{"_buckets": {"deposit": "c0753036-63ba-49f6-b75e-4dfd3c4e486d"}, "_deposit": {"id": "4524", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "4524"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/4524", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "A Comparative Study Using Two Classifiers For Hazardous Audio Event Classification", "subitem_1551255648112": "en_US"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "The hazardous acoustic event classificationsystem is presented and tested in threateningenvironments. The system is based on classifiedwith Support Vector Machine (SVM), k NearestNeighbor (kNN) and modeled with GeneticRegulatory Network (GRN). GRN is adopted asclassification framework and greatly reducedinput feature dimensions. Setting the results thathave already reduced the inputs dimensions fromGRN framework as inputs for SVM and kNN cancorrectly classify audio event with lowcomputational time and cost. Comparative andclassification tests are carried out using threekinds of input sets with SVM and kNN classifier.These input sets are original feature set, reduceddimension feature set by GRN and unique featureset. SVM applies as novel discriminativeapproach for dissimilarity measure in order toaddress a supervised sound-classification taskand then shows good performance in the task ofacoustic event classification. Selecting GRN inevent classification system can not only reducescost and effort but also aims to obtain highperformance and accuracy in varying nature ofenvironments."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Acoustic Surveillance"}, {"interim": "Audio Features"}, {"interim": "Audio events"}, {"interim": "Classification tasks"}, {"interim": "k Nearest neighbor"}, {"interim": "Genetic Regulatory Network"}, {"interim": "Support Vector Machine"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2019-11-15"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "10105.pdf", "filesize": [{"value": "247 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 247000.0, "url": {"url": "https://meral.edu.mm/record/4524/files/10105.pdf"}, "version_id": "901eb6e9-53c0-4ad4-9197-6353994752d3"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Tenth International Conference On Computer Applications (ICCA 2012)", "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": "Kyaw, Tin Ei"}]}]}, "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": "2012-02-28"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/2449"}, "item_title": "A Comparative Study Using Two Classifiers For Hazardous Audio Event Classification", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000004524", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-11-15"}, "publish_date": "2019-11-15", "publish_status": "0", "recid": "4524", "relation": {}, "relation_version_is_last": true, "title": ["A Comparative Study Using Two Classifiers For Hazardous Audio Event Classification"], "weko_shared_id": -1}