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Thesis Defense Presentation

Description

This project explores the potential of an artificial intelligence/machine learning algorithm, K-Means to augment the connection between two individuals through a game interface. Further implementation of such technology is theorized in the form of a two-way chatbot. The role of bias is extensively reported and researched in order to remain…

This project explores the potential of an artificial intelligence/machine learning algorithm, K-Means to augment the connection between two individuals through a game interface. Further implementation of such technology is theorized in the form of a two-way chatbot. The role of bias is extensively reported and researched in order to remain cognizant of these new technological advancements.

ContributorsHatfield, Kacy (Author) / Sha, Xin (Thesis director) / Finn, Ed (Committee member) / Barrett, The Honors College (Contributor) / Arts, Media and Engineering Sch T (Contributor)

Created2022-05

Clustered Friendly Faces Data - Clusters

Description

This project explores the potential of an artificial intelligence/machine learning algorithm, K-Means to augment the connection between two individuals through a game interface. Further implementation of such technology is theorized in the form of a two-way chatbot. The role of bias is extensively reported and researched in order to remain…

This project explores the potential of an artificial intelligence/machine learning algorithm, K-Means to augment the connection between two individuals through a game interface. Further implementation of such technology is theorized in the form of a two-way chatbot. The role of bias is extensively reported and researched in order to remain cognizant of these new technological advancements.

ContributorsHatfield, Kacy (Author) / Sha, Xin (Thesis director) / Finn, Ed (Committee member) / Barrett, The Honors College (Contributor) / Arts, Media and Engineering Sch T (Contributor)

Created2022-05

Clustered Action Data - Sheet 1

Description

This project explores the potential of an artificial intelligence/machine learning algorithm, K-Means to augment the connection between two individuals through a game interface. Further implementation of such technology is theorized in the form of a two-way chatbot. The role of bias is extensively reported and researched in order to remain…

This project explores the potential of an artificial intelligence/machine learning algorithm, K-Means to augment the connection between two individuals through a game interface. Further implementation of such technology is theorized in the form of a two-way chatbot. The role of bias is extensively reported and researched in order to remain cognizant of these new technological advancements.

ContributorsHatfield, Kacy (Author) / Sha, Xin (Thesis director) / Finn, Ed (Committee member) / Barrett, The Honors College (Contributor) / Arts, Media and Engineering Sch T (Contributor)

Created2022-05

Clustered Surreal Images Data - Clusters

Description

This project explores the potential of an artificial intelligence/machine learning algorithm, K-Means to augment the connection between two individuals through a game interface. Further implementation of such technology is theorized in the form of a two-way chatbot. The role of bias is extensively reported and researched in order to remain…

This project explores the potential of an artificial intelligence/machine learning algorithm, K-Means to augment the connection between two individuals through a game interface. Further implementation of such technology is theorized in the form of a two-way chatbot. The role of bias is extensively reported and researched in order to remain cognizant of these new technological advancements.

ContributorsHatfield, Kacy (Author) / Sha, Xin (Thesis director) / Finn, Ed (Committee member) / Barrett, The Honors College (Contributor) / Arts, Media and Engineering Sch T (Contributor)

Created2022-05

Clustered Unfriendly Faces Data - Full Data

Description

This project explores the potential of an artificial intelligence/machine learning algorithm, K-Means to augment the connection between two individuals through a game interface. Further implementation of such technology is theorized in the form of a two-way chatbot. The role of bias is extensively reported and researched in order to remain…

This project explores the potential of an artificial intelligence/machine learning algorithm, K-Means to augment the connection between two individuals through a game interface. Further implementation of such technology is theorized in the form of a two-way chatbot. The role of bias is extensively reported and researched in order to remain cognizant of these new technological advancements.

ContributorsHatfield, Kacy (Author) / Sha, Xin (Thesis director) / Finn, Ed (Committee member) / Barrett, The Honors College (Contributor) / Arts, Media and Engineering Sch T (Contributor)

Created2022-05

Using Machine Learning Classification Techniques to Predict Recessionary Periods in the U.S. Economy

Description

The goal of this research project is to determine how beneficial machine learning (ML) techniquescan be in predicting recessions. Past work has utilized a multitude of classification methods from Probit models to linear Support Vector Machines (SVMs) and obtained accuracies nearing 60-70%, where some models even predicted the Great Recession…

The goal of this research project is to determine how beneficial machine learning (ML) techniquescan be in predicting recessions. Past work has utilized a multitude of classification methods from Probit models to linear Support Vector Machines (SVMs) and obtained accuracies nearing 60-70%, where some models even predicted the Great Recession based off data from the previous 50 years. This paper will build on past work, by starting with less complex classification techniques that are more broadly used in recession forecasting and end by incorporating more complex ML models that produce higher accuracies than their more primitive counterparts. Many models were tested in this analysis and the findings here corroborate past work that the SVM methodology produces more accurate results than currently used probit models, but adds on that other ML models produced sufficient accuracy as well.

