{"_buckets": {"deposit": "fc754291-d47a-411d-a41f-7f1ea2134969"}, "_deposit": {"id": "4415", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "4415"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/4415", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Efficient Rules Extraction using Rough Set Theory for Weather Data Analysis", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "The system extracts optimal rule fromweather data set based on rough set theory. Themain idea of rough set theory is to obtain assimple as rules from the given database byreducing the database while holding the originaldegree of consistency. In order to find theoptimal rule of weather from the historical datait provide easy and accurate for the weatherforecast. This system included the processes ofindiscernibility, set approximation, attributesreduction, rules extraction and optimal ruleselection. GDT-RS (Generalization DistributionTable for Rough Sets) are used for the rulesextraction and optimal rule selection. Thissystem analyzes the relationship between theweather condition attribute and other attributesof weather data set by calculating thedependency and accuracy between them."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value": []}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2019-07-03"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "9079.pdf", "filesize": [{"value": "116 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 116000.0, "url": {"url": "https://meral.edu.mm/record/4415/files/9079.pdf"}, "version_id": "027e66fc-a48c-4734-ab71-85a353e62312"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Ninth International Conference On Computer Applications (ICCA 2011)", "subitem_pages": "", "subitem_volume": ""}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "", "subitem_c_date": "", "subitem_conference_title": "", "subitem_part": "", "subitem_place": "", "subitem_session": "", "subitem_website": ""}]}, "item_1583103211336": {"attribute_name": "Books/reports/chapters", "attribute_value_mlt": [{"subitem_book_title": "", "subitem_isbn": "", "subitem_pages": "", "subitem_place": "", "subitem_publisher": ""}]}, "item_1583103233624": {"attribute_name": "Thesis/dissertations", "attribute_value_mlt": [{"subitem_awarding_university": "", "subitem_supervisor(s)": [{"subitem_supervisor": ""}]}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Ei, Nyein Nyein"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Article"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2011-05-05"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/235"}, "item_title": "Efficient Rules Extraction using Rough Set Theory for Weather Data Analysis", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000004415", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-03"}, "publish_date": "2019-07-03", "publish_status": "0", "recid": "4415", "relation": {}, "relation_version_is_last": true, "title": ["Efficient Rules Extraction using Rough Set Theory for Weather Data Analysis"], "weko_shared_id": -1}