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Artificial Intelligence

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Full-Text Articles in Computer Sciences

Keeping Ai Under Observation: Anticipated Impacts On Physicians' Standard Of Care, Iria Giuffrida, Taylor Treece Apr 2020

Keeping Ai Under Observation: Anticipated Impacts On Physicians' Standard Of Care, Iria Giuffrida, Taylor Treece

Faculty Publications

As Artificial Intelligence (AI) tools become increasingly present across industries, concerns have started to emerge as to their impact on professional liability. Specifically, for the medical industry--in many ways an inherently "risky" business--hospitals and physicians have begun evaluating the impact of Al tools on their professional malpractice risk. This Essay seeks to address that question, zooming in on how AI may affect physicians' standard of care for medical malpractice claims.

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Artificial Agents In Corporate Boardrooms, Sergio Alberto Gramitto Ricci Mar 2020

Artificial Agents In Corporate Boardrooms, Sergio Alberto Gramitto Ricci

Faculty Works

Thousands of years ago, Roman businessmen often ran joint businesses through commonly owned, highly intelligent slaves. Roman slaves did not have full legal capacity and were considered property of their co-owners. Now business corporations are looking to delegate decision-making to uber intelligent machines through the use of artificial intelligence in boardrooms. Artificial intelligence in boardrooms could assist, integrate, or even replace human directors. However, the concept of using artificial intelligence in boardrooms is largely unexplored and raises several issues. This Article sheds light on legal and policy challenges concerning artificial agents in boardrooms. The arguments revolve around two fundamental questions: …

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Autonomous Trading Strategies For Dynamic Energy Markets, Moinul Morshed Porag Chowdhury Jan 2020

Autonomous Trading Strategies For Dynamic Energy Markets, Moinul Morshed Porag Chowdhury

Open Access Theses & Dissertations

With increasing energy demand and an intermittent supply of renewable energy sources, our current energy grid needs a transformation towards a more robust, reliable energy trading architecture. The smart grid promises this architecture as the future of the present energy market, where traders will use digital technologies to automate the management of power delivery. It will improve many issues of the current energy grid such as sustainable, clean, renewable, reliable and secure energy supply, customer participation in markets, distributed generation, and transparency in energy trading. Using autonomous trading agents, we can bridge several dynamic energy markets and ensure an efficient …

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You Might Be A Robot, Bryan Casey, Mark A. Lemley Jan 2020

You Might Be A Robot, Bryan Casey, Mark A. Lemley

Cornell Law Review

As robots and artificial intelligence (Al) increase their influence over society, policymakers are increasingly regulating them. But to regulate these technologies, we first need to know what they are. And here we come to a problem. No one has been able to offer a decent definition of robots arid AI-not even experts. What's more, technological advances make it harder and harder each day to tell people from robots and robots from "dumb" machines. We have already seen disastrous legal definitions written with one target in mind inadvertently affecting others. In fact, if you are reading this you are (probably) not …

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Law, Artificial Intelligence, And Natural Language Processing: A Funny Thing Happened On The Way To My Search Results, Paul D. Callister Jan 2020

Law, Artificial Intelligence, And Natural Language Processing: A Funny Thing Happened On The Way To My Search Results, Paul D. Callister

Faculty Works

Renowned legal educator Roscoe Pound stated, “Law must be stable and yet it cannot stand still.” Yet, as Susan Nevelow Mart has demonstrated in a seminal article that the different online research services (Westlaw, Lexis Advance, Fastcase, Google Scholar, Ravel and Casetext) produce significantly different results when researching case law. Furthermore, a recent study of 325 federal courts of appeals decisions, revealed that only 16% of the cases cited in appellate briefs make it into the courts’ opinions. This does not exactly inspire confidence in legal research or its tools to maintain stability of the law. As Robert Berring foresaw, …

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Perceptions, Potholes, And Possibilities Of Using Digital Voice Assistants To Differentiate Instructions, Adrian A. Weir Jan 2020

Perceptions, Potholes, And Possibilities Of Using Digital Voice Assistants To Differentiate Instructions, Adrian A. Weir

Walden Dissertations and Doctoral Studies

Access to technologies and understanding the potential uses of technology to differentiate instruction have been a concern for the teachers and students in a local school district located in the southeastern United States. Despite the emergence of digital voice assistants (DVAs) as tools for instructions, teachers lack knowledge and strategies for using DVAs to differentiate instruction in their classrooms. The purpose of this qualitative study was to identify teacher knowledge and strategies employed among special education (SPED) teachers using DVAs to differentiate instruction in their classrooms. The concepts of Carol Tomlinson’s differentiation theory and Mishra and Koehler’s TPACK framework served …

