{"_buckets": {"deposit": "c1136cdf-f505-4fc7-821e-3e9e81804e2d"}, "_deposit": {"id": "3343", "owners": [1], "pid": {"revision_id": 0, "type": "depid", "value": "3343"}, "status": "published"}, "_oai": {"id": "oai:nied-repo.bosai.go.jp:00003343", "sets": []}, "author_link": [], "item_10001_biblio_info_7": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2020-09-09", "bibliographicIssueDateType": "Issued"}, "bibliographicPageEnd": "436", "bibliographicPageStart": "431", "bibliographic_titles": [{"bibliographic_title": "第 36 回ファジィシステムシンポジウム 講演論文集 (FSS2020 オンライン)", "bibliographic_titleLang": "ja"}]}]}, "item_10001_description_5": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "Recently, people have become more aware of disaster prevention due to the effects of natural\ndisasters that cause serious damage. The concept of disaster prevention consists of three elements:\n\"pre-disaster prevention\" before disaster, \"prevention of damage expansion\" and \"disaster recovery and\nreconstruction\" after disaster. The current disaster prevention system can observe before the disaster;\nhowever, cannot observe the damage situation after the disaster. Delaying an initial response to a\ndisaster has a great impact on rescue activities and secondary disasters, so a disaster prevention system\nafter disaster is very important for disaster prevention. In recent years, anomaly detection systems\nusing big data have been attracting attention as disaster prevention systems after disasters. In this\nthesis, we focused on people flow data among big data, and discussed a social confusion detection system\nby machine learning using human flow data in normal and abnormal times.", "subitem_description_language": "en", "subitem_description_type": "Other"}]}, "item_10001_publisher_8": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "日本知能情報ファジィ学会", "subitem_publisher_language": "ja"}]}, "item_10001_relation_14": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type_id": {"subitem_relation_type_id_text": "10.14864/fss.36.0_431"}}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "木南優希", "creatorNameLang": "ja"}, {"creatorName": "Yuki Kinami", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "澁谷長史", "creatorNameLang": "ja"}, {"creatorName": "Takeshi Shibuya", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "取出新吾", "creatorNameLang": "ja"}, {"creatorName": "Shingo Toride", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "遠藤靖典", "creatorNameLang": "ja"}, {"creatorName": "Yasunori Endo", "creatorNameLang": "en"}]}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "jpn"}]}, "item_title": "人流データを用いた機械学習による社会的混乱検知システムの基礎的検討", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "人流データを用いた機械学習による社会的混乱検知システムの基礎的検討", "subitem_title_language": "ja"}, {"subitem_title": "A Fundamental Study of Social Confusion Detection System by Machine Learning using Human Flow Data", "subitem_title_language": "en"}]}, "item_type_id": "40001", "owner": "1", "path": ["1670839190650"], "permalink_uri": "https://nied-repo.bosai.go.jp/records/3343", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2023-03-30"}, "publish_date": "2023-03-30", "publish_status": "0", "recid": "3343", "relation": {}, "relation_version_is_last": true, "title": ["人流データを用いた機械学習による社会的混乱検知システムの基礎的検討"], "weko_shared_id": -1}