Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/325473
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DC Field | Value | Language |
---|---|---|
dc.coverage.spatial | ||
dc.date.accessioned | 2021-05-11T12:36:51Z | - |
dc.date.available | 2021-05-11T12:36:51Z | - |
dc.identifier.uri | http://hdl.handle.net/10603/325473 | - |
dc.description.abstract | newline Neuroscience is the most prominent research paradigm in the present era newlineof information technology. Neuroscience has its major application in marketing and newlineunderstanding consumer physiological and psychological responses. For a newlinesustainable future and increased business, neuromarketing techniques need sincere newlineattention. Although, it is an active research area and there are many solutions for newlineachieving efficiency, existing approaches ignore the integrated framework for newlineunderstanding neurometric data. The real challenge is to maintain a satisfactory newlineperformance level without raising costs. An optimal framework that simultaneously newlinehandles performance and maintains low cost is required as the foremost thing for newlinebusiness users. So, modeling algorithms should effectively address the trade-off newlinebetween cost and performance delivered by the system. In the present research newlinework, efforts will be made towards defining a cost-efficient information-driven newlineframework for understanding neuroscientific information for decision making. | |
dc.format.extent | ||
dc.language | English | |
dc.relation | ||
dc.rights | university | |
dc.title | An Information Driven Framework for Processing Neuroscientific Data for Decision Making | |
dc.title.alternative | ||
dc.creator.researcher | Rupali Gill | |
dc.subject.keyword | Computer Science | |
dc.subject.keyword | Computer Science Artificial Intelligence | |
dc.subject.keyword | Engineering and Technology | |
dc.description.note | ||
dc.contributor.guide | Jaiteg Singh | |
dc.publisher.place | Chandigarh | |
dc.publisher.university | Chitkara University, Punjab | |
dc.publisher.institution | Faculty of Computer Science | |
dc.date.registered | 2016 | |
dc.date.completed | 2021 | |
dc.date.awarded | 2021 | |
dc.format.dimensions | ||
dc.format.accompanyingmaterial | DVD | |
dc.source.university | University | |
dc.type.degree | Ph.D. | |
Appears in Departments: | Faculty of Computer Science |
Files in This Item:
File | Description | Size | Format | |
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80_recommendation.pdf | Attached File | 79.65 kB | Adobe PDF | View/Open |
abstract.pdf | 195.58 kB | Adobe PDF | View/Open | |
annexures.pdf | 2.19 MB | Adobe PDF | View/Open | |
chapter 1.pdf | 423.91 kB | Adobe PDF | View/Open | |
chapter 2.pdf | 1.46 MB | Adobe PDF | View/Open | |
chapter 3.pdf | 453.37 kB | Adobe PDF | View/Open | |
chapter 4.pdf | 926.73 kB | Adobe PDF | View/Open | |
chapter 5.pdf | 593.68 kB | Adobe PDF | View/Open | |
chapter 6.pdf | 1.54 MB | Adobe PDF | View/Open | |
chapter 7.pdf | 244.34 kB | Adobe PDF | View/Open | |
references.pdf | 325.11 kB | Adobe PDF | View/Open | |
title pages.pdf | 285.49 kB | Adobe PDF | View/Open |
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