Artificial Intelligence and Robotics Commons^™

Machine Learning Methods For Intrusion Detection And Response In Network Security, Ayomide Oyemaja

College of Graduate Studies: Theses & Dissertations

Intrusion Detection Systems (IDS) play a crucial role in computer network security by identifying malicious activities and potential cyberattacks. This thesis combines machine learning and cybersecurity by applying Reinforcement Learning (RL) in intrusion detection and response using the NSL-KDD dataset.

We designed and implemented a Q-learning framework where an agent learns to classify network traffic over time by interacting with the environment and receiving rewards based on detection accuracy. We also look at the importance of feature selection and classification techniques and how effective they are in improving model performance, reducing the complexity of computation, and producing more desirable results. …

Comparative Analysis Of Eye-Metric Algorithms For Code Comprehension In Introductory Cs Course, Noushin Gauhar

College of Graduate Studies: Theses & Dissertations

Eye-tracking technology offers a non-intrusive way to study cognitive processes by tracking where and how long individuals look. In computer science education, it provides valuable insights into how students understand source code—a task that requires intense visual and mental effort. This study investigates how eye-tracking can reveal differences in code comprehension strategies among students in an Introductory Programming course. By analyzing three key metrics—dwell time, gaze entropy, and the K coefficient—the research explores how students engage with code. Dwell time indicates cognitive focus on specific code elements, the K coefficient measures attentional shifts between scanning and focused reading, and gaze …

Enhancing Adhd Diagnosis In College Students Using Multimodal Integration Of Nicats And Iva-2 Tools, Rushmila Shabneen

College of Graduate Studies: Theses & Dissertations

This study explores enhanced methods for accurately identifying Attention Deficit Hyperactivity Disorder (ADHD) indicators in college students. ADHD, a neurodevelopmental disorder, impacts attention, impulse control, and emotional regulation, often leading to academic and social difficulties. Many students remain undiagnosed due to symptom overlap with stress and other factors. Traditional tools like the IVA-2 assess behavioral responses but may not fully capture ADHD complexity. This research integrates IVA-2 data with multimodal metrics from the Non-Intrusive Classroom Attention Tracking System (NiCATS), which monitors facial expressions, eye movements, and computer interactions. Preliminary results show that combining these tools improves ADHD detection, reduces false …

Is It Getting Better? An Evaluation Of Two Successive Generations Of Chatgpt In Answering Specialized Vascular Surgery Questions, Dongjin Suh, Quang Le, Leana Dogbe, Kedar Lavingia, Michael Amendola

Department Surgery Faculty Publications

Purpose: Large language models (LLMs) can generate clinically relevant text; however, their performance in highly specialized medical domains remains uncertain. This study evaluated ChatGPT-3.5 and ChatGPT-4 (OpenAI) using vascular surgery board–style questions from the Vascular Education and Self-Assessment Program, version 4 (VESAP4) and compared the two public model versions (June and November 2023).

Materials and Methods: All non-image VESAP4 questions (n=384) were presented independently three times to each model version (ChatGPT-3.5 June/November; ChatGPT-4, June/November). Outcomes included accuracy (proportion correct), consistency (same option letter across all three attempts and “consistently correct”), explanation length (word count), and modes of failure classified for …

Generalizing Classification Of Pilot Workload: Transfer Learning Versus A Jepa-Inspired Transformer Architecture, Naim Barnett, Shivani Nagrecha, Morgan Glover, Clayton Harper, Justin Wilson, James Maher, Eric C. Larson

International Journal of Aviation, Aeronautics, and Aerospace

Within the context of learning, there poses difficulty when objectively measuring human performance. In this work, we investigate the evaluation of human performance via its relation to the individual's mental capacity by classification of cognitive load within the domain of aviation. By utilizing a mixed virtual and physical flight simulation environment in conjunction with biometric sensing, we create and evaluate the predictive capabilities of a Joint-Embedding Predictive Architecture (JEPA) and compare the architecture and results to traditional methods for transfer learning and domain adaptation. We find that our JEPA inspired architecture can achieve more than 70% accuracy of cognitive workload, …

Convergence In Cancer: Integrative Multi-Modal Modeling For Pan-Cancer Drug Response Prediction, Jessica Zylla

