Artificial Intelligence and Robotics Commons^™

On Training Neurons With Bounded Compilations, Lance Kennedy

Master of Science in Computer Science Theses

Knowledge compilation offers a formal approach to explaining and verifying the behavior of machine learning systems, such as neural networks. Unfortunately, compiling even an individual neuron into a tractable representation such as an Ordered Binary Decision Diagram (OBDD), is an NP-hard problem. In this thesis, we consider the problem of training a neuron from data, subject to the constraint that it has a compact representation as an OBDD. Our approach is based on the observation that a neuron can be compiled into an OBDD in polytime if (1) the neuron has integer weights, and (2) its aggregate weight is bounded. …

Case Study: The Impact Of Emerging Technologies On Cybersecurity Education And Workforces, Austin Cusak

Journal of Cybersecurity Education, Research and Practice

A qualitative case study focused on understanding what steps are needed to prepare the cybersecurity workforces of 2026-2028 to work with and against emerging technologies such as Artificial Intelligence and Machine Learning. Conducted through a workshop held in two parts at a cybersecurity education conference, findings came both from a semi-structured interview with a panel of experts as well as small workgroups of professionals answering seven scenario-based questions. Data was thematically analyzed, with major findings emerging about the need to refocus cybersecurity STEM at the middle school level with problem-based learning, the disconnects between workforce operations and cybersecurity operators, the …

The Use Of Artificial Intelligence In Higher Education: A Study On Faculty Perspectives In Universities In Egypt, Farah S. Sharawy

Theses and Dissertations

Artificial Intelligence (AI) is an emerging technology that is transforming various aspects of society, including higher education. This paper examines faculty perspectives from five different institutions; The American University in Cairo (AUC), The German University in Cairo (GUC), The Arab Academy for Science and Technology (AAST), Ain Shams University, and Cairo University, on the use of AI in higher education in teaching and learning in Egypt, with all its challenges and resources available to support it, and how it can be used to achieve equity and accessibility. This research was conducted through a qualitative study using semi-structured one- on-one interviews …

A Novel Approach To Extending Music Using Latent Diffusion, Keon Roohparvar, Franz J. Kurfess

Master's Theses

Using deep learning to synthetically generate music is a research domain that has gained more attention from the public in the past few years. A subproblem of music generation is music extension, or the task of taking existing music and extending it. This work proposes the Continuer Pipeline, a novel technique that uses deep learning to take music and extend it in 5 second increments. It does this by treating the musical generation process as an image generation problem; we utilize latent diffusion models (LDMs) to generate spectrograms, which are image representations of music. The Continuer Pipeline is able to …

Artificial Intelligence Cad Tools In Trauma Imaging: A Scoping Review From The American Society Of Emergency Radiology (Aser) Ai/Ml Expert Panel, David Dreizin, Pedro V Staziaki, Garvit D Khatri, Nicholas M Beckmann, Zhaoyong Feng, Yuanyuan Liang, Zachary S Delproposto, Maximiliano Klug, J Stephen Spann, Nathan Sarkar, Yunting Fu

Faculty, Staff and Student Publications

BACKGROUND: AI/ML CAD tools can potentially improve outcomes in the high-stakes, high-volume model of trauma radiology. No prior scoping review has been undertaken to comprehensively assess tools in this subspecialty.

PURPOSE: To map the evolution and current state of trauma radiology CAD tools along key dimensions of technology readiness.

METHODS: Following a search of databases, abstract screening, and full-text document review, CAD tool maturity was charted using elements of data curation, performance validation, outcomes research, explainability, user acceptance, and funding patterns. Descriptive statistics were used to illustrate key trends.

RESULTS: A total of 4052 records were screened, and 233 full-text …

Rapid Assessment Of Fish Freshness For Multiple Supply-Chain Nodes Using Multi-Mode Spectroscopy And Fusion-Based Artificial Intelligence, Hossein Kashani Zadeh, Mike Hardy, Mitchell Sueker, Yicong Li, Angelis Tzouchas, Nicholas Mackinnon, Gregory Bearman, Simon A Haughey, Alireza Akhbardeh, Insuck Baek, Chansong Hwang, Jianwei Qin, Amanda M Tabb, Rosalee S Hellberg, Shereen Ismail, Hassan Reza, Fartash Vasefi, Moon Kim, Kouhyar Tavakolian, Christopher T Elliott

