Artificial Intelligence and Robotics Commons^™

Classification Of Electropherograms Using Machine Learning For Parkinson’S Disease, Soroush Dehghan

Electronic Theses and Dissertations

Parkinson’s disease (PD) is a neurodegenerative movement disorder that progresses gradually over time. The onset of symptoms in people who are suffering from PD can vary from case to case, and it depends on the progression of the disease in each patient. The PD symptoms gradually develop and exacerbate the patient’s movements throughout time. An early diagnosis of PD could improve the outcomes of treatments and could potentially delay the progression of this disorder and that makes discovering a new diagnostic method valuable. In this study, I investigate the feasibility of using a machine learning (ML) approach to classify PD …

Cardiovascular Applications Of Artificial Intelligence In Research, Diagnosis, And Disease Management, Viswanathan Rajagopalan, Houwei Cao

Center for No Boundary Thinking

Despite significant advancements in diagnosis and disease management, cardiovascular (CV) disorders remain the No. 1 killer both in the United States and across the world, and innovative and transformative technologies such as artificial intelligence (AI) are increasingly employed in CV medicine. In this chapter, the authors introduce different AI and machine learning (ML) tools including support vector machine (SVM), gradient boosting machine (GBM), and deep learning models (DL), and their applicability to advance CV diagnosis and disease classification, and risk prediction and patient management. The applications include, but are not limited to, electrocardiogram, imaging, genomics, and drug research in different …

Leveraging Developmental Psychology To Evaluate Artificial Intelligence, David S. Moore, Lisa M. Oakes, Victoria L. Romero, Koleen C. Mccrink

Pitzer Faculty Publications and Research

Artificial intelligence (AI) systems do not exhibit human-like common sense. The principles and practices of experimental psychology – specifically, work on infant cognition – can be used to develop and test AIs, providing insight into the building blocks of common sense. Here, we describe how the evaluation team for DARPA’s Machine Common Sense program is applying conceptual content, experimental design techniques, and analysis tools used in the field of infant cognitive development to the field of AI evaluation.

Liability For Use Of Artificial Intelligence In Medicine, W. Nicholson Price, Sara Gerke, I. Glenn Cohen

Law & Economics Working Papers

While artificial intelligence has substantial potential to improve medical practice, errors will certainly occur, sometimes resulting in injury. Who will be liable? Questions of liability for AI-related injury raise not only immediate concerns for potentially liable parties, but also broader systemic questions about how AI will be developed and adopted. The landscape of liability is complex, involving health-care providers and institutions and the developers of AI systems. In this chapter, we consider these three principal loci of liability: individual health-care providers, focused on physicians; institutions, focused on hospitals; and developers.

Segmentation Of Intracranial Structures From Noncontrast Ct Images With Deep Learning, Evan Porter

Wayne State University Dissertations

Presented in this work is an investigation of the application of artificially intelligent algorithms, namely deep learning, to generate segmentations for the application in functional avoidance radiotherapy treatment planning. Specific applications of deep learning for functional avoidance include generating hippocampus segmentations from computed tomography (CT) images and generating synthetic pulmonary perfusion images from four-dimensional CT (4DCT).A single institution dataset of 390 patients treated with Gamma Knife stereotactic radiosurgery was created. From these patients, the hippocampus was manually segmented on the high-resolution MR image and used for the development of the data processing methodology and model testing. It was determined that …

Using Neural Networks To Model Guitar Distortion, Caleb Koch, Scott Hawley, Andrew Fyfe

Science University Research Symposium (SURS)

Guitar players have been modifying their guitar tone with audio effects ever since the mid-20th century. Traditionally, these effects have been achieved by passing a guitar signal through a series of electronic circuits which modify the signal to produce the desired audio effect. With advances in computer technology, audio “plugins” have been created to produce audio effects digitally through programming algorithms. More recently, machine learning researchers have been exploring the use of neural networks to produce audio effects that yield strikingly similar results to their analog counterparts. Recurrent Neural Networks and Temporal Convolutional Networks have proven to be exceptional at …

A Metric For Machine Learning Vulnerability To Adversarial Examples, Matt Bradley

