A Model-Based Application of Artificial Intelligence for Behavioral Pattern Analysis and Improved Early Intervention in Autism

Artificial Intelligence (AI) and Machine Learning (ML) have immense potential to improve diagnostic research and early intervention using behavioral and computer sciences, and explorations involving the highly prevalent and diverse syndrome of autism spectrum disorder.Current approaches for diagnosing autism have high diagnostic validity but are time consuming and can contribute to delays in arriving at a diagnosis. In this praxis, we derive machine learning classifiers to understand the behavioral patterns for early intervention in Autism.The purpose of this research is to analyze various Machine Learning algorithms to derive predictive models based on Data Analytics that may be used for rapid intervention of individuals likely to have an Autism Spectrum Disorder (ASD). The AI tools can be developed based on predictive data patterns to intervene early and improve learning and social behaviors.Keywords: Machine Learning, Artificial Intelligence, Autism Spectrum Disorder, Behavior Pattern Detection, Systems Architecture, Robotics, Humanoid, Engineering Management

Author

• Sarmah, Rupam Jyoti

Language

• en

Keyword

• Machine Learning
• Artificial Intelligence
• Autism
• Engineering Management
• Behavior Pattern Analysis

Date created

• 2018

Type of Work

• Dissertation

License

• All rights reserved

Rights statement

• In Copyright

GW Unit

• Engineering Management

Degree

• D.Engr.

Advisor

• Young, Stuart

Committee Member(s)

• Bersson, Thomas
• Malalla, Ebrahim
• Etemadi, Amirhossein
• Young, Stuart

Persistent URL

•  https://scholarspace.library.gwu.edu/etd/6t053g25t