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Artificial Neural Network Models For Pattern Discovery From Ecg Time Series, Mehakpreet Kaur Jan 2020

Artificial Neural Network Models For Pattern Discovery From Ecg Time Series, Mehakpreet Kaur

College of Graduate Studies: Theses & Dissertations

Artificial Neural Network (ANN) models have recently become de facto models for deep learning with a wide range of applications spanning from scientific fields such as computer vision, physics, biology, medicine to social life (suggesting preferred movies, shopping lists, etc.). Due to advancements in computer technology and the increased practice of Artificial Intelligence (AI) in medicine and biological research, ANNs have been extensively applied not only to provide quick information about diseases, but also to make diagnostics accurate and cost-effective. We propose an ANN-based model to analyze a patient's electrocardiogram (ECG) data and produce accurate diagnostics regarding possible heart diseases …

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Landing Throttleable Hybrid Rockets With Hierarchical Reinforcement Learning In A Simulated Environment, Francesco Alessandro Stefano Mikulis-Borsoi Jan 2020

Landing Throttleable Hybrid Rockets With Hierarchical Reinforcement Learning In A Simulated Environment, Francesco Alessandro Stefano Mikulis-Borsoi

Honors Theses and Capstones

In this paper, I develop a hierarchical Markov Decision Process (MDP) structure for completing the task of vertical rocket landing. I start by covering the background of this problem, and formally defining its constraints. In order to reduce mistakes while formulating different MDPs, I define and develop the criteria for a standardized MDP definition format. I then decompose the problem into several sub-problems of vertical landing, namely velocity control and vertical stability control. By exploiting MDP coupling and symmetrical properties, I am able to significantly reduce the size of the state space compared to a unified MDP formulation. This paper …

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Exploring Virtual Worlds With Cultural Algorithms: Ancient Alpena-Amberley Land Bridge, Thomas Joseph Palazzolo Jan 2020

Exploring Virtual Worlds With Cultural Algorithms: Ancient Alpena-Amberley Land Bridge, Thomas Joseph Palazzolo

Wayne State University Dissertations

In this thesis the Land Bridge system (DEEPDIVE) is described. The goal of the project is to use Artificial Intelligence technology to aid Archaeologists in the discovery of ancient prehistoric sites, now underwater. The example used here is the Alpena-Amberley Land Bridge that stretched across Lake Huron from Alpena in Michigan to Amberley in Ontario. During the Ice Age (around 10,000 years ago) it was above water for several thousand years. It was postulated that during that time it was used as a migration pathway for caribou, a major food source then. AI techniques were used to create a virtual …

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Potential Impacts Of Artificial Intelligence On Spine Imaging Interpretation And Diagnosis, David Howard Durrant Jan 2020

Potential Impacts Of Artificial Intelligence On Spine Imaging Interpretation And Diagnosis, David Howard Durrant

Walden Dissertations and Doctoral Studies

Spine and related disorders represent one of the most common causes of pain and disability in the United States. Imaging represents an important diagnostic procedure in spine care. Imaging studies contain actionable data and insights undetectable through routine visual analysis. Convergent advances in imaging, artificial intelligence (AI), and radiomic methods has revealed the potential of multiscale in vivo interrogation to improve the assessment and monitoring of pathology. AI offers various types of decision support through the analysis of structured and unstructured data. The primary purpose of this qualitative exploratory case study was to identify the potential impacts of AI solutions …

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Customer And Employee Social Media Comments/Feedback And Stock Purchasing Decisions Enhanced By Sentiment Analysis, Drew Mikel Hall Jan 2020

Customer And Employee Social Media Comments/Feedback And Stock Purchasing Decisions Enhanced By Sentiment Analysis, Drew Mikel Hall

Walden Dissertations and Doctoral Studies

The U.S. Securities and Exchange Commission (SEC) warns professional investors that sentiment analysis tools may lead to impulsive investment decision-making. This warning comes despite evidence showing that aided social sentiment investment decision tools can increase accurate investment decision-making by 18%. Using Fama's theory of efficient market hypothesis, the purpose of this quantitative correlational study was to examine whether customer Twitter comments and employee Glassdoor feedback sentiment predicted successful investing decisions measured by business stock prices. Two thousand records from 3 archival U.S. public NASDAQ 100 datasets from March 28, 2016, to June 15, 2016 (79 days) of 53 companies with …

