Physical Sciences and Mathematics Commons^™

Demonstration Of Visible And Near Infrared Raman Spectrometers And Improved Matched Filter Model For Analysis Of Combined Raman Signals, Alexander Matthew Atkinson

Electrical & Computer Engineering Theses & Dissertations

Raman spectroscopy is a powerful analysis technique that has found applications in fields such as analytical chemistry, planetary sciences, and medical diagnostics. Recent studies have shown that analysis of Raman spectral profiles can be greatly assisted by use of computational models with achievements including high accuracy pure sample classification with imbalanced data sets and detection of ideal sample deviations for pharmaceutical quality control. The adoption of automated methods is a necessary step in streamlining the analysis process as Raman hardware becomes more advanced. Due to limits in the architectures of current machine learning based Raman classification models, transfer from pure …

Success Factors Impacting Artificial Intelligence Adoption --- Perspective From The Telecom Industry In China, Hong Chen

Theses and Dissertations in Business Administration

As the core driving force of the new round of informatization development and the industrial revolution, the disruptive achievements of artificial intelligence (AI) are rapidly and comprehensively infiltrating into various fields of human activities. Although technologies and applications of AI have been widely studied, and factors that affect AI adoption are identified in existing literature, the impact of success factors on AI adoption remains unknown. Accordingly, the main study of this paper proposes a framework to explore the effects of success factors on AI adoption by integrating the technology, organization, and environment (TOE) framework and diffusion of innovation (DOI) theory. …

Using Feature Extraction From Deep Convolutional Neural Networks For Pathological Image Analysis And Its Visual Interpretability, Wei-Wen Hsu

Electrical & Computer Engineering Theses & Dissertations

This dissertation presents a computer-aided diagnosis (CAD) system using deep learning approaches for lesion detection and classification on whole-slide images (WSIs) with breast cancer. The deep features being distinguishing in classification from the convolutional neural networks (CNN) are demonstrated in this study to provide comprehensive interpretability for the proposed CAD system using the domain knowledge in pathology. In the experiment, a total of 186 slides of WSIs were collected and classified into three categories: Non-Carcinoma, Ductal Carcinoma in Situ (DCIS), and Invasive Ductal Carcinoma (IDC). Instead of conducting pixel-wise classification (segmentation) into three classes directly, a hierarchical framework with the …

Developing Algorithms To Detect Incidents On Freeways From Loop Detector And Vehicle Re-Identification Data, Biraj Adhikari

Civil & Environmental Engineering Theses & Dissertations

A new approach for testing incident detection algorithms has been developed and is presented in this thesis. Two new algorithms were developed and tested taking California #7, which is the most widely used algorithm to date, and SVM (Support Vector Machine), which is considered one of the best performing classifiers, as the baseline for comparisons. Algorithm #B in this study uses data from Vehicle Re-Identification whereas the other three algorithms (California #7, SVM and Algorithm #A) use data from a double loop detector for detection of an incident. A microscopic traffic simulator is used for modeling three types of incident …

Confucian Robot Ethics, Qin Zhu, Tom Williams, Ruchen Wen

Computer Ethics - Philosophical Enquiry (CEPE) Proceedings

In the literature of artificial moral agents (AMAs), most work is influenced by either deontological or utilitarian frameworks. It has also been widely acknowledged that these Western “rule-based” ethical theories have encountered both philosophical and computing challenges. To tackle these challenges, this paper explores a non-Western, role-based, Confucian approach to robot ethics. In this paper, we start by providing a short introduction to some theoretical fundamentals of Confucian ethics. Then, we discuss some very preliminary ideas for constructing a Confucian approach to robot ethics. Lastly, we briefly share a couple of empirical studies our research group has recently conducted that …

Autonomous Vehicles And The Ethical Tension Between Occupant And Non-Occupant Safety, Jason Borenstein, Joseph Herkert, Keith W. Miller

Computer Ethics - Philosophical Enquiry (CEPE) Proceedings

Autonomous vehicle manufacturers, people inside an autonomous vehicle (occupants), and people outside the vehicle (non-occupants) are among the distinct stakeholders when addressing ethical issues inherent in systems that include autonomous vehicles. As responses to recent tragic cases illustrate, advocates for autonomous vehicles tend to focus on occupant safety, sometimes to the exclusion of non-occupant safety. Thus, we aim to examine ethical issues associated with non-occupant safety, including pedestrians, bicyclists, motorcyclists, and riders of motorized scooters. We also explore the ethical implications of technical and policy ideas that some might propose to improve non-occupant safety. In addition, if safety (writ large) …

Human Supremacy As Posthuman Risk, Daniel Estrada

Computer Ethics - Philosophical Enquiry (CEPE) Proceedings

Human supremacy is the widely held view that human interests ought to be privileged over other interests as a matter of public policy. Posthumanism is an historical and cultural situation characterized by a critical reevaluation of anthropocentrist theory and practice. This paper draws on Rosi Braidotti’s critical posthumanism and the critique of ideal theory in Charles Mills and Serene Khader to address the use of human supremacist rhetoric in AI ethics and policy discussions, particularly in the work of Joanna Bryson. This analysis leads to identifying a set of risks posed by human supremacist policy in a posthuman context, specifically …

Keeping Anonymity At The Consumer Behavior On The Internet: Proof Of Sacrifice, Sachio Horie

Computer Ethics - Philosophical Enquiry (CEPE) Proceedings

The evolution of the Internet and AI technology has made it possible for the government and the businesses to keep track of their personal lives. GAFA continues to collect information unintended by the individuals. It is a threat that our privacy is violated in this way. In order to solute such problems, it is important to consider a mechanism that enables us to be peaceful lives while protecting privacy in the Internet society.