Faculty, Staff and Student Publications

This study is directed towards developing a fast, non-destructive, and easy-to-use handheld multimode spectroscopic system for fish quality assessment. We apply data fusion of visible near infra-red (VIS-NIR) and short wave infra-red (SWIR) reflectance and fluorescence (FL) spectroscopy data features to classify fish from fresh to spoiled condition. Farmed Atlantic and wild coho and chinook salmon and sablefish fillets were measured. Three hundred measurement points on each of four fillets were taken every two days over 14 days for a total of 8400 measurements for each spectral mode. Multiple machine learning techniques including principal component analysis, self-organized maps, linear and …

Detecting Ai Generated Text Using Neural Networks, Jesus Guerrero

Masters Theses (Archived)

For humans, distinguishing machine generated text from human written text is men- tally taxing and slow. NLP models have been created to do this more effectively and faster. But, what if some adversarial changes have been added to the machine generated text? This thesis discusses this issue and text detectors in general.
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/="/">The primary goal of this thesis is to describe the current state of text detectors in research and to discuss a key adversarial issue in modern NLP transformers. To describe the current state of text detectors a Systematic Literature Review was done on 50 relevant papers …

Analysis Of A Federated Learning Framework For Heterogeneous Medical Image Data: Privacy And Performance Perspective, Julia Brixey

Computer Science and Computer Engineering Undergraduate Honors Theses

The massive amount of data available in our modern world and the increase of computational efficiency and power have allowed for great advancements in several fields such as computer vision, image processing, and natural languages. At the center of these advancements lies a data-centric learning approach termed deep learning. However, in the medical field, the application of deep learning comes with many challenges. Some of the fundamental challenges are the lack of massive training datasets, unbalanced and heterogenous data between health applications and health centers, security and privacy concerns, and the high cost of wrong inference and prediction. One of …

Artificial: A Study On The Use Of Artificial Intelligence In Art, Hayden Ernst

Theses/Capstones/Creative Projects

In the past three to five years there have been significant improvements made in AI due to improvements in computing capacity, the collection and use of big data, and an increase in public interest and funding for research. Programs such as ChatGPT, DALL•E, and Midjourney have also gained tremendous popularity in a relatively short amount of time. This led me to this project in which I aimed to gain a deeper understanding of these art generator AI and where they fit into art as a whole. My goal was to give recommendations to museums and exhibits in Omaha on what …

Achieving Causal Fairness In Recommendation, Wen Huang

Graduate Theses and Dissertations

Recommender systems provide personalized services for users seeking information and play an increasingly important role in online applications. While most research papers focus on inventing machine learning algorithms to fit user behavior data and maximizing predictive performance in recommendation, it is also very important to develop fairness-aware machine learning algorithms such that the decisions made by them are not only accurate but also meet desired fairness requirements. In personalized recommendation, although there are many works focusing on fairness and discrimination, how to achieve user-side fairness in bandit recommendation from a causal perspective still remains a challenging task. Besides, the deployed …

Inaugural Artificial Intelligence For Public Health Practice (Ai4php) Retreat: Ontario, Canada, Jacqueline K. Kueper, Laura C. Rosella, Richard G. Booth, Brent D. Davis, Sarah Nayani, Maxwell J. Smith, Dan Lizotte

Computer Science Publications

The Artificial Intelligence (AI) for Public Health Practice Retreat was a hybrid event held in October 2022 in London, Ontario to achieve three main goals: 1) Identify both the goals of public health practitioners and the tasks that they undertake as part of their practice to achieve those goals that could be supported by AI, 2) Learn from existing examples and the experience of others about facilitators and barriers to AI for public health, and 3) Support new and strengthen existing connections between public health practitioners and AI researchers. The retreat included a keynote presentation, group brainstorming exercises, breakout group …

Of Inventorship And Patent Ownership: Examining The Intersection Between Artificial Intelligence And Patent Law, Cheng Lim Saw, Zheng Wen Samuel Chan

Research Collection Yong Pung How School Of Law

Artificial intelligence (“AI”) has garnered much attention in recent years, with capabilities spanning the operation of self-driving cars to the emulation of the great artistic masters of old. The field has now been ostensibly enlarged in light of the professed abilities of AI machines to autonomously generate patentable inventions. This article examines the present state of AI technology and the suitability of existing patent law frameworks in accommodating it. Looking ahead, the authors also offer two recommendations in a bid to anticipate and resolve the challenges that future developments in AI technology might pose to patent law. In particular, the …

Relationship Between Strategic Dexterity, Absorptive Capacity, And Competitive Advantage, Ifechide Monyei