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Towards Interpretable And Reliable Deep Neural Networks For Visual Intelligence, Ning Xie Jan 2020

Towards Interpretable And Reliable Deep Neural Networks For Visual Intelligence, Ning Xie

Browse all Theses and Dissertations

Deep Neural Networks (DNNs) are powerful tools blossomed in a variety of successful real-life applications. While the performance of DNNs is outstanding, their opaque nature raises a growing concern in the community, causing suspicions on the reliability and trustworthiness of decisions made by DNNs. In order to release such concerns and towards building reliable deep learning systems, research efforts are actively made in diverse aspects such as model interpretation, model fairness and bias, adversarial attacks and defenses, and so on. In this dissertation, we focus on the research topic of DNN interpretations for visual intelligence, aiming to unfold the black-box …

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Development Of A Modeling Algorithm To Predict Lean Implementation Success, Richard Charles Barclay Jan 2020

Development Of A Modeling Algorithm To Predict Lean Implementation Success, Richard Charles Barclay

Doctoral Dissertations

”Lean has become a common term and goal in organizations throughout the world. The approach of eliminating waste and continuous improvement may seem simple on the surface but can be more complex when it comes to implementation. Some firms implement lean with great success, getting complete organizational buy-in and realizing the efficiencies foundational to lean. Other organizations struggle to implement lean. Never able to get the buy-in or traction needed to really institute the sort of cultural change that is often needed to implement change. It would be beneficial to have a tool that organizations could use to assess their …

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A Mathematical Analysis Of The Game Of Santorini, Carson Clyde Geissler Jan 2020

A Mathematical Analysis Of The Game Of Santorini, Carson Clyde Geissler

Senior Independent Study Theses

Santorini is a two player combinatorial board game. Santorini bears resemblance to the graph theory game of Geography, a game of moving and deleting vertices on a graph. We explore Santorini with game theory, complexity theory, and artificial intelligence. We present David Lichtenstein’s proof that Geography is PSPACE-hard and adapt the proof for generalized forms of Santorini. Last, we discuss the development of an AI built for a software implementation of Santorini and present a number of improvements to that AI.

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Human Path Prediction Using Auto Encoder Lstms And Single Temporal Encoders, Hayden Hudgins Jan 2020

Human Path Prediction Using Auto Encoder Lstms And Single Temporal Encoders, Hayden Hudgins

Master's Theses

Due to automation, the world is changing at a rapid pace. Autonomous agents have become more common over the last several years and, as a result, have created a need for improved software to back them up. The most important aspect of this greater software is path prediction, as robots need to be able to decide where to move in the future. In order to accomplish this, a robot must know how to avoid humans, putting frame prediction at the core of many modern day solutions. A popular way to solve this complex problem of frame prediction is Auto Encoder …

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Multi-Agent Narrative Experience Management As Story Graph Pruning, Edward T. Garcia Dec 2019

Multi-Agent Narrative Experience Management As Story Graph Pruning, Edward T. Garcia

LSU New Orleans Theses and Dissertations

In this thesis I describe a method where an experience manager chooses actions for non-player characters (NPCs) in intelligent interactive narratives through story graph representation and pruning. The space of all stories can be represented as a story graph where nodes are states and edges are actions. By shaping the domain as a story graph, experience manager decisions can be made by pruning edges. Starting with a full graph, I apply a set of pruning strategies that will allow the narrative to be finishable, NPCs to act believably, and the player to be responsible for how the story unfolds. By …

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Wuji: Automatic Online Combat Game Testing Using Evolutionary Deep Reinforcement Learning, Yan Zheng, Xiaofei Xie, Ting Su, Lei Ma, Jianye Hao, Zhaopeng Meng, Yang Liu, Ruimin Shen, Yingfeng Chen, Changjie Fan Nov 2019

Wuji: Automatic Online Combat Game Testing Using Evolutionary Deep Reinforcement Learning, Yan Zheng, Xiaofei Xie, Ting Su, Lei Ma, Jianye Hao, Zhaopeng Meng, Yang Liu, Ruimin Shen, Yingfeng Chen, Changjie Fan

