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Machine learning techniques for analyzing brain signals / Farid Ali Mousa Abdel Kader ; Supervised Reda Abdel Wahab Elkhoribi , Mahmoud Ismail Shoman

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Farid Ali Mousa Abdelkader , 2016Description: 122 Leaves : charts , facsimiles ; 30cmOther title:
  • تطبيقات تعليم الآلة لتحليل إشارات المخ [Added title page title]
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  • Issued also as CD
Dissertation note: Thesis (Ph.D.) - Cairo University - Faculty of Computers and Information - Department of Information Technology Summary: 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
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Thesis Thesis 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
CD - Rom CD - Rom 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

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

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