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Development of Deep Learning Methodologies for Modeling Navigational Features in Continuous Spaces
(2021-12)
This thesis proposes generalized methodologies to model navigational features of continuous spaces using deep learning architectural approaches. Navigational features impact how an entity can effectively travel within a ...
Comparison of Machine Learning and Statistical Approaches for Predicting Travel Times in the Oklahoma Highway System
(2020-12)
Traffic management systems play a vital role in supporting the smooth flow of traffic in road networks. By accurately predicting travel time, a traffic condition parameter that is extensively used in such systems, we can ...
A Constraint Driven Approach to Neural and Muscle Recruitment in Wrist Motor Tasks
(2020-07-30)
The transformation from visual stimulus to muscle recruitment is a non-
linear one: it must take into account the configuration of the body and the
lines of action of the muscles. The primary cortex plays a role in the ...
Forecasting landslide events in eastern Oklahoma and western Arkansas using empirical methods and statistical machine learning methods
(2022-12)
Understanding the trend of landslide occurrence in eastern Oklahoma and western Arkansas is crucial to the human and social development of the region. Studies suggest rainfall is one of the major landslide triggering ...
Predicting wine quality and/or taste through the use of a latent ODE-RNN Neural Net
(2019-12-13)
It is common for recommendation systems to use clustering techniques for finding similar products for the downstream user. These models do not always incorporate time as a variable when recommending an item. If our ...
Secure Decentralized Decisions in Consolidated Hospital Systems: Intelligent Agents and Blockchain
(2018)
Shared decision making has become a very important solution in order to build a consolidated healthcare system. While there is some research in the healthcare literature discussing the advantages and disadvantages of the ...