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Articles 1 - 20 of 20
Full-Text Articles in Physical Sciences and Mathematics
Artificial Intelligence In The Medical Field: Medical Review Sentiment Analysis, Nicholas Podlesak
Artificial Intelligence In The Medical Field: Medical Review Sentiment Analysis, Nicholas Podlesak
Honors Capstones
In this research project, natural language processing techniques’ ability to accurately classify medical text was measured to reinforce the relevance of artificial intelligence in the medical field. Sentiment analyses (analyses to determine whether the text was positive or negative) were performed on the prescription drug reviews in an open-source dataset using four different models: lexical, a neural network, a support vector machine, and a logistic regression model. Each model’s effectiveness was gauged by its ability to correctly classify unlabeled drug reviews (i.e., a percentage representing accuracy). The machine learning models were able to accurately classify the text, while the lexical …
Wordmuse, John M. Nelson
Wordmuse, John M. Nelson
Computer Science and Software Engineering
Wordmuse is an application that allows users to enter a song and a list of keywords to create a new song. Built on Spotify's API, this project showcases the fusion of music composition and artificial intelligence. This paper also discusses the motivation, design, and creation of Wordmuse.
Predicting Startup Success Using Publicly Available Data, Emily Gavrilenko
Predicting Startup Success Using Publicly Available Data, Emily Gavrilenko
Master's Theses
Predicting the success of an early-stage startup has always been a major effort for investors and venture funds. Statistically, there are about 305 million total startups created in a year, but less than 10% of them succeed to become profitable businesses. Accurately identifying the signs of startup growth is the work of countless investors, and in recent years, research has turned to machine learning in hopes of improving the accuracy and speed of startup success prediction.
To learn about a startup, investors have to navigate many different internet sources and often rely on personal intuition to determine the startup’s potential …
Autonomous Vehicle Innovation And Implications On Adoption, Liability And Policy, Using Quantum Technologies And Artificial Wisdom, Chia Jie Jun Jeremy
Autonomous Vehicle Innovation And Implications On Adoption, Liability And Policy, Using Quantum Technologies And Artificial Wisdom, Chia Jie Jun Jeremy
Dissertations and Theses Collection (Open Access)
This paper will explore the use of two new innovations for the issues facing autonomous vehicles (AV), those of quantum technologies and artificial wisdom. The issue of delayed at-scale commercialization and adoption of autonomous vehicles due to the extensive dynamic capability required to derive an optimal process solution for any complex, dynamic and adaptive autonomous vehicle ecosystem is shown to be resolved by the use of these innovations, will be shown to be more widely applicable for other issues for AV and for any scenario where automated decision making is required.
QC might open up the door for the application …
An Enterprise Risk Management Framework To Design Pro-Ethical Ai Solutions, Quintin P. Mcgrath
An Enterprise Risk Management Framework To Design Pro-Ethical Ai Solutions, Quintin P. Mcgrath
USF Tampa Graduate Theses and Dissertations
The effective use of Artificial Intelligence (AI) has immediate business benefits for an organization and its stakeholders through efficiency and quality gains, and the potential to explore and implement new business models. However, there are risks of unintended ethical consequences. Enterprise Risk Management (ERM) focuses on managing risk while maximizing business value from exploiting opportunities. Using applied ethics as a basis and the perspective that ethics includes both enabling human flourishing and not violating accepted norms, I argue that greater business value is achieved when an organization simultaneously targets the maximization of benefits and the minimization of harms for the …
Better Understanding Genomic Architecture With The Use Of Applied Statistics And Explainable Artificial Intelligence, Jonathon C. Romero
Better Understanding Genomic Architecture With The Use Of Applied Statistics And Explainable Artificial Intelligence, Jonathon C. Romero
Doctoral Dissertations
