Artificial Intelligence and Robotics Commons^™

Generative Ai For 3d Printed Antenna Design, Jennifer Ann Chavez

Open Access Theses & Dissertations

This research explores the integration of generative artificial intelligence (AI) with a physics-informed particle swarm optimizer (PSO) to develop 3D printable microstrip patch antennas. A neural network was trained on a dataset of microstrip patch antenna geometries and their corresponding performance metrics: return loss and gain. The PSO used a fitness function prioritizing low return loss in potential antennas, eventually yielding novel antenna geometries with parasitic components. 3D printing constraints were also hard coded into the framework, thus preventing any geometries being generated that cannot be fabricated. When simulated using Ansys HFSS, the AI generated microstrip patch antennas exceeded the …

Hidden Layer Reshaping On Convolutional Neural Network, Alan Delgado

Open Access Theses & Dissertations

Artificial Intelligence (AI) technologies have become really popular in recent years. From ChatGPT to Tesla cars, many applications can benefit from these type of technologies. Automotive, healthcare, biomedical, cybersecurity, finances, and retail are some of the fields that take advantage of it. It has been seen that AI can solve complex problems, but there is still work to be done to optimize it. A deep learning neural network (DLNN) tries to simulate how a human brain operates. These DLNNs are made up of artificial neurons which are connected by weight that are modified when the network is trained. These networks …

Enhancing Security And Resiliency In Operational Technology Environments Through Network Slicing And Federated Learning, Brian Giovanni Rodiles Delgado

Open Access Theses & Dissertations

The growing convergence of Information Technology (IT) and Operational Technology (OT) within Industry 4.0 environments has introduced new demands on industrial network infrastructure. As cyber-physical systems become increasingly interconnected, ensuring the secure, timely, and efficient exchange of critical data is essential. This thesis explores how network slicing, a method of creating isolated virtual network segments, can be applied within OT environments to address challenges such as latency, security, and resource allocation.

The first research question addressed in this thesis is: How can OT networks take advantage of NFV and SDN technology to become cyber resilient? This study examines the operational, …

Alphamissense Predictions And Clinvar Annotations: A Deep Learning Approach To Uveal Melanoma, David J. Taylor Gonzalez, Mak B. Djulbegovic, Meghan Sharma, Michael Antonietti, Colin K. Kim, Vladimir N. Uversky, Carol L. Karp, Carol L. Shields, Matthew W. Wilson

Wills Eye Hospital Papers

OBJECTIVE: Uveal melanoma (UM) poses significant diagnostic and prognostic challenges due to its variable genetic landscape. We explore the use of a novel deep learning tool to assess the functional impact of genetic mutations in UM.

DESIGN: A cross-sectional bioinformatics exploratory data analysis of genetic mutations from UM cases.

SUBJECTS: Genetic data from patients diagnosed with UM were analyzed, explicitly focusing on missense mutations sourced from the Catalogue of Somatic Mutations in Cancer (COSMIC) database.

METHODS: We identified missense mutations frequently observed in UM using the COSMIC database, assessed their potential pathogenicity using AlphaMissense, and visualized mutations using AlphaFold. Clinical …

Ai Model For Predicting Asthma Prognosis In Children, Elham Sagheb, Chung-Il Wi, Katherine S King, Bhavani Singh Agnikula Kshatriya, Euijung Ryu, Hongfang Liu, Miguel A Park, Hee Yun Seol, Shauna M Overgaard, Deepak K Sharma, Young J Juhn, Sunghwan Sohn

Faculty, Staff and Student Publications

BACKGROUND: Childhood asthma often continues into adulthood, but some children experience remission. Utilizing electronic health records (EHRs) to predict asthma prognosis can aid health care providers and patients in developing effective prioritized care plans.

OBJECTIVE: We aimed to develop artificial intelligence (AI) models using various clinical variables extracted from EHRs to predict childhood asthma prognosis (remission vs no remission) in different age groups.

