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Khalid Thabit

Work place: Faculty of Computing and Information Technology King Abdulaziz University KAU, Jeddah, Saudi Arabia

E-mail: drthabit@kau.edu.sa

Website:

Research Interests: Speech Synthesis, Speech Recognition

Biography

Dr. Khalid O. Thabit received the Ph.D. degree in Computer Science from the University of Rice, USA in 1981. He received B.S. degree in Computer Science from Massachusetts Institute of Technology, USA and M.S. from University of Southern California, USA. His Ph.D. research thesis was published as a book and the thesis was referenced and cited in more than 30 publications and five books. His thesis was selected as one of the top in the
computer science major among 1000 Universities. His research interests span a broad range of areas from compilers to Arabic text and speech recognition and Knowledge-based systems. Dr. Thabit is an Assistant Professor at the Department of Computer Science in the Faculty of Computing and Information Technology, King Abdul Aziz University (KAU), Jeddah, Saudi Arabia. He served as the Chairman of the Department of Mathematics for 2 years (1982-1984) at KAU, and as the Chairman of the Department of Computer Science for 8 years (1985-1991) at KAU. Dr. Thabit is serving as a member in various administrative committees at KAU. He
also served as a supervisor for many Master students, and one PhD. His main research interests are development of Arabic text to speech system and the computer generation of more than 28 million Arabic compound words. His current research interest is in Brain Computer interface.

Author Articles
EEG based Autism Diagnosis Using Regularized Fisher Linear Discriminant Analysis

By Mahmoud I. Kamel Mohammed J. Alhaddad Hussein M. Malibary Khalid Thabit Foud Dahlwi Ebtehal A. Alsaggaf Anas A. Hadi

DOI: https://doi.org/10.5815/ijigsp.2012.03.06, Pub. Date: 8 Apr. 2012

Diagnosis of autism is one of the difficult problems facing researchers. To reveal the discriminative pattern between autistic and normal children via electroencephalogram (EEG) analysis is a big challenge. The feature extraction is averaged Fast Fourier Transform (FFT) with the Regulated Fisher Linear Discriminant (RFLD) classifier. 
Gaussinaty condition for the optimality of Regulated Fisher Linear Discriminant (RFLD) has been achieved by a well-conditioned appropriate preprocessing of the data, as well as optimal shrinkage technique for the Lambda parameter. Winsorised Filtered Data gave the best result.

[...] Read more.
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