{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1388"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1388","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Application-specific transfer learning over edge networks","abstract":"Transfer learning uses a profound labeled set of data from the source domain to deal with a similar problem for the target domain. Transfer learning provides accurate decision- making when insufficient data samples are available and when building a new prediction model takes more time and effort. This study explains comparative analysis of traditional machine learning techniques and transfer learning approaches over edge networks to enhance the performance and networking latency within discrete nodes. Edge networks are widely used to improve the efficiency and staging of any algorithm as the embedded systems focus on implementing some particular events based on the microprocessors and, at the same time, working on the least resources that result in having less power consumption. Moreover, we generated a hybrid-based transfer learning model to avoid negative transfer. This thesis uses two case studies: mushroom sales prediction and heart attack detection system.","abstract_html":"Transfer learning uses a profound labeled set of data from the source domain to deal with a similar problem for the target domain. Transfer learning provides accurate decision- making when insufficient data samples are available and when building a new prediction model takes more time and effort. This study explains comparative analysis of traditional machine learning techniques and transfer learning approaches over edge networks to enhance the performance and networking latency within discrete nodes. Edge networks are widely used to improve the efficiency and staging of any algorithm as the embedded systems focus on implementing some particular events based on the microprocessors and, at the same time, working on the least resources that result in having less power consumption. Moreover, we generated a hybrid-based transfer learning model to avoid negative transfer. This thesis uses two case studies: mushroom sales prediction and heart attack detection system.","abstract_has_math":false,"creators":["Saggu, Deepak"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Applied Science (MASc)","degree_level":null,"degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Azim, Akramul"],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-11-01","date_published":"2021-11-01","updated_at":"2026-07-24T05:35:20Z","subjects":["Machine learning","Transfer learning","Embedded systems","Edge networks","Classification and regression"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1388","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Azim, Akramul"]},{"key":"dc:creator","label":"Author","values":["Saggu, Deepak"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2021-11-29T20:13:47Z","2022-03-29T16:46:30Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2021-11-29T20:13:47Z","2022-03-29T16:46:30Z"]},{"key":"dc:date.issued","label":"Date","values":["2021-11-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (MASc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine learning","Transfer learning","Embedded systems","Edge networks","Classification and regression"]}]},{"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.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/1388"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Transfer learning uses a profound labeled set of data from the source domain to deal with a similar problem for the target domain. Transfer learning provides accurate decision- making when insufficient data samples are available and when building a new prediction model takes more time and effort. This study explains comparative analysis of traditional machine learning techniques and transfer learning approaches over edge networks to enhance the performance and networking latency within discrete nodes. Edge networks are widely used to improve the efficiency and staging of any algorithm as the embedded systems focus on implementing some particular events based on the microprocessors and, at the same time, working on the least resources that result in having less power consumption. Moreover, we generated a hybrid-based transfer learning model to avoid negative transfer. This thesis uses two case studies: mushroom sales prediction and heart attack detection system."]},{"key":"dc:title","label":"Title","values":["Application-specific transfer learning over edge networks"]}]}],"canonical_facts":{"dc:contributor.advisor":["Azim, Akramul"],"dc:creator":["Saggu, Deepak"],"dc:date.accessioned":["2021-11-29T20:13:47Z","2022-03-29T16:46:30Z"],"dc:date.available":["2021-11-29T20:13:47Z","2022-03-29T16:46:30Z"],"dc:date.issued":["2021-11-01"],"dc:description.abstract":["Transfer learning uses a profound labeled set of data from the source domain to deal with a similar problem for the target domain. Transfer learning provides accurate decision- making when insufficient data samples are available and when building a new prediction model takes more time and effort. This study explains comparative analysis of traditional machine learning techniques and transfer learning approaches over edge networks to enhance the performance and networking latency within discrete nodes. Edge networks are widely used to improve the efficiency and staging of any algorithm as the embedded systems focus on implementing some particular events based on the microprocessors and, at the same time, working on the least resources that result in having less power consumption. Moreover, we generated a hybrid-based transfer learning model to avoid negative transfer. This thesis uses two case studies: mushroom sales prediction and heart attack detection system."],"dc:identifier.uri":["https://hdl.handle.net/10155/1388"],"dc:language.iso":["en"],"dc:subject":["Machine learning","Transfer learning","Embedded systems","Edge networks","Classification and regression"],"dc:title":["Application-specific transfer learning over edge networks"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_name":["Master of Applied Science (MASc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:20Z"}