Walden Dissertations and Doctoral Studies

Small- and medium-sized enterprise (SME) manufacturing executives and managers are concerned with the rapid technological changes involving artificial intelligence (AI), machine learning, and big data. To compete in the global landscape, effectively managing digital and artificial intelligence changes among SME manufacturing executives and managers is critical for leaders to compete in 2023 and beyond. Grounded in the dynamic capabilities view theory, the purpose of this quantitative correlation study was to examine the relationship between strategic dexterity, absorptive capacity, and competitive advantage. The participants were 66 executives and managers of SME manufacturing organizations who use big data and analytics daily and …

Relationship Between Strategic Dexterity, Absorptive Capacity, And Competitive Advantage, Ifechide Monyei

Walden Dissertations and Doctoral Studies

Small- and medium-sized enterprise (SME) manufacturing executives and managers are concerned with the rapid technological changes involving artificial intelligence (AI), machine learning, and big data. To compete in the global landscape, effectively managing digital and artificial intelligence changes among SME manufacturing executives and managers is critical for leaders to compete in 2023 and beyond. Grounded in the dynamic capabilities view theory, the purpose of this quantitative correlation study was to examine the relationship between strategic dexterity, absorptive capacity, and competitive advantage. The participants were 66 executives and managers of SME manufacturing organizations who use big data and analytics daily and …

Utilizing Machine Learning In Healthcare In An Ethical Fashion, Nishka Ayyar

CMC Senior Theses

This thesis paper explores the ethical considerations surrounding the use of machine learning (ML) solutions in healthcare. The background section discusses the basics of machine learning techniques and algorithms, and the increasing interest in their utilization in the healthcare sector. The paper then reviews and critically analyzes four studies that highlight concerns related to using ML in healthcare, including issues of bias, privacy, accountability, and transparency. Based on the analysis of these studies, the paper presents several recommendations for addressing these concerns. The paper concludes with a discussion on the potential benefits of using machine learning technology in healthcare. Ultimately, …

Generalizable And Adaptable Data-Driven Methods For Overcoming Barriers To Practical Industrial Condition Monitoring, Matthew B. Russell

Theses and Dissertations--Electrical and Computer Engineering

The future of smart manufacturing relies on predictive maintenance systems that intelligently minimize expensive downtime through timely assessment of machine condition. Deep Learning (DL) has achieved excellent performance in industrial condition monitoring experiments, but the constraints of the manufacturing environment prevent many algorithms from being practically deployed on the factory floor. Ubiquitous sensing from online machines generates high velocity data streams that require new techniques for efficient transmission and storage. Despite these ever-increasing data lakes, many applications still lack the data needed for training DL fault diagnosis and wear tracking models since most data is unlabeled and only from nominal …

Enhancing Early-Stage Xai Projects Through Designer-Led Visual Ideation Of Ai Concepts, Helen Sheridan, Emma Murphy, Dympna O'Sullivan

Academic Posters Collection

The pervasive use of artificial intelligence (AI) in processing users’ data is well documented with the use of AI believed to profoundly change users’ way of life in the near future. However, there still exists a sense of mistrust among users who engage with AI systems some of this stemming from lack of transparency, including users failing to understand what AI is, what it can do and its impact on society. From this, the emerging discipline of explainable artificial intelligence (XAI) has emerged, a method of designing and developing AI where a systems decisions, processes and outputs are explained and …

Unlocking The Black Box: Evaluating Xai Through A Mixed Methods Approach Combining Quantitative Standardised Scales And Qualitative Techniques, Helen Sheridan, Dympna O'Sullivan, Emma Murphy

Academic Posters Collection

In 1950 when Alan Turing first published his groundbreaking paper, computing machinery and intelligence and asked “Can machines think?” a new era of research exploring the intelligence of digital computers and their ability to deceive and/or imitate a human was ignited. From these first explorations of AI to modern day artificial intelligence and machine learning systems many advances, breakthroughs and improved algorithms have been developed usually advancing at an exponential pace. This has resulted in the pervasive use of AI systems in the processing of data. Concerns have been expressed related to biased decisions by AI systems around the processing …

Object Detection And Image Categorization By Transferring Commonsense Knowledge With Premises And Quantifiers, Irina Chernyavsky

Theses, Dissertations and Culminating Projects

Domestic, or household robots, are autonomous robots designed to make our home-life easier by performing chores and mundane tasks such as cleaning, or cooking. Currently domestic robots are specialized to complete a specific task and, therefore, are confined by factors such as mobility, size, and complexity. With the fast development of computer vision and robotics, the need for more compact, advanced and multi-task robots has emerged. Therefore, the robot needs to be multi-functional, able to discern the environment and the tasks. The aim of this paper is to categorize images in domestic robots as relevant to the culinary, laundry, vacuum …