Masters Theses & Doctoral Dissertations

Machine learning is used in myriad aspects, both in academic research and in everyday life, including safety-critical applications such as robust robotics, cybersecurity products, medial testing and diagnosis where a false positive or negative could have catastrophic results. Despite the increasing prevalence of machine learning applications and their role in critical systems we rely on daily, the security and robustness of machine learning models is still a relatively young field of research with many open questions, particularly on the defensive side of adversarial machine learning. Chief among these open questions is how best to quantify a model’s attack surface against …

Learning A Scalable Algorithm For Improving Betweenness In The Lightning Network, Vincent Davis

Theses and Dissertations--Computer Science

This paper presents a scalable algorithm for solving the Maximum Betweenness Improvement Problem as it occurs in the Bitcoin Lightning Network. In this approach, each node is embedded with a feature vector whereby an Advantage Actor-Critic model identifies key nodes in the network that a joining node should open channels with to maximize its own expected routing opportunities. This model is trained using a custom built environment, lightning-gym, which can randomly generate small scale-free networks or import snapshots of the Lightning Network. After 100 training episodes on networks with 128 nodes, this A2C agent can recommend channels in the Lightning …

Robophobia, Andrew Keane Woods

University of Colorado Law Review

Robots-machines, algorithms, artificial intelligence-play an increasingly important role in society, often supplementing or even replacing human judgment. Scholars have rightly become concerned with the fairness, accuracy, and humanity of these systems. Indeed, anxiety about machine bias is at a fever pitch. While these concerns are important, they nearly all run in one direction: we worry about robot bias against humans; we rarely worry about human bias against robots.

This is a mistake. Not because robots deserve, in some deontological sense, to be treated fairly-although that may be true-but because our bias against nonhuman deciders is bad for us. For example, …

Part I - Ai And Data As Medical Devices, W. Nicholson Price Ii

Other Publications

It may seem counterintuitive to open a book on medical devices with chapters on software and data, but these are the frontiers of new medical device regulation and law. Physical devices are still crucial to medicine, but they – and medical practice as a whole – are embedded in and permeated by networks of software and caches of data. Those software systems are often mindbogglingly complex and largely inscrutable, involving artificial intelligence and machine learning. Ensuring that such software works effectively and safely remains a substantial challenge for regulators and policymakers. Each of the three chapters in this part examines …

Addressing Human-Centered Artificial Intelligence: Fair Data Generation And Classification And Analyzing Algorithmic Curation In Social Media, Amirarsalan Rajabi

Electronic Theses and Dissertations, 2020-2023

With the growing impact of artificial intelligence, the topic of fairness in AI has received increasing attention. Artificial intelligence is observed to have caused unanticipated negative consequences. In this dissertation, we address two critical aspects regarding human-centered artificial intelligence (HCAI), a new paradigm for developing artificial intelligence that is ethical, fair, and helps to improve the human condition. In the first part of this dissertation, we investigate the effect that AI curation of contents by social media platforms has on an online discussions, by studying a polarized discussion in the Twitter network. We then develop a network communication model that …

Theoretical And Experimental Application Of Neural Networks In Spaceflight Control Systems, Pavel Galchenko

Doctoral Dissertations

“Spaceflight systems can enable advanced mission concepts that can help expand our understanding of the universe. To achieve the objectives of these missions, spaceflight systems typically leverage guidance and control systems to maintain some desired path and/or orientation of their scientific instrumentation. A deep understanding of the natural dynamics of the environment in which these spaceflight systems operate is required to design control systems capable of achieving the desired scientific objectives. However, mitigating strategies are critically important when these dynamics are unknown or poorly understood and/or modelled. This research introduces two neural network methodologies to control the translation and rotation …

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 …

A Novel Framework For Mixed Reality–Based Control Of Collaborative Robot: Development Study, Md. Tanzil Shahria, Md. Samiul Haque Sunny, Md. Ishrak Islam Zarif, Md. Mahafuzur Rahaman Khan, Preet Parag Modi, Sheikh Iqbal Ahamed, Mohammad H. Rahman

Computer Science Faculty Research and Publications

Background:

Applications of robotics in daily life are becoming essential by creating new possibilities in different fields, especially in the collaborative environment. The potentials of collaborative robots are tremendous as they can work in the same workspace as humans. A framework employing a top-notch technology for collaborative robots will surely be worthwhile for further research.