Dissertations and Theses

Heterogeneity and rarity can make cancer difficult to study and treat. Cancer's heterogeneity, evident in tumor locations, tumor cell types, subtypes, and microbiota within the tumor microenvironment, complicates diagnosis, prognosis, and treatment. Research into the tumor microenvironment and its microbiota is an evolving area of oncology that may advance cancer drug-patient response. Cancer-omics signatures are crucial to drug response, yet there is no tool that physicians use as a gold standard. Research indicates that microbial presence can alter drug metabolism and immune response, affecting cancer-drug efficacy on a patient-specific basis. This work highlights the need for training readiness in transdisciplinary …

Afit Generative Ai Teaching Guidebook Synopsis, Afit Faculty Learning Community, Mark G. Bateman, Brett J. Borghetti, Allen W. Dukes, Nicholas C. Francis, Mike Frick, Bobbie Oh, Kevin Patterson, Hiren J. Patel, Mark G. Reith, Erick S. Tyndall, Teresa M. Walton, Torrey J. Wagner, Timothy S. Wolfe, Jonathan Zemmer

AFIT Documents

The main objective of this work was to bring together various perspectives on how to envision incorporating Gen AI capabilities into the learning environment and identify some best practices for their implementation. Any instructor who is interested in these capabilities but does not necessarily have a technical background can find pragmatic use of the examples provided. While the examples have a wide range of applicability, they are meant to serve as a starting point for educators to explore what would be beneficial to their educational environment, from traditional classroom settings to online continuing education courses.

Differentiating Opioid Use Disorder From Healthy Controls Via Ml Analysis Of Rs-Fmri Networks, Ahmed Temtam, Megan A. Witherow, Liangsuo Ma, M. Shibly Sadique, F. Gerard Moeller, C. Kenneth, Dianne Wright, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Objectives/Goals: This work aims to identify functional brain networks that differentiate opioid use disorder (OUD) subjects from healthy controls (HC) using machine learning (ML) analysis of resting-state fMRI (rs-fMRI). We investigate the default mode network (DMN), salience network (SN), and executive control network (ECN), as well as demographic features. Methods/Study Population: This work uses high-resolution rs-fMRI data from a National Institute on Drug Abuse study (IRB #HM20023630) with 31 OUD and 45 HC subjects. We extract rs-fMRI blood oxygenation level-dependent (BOLD) features from the DMN, SN, and ECN. The Boruta ML algorithm identifies statistically significant features and brain activity mapping …

Am I As Effective At Identifying Emotions As Artificial Intelligence? A Comparative Study Of Emotional Recognition, Traci R. Grove, Alexandra T. Lucas, Maryann Martin, Cathleen M. Deckers, Lulu Sherif Mahmood, Nicole Danaher-Garcia, Mark W. Scerbo, Suzan Kardong-Edgren, Janice C. Palaganas

Psychology Faculty Publications

Background

Learning conversations, or dialogues aimed at deepening understanding and reflection, are deeply influenced by emotions. Effective communication is influenced by emotional intelligence - the ability to recognize, understand, and manage both one’s own and others’ emotions. While advances in artificial intelligence (AI) offer new tools for emotion recognition, these technologies still struggle with accurately interpreting subtle and culturally diverse emotional expressions, sparking debate about their reliability and effectiveness. This article provides a comparative analysis of human versus AI recognition of emotions during an end-of-course reflective learning conversation.

Methods

Emotions during a structured post-conference debriefing were analyzed and coded by …

Motion Planning For A Flexible Modular Raft Robot, Chun-Yi She

Dartmouth College Master’s Theses

This thesis presents a hierarchical motion planning framework for SoftRafts, a modular and deformable aquatic robot capable of performing locomotion and manipulation tasks on water surfaces. SoftRafts consist of soft and rigid components that enable structural reconfiguration, offering adaptability in unstructured aquatic environments.

