Artificial Intelligence and Robotics Commons^™

Airport Assignment For Emergency Aircraft Using Reinforcement Learning, Saketh Kamatham

Master's Projects

The volume of air traffic is increasing exponentially every day. The Air Traffic Control (ATC) at the airport has to handle aircraft runway assignments for landing and takeoff and airspace maintenance by directing passing aircraft through the airspace safely. If any aircraft is facing a technical issue or problem and is in a state of emergency, it requires expedited landing to respond to that emergency. The ATC gives this aircraft priority to landing and assistance. This process is very strenuous as the ATC has to deal with multiple aspects along with the emergency aircraft. It is the duty of the …

Rideshare Using Degrees Of Separation: A Social Network-Based Approach, Gokul Garikipati

Master's Projects

Conventional ride-sharing services, such as Lyft and Uber, routinely match drivers with riders based on their proximity to each other, using GPS coordinates and mapping technology. The application then calculates the cost of the ride based on factors such as distance traveled and time spent in the car. The concept of six degrees of separation suggests that a maximum of 6 steps or relationships can connect any two individuals in the world. This idea could be applied to a ride-share service to provide a more personalized and efficient experience for users. Instead of just matching riders with drivers based on …

Federated Learning For Protecting Medical Data Privacy, Abhishek Reddy Punreddy

Master's Projects

Deep learning is one of the most advanced machine learning techniques, and its prominence has increased in recent years. Language processing, predictions in medical research and pattern recognition are few of the numerous fields in which it is widely utilized. Numerous modern medical applications benefit greatly from the implementation of machine learning (ML) models and the disruptive innovations in the entire modern health care system. It is extensively used for constructing accurate and robust statistical models from large volumes of medical data collected from a variety of sources in contemporary healthcare systems [1]. Due to privacy concerns that restrict access …

Gender Classification Via Human Joints Using Convolutional Neural Network, Cheng-En Sung

Master's Projects

With the growing demand for gender-related data on diverse applications, including security systems for ascertaining an individual’s identity for border crossing, as well as marketing purposes of digging the potential customer and tailoring special discounts for them, gender classification has become an essential task within the field of computer vision and deep learning. There has been extensive research conducted on classifying human gender using facial expression, exterior appearance (e.g., hair, clothes), or gait movement. However, within the scope of our research, none have specifically focused gender classification on two-dimensional body joints. Knowing this, we believe that a new prediction pipeline …

Codeval, Aditi Agrawal

Master's Projects

Grading coding assignments call for a lot of work. There are numerous aspects of the code that need to be checked, such as compilation errors, runtime errors, the number of test cases passed or failed, and plagiarism. Automated grading tools for programming assignments can be used to help instructors and graders in evaluating the programming assignments quickly and easily. Creating the assignment on Canvas is again a time taking process and can be automated. We developed CodEval, which instantly grades the student assignment submitted on Canvas and provides feedback to the students. It also uploads, creates, and edits assignments, thereby …

Relationalnet Using Graph Neural Networks For Social Recommendations, Dharahas Tallapally

Master's Projects

Traditional recommender systems create models that can predict user interests based on the user-item relationships. However, these systems often have limited performance due to sparse user behavior data. To address this challenge, researchers are now exploring models for social recommendation that can account for both user- user and user-item relationships based on social networks, and user past behavior, respectively. These models aim to understand each user’s behavior by considering their trusted neighbors and their influence on each other. Specifically, the potential embedding of each user is influenced by their trusted neighbors, who are, in turn, influenced by their own trusted …

Murder On The Vr Express: Studying The Impact Of Thought Experiments At A Distance In Virtual Reality, Andrew Kissel, Krzysztof J. Rechowicz, John B. Shull

Philosophy Faculty Publications

Hypothetical thought experiments allow researchers to gain insights into widespread moral intuitions and provide opportunities for individuals to explore their moral commitments. Previous thought experiment studies in virtual reality (VR) required participants to come to an on-site laboratory, which possibly restricted the study population, introduced an observer effect, and made internal reflection on the participants’ part more difficult. These shortcomings are particularly crucial today, as results from such studies are increasingly impacting the development of artificial intelligence systems, self-driving cars, and other technologies. This paper explores the viability of deploying thought experiments in commercially available in-home VR headsets. We conducted …

Application Of Knowledge Graph Techniques On Textbooks, Yutong Yao

Master's Projects

Textbooks are written and organized in a way that facilitates learning and understanding. Sections like glossary terms at the end of a textbook provide guidance on the topic of interest. However, it takes manual effort to create the index terms in the glossary that highlight the key referenced terminologies and related terms. Knowledge graphs, which have been used to represent and even reason over data and knowledge, can potentially capture textbook’s important terms, concepts, and their relations. Popular since the initial introduction by Google Knowledge Graphs (KGs), they combine graph and data to capture and model enormous amounts of relational …

