Log in
Language:

MERAL Myanmar Education Research and Learning Portal

  • Top
  • Universities
  • Ranking
To
lat lon distance
To

Field does not validate



Index Link

Index Tree

Please input email address.

WEKO

One fine body…

WEKO

One fine body…

Item

{"_buckets": {"deposit": "6394bc05-47ea-45f8-992f-b0a435fddf1d"}, "_deposit": {"id": "3412", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "3412"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/3412", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Evaluation of Diagnosis according to Myanmar Traditional Medicine by using Expectation Maximization", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Nowadays, computer based medical system is playing a role in assisting both diagnosis and treatment. Thus, this system intends to provide information for junior traditional medicine practitioners and user who interested traditional medicine. Before evaluating, this system stores the knowledge of traditional medical experts and medical records from previous cases as training database. And, it produces the generate rules from training data set by using Naïve Bayesian Classifier. When user inputs symptoms, this system analyzes corrected diagnosis and suitable dosage. If user inputted symptoms are not evolved by NB classification, we use Expectation Maximization (EM) step that computes maximum likelihood estimation of unlabeled data. This EM step probabilistically evaluates unlabeled data by using available labeled data which is training by NB. As a result, in this paper, we evaluate corrected diagnosis and proper dosage by using semisupervised learning method (EM with NB classification) in order to improve correctness of classifier."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "traditional medicine"}, {"interim": "naïve bayesian classifier"}, {"interim": "expectation maximization step"}, {"interim": "symptoms"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2019-07-22"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "psc2010paper (222).pdf", "filesize": [{"value": "334 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 334000.0, "url": {"url": "https://meral.edu.mm/record/3412/files/psc2010paper (222).pdf"}, "version_id": "78899887-bf4a-4e60-9b11-983f9e80ae11"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Fifth 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": "Hlaing, Hnin Wai Wai"}, {"subitem_authors_fullname": "Tun, Myint Thuzar"}]}]}, "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": "2010-12-16"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/1166"}, "item_title": "Evaluation of Diagnosis according to Myanmar Traditional Medicine by using Expectation Maximization", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000003412", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-22"}, "publish_date": "2019-07-22", "publish_status": "0", "recid": "3412", "relation": {}, "relation_version_is_last": true, "title": ["Evaluation of Diagnosis according to Myanmar Traditional Medicine by using Expectation Maximization"], "weko_shared_id": -1}
  1. University of Computer Studies, Yangon
  2. Conferences

Evaluation of Diagnosis according to Myanmar Traditional Medicine by using Expectation Maximization

http://hdl.handle.net/20.500.12678/0000003412
http://hdl.handle.net/20.500.12678/0000003412
90f1bdab-d2a3-42d0-8f40-43a5b09a33a8
6394bc05-47ea-45f8-992f-b0a435fddf1d
None
Preview
Name / File License Actions
psc2010paper psc2010paper (222).pdf (334 Kb)
Publication type
Article
Upload type
Publication
Title
Title Evaluation of Diagnosis according to Myanmar Traditional Medicine by using Expectation Maximization
Language en
Publication date 2010-12-16
Authors
Hlaing, Hnin Wai Wai
Tun, Myint Thuzar
Description
Nowadays, computer based medical system is playing a role in assisting both diagnosis and treatment. Thus, this system intends to provide information for junior traditional medicine practitioners and user who interested traditional medicine. Before evaluating, this system stores the knowledge of traditional medical experts and medical records from previous cases as training database. And, it produces the generate rules from training data set by using Naïve Bayesian Classifier. When user inputs symptoms, this system analyzes corrected diagnosis and suitable dosage. If user inputted symptoms are not evolved by NB classification, we use Expectation Maximization (EM) step that computes maximum likelihood estimation of unlabeled data. This EM step probabilistically evaluates unlabeled data by using available labeled data which is training by NB. As a result, in this paper, we evaluate corrected diagnosis and proper dosage by using semisupervised learning method (EM with NB classification) in order to improve correctness of classifier.
Keywords
traditional medicine, naïve bayesian classifier, expectation maximization step, symptoms
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/1166
Journal articles
Fifth Local Conference on Parallel and Soft Computing
Conference papers
Books/reports/chapters
Thesis/dissertations
Back
0
0
views
downloads
See details
Views Downloads

Versions

Ver.1 2020-09-01 10:06:48.085247
Show All versions

Share

Mendeley Twitter Facebook Print Addthis

Export

OAI-PMH
  • OAI-PMH DublinCore
Other Formats
  • JSON

Confirm


Back to MERAL


Back to MERAL