{"created":"2020-09-01T09:51:34.184787+00:00","id":3263,"links":{},"metadata":{"_buckets":{"deposit":"7edf917f-4d0c-46b0-ae4c-740291f00c75"},"_deposit":{"id":"3263","owners":[],"pid":{"revision_id":0,"type":"recid","value":"3263"},"status":"published"},"_oai":{"id":"oai:meral.edu.mm:recid/3263","sets":["1582963302567:1597824273898"]},"communities":["ucsy"],"item_1583103067471":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"Enhancing Neural Network Training by Using Artificial Bee Colony Algorithm","subitem_1551255648112":"en"}]},"item_1583103085720":{"attribute_name":"Description","attribute_value_mlt":[{"interim":"Multilayer feedforward networks are oneof the most used neural networks in variousdomains because of their universal approximationability. One of the popular algorithms for trainingmultilayer feedforward network isbackpropagation which uses two phase namelyfeedforward and backpropagate to learn theweight in the network. The main disadvantage ofthe backpropagation algorithm is its convergencerate is slow at it always being trapped in localminima.Artificial Bee Colony (ABC) algorithm isone of the most recently introduced swarmbasedalgorithms.ABC simulates the intelligentforaging behavior of a honeybee swarm.Theproposed method in this paper includes anartificial bee colony algorithm based neuralnetwork training method and back-propagationbased neural network. And then compare accuracyand mean square rate for both neural networktraining. Four type of UCI datasets are used forboth neural network training."}]},"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":"161_PDFsam_PSC_final proof.pdf","filesize":[{"value":"244 Kb"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"url":"https://meral.edu.mm/record/3263/files/161_PDFsam_PSC_final proof.pdf"},"version_id":"e3b17296-6e51-4afe-b62f-b9f3f16ad9ca"}]},"item_1583103131163":{"attribute_name":"Journal articles","attribute_value_mlt":[{"subitem_issue":"","subitem_journal_title":"","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":"Thuzar, Pyae Phyo"},{"subitem_authors_fullname":"Kham, Nang Saing Moon"}]}]},"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/123456789/1030"},"item_title":"Enhancing Neural Network Training by Using Artificial Bee Colony Algorithm","item_type_id":"21","owner":"1","path":["1597824273898"],"publish_date":"2019-07-19","publish_status":"0","recid":"3263","relation_version_is_last":true,"title":["Enhancing Neural Network Training by Using Artificial Bee Colony Algorithm"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2022-03-24T23:11:27.057356+00:00"}