Research Collection School Of Computing and Information Systems

—Game testing has been long recognized as a notoriously challenging task, which mainly relies on manual playing and scripting based testing in game industry. Even until recently, automated game testing still remains to be largely untouched niche. A key challenge is that game testing often requires to play the game as a sequential decision process. A bug may only be triggered until completing certain difficult intermediate tasks, which requires a certain level of intelligence. The recent success of deep reinforcement learning (DRL) sheds light on advancing automated game testing, without human competitive intelligent support. However, the existing DRLs mostly focus …

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Liability For Ai Decision-Making: Some Legal And Ethical Considerations, Iria Giuffrida Nov 2019

Liability For Ai Decision-Making: Some Legal And Ethical Considerations, Iria Giuffrida

Faculty Publications

No abstract provided.

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Artificial Intelligence And The Challenge For Rural Medicine, James Denvir Oct 2019

Artificial Intelligence And The Challenge For Rural Medicine, James Denvir

Marshall Journal of Medicine

Recent advances in artificial intelligence, machine learning, and deep learning are beginning to have an impact on everyday experiences, from natural language processing used in automated telephone call centers to semi-autonomous vehicles. These techniques have also been applied to medical care. In this editorial we discuss applications of AI to medicine and argue for a proactive approach to include rural medicine in this paradigm shift.

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Court Record In The Age Of Artificial Intelligence, Fredric I. Lederer Oct 2019

Court Record In The Age Of Artificial Intelligence, Fredric I. Lederer

Popular Media

No abstract provided.

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Designing Women: Essentializing Femininity In Ai Linguistics, Ellianie S. Vega Oct 2019

Designing Women: Essentializing Femininity In Ai Linguistics, Ellianie S. Vega

Student Publications

Since the eighties, feminists have considered technology a force capable of subverting sexism because of technology’s ability to produce unbiased logic. Most famously, Donna Haraway’s “A Cyborg Manifesto” posits that the cyborg has the inherent capability to transcend gender because of its removal from social construct and lack of loyalty to the natural world. But while humanoids and artificial intelligence have been imagined as inherently subversive to gender, current artificial intelligence perpetuates gender divides in labor and language as their programmers imbue them with traits considered “feminine.” A majority of 21st century AI and humanoids are programmed to fit female …

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A Study On Large-Scale Deep Learning In Bioinformatics And Biomedical Applications, Shayan Shams Jun 2019

A Study On Large-Scale Deep Learning In Bioinformatics And Biomedical Applications, Shayan Shams

LSU Doctoral Dissertations

Recent advances in Artificial Intelligence and deep learning have provided researchers in various fields insights into the analysis of multiple datasets. These applications include image analysis, text analysis, and many more. However, the effectiveness of deep learning in some areas, such as biomedical imaging and genomic research, has been overshadowed by the variance in the types and complexity of data. This is in addition to the expensive labeling process and the limited size of datasets in these fields. These challenges require advanced deep learning models capable of learning from a small dataset and also from a small number of labeled …

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Deep Learning Based Real Time Devanagari Character Recognition, Aseem Chhabra May 2019

Deep Learning Based Real Time Devanagari Character Recognition, Aseem Chhabra

Master's Projects

The revolutionization of the technology behind optical character recognition (OCR) has helped it to become one of those technologies that have found plenty of uses in the entire industrial space. Today, the OCR is available for several languages and have the capability to recognize the characters in real time, but there are some languages for which this technology has not developed much. All these advancements have been possible because of the introduction of concepts like artificial intelligence and deep learning. Deep Neural Networks have proven to be the best choice when it comes to a task involving recognition. There are …

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Commonsense Knowledge In Sentiment Analysis Of Ordinance Reactions For Smart Governance, Manish Puri May 2019

Commonsense Knowledge In Sentiment Analysis Of Ordinance Reactions For Smart Governance, Manish Puri

Theses, Dissertations and Culminating Projects

Smart Governance is an emerging research area which has attracted scientific as well as policy interests, and aims to improve collaboration between government and citizens, as well as other stakeholders. Our project aims to enable lawmakers to incorporate data driven decision making in enacting ordinances. Our first objective is to create a mechanism for mapping ordinances (local laws) and tweets to Smart City Characteristics (SCC). The use of SCC has allowed us to create a mapping between a huge number of ordinances and tweets, and the use of Commonsense Knowledge (CSK) has allowed us to utilize human judgment in mapping. …