Artificial Intelligence-Based Medical Device Technologies Implementation Strategies In The Nigerian Health Care Industry, Oliver Chikaodinaka Iheme

Walden Dissertations and Doctoral Studies

Artificial intelligence (AI)-based medical device technologies can aid medical professionals in delivering faster and more accurate treatment, but health care leaders are concerned with eliminating challenges that impede implementation. Grounded in the technology-organization-environment and technology acceptance models, the purpose of this qualitative multi-case study was to explore strategies health care leaders in Nigeria use to obtain, adopt, and implement AI-based medical device technologies. The participants were 11 health care leaders in Nigeria who successfully implemented AI-based medical device technologies in their hospitals. Data were collected using semi-structured interviews and the review of organizational documents. Through thematic analysis, five themes were …

Predicting Housing Prices Using Ai, Eric Sconyers

Williams Honors College, Honors Research Projects

I have created an AI model that can predict housing prices with 70 percent accuracy in Ames Iowa. I was able to use data from a website called Kaggle.com which is a website that provides datasets to the public so they can create AI models with the data. I found the dataset pertaining to housing prices in Ames Iowa. With this data, I was able to create an AI model that can predict the housing price of these homes. The technology I used in this project was Python as the programming language, and I used the scikit-learn library which has …

Robots And Reference Services, Abdullahi Olayinka Isiaka, Biliamin Abiola Aremu, Abdulfatai Soliu, Fahisat Romoke Isiaq

Library Philosophy and Practice (e-journal)

Abstract

This paper explores the integration of robots into reference services in various library and information settings. The use of robots in these contexts has gained momentum in recent years, offering innovative solutions to enhance user experiences, improve access to information, and expand the capabilities of reference librarians. This paper reviews the current state of robots in reference services, discusses their potential benefits and challenges, and examines case studies to illustrate their practical applications. Furthermore, it offers insights into the future prospects and ethical considerations associated with the integration of robots in this domain. This paper delves into the fascinating …

Artificial Intelligence-Based Medical Device Technologies Implementation Strategies In The Nigerian Health Care Industry, Oliver Chikaodinaka Iheme

Walden Dissertations and Doctoral Studies

Artificial intelligence (AI)-based medical device technologies can aid medical professionals in delivering faster and more accurate treatment, but health care leaders are concerned with eliminating challenges that impede implementation. Grounded in the technology-organization-environment and technology acceptance models, the purpose of this qualitative multi-case study was to explore strategies health care leaders in Nigeria use to obtain, adopt, and implement AI-based medical device technologies. The participants were 11 health care leaders in Nigeria who successfully implemented AI-based medical device technologies in their hospitals. Data were collected using semi-structured interviews and the review of organizational documents. Through thematic analysis, five themes were …

Development Of Machine Learning Based Approach To Predict Fuel Consumption And Maintenance Cost Of Heavy-Duty Vehicles Using Diesel And Alternative Fuels, Sasanka Katreddi

Graduate Theses, Dissertations, and Problem Reports (ETD)

One of the major contributors of human-made greenhouse gases (GHG) namely carbon dioxide (CO2), methane (CH4), and nitrous oxide (NOX) in the transportation sector and heavy-duty vehicles (HDV) contributing to about 27% of the overall fraction. In addition to the rapid increase in global temperature, airborne pollutants from diesel vehicles also present a risk to human health. Even a small improvement that could potentially drive energy savings to the century-old mature diesel technology could yield a significant impact on minimizing greenhouse gas emissions. With the increasing focus on reducing emissions and operating costs, there is a need for efficient and …

Comments Of The Cordell Institute On Ai Accountability, Neil M. Richards, Woodrow Hartzog, Jordan Francis

Scholarship@WashULaw

These comments are a response to the National Telecommunications and Information Administration's 2023 request for comment on AI accountability (AI Accountability RFC, NTIA–2023–0005).

Responding to NTIA’s recent inquiry into AI assurance and accountability, we offer two main arguments regarding the importance of substantive legal protections. First, a myopic focus on concepts of transparency, bias mitigation, and ethics (for which procedural compliance efforts such as audits, assessments, and certifications are proxies) is insufficient when it comes to the design and implementation of accountable AI systems. We call rules built around transparency and bias mitigation “AI half-measures,” because they provide the appearance …

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

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

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

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

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 …