Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/346406
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.coverage.spatial | ||
dc.date.accessioned | 2021-11-01T05:43:02Z | - |
dc.date.available | 2021-11-01T05:43:02Z | - |
dc.identifier.uri | http://hdl.handle.net/10603/346406 | - |
dc.description.abstract | newline | |
dc.format.extent | ||
dc.language | English | |
dc.relation | ||
dc.rights | university | |
dc.title | an extensive performance analysis on framework of customer churn prediction through stochastic gradient descent based deep learning model | |
dc.title.alternative | ||
dc.creator.researcher | Isabella Amali A | |
dc.subject.keyword | Arts and Humanities | |
dc.subject.keyword | Arts and Recreation | |
dc.subject.keyword | Art | |
dc.description.note | ||
dc.contributor.guide | R. ARUNKUMAR | |
dc.publisher.place | Annamalai Nagar | |
dc.publisher.university | Annamalai University | |
dc.publisher.institution | Department of Computer and Information Science | |
dc.date.registered | 2017 | |
dc.date.completed | 2020 | |
dc.date.awarded | 2021 | |
dc.format.dimensions | ||
dc.format.accompanyingmaterial | DVD | |
dc.source.university | University | |
dc.type.degree | Ph.D. | |
Appears in Departments: | Department of Computer and Information Science |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
10.chapter.1.pdf | Attached File | 825.46 kB | Adobe PDF | View/Open |
11.chapter.2.pdf | 825.4 kB | Adobe PDF | View/Open | |
12.chapter.3.pdf | 745.5 kB | Adobe PDF | View/Open | |
13.chapter.4.pdf | 464.15 kB | Adobe PDF | View/Open | |
14.chapter.5.pdf | 533.34 kB | Adobe PDF | View/Open | |
15.chapter.6.pdf | 2.6 MB | Adobe PDF | View/Open | |
16.chapter.7.pdf | 488.64 kB | Adobe PDF | View/Open | |
17.chapter.8.pdf | 35.04 kB | Adobe PDF | View/Open | |
1.cover.pdf | 38.01 kB | Adobe PDF | View/Open | |
2.certificate.pdf | 43.75 kB | Adobe PDF | View/Open | |
4.acknowledement.pdf | 35.16 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 35.04 kB | Adobe PDF | View/Open |
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