METHODS: We developed AI models utilizing patients' EHRs during the first 6, 9, or 12 years of their lives to predict their asthma prognosis status at ages 6 to 9, 9 to 12, or 12 to 15 …

Exploring The Biasing Effects Of Gender On Personality Disorder Diagnoses Formulated By Artificial Intelligence, Zoe Colclough

Student Theses

Gender bias is prevalent in personality disorder assessments, and while artificial intelligence has been posited as a solution to improve diagnostic objectivity and accuracy, the potential for such technologies to propagate human gender bias in mental health contexts remains underexplored. This study investigated the influences of gender bias on the diagnostic performance of ChatGPT-4o for personality disorders using three factorial research designs, which involved experimentally manipulating patient gender in a combined sample of 360 vignettes and case studies. Vignettes were synthesized through a novel artificial intelligence-assisted methodology established for this research, and case studies were identified from the literature. Significant …

Time Series Deep Learning Approach For The Intermittent Operational Performance Of A Wellhead Water Treatment And Desalination System, Michael G. Clement

Electronic Theses, Projects, and Dissertations

Distributed water treatment and desalination (DWTD) systems are becoming significant for serving disadvantaged communities that are geographically segregated from centralized water distribution networks. However, given the remote nature of the communities, these systems must operate autonomously adapting to intermittent operations due to varying water use patterns and unavailability of continuous manual labor support. Machine Learning models describing and forecasting system performance are critical, allowing for model-based control, performance forecasting, fault detection, and determination of causal relationships among process attributes. Accordingly, graph convolutional neural networks with an attention mechanism (GATConv) were developed to describe the intermittent operational profiles of a wellhead …

Learning Behaviors In Physics-Informed Deep Learning, Alex Glover

Electronic Theses and Dissertations

Physics-informed deep learning is a methodology in artificial intelligence aimed at combating the large training data requirement and the barrier of domain awareness that deep learning architectures commonly face in applications. Stochastic modeling integrated into the predictive models provides that domain knowledge. Variations of the Intelligent Driving Model impact the learning behaviors of the joint-training architecture. This thesis examines the effect of substituting the standard linear Intelligent Driving Model with a modified nonlinear version, as applied to real human driving behavior on the I-80 interstate. The experimentation also critically evaluates the complications that impede the viability of this architecture in …

Towards Reliable Ml: Data Attribution And Adversarial Robustness, Xiaosen Zheng

Dissertations and Theses Collection (Open Access)

Modern machine learning (ML) models achieve remarkable success, but face critical reliability challenges. This thesis advances two pillars of reliable ML systems: interpretability through data attribution and robustness against adversarial threats.

In the first part, we develop novel data attribution methods to elucidate the data-model relationship. We establish the critical role of memorization in model generalization through token-level influence analysis, extend sample-level attribution to diffusion models with effective approximation techniques, and introduce REGMIX, a group-level approach that predicts data mixture performance using small-scale experiments. These contributions provide practitioners with scalable tools to audit training data impacts across modalities.

The second …

Disentangling User Preferences Towards Self-Interpretable Recommender Systems, Nhu Thuat Tran

Dissertations and Theses Collection (Open Access)

Understanding user preferences remains a central challenge in recommender systems due to their inherently complex, unstructured, and multi-faceted nature, exacerbated by the sparsity of user interaction data. Traditional approaches often compress user interests into a single latent vector, overlooking the fact that user preferences are typically shaped by multiple underlying factors that differ across individuals. These latent drivers are not directly observable and must be discovered through unsupervised modeling, further complicated by limited historical interactions per user.

This dissertation addresses these challenges by introducing a principled framework for multiinterest modeling, which disentangles user behaviors into multiple latent factors to better …

Analytics Insights From Text: Machine Learning, Ai, And Sentiment Analysis On Beige Books, Charlie Smith

Graduate Theses and Dissertations (2019 - present)

Business analytics is about drawing actionable insights from data. These distinct but connected essays represent a novel approach to explore how natural language processing (NLP) advances and machine learning can transform unstructured text data into actionable conclusions. Essay 1 provides a broad framework. Essay 2 strengthens the sentiment analysis with the most recent artificial intelligence methodologies for capturing nuanced sentiment in complex texts. Essay 3 applies those insights to forecast recessions using topics that can be readily interpreted and applied.

The research demonstrates how these methodologies can be applied to enhance understanding of the same dataset, Beige Books. Published by …

Greening Intelligence: Why Ai Infrastructure And Governance Must Evolve Together, Heng Wang, Poh Seng Lee

Research Collection Yong Pung How School Of Law

AI infrastructure is evolving faster than the regulation and governance needed to ensure it serves public and planetary interests.

