header
Image from OpenLibrary

Text 2.0 author tools : Robust gaze - based objective quality measures for text / Mostafa Elhosseiny ; Supervised Slim Abdennadher , Ralf Biedert

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Mostafa Elhosseiny , 2012Description: 52 Leaves : charts ; 30cmDissertation note: Thesis (M.Sc.) - German University - Faculty of Postgraduate Studies and Scientific Research - Department of Computer Science and Engineering Summary: Eye tracking devices integrated in e-book readers; an increasingly likely eventuality due to recent trends, will allow us to aggregate the reading data of multiple readers in order to provide authors and editors with objective and implicitly gathered quality feedback. In this thesis we improve an initial system that is able to classify the comprehensibility of text automatically by employing machine learning techniques with an overall accuracy of 62%, an inaccurate figure due to an error in the evaluation of the initial system which exaggerated its true accuracy of 54%
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
Holdings
Item type Current library Home library Call number Status Date due Barcode
Thesis Thesis قاعة الثقاقات الاجنبية - الدور الثالث المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.34.M.Sc.2012.Mo.T (Browse shelf(Opens below)) Not for loan 01010110063668000

Thesis (M.Sc.) - German University - Faculty of Postgraduate Studies and Scientific Research - Department of Computer Science and Engineering

Eye tracking devices integrated in e-book readers; an increasingly likely eventuality due to recent trends, will allow us to aggregate the reading data of multiple readers in order to provide authors and editors with objective and implicitly gathered quality feedback. In this thesis we improve an initial system that is able to classify the comprehensibility of text automatically by employing machine learning techniques with an overall accuracy of 62%, an inaccurate figure due to an error in the evaluation of the initial system which exaggerated its true accuracy of 54%

There are no comments on this title.

to post a comment.