{"id":{"repo_id":"regina","oai_identifier":"oai:uregina.scholaris.ca:10294/5776"},"canonical_url":"https://search.dev.ndltd.org/etd/regina/oai:uregina.scholaris.ca:10294/5776","repository":{"repo_id":"regina","name":"University of Regina","base_url":"https://uregina.scholaris.ca/server/oai/request"},"display":{"title":"Development of Enhanced Neural Decision Tree Model and Application of Data Mining Techniques for Modeling of Petroleum Datasets","abstract":"In this work, two data mining techniques are studied and applied to petroleum datasets. so as to extract useful information such as: correlations and interdependencies among the attributes. This helps to better understand important characteristics that can increase oil production. The two data mining techniques, Artificial Neural Networks and the Decision Tree algorithm, are combined to form the Neural Decision Tree (NDT) model. The NDT model enhances classification accuracy of the decision tree algorithm. The Neural Decision Tree model generates statistical results of classification and graphical representation of data in the form of a tree data structure, which supports data analysis in more detail, and useful knowledge can be extracted from it. The NDT model is useful for discovering interdependencies and for explaining correlations among the attributes of the data. The results generated from application of the Neural Decision Tree model can provide initial estimates of some parameters in the statistical modeling so that correlations among the parameters can be derived. The knowledge on correlations can support petroleum experts in making important decisions such as cost estimation and required resources for oil production.","abstract_html":"In this work, two data mining techniques are studied and applied to petroleum datasets. so as to extract useful information such as: correlations and interdependencies among the attributes. This helps to better understand important characteristics that can increase oil production. The two data mining techniques, Artificial Neural Networks and the Decision Tree algorithm, are combined to form the Neural Decision Tree (NDT) model. The NDT model enhances classification accuracy of the decision tree algorithm. The Neural Decision Tree model generates statistical results of classification and graphical representation of data in the form of a tree data structure, which supports data analysis in more detail, and useful knowledge can be extracted from it. The NDT model is useful for discovering interdependencies and for explaining correlations among the attributes of the data. The results generated from application of the Neural Decision Tree model can provide initial estimates of some parameters in the statistical modeling so that correlations among the parameters can be derived. The knowledge on correlations can support petroleum experts in making important decisions such as cost estimation and required resources for oil production.","abstract_has_math":false,"creators":["Omer, Sardar Usman"],"institution":"Faculty of Graduate Studies and Research, University of Regina","degree_name":"Master of Applied Science (MASc)","degree_level":"Master's","degree_discipline":"Engineering - Software Systems","degree_department":null,"school":null,"contributors":[],"advisors":["Chan, Christine"],"committee_chairs":[],"committee_members":["Laforge, Paul","Gelowitz, Craig"],"year":2014,"date_issued":"2014-07","date_published":"2014-07","updated_at":"2026-07-24T04:03:49Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.82465/5023"],"render_values":[{"text":"https://doi.org/10.82465/5023","href":"https://doi.org/10.82465/5023","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10294/5776","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Chan, Christine"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Laforge, Paul","Gelowitz, Craig"]},{"key":"dc:creator","label":"Author","values":["Omer, Sardar Usman"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2015-07-06T19:56:01Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2015-07-06T19:56:01Z"]},{"key":"dc:date.issued","label":"Date","values":["2014-07"]},{"key":"dc:publisher","label":"Institution","values":["Faculty of Graduate Studies and Research, University of Regina"]},{"key":"dc:type","label":"Dc Type","values":["master thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering - Software Systems"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master's"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (MASc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Faculty of Graduate Studies and Research, University of Regina"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.82465/5023"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10294/5776"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Software Systems Engineering, University of Regina. viii, 105 p."]},{"key":"dc:description.abstract","label":"Abstract","values":["In this work, two data mining techniques are studied and applied to petroleum datasets. so as to extract useful information such as: correlations and interdependencies among the attributes. 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