Twitter Bot Detection Using Nlp And Graph Classification, Warada Jayant Kulkarni

Master's Projects

Social media platforms are one of the primary resources for information as it is easily accessible, low in cost, and provides a high rate of information spread. Online social media (OSM) have become the main source of news information around the world, but because of the distributed nature of the web, it has increased the risk of fake news spread. Fake news is misleading information that is published as real news. Therefore, identifying fake news and flagging them as such, as well as detecting sources that generate them is an ongoing task for researchers and OSM companies. Bots are artificial …

Machine Learning And Protein Allostery, Sian Xiao, Gennady M. Verkhivker, Peng Tao

Mathematics, Physics, and Computer Science Faculty Articles and Research

The fundamental biological importance and complexity of allosterically regulated proteins stem from their central role in signal transduction and cellular processes. Recently, machine-learning approaches have been developed and actively deployed to facilitate theoretical and experimental studies of protein dynamics and allosteric mechanisms. In this review, we survey recent developments in applications of machine-learning methods for studies of allosteric mechanisms, prediction of allosteric effects and allostery-related physicochemical properties, and allosteric protein engineering. We also review the applications of machine-learning strategies for characterization of allosteric mechanisms and drug design targeting SARS-CoV-2. Continuous development and task-specific adaptation of machine-learning methods for protein allosteric …

Explainable Ai Helps Bridge The Ai Skills Gap: Evidence From A Large Bank, Selina Carter, Jonathan Hersh

Economics Faculty Articles and Research

Advances in machine learning have created an “AI skills gap” both across and within firms. As AI becomes embedded in firm processes, it is unknown how this will impact the digital divide between workers with and without AI skills. In this paper we ask whether managers trust AI to predict consequential events, what manager characteristics are associated with increasing trust in AI predictions, and whether explainable AI (XAI) affects users’ trust in AI predictions. Partnering with a large bank, we generated AI predictions for whether a loan will be late in its final disbursement. We embedded these predictions into a …

College Teaching And Ai, Leo Irakliotis

Computer Science: Faculty Publications and Other Works

Artificial Intelligence will reshape the way we assess student learning in ways that no one has prepared us for.

Agglomerative Hierarchical Clustering With Dynamic Time Warping For Household Load Curve Clustering, Fadi Almahamid, Katarina Grolinger

Electrical and Computer Engineering Publications

Energy companies often implement various demand response (DR) programs to better match electricity demand and supply by offering the consumers incentives to reduce their demand during critical periods. Classifying clients according to their consumption patterns enables targeting specific groups of consumers for DR. Traditional clustering algorithms use standard distance measurement to find the distance between two points. The results produced by clustering algorithms such as K-means, K-medoids, and Gaussian Mixture Models depend on the clustering parameters or initial clusters. In contrast, our methodology uses a shape-based approach that combines Agglomerative Hierarchical Clustering (AHC) with Dynamic Time Warping (DTW) to classify …

Finite Gaussian Neurons: Defending Against Adversarial Attacks By Making Neural Networks Say "I Don’T Know", Felix Grezes

Dissertations, Theses, and Capstone Projects

In this work, I introduce the Finite Gaussian Neuron (FGN), a novel neuron architecture for artificial neural networks aimed at protecting against adversarial attacks.
Since 2014, artificial neural networks have been known to be vulnerable to adversarial attacks, which can fool the network into producing wrong or nonsensical outputs by making humanly imperceptible alterations to inputs. While defenses against adversarial attacks have been proposed, they usually involve retraining a new neural network from scratch, a costly task.

My works aims to:
- easily convert existing models to Finite Gaussian Neuron architecture,
- while preserving the existing model's behavior on real …

Influence Level Prediction On Social Media Through Multi-Task And Sociolinguistic User Characteristics Modeling, Denys Katerenchuk

Dissertations, Theses, and Capstone Projects

Prediction of a user’s influence level on social networks has attracted a lot of attention as human interactions move online. Influential users have the ability to influence others’ behavior to achieve their own agenda. As a result, predicting users’ level of influence online can help to understand social networks, forecast trends, prevent misinformation, etc. The research on user influence in social networks has attracted much attention across multiple disciplines, from social sciences to mathematics, yet it is still not well understood. One of the difficulties is that the definition of influence is specific to a particular problem or a domain, …

Artificial Intelligence In The Radiomic Analysis Of Glioblastomas: A Review, Taxonomy, And Perspective, Ming Zhu, Sijia Li, Yu Kuang, Virginia B. Hill, Amy B. Heimberger, Lijie Zhai, Shenjie Zhai