Objective:

This study aims to present the development of a novel framework for the collaborative robot using mixed reality.

Methods:

The framework uses Unity and Unity Hub as a cross-platform gaming engine and project management tool to design the mixed reality interface and digital twin. …

Universal Design In Bci: Deep Learning Approaches For Adaptive Speech Brain-Computer Interfaces, Srdjan Lesaja

Theses and Dissertations

In the last two decades, there have been many breakthrough advancements in non-invasive and invasive brain-computer interface (BCI) systems. However, the majority of BCI model designs still follow a paradigm whereby neural signals are preprocessed and task-related features extracted using static, and generally customized, data-independent designs. Such BCI designs commonly optimize narrow task performance over generalizability, adaptability, and robustness, which is not well suited to meeting individual user needs. If one day BCIs are to be capable of decoding our higher-order cognitive commands and conceptual maps, their designs will need to be adaptive architectures that will evolve and grow in …

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 …

Why Do Family Members Reject Ai In Health Care? Competing Effects Of Emotions, Eun Hee Park, Karl Werder, Lan Cao, Balasubramaniam Ramesh

Information Technology & Decision Sciences Faculty Publications

Artificial intelligence (AI) enables continuous monitoring of patients’ health, thus improving the quality of their health care. However, prior studies suggest that individuals resist such innovative technology. In contrast to prior studies that investigate individuals’ decisions for themselves, we focus on family members’ rejection of AI monitoring, as family members play a significant role in health care decisions. Our research investigates competing effects of emotions toward the rejection of AI monitoring for health care. Based on two scenario-based experiments, our study reveals that emotions play a decisive role in family members’ decision making on behalf of their parents. We find …

Artificial Intelligence System For Automatic Imaging, Quantification, And Identification Of Arthropods In Leaf Litter And Pitfall Samples, Pierce Helton, Khoa Luu, Ashley Dowling

Inquiry: The University of Arkansas Undergraduate Research Journal

It is well known that arthropods are the most diverse and abundant eukaryotic organisms on the planet. Museum and research collections have huge insect accumulations from expeditions conducted over history that contain specimens of both temporal and spatial value, including hundreds of thousands of species. This biodiversity data is inaccessible to the research community, resulting in a vast amount of “dark data”. The primary objective of this study is to develop an artificial intelligence-driven system for specimen identification that greatly minimizes the time and expertise required to identify specimens in atypical environments. Successful development will have profound impacts on both …

Image-Data-Driven Deep Learning For Slope Stability Analysis, Behnam Azmoon

Dissertations, Master's Theses and Master's Reports

Landslides cause major infrastructural issues, damage the environment, and cause socio-economic disruptions. Therefore, various slope stability analysis methods have been developed to evaluate the stability of slopes and the probability of their failure. This dissertation attempts to take advantage of the recent advancements in remote sensing and computer technology to implement a deep-learning-based landslide prediction method.

Considering the novelty of this approach, this dissertation leads with proof-of-concept studies to evaluate and establish the suitability of deep learning models for slope stability analysis. To achieve this, a simulated 2D dataset of slope images was created with different geometries and soil properties. …

A Comparison Of Deep Learning Algorithms On Image Data For Detecting Floodwater On Roadways, Sarp Salih, Kuzlu Murat, Zhao Yanxiao, Cetin Mecit

Engineering Technology Faculty Publications

Object detection and segmentation algorithms evolved significantly in the last decade. Simultaneous object detection and segmentation paved the way for real-time applications such as autonomous driving. Detection and segmentation of (partially) flooded roadways are essential inputs for vehicle routing and traffic management systems. This paper proposes an automatic floodwater detection and segmentation method utilizing the Mask Region-Based Convolutional Neural Networks (Mask-R-CNN) and Generative Adversarial Networks (GAN) algorithms. To train the model, manually labeled images with urban, suburban, and natural settings are used. The performances of the algorithms are assessed in accurately detecting the floodwater captured in images. The results show …