With the continuous improvements in biological data collection, new techniques are needed to better understand the complex relationships in genomic and other biological data sets. Explainable Artificial Intelligence (X-AI) techniques like Iterative Random Forest (iRF) excel at finding interactions within data, such as genomic epistasis. Here, the introduction of new methods to mine for these complex interactions is shown in a variety of scenarios. The application of iRF as a method for Genomic Wide Epistasis Studies shows that the method is robust in finding interacting sets of features in synthetic data, without requiring the exponentially increasing computation time of many …
Developing Artificial Intelligence And Machine Learning To Support Primary Care Research And Practice, Jacqueline K. Kueper
Developing Artificial Intelligence And Machine Learning To Support Primary Care Research And Practice, Jacqueline K. Kueper
Electronic Thesis and Dissertation Repository
This thesis was motivated by the potential to use "everyday data", especially that collected in electronic health records (EHRs) as part of healthcare delivery, to improve primary care for clients facing complex clinical and/or social situations. Artificial intelligence (AI) techniques can identify patterns or make predictions with these data, producing information to learn about and inform care delivery. Our first objective was to understand and critique the body of literature on AI and primary care. This was achieved through a scoping review wherein we found the field was at an early stage of maturity, primarily focused on clinical decision support …
Interdisciplinary Communication By Plausible Analogies: The Case Of Buddhism And Artificial Intelligence, Michael Cooper
Interdisciplinary Communication By Plausible Analogies: The Case Of Buddhism And Artificial Intelligence, Michael Cooper
USF Tampa Graduate Theses and Dissertations
Communicating interdisciplinary information is difficult, even when two fields are ostensibly discussing the same topic. In this work, I’ll discuss the capacity for analogical reasoning to provide a framework for developing novel judgments utilizing similarities in separate domains. I argue that analogies are best modeled after Paul Bartha’s By Parallel Reasoning, and that they can be used to create a Toulmin-style warrant that expresses a generalization. I argue that these comparisons provide insights into interdisciplinary research. In order to demonstrate this concept, I will demonstrate that fruitful comparisons can be made between Buddhism and Artificial Intelligence research.
Building An Artificial Intelligence Framework For Hypertension Diagnosis: A Use Case Of The Problem List Curation, Ketemwabi Yves Shamavu
Building An Artificial Intelligence Framework For Hypertension Diagnosis: A Use Case Of The Problem List Curation, Ketemwabi Yves Shamavu
Theses & Dissertations
Hypertension is the world's leading factor in cardiovascular disease. Forty-seven percent or close to one in two Americans aged 18 and older are affected. It predicts approximately a thousand deaths per day. Based on recent statistics from the Centers for Disease Control and Prevention, one in three patients with hypertension does not know they are hypertensive. Seventy-five percent of hypertensive patients have uncontrolled hypertension - meaning that they are not treated to target. While there is extensive literature on hypertension diagnosis and management, there is an apparent gap in understanding and acknowledging that a person is hypertensive. Moreover, blood pressure …
Risk Gameplay Analysis Using Stochastic Beam Search, Jacob Gillenwater
Risk Gameplay Analysis Using Stochastic Beam Search, Jacob Gillenwater
Electronic Theses and Dissertations
Hasbro’s RISK, first published in 1959, is a complex multiplayer strategy game that has received little attention from the scientific community. Training artificial intelligence (AI) agents using stochastic beam search gives insight into effective strategy when playing RISK. A comprehensive analysis of the systems of play challenges preconceptions about good strategy in some areas of the game while reinforcing those preconceptions in others. This study applies stochastic beam search to discover optimal strategies in RISK. Results of the search show both support for and challenges to traditionally held positions about RISK gameplay. While stochastic beam search competently investigates gameplay on …
The Bracelet: An American Sign Language (Asl) Interpreting Wearable Device, Samuel Aba, Ahmadre Darrisaw, Pei Lin, Thomas Leonard
The Bracelet: An American Sign Language (Asl) Interpreting Wearable Device, Samuel Aba, Ahmadre Darrisaw, Pei Lin, Thomas Leonard
Chancellor’s Honors Program Projects
No abstract provided.