Electrical & Computer Engineering Faculty Research

Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications …

Reduced Fuel Emissions Through Connected Vehicles And Truck Platooning, Paul D. Brummitt

Electronic Theses and Dissertations

Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication enable the sharing, in real time, of vehicular locations and speeds with other vehicles, traffic signals, and traffic control centers. This shared information can help traffic to better traverse intersections, road segments, and congested neighborhoods, thereby reducing travel times, increasing driver safety, generating data for traffic planning, and reducing vehicular pollution. This study, which focuses on vehicular pollution, used an analysis of data from NREL, BTS, and the EPA to determine that the widespread use of V2V-based truck platooning—the convoying of trucks in close proximity to one another so as to reduce air drag …

Comparing Learned Representations Between Unpruned And Pruned Deep Convolutional Neural Networks, Parker Mitchell

Master's Theses

While deep neural networks have shown impressive performance in computer vision tasks, natural language processing, and other domains, the sizes and inference times of these models can often prevent them from being used on resource-constrained systems. Furthermore, as these networks grow larger in size and complexity, it can become even harder to understand the learned representations of the input data that these networks form through training. These issues of growing network size, increasing complexity and runtime, and ambiguity in the understanding of internal representations serve as guiding points for this work.

In this thesis, we create a neural network that …

The Significance Of Sonic Branding To Strategically Stimulate Consumer Behavior: Content Analysis Of Four Interviews From Jeanna Isham’S “Sound In Marketing” Podcast, Ina Beilina

Student Theses and Dissertations

Purpose:
Sonic branding is not just about composing jingles like McDonald’s “I’m Lovin’ It.” Sonic branding is an industry that strategically designs a cohesive auditory component of a brand’s corporate identity. This paper examines the psychological impact of music and sound on consumer behavior reviewing studies from the past 40 years and investigates the significance of stimulating auditory perception by infusing sound in consumer experience in the modern 2020s.

Design/methodology/approach:
Qualitative content analysis of audio media was used to test two hypotheses. Four archival oral interview recordings from Jeanna Isham’s podcast “Sound in Marketing” featuring the sonic branding experts …

Human Trafficking And Machine Learning: A Data Pipeline From Law Agencies To Research Groups, Nathaniel Hites

Computer Science and Engineering Theses and Dissertations

Human trafficking is a form of modern-day slavery that, while highly illegal, is more dangerous with the advancements of modern technology (such as the Internet), which allows such a practice to spread more easily and quickly all over the world. While the number of victims of human trafficking is large (according to non-profit organization Safe House, there are estimated to be about 20.5 million human trafficking victims, worldwide (“Human Trafficking Statistics & Facts.” Safe Horizon)- co-erced or manipulated by traffickers into either forced labor, or sexual exploitation and encounters), the number of heard cases is proportionally low- several thousand successful …

Computational Approaches To Facilitate Automated Interchange Between Music And Art, Rao Hamza Ali

Computational and Data Sciences (PhD) Dissertations

Recently, there has been a tremendous increase in generating and synthesizing music and art using various computational techniques. An area that is still under-researched, however, is how one medium can be converted into the other, while maintaining the overall aesthetics. Over the last few centuries, artists, composers, and scholars, have attempted to use substitute one form of art for the other: by proposing techniques where music notes are synonymous to colors, by inventing instruments that combine the aesthetics of music and visual art, and by incorporating the two media in live performances. A widely accepted computational approach, for the conversion, …

An Application Of Machine Learning To Analysis Of Packed Mac Malware, Kimo Bumanglag

Masters Theses & Doctoral Dissertations

The macOS operating system is increasingly targeted by malware. Software written for macOS, both benign and malicious, is in the Mach-O executable format. Malware authors may frustrate analysts through obfuscation methods such as packing. The field of malware research on Windows is well-established but is less so on the macOS platform. Thus far, no research has been identified that studies how machine learning can be used to detected packed Mach-O malware. This research applies supervised machine learning techniques to the classification of packed Mach-O malware. This research will answer three research questions. First, whether machine learning can classify packed Mach-O …

Video Games, Grief, And The Character Link System, Nam Nguyen

LSU New Orleans Theses and Dissertations

Grief can encompass more than just the loss of real-life people. It can be felt with the loss of a pet, changes in daily structure, and even the loss of video game characters. The topic of grief related to video games and video game characters comes at a time when games as a service (GaaS) continue to increase in popularity and the phenomenon where these games also inevitably terminate service. To combat this unique form of grief, the Character LINK System was created as a tool that uses simple natural language processing (NLP) techniques to offer support to the bereaved …

Considering The Context To Build Theory In Hci, Hri, And Hmc: Explicating Differences In Processes Of Communication And Socialization With Social Technologies, Andrew Gambino, Bingjie Liu