To address the complexity of planning in high-dimensional, deformable systems, the proposed method uses a bounding-shape abstraction, specifically, enclosing circles and rectangular bounding boxes to simplify motion planning. These enclosures abstract the robot's overall shape, reducing the high-dimensional planning problem into a lower-dimensional problem. A global planner uses a probabilistic roadmap (PRM) to compute a collision-free path …

Ai-Generated Messaging For Life Events Using Structured Prompts: A Comparative Study Of Gpt With Human Experts And Machine Learning, Christopher Lynch, Erik Jensen, Ross Gore, Virginia Zamponi, Kevin O'Brien, Brandon Feldhaus, Katherine Smith, Joseph Martínez, Madison H. Munro, Timur E. Ozkose, Tugce B. Gundogdu, Ann Marie Reinhold, Hamdi Kavak, Barry Ezell

VMASC Publications

Large Language Models (LLMs) play an increasingly integrated and pivotal role in generating diverse types of texts, such as social media messages, emails, narratives, and technical reports, among other textual communication forms. As AI-generated messaging filters into human communication, a systematic exploration of their effectiveness for mimicking human-like communication of life events is needed. In this study, we employ a zero-shot structured narrative prompt to generate 24,000 life event messages for birth, death, hiring, and firing events using OpenAI's GPT-4. From this dataset, we manually classify 2880 messages and evaluate their validity in conveying these life events through the form …

Qos-Aware Link Adaptation For Beyond 5g Networks: A Deep Reinforcement Learning Approach, Ali Parsa, Neda Moghim, Sachin Shetty

Center for Secure and Intelligent Critical Systems (CSICS) Publications

Modern wireless communication systems face increasingly complex challenges due to rapidly changing channel conditions and the growing diversity of application-specific Quality of Service (QoS) requirements. Traditional link adaptation mechanisms primarily aim to maximize throughput and often lack the flexibility to support emerging applications, such as Extended Reality (XR) and Virtual Reality (VR), which demand simultaneous guarantees for high data rates, ultra low latency, and high reliability. These stringent and multidimensional QoS needs call for more intelligent and adaptive solutions. In this paper, we propose QDRLLA (QoS-aware Deep Reinforcement Learning-based Link Adaptation), a novel framework that employs deep reinforcement learning to …

A Trust-By-Learning Framework For Secure 6g Wireless Networks Under Native Generative Ai Attacks, Md Shirajum Munir, Sravanthi Proddatoori, Manjushree Muralidhara, Trinidad Mario Dena, Walid Saad, Zhu Han, Sachin Shetty

Center for Secure and Intelligent Critical Systems (CSICS) Publications

Sixth-generation (6G) wireless networks will become vulnerable due to native generative AI (GenAI)-driven intelligent poisoning attacks in both the radio unit and the core network. In particular, network parameters and metrics in cross-layer design pose fundamentally uncertain conditions and can be compromised through the native GenAI mechanism, which leverages data augmentation and reconstruction capabilities. This work investigates the capabilities of native GenAI to create novel poisoning attacks in wireless networks, while investigating their impact through uncertainty-informed root analysis. Then, detected attacks are mitigated by developing a trustworthy service aggregation in the wireless network. First, a joint decision problem is formulated …

Examining Non-Traditional Online Learners' Ownership Of Learning In The Context Of Chatgpt-Facilitated Design, Mohan Yang, Tian Luo, Kristin Herman, Belle Li, Shanan Chappell Moots, Noah Glaser, Shiyan Jiang

STEMPS Faculty Publications

Generative artificial intelligence (GenAI) offers potential solutions to educational challenges by personalizing learning experiences for diverse learners. However, it also introduces complexities around issues of ownership of learning. As an indicator of empowered learning, ownership is a multifaceted but underexplored concept. In this study, the authors adopted a mixed-methods multiple-case study design to explore non-traditional online students’ perceived ownership of learning when using ChatGPT for instructional design. The authors adopted the psychology ownership construct consisting of self-efficacy, accountability, belongingness, and self-identity as the theoretical framework. Findings revealed students’ prior AI experiences and educational levels played a significant role in their …

Socially Shared Regulation Of Learning And Artificial Intelligence: Opportunities To Support Socially Shared Regulation, Jinhee Kim, Rita Detrick, Seongryeong Yu, Yukyeong Song, Linda Bol, Na Li

STEMPS Faculty Publications

Supporting learners in achieving high-level socially shared regulation of learning (SSRL) in the online collaborative learning (OCL) context presents challenges that the utilization of artificial intelligence (AI) technologies may help solve. However, the effective uses of AI to support multifaceted areas (cognition, metacognition, and motivation) and phases (forethought, performance, and reflection) of SSRL remain elusive. Furthermore, research on developing an educational AI and what pedagogical attributes and elements are required for AI to support students' SSRL effectively is limited. This study, therefore, aims to investigate students' perceptions of AI applications in enhancing SSRL and to explore the essential pedagogical elements …