ContributorsHogan, Carter (Author) / McCulloch, Robert (Thesis director) / Pereira, Claudiney (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / Economics Program in CLAS (Contributor) / School of Mathematical and Statistical Sciences (Contributor)

Created2022-05

Surface Pattern Recognition for Image-Based Inference of Mechanical Properties

Description

A key aspect of understanding the behavior of materials and structures is the analysis of how they fail. A key aspect of failure analysis is the discipline of fractography, which identifies features of interest on fracture surfaces with the goal of revealing insights on the nature of defects and microstructure,…

A key aspect of understanding the behavior of materials and structures is the analysis of how they fail. A key aspect of failure analysis is the discipline of fractography, which identifies features of interest on fracture surfaces with the goal of revealing insights on the nature of defects and microstructure, and their interactions with the environment such as loading conditions. While fractography itself is a decades-old science, two aspects drive the need for this research: (i) Fractography remains a specialized domain of materials science where human subjectivity and experience play a large role in accurate determination of fracture modes and their relationship to the loading environment. (ii) Secondly, Additive Manufacturing (AM) is increasingly being used to create critical functional parts, where our understanding of failure mechanisms and how they relate to process and post-process conditions is nascent. Given these two challenges, this thesis conducted work to train convolutional neural network (CNN) models to analyze fracture surfaces in place of human experts and applies this to Inconel 718 specimens fabricated with the Laser Powder Bed Fusion (LPBF) process, as well as to traditional sheet metal specimens of the same alloy. This work intends to expand on previous work utilizing clustering methods through comparison of models developed using both manufacturing processes to demonstrate the effectiveness of the CNN approach, as well as elucidate insights into the nature of fracture modes in additively and traditionally manufactured thin-wall Inconel 718 specimens.

ContributorsVan Handel, Nicole (Author) / Bhate, Dhruv (Thesis director, Committee member) / Guo, Shenghan (Thesis director, Committee member) / Barrett, The Honors College (Contributor) / Engineering Programs (Contributor) / Dean, W.P. Carey School of Business (Contributor) / School of Mathematical and Statistical Sciences (Contributor)

Created2022-05

Population Receptive Field Prediction with Convolutional Neural Networks

Description

The Population Receptive Field (pRF) model is widely used to predict the location (retinotopy) and size of receptive fields on the visual space. Doing so allows for the creation of a mapping from locations in the visual field to the associated groups of neurons in the cortical region (within the…

The Population Receptive Field (pRF) model is widely used to predict the location (retinotopy) and size of receptive fields on the visual space. Doing so allows for the creation of a mapping from locations in the visual field to the associated groups of neurons in the cortical region (within the visual cortex of the brain). However, using the pRF model is very time consuming. Past research has focused on the creation of Convolutional Neural Networks (CNN) to mimic the pRF model in a fraction of the time, and they have worked well under highly controlled conditions. However, these models have not been thoroughly tested on real human data. This thesis focused on adapting one of these CNNs to accurately predict the retinotopy of a real human subject using a dataset from the Human Connectome Project. The results show promise towards creating a fully functioning CNN, but they also expose new challenges that must be overcome before the model could be used to predict the retinotopy of new human subjects.

ContributorsBurgard, Braeden (Author) / Wang, Yalin (Thesis director) / Ta, Duyan (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)

Created2022-05

Recognizing Emotions: Developing a Linguistic Understanding of How Emotions Feed Into Speech Through Semantics and Facial Expressions

Description

The importance of nonverbal communication has been well established through several theories including Albert Mehrabian's 7-38-55 rule that proposes the respective importance of semantics, tonality and facial expressions in communication. Although several studies have examined how emotions are expressed and preceived in communication, there is limited research investigating the relationshi…

The importance of nonverbal communication has been well established through several theories including Albert Mehrabian's 7-38-55 rule that proposes the respective importance of semantics, tonality and facial expressions in communication. Although several studies have examined how emotions are expressed and preceived in communication, there is limited research investigating the relationship between how emotions are expressed through semantics and facial expressions. Using a facial expression analysis software to deconstruct facial expressions into features and a K-Nearest-Neighbor (KNN) machine learning classifier, we explored if facial expressions can be clustered based on semantics. Our findings indicate that facial expressions can be clustered based on semantics and that there is an inherent congruence between facial expressions and semantics. These results are novel and significant in the context of nonverbal communication and are applicable to several areas of research including the vast field of emotion AI and machine emotional communication.

ContributorsEverett, Lauren (Author) / Coza, Aurel (Thesis director) / Santello, Marco (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor) / Dean, W.P. Carey School of Business (Contributor)

Created2022-05

Find My College

Description

Find My College is an app to help people who are interested in pursuing a collegiate degree; find a college/s that is right for them. This app is designed using the Ionic Framework, to allow access across all operating systems such as Android and MacOS. We wanted to create an…

Find My College is an app to help people who are interested in pursuing a collegiate degree; find a college/s that is right for them. This app is designed using the Ionic Framework, to allow access across all operating systems such as Android and MacOS. We wanted to create an app that people using Android or Apple can use, and this framework allows us to do that. The app is very user friendly and straightforward, which makes it usable to all types of people. It will be a free to use app that can be improved and adjusted if changes are needed/wanted.

ContributorsSolis, Jalen (Author) / Vadlamudi, Sai (Co-author) / Miller, Phillip (Thesis director) / De Luca, Gennaro (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)

Created2022-05

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