Author Type

Graduate Student

Date of Award

2016

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Huang, Shihong

Abstract

A self-adaptive software is developed to predict the stock market. It’s Stock Prediction Engine functions autonomously when its skill-set suffices to achieve its goal, and it includes human-in-the-loop when it recognizes conditions benefiting from more complex, expert human intervention. Key to the system is a module that decides of human participation. It works by monitoring three mental states unobtrusively and in real time with Electroencephalography (EEG). The mental states are drawn from the Opportunity-Willingness-Capability (OWC) model. This research demonstrates that the three mental states are predictive of whether the Human Computer Interaction System functions better autonomously (human with low scores on opportunity and/or willingness, capability) or with the human-in-the-loop, with willingness carrying the largest predictive power. This transdisciplinary software engineering research exemplifies the next step of self-adaptive systems in which human and computer benefit from optimized autonomous and cooperative interactions, and in which neural inputs allow for unobtrusive pre-interactions.

Extent

159 p.

Comments

Includes bibliography.|Thesis (M.S.)--Florida Atlantic University, 2016.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.

Identifier

FA00004764

Format

pdf

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