{"_buckets": {"deposit": "91cd92f5-92b6-4598-8aa9-90540f586489"}, "_deposit": {"id": "4823", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "4823"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/4823", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Automatic Text Classification in Conference Management System using Naïve Bayesian learning and Information Gain", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "This paper presents an automatic text classification system enhanced conference management system. A conference management system is a digital library of a particular conference, where users provide, route and seek for information. In traditional conference management system’s problem can occur in the phase where the author has to choose the research topic under which the paper should be filed. The author can be irresolute and uncertain in selecting the topic of the paper. This feeling can be reinforced when the categories are not precisely described. We also explore the use of information gain for the naive Bayes text classification. In this paper, the classifier is trained with examples from previous conferences and so the author should be guided to the correct category, Furthermore, how the automatic text classification system is trained, where it is integrated into the conference management system can also be presented. Naïve Bayes Classifier is used to classify the conference papers. In this paper, when the conference paper comes to the system, Information gain calculate the probability for the unique text in the paper and then send this text (keyword) to Naïve Bayesian algorithm to classify the correct class for this paper."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value": []}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value": []}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Eleventh International Conference On Computer Applications (ICCA 2013)", "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": "San, Pan 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": "2013-02-26"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/723"}, "item_title": "Automatic Text Classification in Conference Management System using Naïve Bayesian learning and Information Gain", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000004823", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-11"}, "publish_date": "2019-07-11", "publish_status": "0", "recid": "4823", "relation": {}, "relation_version_is_last": true, "title": ["Automatic Text Classification in Conference Management System using Naïve Bayesian learning and Information Gain"], "weko_shared_id": -1}