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Intelligent Technology Of Command And Control System In The Rts Perspective, Wenfeng Wu, Zhang Yu, Rong Ming Jan 2019

Intelligent Technology Of Command And Control System In The Rts Perspective, Wenfeng Wu, Zhang Yu, Rong Ming

Journal of System Simulation

Abstract: Real-Time Strategy (RTS) games have important reference value for studying the intelligent technology of command and control systems. The similarities between RTS games and the strategic battle level command and control systems are described according to the decision process. The challenges brought by the problems of planning, learning, uncertainty and space-time reasoning in the intelligent technology of RTS games are analyzed. The key technologies and latest research progress of action sequence planning, plan recognition, state assessment, multi-agent collaboration and multi-scale AI are studied. The trend of intelligent technology development of strategic and operational level command and control systems is …

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Knowledge-Enabled Entity Extraction, Hussein S. Al-Olimat Jan 2019

Knowledge-Enabled Entity Extraction, Hussein S. Al-Olimat

Browse all Theses and Dissertations

Information Extraction (IE) techniques are developed to extract entities, relationships, and other detailed information from unstructured text. The majority of the methods in the literature focus on designing supervised machine learning techniques, which are not very practical due to the high cost of obtaining annotations and the difficulty in creating high quality (in terms of reliability and coverage) gold standard. Therefore, semi-supervised and distantly-supervised techniques are getting more traction lately to overcome some of the challenges, such as bootstrapping the learning quickly. This dissertation focuses on information extraction, and in particular entities, i.e., Named Entity Recognition (NER), from multiple domains, …

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Efficient Local Comparison Of Images Using Krawtchouk Descriptors, Julian Deville Jan 2019

Efficient Local Comparison Of Images Using Krawtchouk Descriptors, Julian Deville

Online Theses and Dissertations

It is known that image comparison can prove cumbersome in both computational complexity and runtime, due to factors such as the rotation, scaling, and translation of the object in question. Due to the locality of Krawtchouk polynomials, relatively few descriptors are necessary to describe a given image, and this can be achieved with minimal memory usage. Using this method, not only can images be described efficiently as a whole, but specific regions of images can be described as well without cropping. Due to this property, queries can be found within a single large image, or collection of large images, which …

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Efficient Detection Of Diseases By Feature Engineering Approach From Chest Radiograph, Avishek Mukherjee Jan 2019

Efficient Detection Of Diseases By Feature Engineering Approach From Chest Radiograph, Avishek Mukherjee

Legacy Theses & Dissertations (2009 - 2024)

Deep Learning is the new state-of-the-art technology in Image Processing. We applied Deep Learning techniques for identification of diseases from Radiographs made publicly available by NIH. We applied some Feature Engineering approach to augment the data from Anterior-Posterior position to Posterior-Anterior position and vice-versa for all the diseases, at the same point we suppressed ‘No Finding’ radiographs which contributed to more than 50% (approximately 60,000) of the dataset to top 1000 images. We also prepared a model by adding a huge amount of noise to the augmented data, which if need be can be deployed at rural locations which lack …

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Utilizing Various Neural Network Architectures To Play A Game Developed For Human Players, Michael Blake Arender Jan 2019

Utilizing Various Neural Network Architectures To Play A Game Developed For Human Players, Michael Blake Arender

Electronic Theses and Dissertations

Neural Networks have received an explosive amount of attention and interest in recent years. Despite the fact that Neural Network algorithms having existed for many decades, it was not until recent advances in computer hardware that they saw widespread use. This is in no small part due to the success these algorithms have had in tasks ranging from image classification, voice recognition, game theory, and many other applications. Thanks to recent strides in hardware development, most importantly in the advancements in Graphics Processor Units including the capabilities of modern GPU Computing, Neural Networks are now capable of solving tasks at …

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Curtus: An Nlp Tool To Map Job Skills To Academic Courses, Daniel Rockwell Jan 2019

Curtus: An Nlp Tool To Map Job Skills To Academic Courses, Daniel Rockwell

Theses and Dissertations

Many businesses are burdened with the need to train students for the job instead of finding them prepared for it. Few business leaders feel that colleges prepare students for future jobs from day one. It can be a challenge for colleges to determine if their curricula meet the industry needs. Mapping industry needs to academic courses can be advantageous to both parties as it will allow colleges to be aligned with the industry needs and accordingly satisfy those needs and will allow the industry to hire better prepared graduates. In an attempt to address this, a system prototype that uses …

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