{"created":"2020-09-01T09:51:43.869456+00:00","id":3265,"links":{},"metadata":{"_buckets":{"deposit":"0265dc56-3798-47ec-88f5-23b99e9fa519"},"_deposit":{"id":"3265","owners":[],"pid":{"revision_id":0,"type":"recid","value":"3265"},"status":"published"},"_oai":{"id":"oai:meral.edu.mm:recid/3265","sets":["1582963302567:1597824273898"]},"communities":["ucsy"],"item_1583103067471":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"Classification of Myanmar Rice using Back Propagation Neural Network","subitem_1551255648112":"en"}]},"item_1583103085720":{"attribute_name":"Description","attribute_value_mlt":[{"interim":"Neural networks(NN) are a very populardata mining, classification, and image-processingtool.In this paper, neural network model is used toclassify the specific features of rice. The purposeof rice grading is to ensure that the rice producedfor the market meets the quality requirements ofconsumer and to help the experts whose decisionprocess will benefit for grading of the product.Thegrading of rice is important in the rice productionindustry becauserice quality affects the price andmarket demand. The purpose of this paper is todesign a NN model for rice grading based on thetype of rice (Emata, Zeera, Ngasein) and toshowthe accuracy of NN based on mean squareerror. Rules are extracted from trained datasetsand these rules are used for grading of rice. Thereare 300 data in the dataset. These data are thefeatures of rice that are received from theDepartment of Consumer Affairs, Ministry ofCommerce."}]},"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":"2019-07-19"}],"displaytype":"preview","filename":"163_PDFsam_PSC_final proof.pdf","filesize":[{"value":"137 Kb"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"url":"https://meral.edu.mm/record/3265/files/163_PDFsam_PSC_final proof.pdf"},"version_id":"c8127f8c-e6a4-4fe6-aed1-18188469852b"}]},"item_1583103131163":{"attribute_name":"Journal articles","attribute_value_mlt":[{"subitem_issue":"","subitem_journal_title":"Eighth Local Conference on Parallel and Soft Computing","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":"Win, Thu Zar"},{"subitem_authors_fullname":"Khaing, Wint Aye"}]}]},"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":"2017-12-27"},"item_1583159847033":{"attribute_name":"Identifier","attribute_value":"http://onlineresource.ucsy.edu.mm/handle/123456789/1032"},"item_title":"Classification of Myanmar Rice using Back Propagation Neural Network","item_type_id":"21","owner":"1","path":["1597824273898"],"publish_date":"2019-07-19","publish_status":"0","recid":"3265","relation_version_is_last":true,"title":["Classification of Myanmar Rice using Back Propagation Neural Network"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2021-12-13T00:42:15.615223+00:00"}