On Bed Posture Recognition Using Deep Learning With Pressure Sensors, Farheen Akhter Ms

Electronic Theses, Projects, and Dissertations

In healthcare applications such as disease prevention, sleep quality evaluation, and patient monitoring, bed posture recognition is essential. Using pressure sensor arrays placed on top of or embedded in mattresses, this study investigates the application of deep learning models for non-invasive posture classification. Although they have been widely employed, traditional machine learning approaches like support vector machines (SVM) and k-nearest neighbors (KNN) sometimes struggle with feature extraction and real-time performance necessitating considerable processing resources. I implemented a model using conventional approaches to get over these restrictions, then fine-tuned it using the following deep learning architectures for bed posture recognition: ResNet-50, …

A Systemic Approach To Maximize Heterogeneous System Performance, Thomas L. Randall

All Dissertations

Continuous increases in high performance computing (HPC) throughput have served as catalysts for industry and scientific advancement in countless manners that have fundamentally shaped our modern world. Our demands on compute resources continue to scale, but the limitations of Ahmdal’s law and Dennard scaling have proven increasingly difficult to overcome when approached solely through hardware or software design. Furthermore, many HPC applications fail to utilize the collective system’s performance, even on the most advanced supercomputers.

However, the resurgence of AI in the industry has promoted an explosion of hardware and software codesign that have fueled massive improvements in GPU design …

Oculomics: Current Concepts And Evidence, Zhuoting Zhu, Yueye Wang, Ziyi Qi, Wenyi Hu, Xiayin Zhang, Siegfried K Wagner, Yujie Wang, An Ran Ran, Joshua Ong, Ethan Waisberg, Mouayad Masalkhi, Alex Suh, Yih Chung Tham, Carol Y Cheung, Xiaohong Yang, Honghua Yu, Zongyuan Ge, Wei Wang, Bin Sheng, Yun Liu, Andrew G Lee, Alastair K Denniston, Peter Van Wijngaarden, Pearse A Keane, Ching-Yu Cheng, Mingguang He, Tien Yin Wong

Faculty, Staff and Student Publications

The eye provides novel insights into general health, as well as pathogenesis and development of systemic diseases. In the past decade, growing evidence has demonstrated that the eye's structure and function mirror multiple systemic health conditions, especially in cardiovascular diseases, neurodegenerative disorders, and kidney impairments. This has given rise to the field of oculomics-the application of ophthalmic biomarkers to understand mechanisms, detect and predict disease. The development of this field has been accelerated by three major advances: 1) the availability and widespread clinical adoption of high-resolution and non-invasive ophthalmic imaging ("hardware"); 2) the availability of large studies to interrogate associations …

The Role Of Ai In Enhancing Teamwork, Resilience And Decision-Making: Review Of Recent Developments, Satyadhar Joshi

Harrisburg University Other Works

This paper explores the transformative impact of artificial intelligence (AI) on organizational teamwork, decision-making, and resilience. This paper furthur reviews recent literature on the integration of Artificial Intelligence (AI) in various organizational functions, focusing on its impact on innovation management, leadership paradigms, and organizational resilience. We provide groundwork required to enhance frameworks that can integrate cognitive scaffolding with antifragile team dynamics, employing behavioral economics and neurocognitive principles. We introduce methodologies for enhancing team resilience through adaptive AI systems, cross-training interventions, and pre-mortem simulation techniques. The framework addresses key challenges in confirmation bias mitigation, cultural dimension alignment, and vigilance decrement prevention. …

Robot-Integrated 4d Building Information Modeling (4d Bim): Framework For Planning Safe Autonomous Construction Operations In Dynamic Environments, Hafiz Oyedimeji Oyediran

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

In the construction industry, the use of autonomous robots is considered a solution to overcome the heavy reliance on human workers to perform repetitive, strenuous, and hazardous tasks. While these robots offer the advantage of autonomous operation, ensuring their safe and efficient integration within construction sites requires precise planning. Such planning must account for the varying project complexities such as scope, site layout, tasks, timelines, existence of human workers, and other spatiotemporal conditions of the construction site. Currently, there are no methods to safely plan autonomous robot operations considering these factors within the overarching construction planning process. Thus, autonomous robots …

Empirically Exploring The Physical Realizability Of Adversarial Examples, Ruoyao Wen

McKelvey School of Engineering Graduate Student Theses & Dissertations

The development of autonomous vehicles (AVs) has been accelerated by advancements in deep neural networks (DNNs), which power the complex perception systems necessary for safe and efficient real-world navigation. However, as AVs increasingly integrate into public transportation networks, the robustness of their perception systems against potential vulnerabilities is critical. Among these threats, adversarial attacks—particularly through the use of adversarial patches—pose significant risks. These patches are carefully crafted perturbations designed to mislead DNNs, potentially compromising AV safety by causing incorrect object recognition or misclassification.

