Machine learning techniques for analyzing brain signals / Farid Ali Mousa Abdel Kader ; Supervised Reda Abdel Wahab Elkhoribi , Mahmoud Ismail Shoman
Material type:
- تطبيقات تعليم الآلة لتحليل إشارات المخ [Added title page title]
- Issued also as CD
Item type | Current library | Home library | Call number | Copy number | Status | Barcode | |
---|---|---|---|---|---|---|---|
![]() |
قاعة الرسائل الجامعية - الدور الاول | المكتبة المركزبة الجديدة - جامعة القاهرة | Cai01.20.01.Ph.D.2016.Fa.M (Browse shelf(Opens below)) | Not for loan | 01010110069893000 | ||
![]() |
مخـــزن الرســائل الجـــامعية - البدروم | المكتبة المركزبة الجديدة - جامعة القاهرة | Cai01.20.01.Ph.D.2016.Fa.M (Browse shelf(Opens below)) | 69893.CD | Not for loan | 01020110069893000 |
Browsing المكتبة المركزبة الجديدة - جامعة القاهرة shelves Close shelf browser (Hides shelf browser)
No cover image available | No cover image available | No cover image available | No cover image available | No cover image available | No cover image available | No cover image available | ||
Cai01.20.01.Ph.D.2016.Eh.S A security framework for Mobile Ad-hHoc Networks / | Cai01.20.01.Ph.D.2016.Eh.S A security framework for Mobile Ad-hHoc Networks / | Cai01.20.01.Ph.D.2016.Fa.M Machine learning techniques for analyzing brain signals / | Cai01.20.01.Ph.D.2016.Fa.M Machine learning techniques for analyzing brain signals / | Cai01.20.01.Ph.D.2016.Hu.R Recognition of human activities in spatio temporal domain / | Cai01.20.01.Ph.D.2016.Hu.R Recognition of human activities in spatio temporal domain / | Cai01.20.01.Ph.D.2016.Ra.S Speech enhancement and its effects on speech recognition / |
Thesis (Ph.D.) - Cairo University - Faculty of Computers and Information - Department of Information Technology
The purpose behind this research is to improve a model for brain signals analysis. Different techniques have been developed in the literature for the classification of brain signals. The purpose of this work is to develop novel methods of analyzing the brain signals. We developed four experiments that used to classify brain. We have used artificial neural networks, support vector machine, k-nearest neighbor, fuzzy k-nearest neighbor, weighted k-nearest neighbor in the classification step. It has been depicted from results that the proposed integrated techniques outperform a better performance than methods mentioned in literature
Issued also as CD
There are no comments on this title.