This paper focuses on the consumption behavior on the Internet and addresses anonymity. We consider some network protocols that enable sustainable consensus by combining anonymity methods such …

Variable Speed Limit Control At Sag Curves Through Connected Vehicles: Implications Of Alternative Communications And Sensing Technologies, Reza Vatani Nezafat

Civil & Environmental Engineering Theses & Dissertations

Connected vehicles (CVs) will enable new applications to improve traffic flow. This study’s focus is to investigate how potential implementation of variable speed limit (VSL) through different types of communication and sensing technologies on CVs may improve traffic flow at a sag curve. At sag curves, the gradient changes from negative to positive values which causes a reduction in the roadway capacity and congestion. A VSL algorithm is developed and implemented in a simulation environment for controlling the inflow of vehicles to a sag curve on a freeway to minimize delays and increase throughput. Both vehicle-to-vehicle (V2V) and infrastructure-to-vehicle (I2V) …

Development And Initial Evaluation Of A Reinforced Cue Detection Model To Assess Situation Awareness In Commercial Aircraft Cockpits, Aysen K. Taylor

Engineering Management & Systems Engineering Theses & Dissertations

Commercial transport aircraft of today vary greatly from early aircraft with regards to how the aircraft are controlled and the feedback provided from the machine to the human operator. Over time, as avionics systems became more automated, pilots had less direct control over their aircraft. Much research exists in the literature about automation issues, and several major accidents over the last twenty years spurred interest about how to maintain the benefits of automation while improving the overall human-machine interaction as the pilot is considered the last line of defense.

An important reason for maintaining or even improving overall pilot situation …

A Data-Driven Approach For Modeling Agents, Hamdi Kavak

Computational Modeling & Simulation Engineering Theses & Dissertations

Agents are commonly created on a set of simple rules driven by theories, hypotheses, and assumptions. Such modeling premise has limited use of real-world data and is challenged when modeling real-world systems due to the lack of empirical grounding. Simultaneously, the last decade has witnessed the production and availability of large-scale data from various sensors that carry behavioral signals. These data sources have the potential to change the way we create agent-based models; from simple rules to driven by data. Despite this opportunity, the literature has neglected to offer a modeling approach to generate granular agent behaviors from data, creating …

Reimagining Medical Education In The Age Of Ai, Steven A. Wartman, C. Donald Combs

Computational Modeling & Simulation Engineering Faculty Publications

Available medical knowledge exceeds the organizing capacity of the human mind, yet medical education remains based on information acquisition and application. Complicating this information overload crisis among learners is the fact that physicians' skill sets now must include collaborating with and managing artificial intelligence (AI) applications that aggregate big data, generate diagnostic and treatment recommendations, and assign confidence ratings to those recommendations. Thus, an overhaul of medical school curricula is due and should focus on knowledge management (rather than information acquisition), effective use of AI, improved communication, and empathy cultivation.

Transfer Learning For Detecting Unknown Network Attacks, Juan Zhao, Sachin Shetty, Jan Wei Pan, Charles Kamhoua, Kevin Kwiat

VMASC Publications

Network attacks are serious concerns in today’s increasingly interconnected society. Recent studies have applied conventional machine learning to network attack detection by learning the patterns of the network behaviors and training a classification model. These models usually require large labeled datasets; however, the rapid pace and unpredictability of cyber attacks make this labeling impossible in real time. To address these problems, we proposed utilizing transfer learning for detecting new and unseen attacks by transferring the knowledge of the known attacks. In our previous work, we have proposed a transfer learning-enabled framework and approach, called HeTL, which can find the common …

Healthcare Robotics: Key Factors That Impact Robot Adoption In Healthcare, Sujatha Alla, Pilar Pazos

Engineering Management & Systems Engineering Faculty Publications

In the current dynamic business environment, healthcare organizations are focused on improving patient satisfaction, performance, and efficiency. The healthcare industry is considered a complex system that is highly reliant of new technologies to support clinical as well as business processes. Robotics is one of such technologies that is considered to have the potential to increase efficiency in a wide range of clinical services. Although the use of robotics in healthcare is at the early stages of adoption, some studies have shown the capacity of this technology to improve precision, accessibility through less invasive procedures, and reduction of human error during …

Computational Modeling Of Trust Factors Using Reinforcement Learning, C. M. Kuzio, A. Dinh, C. Stone, L. Vidyaratne, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