While extensive research has demonstrated high attack success rates for adversarial patches in controlled digital environments, their …

The Efficacy Of Incorporating Artificial Intelligence (Ai) Chatbots In Brief Gratitude And Self-Affirmation Interventions: Evidence From Two Exploratory Experiments, Jing Wen Hung, Andree Hartanto, Adalia Y.H. Goh, Zoey K.Y. Eun, K. T. A. Sandeeshwara Kasturiratna, Zhi Xuan Lee, Nadyanna M. Majeed

Research Collection School of Social Sciences

Numerous studies have demonstrated that positive psychology interventions, including brief interventions, can significantly improve well-being outcomes. These findings are particularly important given that many of these interventions are brief and self-administered, making them both accessible and scalable for large populations. However, the efficacy of positive psychology interventions is often constrained by small effect sizes. In light of advancements in generative Artificial Intelligence (AI), this study explored whether integrating AI chatbots into positive psychology interventions could enhance their efficacy compared to traditional self-administered approaches. Study 1 examined the efficacy of a gratitude intervention delivered through Snapchat's My AI, while Study 2 …

Advancing Precision And Autonomy In Agriculture And Medical Imaging Through Ai And Computer Visions, Tan-Hanh Pham

Theses and Dissertations

Deep learning has revolutionized numerous fields by enhancing precision, automation, and decision-making capabilities. This dissertation explores its applications in agriculture and medical image processing, introducing novel methodologies to improve accuracy and efficiency in these domains. These fields hold critical societal importance -- agriculture underpins global food security and sustainability, while medical imaging drives advancements in diagnostics and personalized healthcare, both benefiting significantly from data-driven innovations. In agriculture, deep learning is applied to precision spray systems through droplet analysis. Specifically, a generative model is designed to create synthetic droplet images, addressing the challenge of limited training samples, which are expensive …

Expressive And Interpretable User Engagement Prediction Using Multivariate Survival Processes, Akshay Aravamudan

Theses and Dissertations

The ability to characterize how information diffuses online is of paramount importance to stakeholders that are interested in tasks such as proposing solutions for mitigating and countering dis/misinformation, predicting user engagement of content in social media, planning marketing campaigns to roll-out products and planning dissemination of political campaign messaging among others. One such facet of learning the dynamics of information diffusion is the ability to predict user engagement or the popularity of a single piece of information as it spreads through an online medium. Existing works in this regard mainly either obfuscate user level information or utilize frameworks that are …

Adversarial Machine Learning: Methods For Attacks And Defenses, Minh Hao Van

Graduate Theses and Dissertations

With the rapid development of machine learning in real-world applications, enhancing security plays an important role. Adversarial machine learning focuses on understanding malicious actions from attackers and developing defensive techniques against such threats when deploying machine learning systems. An attack can occur in different scenarios, such as poisoning attacks during the training stage and evasion attacks during the testing stage. Although extensive research has explored defense strategies to deal with these harmful attacks, there is a need for further research into areas such as how to counteract malicious attacks with healthy noise or how to train an adaptive defense against …

Achieving Flexible Fairness And Privacy In Federated Learning, Alycia N. Carey

Graduate Theses and Dissertations

Having access to large, high-quality datasets is crucial for training machine learning models that achieve satisfactory performance. Unfortunately, it is common that a single entity (e.g., mobile device or organization) does not have access to such datasets due to monetary or resource constraints. Traditional machine learning requires that all training data reside in a centralized location during the entire duration of model training, however, in many circumstances it is difficult or even impossible (e.g., due to governmental regulations) for multiple parties to combine their data to meet this constraint. Federated learning is a machine learning paradigm that facilitates the joint …

Seaexam And Seabench: Benchmarking Llms With Local Multilingual Questions In Southeast Asia, Chaoqun Liu, Wenxuan Zhang, Jiahao Ying, Mahani Aljunied, Anh Tuan Luu, Lidong Bing

Research Collection School Of Computing and Information Systems

This study introduces two novel benchmarks, SeaExam and SeaBench, designed to evalu ate the capabilities of Large Language Models (LLMs) in Southeast Asian (SEA) application scenarios. Unlike existing multilingual datasets primarily derived from English translations, these benchmarks are constructed based on real world scenarios from SEA regions. SeaExam draws from regional educational exams to form a comprehensive dataset that encompasses sub jects such as local history and literature. In contrast, SeaBench is crafted around multi turn, open-ended tasks that reflect daily inter actions within SEA communities. Our evalua tions demonstrate that SeaExam and SeaBench more effectively discern LLM performance on …

Using Natural Language Processing And Machine Learning To Detect Online Radicalisation In The Maldivian Language, Dhivehi, Hussain Ibrahim, Ahmed Ibrahim, Michael N. Johnstone

