{"id":{"repo_id":"regina","oai_identifier":"oai:uregina.scholaris.ca:10294/14458"},"canonical_url":"https://search.dev.ndltd.org/etd/regina/oai:uregina.scholaris.ca:10294/14458","repository":{"repo_id":"regina","name":"University of Regina","base_url":"https://uregina.scholaris.ca/server/oai/request"},"display":{"title":"A New Machine Learning Algorithm for Detection of Stray Clays","abstract":"This paper discusses various machine learning algorithms utilized with Ground Penetrating Radar (GPR) for differentiating between clay seam number 414 and stray clays in potash mines to evaluate mine-room safety. Although different strategies have been used to find and recognize anomalies from GPR data through machine learning frameworks, but the frameworks which provide precise execution are still under investigation. In this research, we propose difference-of-gradients (DOG) method for detecting the presence of stray clays from the simulated and the actual GPR data, which is based on gated recurrent units (GRU) in neural networks. The combination of DOG and GRU methods assists to discover the accurate position of stray clays with time-series analysis. We optimize the strength of stray clay from clay seam number 414 by suppressing the stray clay signal strength from GPR data. A new window function in neural networks SmoothGrad which helps to filter the most of the anomalies effects on clay seam number 414 is utilized. This window length which tries to smooth GPR signal by supressing the noise, can be measured by adding the first and second gradients of GPR signal’s power. Therefore, my outcomes on the overall significances is to get the most accurate position of stray clays by differentiating the characteristics between the clay seam number 414 and the stray clays.","abstract_html":"This paper discusses various machine learning algorithms utilized with Ground Penetrating Radar (GPR) for differentiating between clay seam number 414 and stray clays in potash mines to evaluate mine-room safety. Although different strategies have been used to find and recognize anomalies from GPR data through machine learning frameworks, but the frameworks which provide precise execution are still under investigation. In this research, we propose difference-of-gradients (DOG) method for detecting the presence of stray clays from the simulated and the actual GPR data, which is based on gated recurrent units (GRU) in neural networks. The combination of DOG and GRU methods assists to discover the accurate position of stray clays with time-series analysis. We optimize the strength of stray clay from clay seam number 414 by suppressing the stray clay signal strength from GPR data. A new window function in neural networks SmoothGrad which helps to filter the most of the anomalies effects on clay seam number 414 is utilized. This window length which tries to smooth GPR signal by supressing the noise, can be measured by adding the first and second gradients of GPR signal’s power. Therefore, my outcomes on the overall significances is to get the most accurate position of stray clays by differentiating the characteristics between the clay seam number 414 and the stray clays.","abstract_has_math":false,"creators":["Aysha, Umme"],"institution":"Faculty of Graduate Studies and Research, University of Regina","degree_name":"Master of Applied Science (MASc)","degree_level":"Master's","degree_discipline":"Engineering - Electronic Systems","degree_department":null,"school":null,"contributors":[],"advisors":["Paranjape, Raman"],"committee_chairs":[],"committee_members":["Wang, Zhanle"],"year":2021,"date_issued":"2021-07","date_published":"2021-07","updated_at":"2026-07-24T04:03:30Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.82465/4058"],"render_values":[{"text":"https://doi.org/10.82465/4058","href":"https://doi.org/10.82465/4058","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10294/14458","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Paranjape, Raman"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Wang, Zhanle"]},{"key":"dc:creator","label":"Author","values":["Aysha, Umme"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2021-12-13T16:58:21Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2021-12-13T16:58:21Z"]},{"key":"dc:date.issued","label":"Date","values":["2021-07"]},{"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 - Electronic 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/10.82465/4058"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10294/14458"]}]},{"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 Electronic Systems Engineering, University of Regina. viii, 99 p."]},{"key":"dc:description.abstract","label":"Abstract","values":["This paper discusses various machine learning algorithms utilized with Ground Penetrating Radar (GPR) for differentiating between clay seam number 414 and stray clays in potash mines to evaluate mine-room safety. Although different strategies have been used to find and recognize anomalies from GPR data through machine learning frameworks, but the frameworks which provide precise execution are still under investigation. In this research, we propose difference-of-gradients (DOG) method for detecting the presence of stray clays from the simulated and the actual GPR data, which is based on gated recurrent units (GRU) in neural networks. The combination of DOG and GRU methods assists to discover the accurate position of stray clays with time-series analysis. We optimize the strength of stray clay from clay seam number 414 by suppressing the stray clay signal strength from GPR data. A new window function in neural networks SmoothGrad which helps to filter the most of the anomalies effects on clay seam number 414 is utilized. This window length which tries to smooth GPR signal by supressing the noise, can be measured by adding the first and second gradients of GPR signal’s power. Therefore, my outcomes on the overall significances is to get the most accurate position of stray clays by differentiating the characteristics between the clay seam number 414 and the stray clays."]},{"key":"dc:title","label":"Title","values":["A New Machine Learning Algorithm for Detection of Stray Clays"]}]}],"canonical_facts":{"dc:contributor.advisor":["Paranjape, Raman"],"dc:contributor.committeemember":["Wang, Zhanle"],"dc:creator":["Aysha, Umme"],"dc:date.accessioned":["2021-12-13T16:58:21Z"],"dc:date.available":["2021-12-13T16:58:21Z"],"dc:date.issued":["2021-07"],"dc:description":["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 Electronic Systems Engineering, University of Regina. viii, 99 p."],"dc:description.abstract":["This paper discusses various machine learning algorithms utilized with Ground Penetrating Radar (GPR) for differentiating between clay seam number 414 and stray clays in potash mines to evaluate mine-room safety. Although different strategies have been used to find and recognize anomalies from GPR data through machine learning frameworks, but the frameworks which provide precise execution are still under investigation. In this research, we propose difference-of-gradients (DOG) method for detecting the presence of stray clays from the simulated and the actual GPR data, which is based on gated recurrent units (GRU) in neural networks. The combination of DOG and GRU methods assists to discover the accurate position of stray clays with time-series analysis. We optimize the strength of stray clay from clay seam number 414 by suppressing the stray clay signal strength from GPR data. A new window function in neural networks SmoothGrad which helps to filter the most of the anomalies effects on clay seam number 414 is utilized. This window length which tries to smooth GPR signal by supressing the noise, can be measured by adding the first and second gradients of GPR signal’s power. Therefore, my outcomes on the overall significances is to get the most accurate position of stray clays by differentiating the characteristics between the clay seam number 414 and the stray clays."],"dc:identifier.doi":["https://doi.org/10.82465/4058"],"dc:identifier.uri":["https://hdl.handle.net/10294/14458"],"dc:language.iso":["en"],"dc:publisher":["Faculty of Graduate Studies and Research, University of Regina"],"dc:title":["A New Machine Learning Algorithm for Detection of Stray Clays"],"dc:type":["master thesis"],"thesis:degree_discipline":["Engineering - Electronic Systems"],"thesis:degree_level":["Master's"],"thesis:degree_name":["Master of Applied Science (MASc)"],"thesis:institution_name":["Faculty of Graduate Studies and Research, University of Regina"]},"updated_at":"2026-07-24T04:03:30Z"}