As machine-learning algorithms continue to expand their scope and approach more ambiguous goals, they may be required to make decisions based on data that is often incomplete, imprecise, and uncertain. The capabilities of these models must, in turn, evolve to meet the increasingly complex challenges associated with the deployment and integration of intelligent systems into modern society. Historical variability in the performance of traditional machine-learning models in dynamic environments leads to ambiguity of trust in decisions made by such algorithms. Consequently, the objective of this work is to develop a novel computational model that effectively quantifies the reliability of autonomous …

End-To-End Learning Via A Convolutional Neural Network For Cancer Cell Line Classification, Darlington A. Akogo, Xavier-Lewis Palmer

Electrical & Computer Engineering Faculty Publications

Purpose: Computer vision for automated analysis of cells and tissues usually include extracting features from images before analyzing such features via various machine learning and machine vision algorithms. The purpose of this work is to explore and demonstrate the ability of a Convolutional Neural Network (CNN) to classify cells pictured via brightfield microscopy without the need of any feature extraction, using a minimum of images, improving work-flows that involve cancer cell identification.

Design/methodology/approach: The methodology involved a quantitative measure of the performance of a Convolutional Neural Network in distinguishing between two cancer lines. In their approach, they trained, validated and …

Emerging Roles Of Virtual Patients In The Age Of Ai, C. Donald Combs, P. Ford Combs

Computational Modeling & Simulation Engineering Faculty Publications

Today's web-enabled and virtual approach to medical education is different from the 20th century's Flexner-dominated approach. Now, lectures get less emphasis and more emphasis is placed on learning via early clinical exposure, standardized patients, and other simulations. This article reviews literature on virtual patients (VPs) and their underlying virtual reality technology, examines VPs' potential through the example of psychiatric intake teaching, and identifies promises and perils posed by VP use in medical education.

Clinical Big Data And Deep Learning: Applications, Challenges, And Future Outlooks, Ying Yu, Liangliang Liu, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

The explosion of digital healthcare data has led to a surge of data-driven medical research based on machine learning. In recent years, as a powerful technique for big data, deep learning has gained a central position in machine learning circles for its great advantages in feature representation and pattern recognition. This article presents a comprehensive overview of studies that employ deep learning methods to deal with clinical data. Firstly, based on the analysis of the characteristics of clinical data, various types of clinical data (e.g., medical images, clinical notes, lab results, vital signs and demographic informatics) are discussed and details …

Drug Repositioning Based On Bounded Nuclear Norm Regularization, Mengyun Yang, Huimin Lao, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation: Computational drug repositioning is a cost-effective strategy to identify novel indications for existing drugs. Drug repositioning is often modeled as a recommendation system problem. Taking advantage of the known drug–disease associations, the objective of the recommendation system is to identify new treatments by filling out the unknown entries in the drug–disease association matrix, which is known as matrix completion. Underpinned by the fact that common molecular pathways contribute to many different diseases, the recommendation system assumes that the underlying latent factors determining drug–disease associations are highly correlated. In other words, the drug–disease matrix to be completed is low-rank. Accordingly, …

Automatic Slide Generation For Scientific Papers, Athar Sefid, Jian Wu, Prasenjit Mitra, C. Lee Giles

Computer Science Faculty Publications

We describe our approach for automatically generating presentation slides for scientific papers using deep neural networks. Such slides can help authors have a starting point for their slide generation process. Extractive summarization techniques are applied to rank and select important sentences from the original document. Previous work identified important sentences based only on a limited number of features that were extracted from the position and structure of sentences in the paper. Our method extends previous work by (1) extracting a more comprehensive list of surface features, (2) considering semantic or meaning of the sentence, and (3) using context around the …

The Role Of Data Analytics In Education: Possibilities & Limitations, Robert L. Moore

STEMPS Faculty Publications

In the last decade, we have seen dramatic increases in the integration of technology within education. It has now become commonplace for K-5 educators to apply learning management systems (LMS) in ways that were previously only seen in higher education contexts. Similarly, on the higher education side, we are seeing a significant increase in online learning evidenced by the growing number of for-profit online colleges and universities (Picciano, 2012). This chapter utilizes Khan’s Learning Framework (Khan, 2001, 2005) to explore the role data analytics can play in education by looking at the possibilities and limitations of analytics.

Electroencephalogram (Eeg) For Delineating Objective Measure Of Autism Spectrum Disorder, Sampath Jayarathna, Yasith Jayawardana, Mark Jaime, Sashi Thapaliya

Computer Science Faculty Publications

Autism spectrum disorder (ASD) is a developmental disorder that often impairs a child's normal development of the brain. According to CDC, it is estimated that 1 in 6 children in the US suffer from development disorders, and 1 in 68 children in the US suffer from ASD. This condition has a negative impact on a person's ability to hear, socialize, and communicate. Subjective measures often take more time, resources, and have false positives or false negatives. There is a need for efficient objective measures that can help in diagnosing this disease early as possible with less effort. EEG measures the …