{"_buckets": {"deposit": "bc8904cc-fb18-4be0-a2a5-7ef08a2829a3"}, "_deposit": {"id": "4872", "owners": [1], "pid": {"revision_id": 0, "type": "depid", "value": "4872"}, "status": "published"}, "_oai": {"id": "oai:nied-repo.bosai.go.jp:00004872", "sets": []}, "author_link": [], "item_10001_biblio_info_7": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2017", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "0", "bibliographicPageEnd": "277", "bibliographicPageStart": "271", "bibliographicVolumeNumber": "31", "bibliographic_titles": [{"bibliographic_title": "地域安全学会論文集", "bibliographic_titleLang": "ja"}, {"bibliographic_title": "Journal of Social Safety Science", "bibliographic_titleLang": "en"}]}]}, "item_10001_description_5": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "\u003cp\u003eIn order to conduct regional disaster prevention activities continuously and appropriately, it is necessary to evaluate the effectiveness and appropriateness, and make the improvements based on evaluation. However, there are insufficient human resources of evaluators and it is difficult to foster the talent, since extensive field experience and broad disaster prevention knowledge is required for appropriate evaluation. This study constructed a prototype of a machine learning system for automatically evaluating regional disaster prevention activities. It also performs the machine learning with the data from activity records of the Bosai Contest as input variables, and the winning judgment data evaluated by the expert\u0027s review committee as output variables. As a result, the same result as experts\u0027 judgment was obtained with the probability of 94% for learning data and 79% for verification data.\u003c/p\u003e", "subitem_description_language": "ja", "subitem_description_type": "Other"}, {"subitem_description": "\u003cp\u003eIn order to conduct regional disaster prevention activities continuously and appropriately, it is necessary to evaluate the effectiveness and appropriateness, and make the improvements based on evaluation. However, there are insufficient human resources of evaluators and it is difficult to foster the talent, since extensive field experience and broad disaster prevention knowledge is required for appropriate evaluation. This study constructed a prototype of a machine learning system for automatically evaluating regional disaster prevention activities. It also performs the machine learning with the data from activity records of the Bosai Contest as input variables, and the winning judgment data evaluated by the expert\u0027s review committee as output variables. As a result, the same result as experts\u0027 judgment was obtained with the probability of 94% for learning data and 79% for verification data.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e", "subitem_description_language": "en", "subitem_description_type": "Other"}]}, "item_10001_publisher_8": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "一般社団法人 地域安全学会", "subitem_publisher_language": "ja"}, {"subitem_publisher": "Institute of Social Safety Science", "subitem_publisher_language": "en"}]}, "item_10001_relation_14": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type_id": {"subitem_relation_type_id_text": "10.11314/jisss.31.271"}}]}, "item_10001_source_id_9": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "1345-2088", "subitem_source_identifier_type": "ISSN"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "崔 青林", "creatorNameLang": "ja"}, {"creatorName": "CUI Qinglin", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "島崎 敢", "creatorNameLang": "ja"}, {"creatorName": "SHIMAZAKI Kan", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "李 泰榮", "creatorNameLang": "ja"}, {"creatorName": "YI Taiyoung", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "臼田 裕一郎", "creatorNameLang": "ja"}, {"creatorName": "USUDA Yuichiro", "creatorNameLang": "en"}]}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "jpn"}]}, "item_title": "機械学習を用いた地域防災活動の評価モデルの自動生成方法に関する研究", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "機械学習を用いた地域防災活動の評価モデルの自動生成方法に関する研究", "subitem_title_language": "ja"}, {"subitem_title": "Automatic Performing Method of Regional Disaster Prevention Activities Evaluation Model Using Machine Learning", "subitem_title_language": "en"}]}, "item_type_id": "40001", "owner": "1", "path": ["1670839190650"], "permalink_uri": "https://nied-repo.bosai.go.jp/records/4872", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2023-03-30"}, "publish_date": "2023-03-30", "publish_status": "0", "recid": "4872", "relation": {}, "relation_version_is_last": true, "title": ["機械学習を用いた地域防災活動の評価モデルの自動生成方法に関する研究"], "weko_shared_id": -1}