Leveraging Custom Gpts For Formative Assessment And Feedback, Nari Kim, Jason Vickers, Shaimaa Alyehaibi, Chikezie Ozuzu, Xinyue Ren, Suhasini Kotcherlakota

STEMPS Faculty Publications

As artificial intelligence (AI) advances, the use of custom generative pre-trained transformers (Custom GPTs) in formative assessments opens new opportunities for improving student learning and engagement. Custom GPTs increase cognitive and emotional engagement by providing timely, context-sensitive, and tailored responses based on unique learner profiles. This paper aims to investigate how custom GPT-driven formative feedback can enhance individualized learning experiences and improve student performance. This paper will discuss the promise of this AI tool, the challenges associated with its application, and recommendations for educators and academics looking to use these technologies for formative assessments.

The Integration Of Artificial Intelligence And Ontologies: Transformations In Knowledge Representation And Application, Grazia Serratore, Julaine Clunis

STEMPS Faculty Publications

Artificial Intelligence (AI) is reshaping the landscape of knowledge representation. There is an increasingly strong bidirectional relationship, between AI techniques and ontologies. AI techniques revolutionized traditional, manual ontology development and contribute to automated ontology construction, while ontologies enhance the performance of AI systems and their semantic accuracy. Through a comprehensive review of current literature, this paper aims to examine: i) how Machine Learning (ML) techniques contribute to the automated construction, refinement, and validation of ontologies; ii) the most widely used and effective ML approaches for ontology construction; iii) how domain-specific requirements influence the selection and adaptation of AI techniques for …

Exploring The Impact Of Generative Ai Chatgpt On Critical Thinking In Higher Education: Passive Ai-Directed Use Or Human-Ai Supported Collaboration?, Nesma Ragab Nasr, Chih-Hsiung Tu, Jennifer Werner, Tonia Bauer, Cherng-Jyh Yen, Laura Sujo-Montes

STEMPS Faculty Publications

Generative AI is weaving into the fabric of many human aspects through its transformative power to mimic human-generated content. It is not a mere technology; it functions as a generative virtual assistant, raising concerns about its impact on cognition and critical thinking. This mixed-methods study investigates how GenAI ChatGPT affects critical thinking across cognitive presence (CP) phases. Forty students from a four-year university in the southwestern United States completed a survey; six provided their ChatGPT scripts, and two engaged in semi-structured interviews. Students’ self-reported survey responses suggested that GenAI ChatGPT improved triggering events (M = 3.60), exploration (M = 3.70), …

Designing Ai-Powered Learning: Adult Learners' Expectations For Curriculum And Human-Ai Interaction, Jinhee Kim, Seongryeong Yu, Rita Detrick, Xi Lin, Na Li

STEMPS Faculty Publications

Despite the potential benefits offered by GenAI technologies to provide innovative solutions to address distinct challenges faced by working adult learners (ALs) in higher education and beyond, there is limited understanding of how best to structure AI-powered learning for this population while ensuring their distinct needs and perspectives are considered. Hence, this study aimed to determine what curriculum and student-AI interaction would be required by situating ALs’ views. Through analyzing 48 e-portfolios and in-depth interviews with 20 ALs from diverse educational and professional backgrounds, the study found that ALs perceived content mastery and developing a lifelong habit of learning as …

Artificial Intelligence In Cancer-Related Malnutrition And Cachexia: A Transformative Tool In Clinical Nutrition, Salvatore Carbone

EVMS School of Health Professions Faculty Publications

[Introduction] Malnutrition and cachexia are common complications in cancer patients, and they negatively influence prognosis, treatment efficacy, and tolerability as well as quality of life [[1], [2], [3]]. Accurately identifying and effectively managing malnutrition and cachexia in this population remains a clinical challenge. Conventional validated screening tools may lack the sensitivity and specificity required for early detection and personalized intervention in diverse cancer types and treatment settings [4,5]. Over the last decade, the use of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) strategies, has shown promising results in clinical nutrition, with the potential to revolutionize nutritional …