Artificial Intelligence And Deep Reinforcement Learning Stock Market Predictions, Andrew W. Brim
Artificial Intelligence And Deep Reinforcement Learning Stock Market Predictions, Andrew W. Brim
All Graduate Theses and Dissertations, Spring 1920 to Summer 2023
Billions of dollars are traded automatically in the stock market every day, including algorithms that use artificial intelligence (AI) techniques, but there are still questions regarding how AI trades successfully. The black box nature of these AI techniques, namely neural networks, gives pause to entrusting it with valuable trading funds. This dissertation applies AI techniques to stock market trading strategies, but it also provides exploratory research into how these techniques predict the stock market successfully.
This dissertation presents the work of three research papers. The first paper presented in this dissertation applies a artificial intelligence technique, reinforcement learning, to candlestick …
Reliable Decision-Making With Imprecise Models, Sandhya Saisubramanian
Reliable Decision-Making With Imprecise Models, Sandhya Saisubramanian
Doctoral Dissertations
The rapid growth in the deployment of autonomous systems across various sectors has generated considerable interest in how these systems can operate reliably in large, stochastic, and unstructured environments. Despite recent advances in artificial intelligence and machine learning, it is challenging to assure that autonomous systems will operate reliably in the open world. One of the causes of unreliable behavior is the impreciseness of the model used for decision-making. Due to the practical challenges in data collection and precise model specification, autonomous systems often operate based on models that do not represent all the details in the environment. Even if …
Deep Features To Analyze Pulmonary Abnormalities In Chest X-Rays Due To Covid-19, Supriti Ghosh
Deep Features To Analyze Pulmonary Abnormalities In Chest X-Rays Due To Covid-19, Supriti Ghosh
Dissertations and Theses
Artificial Intelligence (AI) has contributed a lot since the beginning. Healthcare is no exception. Detecting anomaly/abnormality in (bio)medical image is crucial. In this thesis, we aim at detecting/screening pulmonary abnormalities due to Covid-19 in chest X-rays using deep features. We study CheXNet, DenseNet169, ResNet50 and VggNet16 to analyze CXRs to detect the evidence of Covid-19 in this research. CheXNet was primarily designed for radiologist-level pneumonia detection in Chest X-rays (CXRs). We created a benchmark dataset size of 4,716 CXRs (2,358 Covid-19 positive cases and 2,358 non-Covid cases (Healthy and Pneumonia cases)) and with k(=5) fold cross-validation technique, using the DenseNet, …
A Machine Learning Approach To Intended Motion Prediction For Upper Extremity Exoskeletons, Justin Berdell
A Machine Learning Approach To Intended Motion Prediction For Upper Extremity Exoskeletons, Justin Berdell
Graduate Research Theses & Dissertations
A fully solid-state, software-defined, one-handed, handle-type control device built around a machine-learning (ML) model that provides intuitive and simultaneous control in position and orientation each in a full three degrees-of-freedom (DOF) is proposed in this paper. The device, referred to as the “Smart Handle”, and it is compact, lightweight, and only reliant on low-cost and readily available sensors and materials for construction. Mobility chairs for persons with motor difficulties could make use of a control device that can learn to recognize arbitrary inputs as control commands. Upper-extremity exoskeletons used in occupational settings and rehabilitation require a natural control device like …
Use Of Human–Computer Interaction Devices And Web 3.0 Skills Among Engineers, Dr. Robbie L. Walker
Use Of Human–Computer Interaction Devices And Web 3.0 Skills Among Engineers, Dr. Robbie L. Walker
Walden Dissertations and Doctoral Studies
Despite massive company investments in human–computer interaction devices and software, such as Web 3.0 technologies, engineers are not demonstrating measurable performance and productivity increases. There is a lack of knowledge and understanding related to the motivation of engineers to use Web 3.0 technologies including the semantic web and cloud applications for increased performance. The purpose of this quantitative correlational study was to investigate whether the use of human–computer interaction devices predict Web 3.0 skills among engineers. Solow’s information technology productivity paradox was the theoretical foundation for this study. Convenience sampling was used for a sample of 214 participants from metropolitan …