Research outputs 2022 to 2026

Early detection of online radical content is important for intelligence services to combat radicalisation and terrorism. The motivation for this research was the lack of language tools in the detection of radicalisation in the Maldivian language, Dhivehi. This research applied Machine Learning and Natural Language Processing (NLP) to detect online radicalisation content in Dhivehi, with the incorporation of domain-specific knowledge. The research used Machine Learning to evaluate the most effective technique for detection of radicalisation text in Dhivehi and used interviews with Subject Matter Experts and self-deradicalised individuals to validate the results, add contextual information and improve recognition accuracy. The …

Multiobjective Linear Ensembles For Robust And Sparse Training Of Few-Bit Neural Networks, Ambrogio Maria Bernardelli, Stefano Gualandi, Simone Milanesi, Hoong Chuin Lau, Neil Yorke-Smith

Research Collection School Of Computing and Information Systems

Training neural networks (NNs) using combinatorial optimization solvers has gained attention in recent years. In low-data settings, the use of state-of-the-art mixed integer linear programming solvers, for instance, has the potential to exactly train an NN while avoiding computing-intensive training and hyperparameter tuning and simultaneously training and sparsifying the network. We study the case of few-bit discrete-valued neural networks, both binarized neural networks (BNNs) whose values are restricted to ±1 and integer-valued neural networks (INNs) whose values lie in the range {−P,…,P}. Few-bit NNs receive increasing recognition because of their lightweight architecture and ability to run on low-power devices: for …

Reverse Modeling In Large Language Models, Sicheng Yu, Yuanchen Xu, Cunxiao Du, Yanying Zhou, Minghui Qiu, Qianru Sun, Hao Zhang, Jiawei Wu

Research Collection School Of Computing and Information Systems

Humans are accustomed to reading and writing in a forward manner, and this natural bias extends to text understanding in auto-regressive large language models (LLMs). This paper investigates whether LLMs, like humans, struggle with reverse modeling, specifically with reversed text inputs. We found that publicly available pre-trained LLMs cannot understand such inputs. However, LLMs trained from scratch with both forward and reverse texts can understand them equally well during inference. Our case study shows that different-content texts result in different losses if input (to LLMs) in different directions---some get lower losses for forward while some for reverse. This leads us …

“Ronaldo’S A Poser!”: How The Use Of Generative Ai Shapes Debates In Online Forums, Yuhan Zeng, Yingxuan Shi, Xuehan Huang, Fiona Fui-Hoon Nah, Ray Lc

Research Collection School Of Computing and Information Systems

Online debates can enhance critical thinking but may escalate into hostile attacks. As humans are increasingly reliant on Generative AI (GenAI) in writing tasks, we need to understand how people utilize GenAI in online debates. To examine the patterns of writing behavior while making arguments with GenAI, we created an online forum for soccer fans to engage in turn-based and free debates in a post format with the assistance of ChatGPT, arguing on the topic of "Messi vs Ronaldo". After 13 sessions of two-part study and semi-structured interviews with 39 participants, we conducted content and thematic analyses to integrate insights …

Iot In Sustainability And Iot In The Ai And Metaverse Age, Yuzhou Qian, Keng Siau

Research Collection School Of Computing and Information Systems

The Internet of Things (IoT) is a modern technology that has gained large popularity and is still developing. Connecting heterogeneous devices, such as phones, vehicles, and household appliances, IoT has brought convenience to our lives. Further, IoT plays a significant role in enhancing environmental sustainability. It provides timely data about different devices and enables users and managers to directly control the objects. IoT can optimize the existing energy systems and promote the usage of renewable technologies. In this paper, we discuss how IoT supports green initiatives (i.e., how it is applied in different sectors), how it can be "green" itself …

Cyberoception: Finding A Painlessly-Measurable New Sense In The Cyberworld Towards Emotion-Awareness In Computing, Tadashi Okoshi, Zexiong Gao, Yi Zhen Tan, Takumi Karasawa, Takeshi Miki, Wataru Sasaki, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

In Affective computing, recognizing users’ emotions accurately is the basis of affective human–computer interaction. Understanding users’ interoception contributes to a better understanding of individually different emotional abilities, which is essential for achieving inter-individually accurate emotion estimation. However, existing interoception measurement methods, such as the heart rate discrimination task, have several limitations, including their dependence on a well-controlled laboratory environment and precision apparatus, making monitoring users’ interoception challenging. This study aims to determine other forms of data that can explain users’ interoceptive or similar states in their real-world lives and propose a novel hypothetical concept “cyberoception,” a new sense (1) which …