{"_buckets": {"deposit": "258ae373-7e2d-4505-840d-0976745b3eaf"}, "_deposit": {"id": "4441", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "4441"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/4441", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Forecasting of Maximum and Minimum Temperature in Mandalay by Evolving Artificial Neural Networks Using Genetic Algorithms", "subitem_1551255648112": "en_US"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Forecasting of weather is very popular innowadays. But forecasting the future from theobserved past is very difficult. There are severalforecasting methods for weather data. Amongthem, evolving artificial neural networks aresuitable for weather time series forecastingbecause of their abilities to learn and adapt anew situation by recognizing new patterns inprevious data. However, ANNs present somedrawbacks such as over fitting and long timeprocessing. Using ANNs together with geneticalgorithm comes to solutions of these problems.Genetic artificial neural networks (GANNs) cangive optimal forecasting result from the observedpast. In order to provide more effective ANNs,the proposed system use cascade backpropagation instead of back propagationmethod. Weather parameters (attributes) such asrain fall (precipitation), humidity, wind force,dew point, sea level, wind direction will also beused to forecast maximum and minimumtemperature."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Artificial Neural Networks (ANN)"}, {"interim": "Genetic Algorithms (GA)"}, {"interim": "Time Series (TS)"}, {"interim": "Mean Square Error (MSE)"}, {"interim": "Specific Mean Square Error (SMSE)"}, {"interim": "Multi Layer Perceptron(MLP)"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2019-10-29"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "10002.pdf", "filesize": [{"value": "505 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 505000.0, "url": {"url": "https://meral.edu.mm/record/4441/files/10002.pdf"}, "version_id": "24c91332-b9e8-40e8-9168-b5274b4fcd80"}]}, "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": "Wutyi, Khaing Shwe"}]}]}, "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/123456789/2373"}, "item_title": "Forecasting of Maximum and Minimum Temperature in Mandalay by Evolving Artificial Neural Networks Using Genetic Algorithms", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000004441", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-10-29"}, "publish_date": "2019-10-29", "publish_status": "0", "recid": "4441", "relation": {}, "relation_version_is_last": true, "title": ["Forecasting of Maximum and Minimum Temperature in Mandalay by Evolving Artificial Neural Networks Using Genetic Algorithms"], "weko_shared_id": -1}