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Applied machine learning for personalised early intervention in autism

Vellanki, Pratibha 2016, Applied machine learning for personalised early intervention in autism, PhD. thesis, School of Information Technology, Deakin University.

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Title Applied machine learning for personalised early intervention in autism
Author Vellanki, Pratibha
Institution Deakin University
School School of Information Technology
Faculty Faculty of Science, Engineering and Built Environment
Degree type Research doctorate
Degree name PhD.
Thesis advisor Venkatesh, Svetha
Duong, Thi
Phung, Dinh
Date submitted 2016-05
Keyword(s) Autism
Early intervention
Assistive technology
Developmental disorders
Computing
Summary This thesis is the first to address the problems of early intervention in Autism Spectrum Disorder through the lens of machine learning and data analytics. The key contribution is the establishment of large datasets in this domain for the first time together with a systematic data-based approach to extract knowledge relevant to Autism.
Language eng
Field of Research 080109 - Pattern Recognition and Data Mining 70%
080699 - Information Systems not elsewhere classified 30%
Socio Economic Objective 970108 - Expanding Knowledge in the Information and Computing Sciences 50% 970117 - Expanding Knowledge in Psychology and Cognitive Sciences 20% 970110 - Expanding Knowledge in Technology 30%
Description of original xvi, 227 pages : illustrations, figures, tables, some coloured, appendices
Restricted until 2017-01-31
Access conditions No online access
Copyright notice ┬ęThe author
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30089075

Document type: Thesis
Collection: Higher degree theses (citation only)
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Created: Wed, 16 Nov 2016, 13:47:31 EST by Deb Gray

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