Human-Machine Communication

The proliferation and integration of social technologies has occurred quickly, and the specific technologies with which we engage are ever-changing. The dynamic nature of the development and use of social technologies is often acknowledged by researchers as a limitation. In this manuscript, however, we present a discussion on the implications of our modern technological context by focusing on processes of socialization and communication that are fundamentally different from their interpersonal corollary. These are presented and discussed with the goal of providing theoretical building blocks toward a more robust understanding of phenomena of human-computer interaction, human-robot interaction, human-machine communication, and interpersonal …

The Global Rise Of Online Devices, Cyber Crime And Cyber Defense: Enhancing Ethical Actions, Counter Measures, Cyber Strategy, And Approaches, Naresh Kshetri

Dissertations

The rise of online devices, online users, online shopping, online gaming, and online teaching has ultimately given rise to online attacks and online crimes. As cases of COVID-19 seem to increase day by day, so do online crimes and attacks (as many sectors and organizations went 100% online). Technological advancements and cyber warfare already generated many ethical issues, as internet users increasingly need ethical cyber defense strategies.

Individual internet users have challenges on their end; and on the other end, nation states (some secretly, some openly), are investing in robot weapons and autonomous weapons systems (AWS). New technologies have combined …

Autonomous, Long-Range, Sensor Emplacement Using Unmanned Aircraft Systems, Adam Plowcha, Justin Bradley, Jacob Hoberg, Thomas Ammon, Mark Nail, Brittany Duncan, Carrick Detweiler

School of Computing: Faculty Publications

Automated, in-ground sensor emplacement can significantly improve remote, terrestrial, data collection capabilities. Utilizing a multicopter, unmanned aircraft system (UAS) for this purpose allows sensor insertion with minimal disturbance to the target site or surrounding area. However, developing an emplacement mechanism for a small multicopter, autonomy to manage the target selection and implantation process, as well as long-range deployment are challenging to address. We have developed an autonomous, multicopter UAS that can implant subsurface sensor devices. We enhanced the UAS autopilot with autonomy for target and landing zone selection, as well as ensuring the sensor is implanted properly in the ground. …

The Effect Of Using The Gamification Strategy On Academic Achievement And Motivation Towards Learning Problem-Solving Skills In Computer And Information Technology Course Among Tenth Grade Female Students, Mazyunah Almutairi, Prof. Ahmad Almassaad

International Journal for Research in Education

Abstract

This study aimed to identify the effect of using the gamification strategy on academic achievement and motivation towards learning problem-solving skills in computer and information technology course. A quasi-experimental method was adopted. The study population included tenth-grade female students in Al-Badi’ah schools in Riyadh. The sample consisted of 54 students divided into two equal groups: control group and experimental group. The study tools comprised an achievement test and the motivation scale. The results showed that there were statistically significant differences between the two groups in the academic achievement test in favor of the experimental group, with a large effect …

Classifying Blood Glucose Levels Through Noninvasive Features, Rishi Reddy

Graduate Theses, Dissertations, and Problem Reports (ETD)

Blood glucose monitoring is a key process in the prevention and management of certain chronic diseases, such as diabetes. Currently, glucose monitoring for those interested in their blood glucose levels are confronted with options that are primarily invasive and relatively costly. A growing topic of note is the development of non-invasive monitoring methods for blood glucose. This development holds a significant promise for improvement to the quality of life of a significant portion of the population and is overall met with great enthusiasm from the scientific community as well as commercial interest. This work aims to develop a potential pipeline …

Deep Learning Detection In The Visible And Radio Spectrums, Greg Clancy Murray

Graduate Theses, Dissertations, and Problem Reports (ETD)

Deep learning models with convolutional neural networks are being used to solve some of the most difficult problems in computing today. Complicating factors to the use and development of deep learning models include lack of availability of large volumes of data, lack of problem specific samples, and the lack variations in the specific samples available. The costs to collect this data and to compute the models for the task of detection remains a inhibitory condition for all but the most well funded organizations. This thesis seeks to approach deep learning from a cost reduction and hybrid perspective — incorporating techniques …

Analysis Of Public Sentiment Of Covid-19 Pandemic, Vaccines, And Lockdowns, Devinesh Singh

Master's Projects

CoV-2 pandemic prompted lockdown measures to be implemented worldwide; these directives were implemented nationwide to stunt the spread of the infection. Throughout the lockdowns, millions of individuals resorted to social media for entertainment, communicate with friends and family, and express their opinions about the pandemic. Simultaneously, social media aided in the dissemination of misinformation, which has proven to be a threat to global health. Sentiment analysis, a technique used to analyze textual data, can be used to gain an overview of public opinion behind CoV-2 from Twitter and TikTok. The primary focus of the project is to build a deep …