Novel Natural Language Processing Models For Medical Terms And Symptoms Detection In Twitter, Farahnaz Golrooy Motlagh
Novel Natural Language Processing Models For Medical Terms And Symptoms Detection In Twitter, Farahnaz Golrooy Motlagh
Browse all Theses and Dissertations
This dissertation focuses on disambiguation of language use on Twitter about drug use, consumption types of drugs, drug legalization, ontology-enhanced approaches, and prediction analysis of data-driven by developing novel NLP models. Three technical aims comprise this work: (a) leveraging pattern recognition techniques to improve the quality and quantity of crawled Twitter posts related to drug abuse; (b) using an expert-curated, domain-specific DsOn ontology model that improve knowledge extraction in the form of drug-to-symptom and drug-to-side effect relations; and (c) modeling the prediction of public perception of the drug’s legalization and the sentiment analysis of drug consumption on Twitter. We collected …
Deep Understanding Of Technical Documents : Automated Generation Of Pseudocode From Digital Diagrams & Analysis/Synthesis Of Mathematical Formulas, Nikolaos Gkorgkolis
Deep Understanding Of Technical Documents : Automated Generation Of Pseudocode From Digital Diagrams & Analysis/Synthesis Of Mathematical Formulas, Nikolaos Gkorgkolis
Browse all Theses and Dissertations
The technical document is an entity that consists of several essential and interconnected parts, often referred to as modalities. Despite the extensive attention that certain parts have already received, per say the textual information, there are several aspects that severely under researched. Two such modalities are the utility of diagram images and the deep automated understanding of mathematical formulas. Inspired by existing holistic approaches to the deep understanding of technical documents, we develop a novel formal scheme for the modelling of digital diagram images. This extends to a generative framework that allows for the creation of artificial images and their …
Advances And Applications In High-Dimensional Heuristic Optimization, Samuel Alexander Vanfossan
Advances And Applications In High-Dimensional Heuristic Optimization, Samuel Alexander Vanfossan
Doctoral Dissertations
“Applicable to most real-world decision scenarios, multiobjective optimization is an area of multicriteria decision-making that seeks to simultaneously optimize two or more conflicting objectives. In contrast to single-objective scenarios, nontrivial multiobjective optimization problems are characterized by a set of Pareto optimal solutions wherein no solution unanimously optimizes all objectives. Evolutionary algorithms have emerged as a standard approach to determine a set of these Pareto optimal solutions, from which a decision-maker can select a vetted alternative. While easy to implement and having demonstrated great efficacy, these evolutionary approaches have been criticized for their runtime complexity when dealing with many alternatives or …
Application Of Artificial Intelligence For Co2 Storage In Saline Aquifer (Smart Proxy For Snap-Shot In Time), Marwan Mohammed Alnuaimi
Application Of Artificial Intelligence For Co2 Storage In Saline Aquifer (Smart Proxy For Snap-Shot In Time), Marwan Mohammed Alnuaimi
Graduate Theses, Dissertations, and Problem Reports
In recent years, artificial intelligence (AI) and machine learning (ML) technology have grown in popularity. Smart Proxy Models (SPM) are AI/ML based data-driven models which have proven to be quite crucial in petroleum engineering domain with abundant data, or operations in which large surface/ subsurface volume of data is generated. Climate change mitigation is one application of such technology to simulate and monitor CO2 injection into underground formations.
The goal of the SPM developed in this study is to replicate the results (in terms of pressure and saturation outputs) of the numerical reservoir simulation model (CMG) for CO2 injection into …