に基づく高血圧検出のための個人モデルを構築した.応用に際し,個人モデルではなく一般モデルの構築が求められる.さらに, CNNの構造や学習に関するパラメータの最適化は高血圧検出の精度向上において必要不可欠である.本研究では,顔面可視画像に基づく高血圧検出のための深層学習パラメータの最適化を行う.","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"402","bibliographic_titles":[{"bibliographic_title":"第84回全国大会講演論文集"}],"bibliographicPageStart":"401","bibliographicIssueDates":{"bibliographicIssueDate":"2022-02-17","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2022"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:20:41.830598+00:00","updated":"2025-01-19T14:28:47.836228+00:00","id":220665}