Artificial Intelligence and Robotics Commons^™

Path Planning Of Mobile Robot Based On The Integration Of Multi-Scale A* And Optimized Dwa Algorithm, Jianmin Xu, Lei Song, Dongdong Deng, Yaoruo Chen, Wei Yang

Journal of System Simulation

Abstract: In order to solve the problems of sharply increasing computational and time costs, as well as poor flexibility of the traditional A* algorithm and dynamic window approach (DWA) in the face of largescale complex environmental path planning, a fusion algorithm based on the A* algorithm of the multiscale map approach(MMA) and the improved DWA algorithm is proposed. A multi-scale map set is established and an obstacle proportion factor is added to the heuristic function of the A* algorithm. The A* algorithm is used to calculate the optimal path on the coarse-scale map, and the optimal path is mapped onto …

Making The Most Of Artificial Intelligence And Large Language Models To Support Collection Development In Health Sciences Libraries, Ivan Portillo, David Carson

Library Articles and Research

This project investigated the potential of generative AI models in aiding health sciences librarians with collection development. Researchers at Chapman University’s Harry and Diane Rinker Health Science campus evaluated four generative AI models—ChatGPT 4.0, Google Gemini, Perplexity, and Microsoft Copilot—over six months starting in March 2024. Two prompts were used: one to generate recent eBook titles in specific health sciences fields and another to identify subject gaps in the existing collection. The first prompt revealed inconsistencies across models, with Copilot and Perplexity providing sources but also inaccuracies. The second prompt yielded more useful results, with all models offering helpful analysis …

Effects Of Technical Debt On Software Interoperability, Leonard Peter Binamungu

Tanzania Journal of Engineering and Technology (TJET)

Technical debt (TD) refers to sub-optimal development decisions that make the software costly to maintain and evolve. Examples of TD include structural complexity, violation of coding styles, and code complexity. Existing research has investigated the nature, causes and indicators of TD, as well as tools and strategies for managing TD. However, although TD could hinder the ability of a software system to be interoperable with others, existing literature has limited evidence on how TD affects systems interoperability. This limits the ability of software engineering teams to manage TD in ways that do not hinder systems interoperability. To fill this void, …

Using Satellite Image Segmentation To Detect Trails, Jeremy Reynolds

Theses and Dissertations

This masters thesis proposes an innovative approach to satellite image segmentation by focusing on the detection and mapping of walking, hiking, and biking trails. The motivation behind this project comes from the underexplored area in segmentation techniques for trail identification and offers potential benefits for urban planning, environmental monitoring, and public health. The problem statement addresses the need for a model that can differentiate between various trail types and other natural or man-made elements. The project aims for efficiency and scalability in processing satellite imagery across different compute hardware. The work details several stages: researching existing segmentation techniques, specifically road …

Multiparametric Mri Along With Machine Learning Predicts Prognosis And Treatment Response In Pediatric Low-Grade Glioma, Anahita Fathi Kazerooni, Adam Kraya, Komal Rathi, Meen Chul Kim, Arastoo Vossough, Nastaran Khalili, Ariana Familiar, Deep Gandhi, Neda Khalili, Varun Kesherwani, Debanjan Haldar, Hannah Anderson, Run Jin, Aria Mahtabfar, Sina Bagheri, Yiran Guo, Qi Li, Xiaoyan Huang, Yuankun Zhu, Alex Sickler, Matthew R Lueder, Saksham Phul, Mateusz Koptyra, Phillip Storm, Jeffrey Ware, Yuanquan Song, Christos Davatzikos, Jessica Foster, Sabine Mueller, Michael J Fisher, Adam Resnick, Ali Nabavizadeh

Department of Neurosurgery Faculty Papers

Pediatric low-grade gliomas (pLGGs) exhibit heterogeneous prognoses and variable responses to treatment, leading to tumor progression and adverse outcomes in cases where complete resection is unachievable. Early prediction of treatment responsiveness and suitability for immunotherapy has the potential to improve clinical management and outcomes. Here, we present a radiogenomic analysis of pLGGs, integrating MRI and RNA sequencing data. We identify three immunologically distinct clusters, with one group characterized by increased immune activity and poorer prognosis, indicating potential benefit from immunotherapies. We develop a radiomic signature that predicts these immune profiles with over 80% accuracy. Furthermore, our clinicoradiomic model predicts progression-free …

Scalable And Ethical Insider Threat Detection Through Data Synthesis And Analysis By Llms, Haywood Gelman, John Hastings

Research & Publications

Insider threats wield an outsized influence on organizations, disproportionate to their small numbers. This is due to the internal access insiders have to systems, information, and infrastructure. Signals for such risks may be found in anonymous submissions to public web-based job search site reviews. This research studies the potential for large language models (LLMs) to analyze and detect insider threat sentiment within job site reviews. Addressing ethical data collection concerns, this research utilizes synthetic data generation using LLMs alongside existing job review datasets. A comparative analysis of sentiment scores generated by LLMs is benchmarked against expert human scoring. Findings reveal …

Artificial Intelligence In Health Care: Business Opportunities And Ethical Challenges, Ankita Srivastava,, Marco Marabelli, Jeffrey Moriarty

Ethics Publication

Artificial intelligence (AI), and in particular generative AI (GAI), are making incredible progress toward automation. The pervasive and invasive nature of these technologies are affecting every industry, and health care is no exception. This paper summarizes insights derived from a panel that the Hoffman Center for Business Ethics and the Center for Health and Business at Bentley University hosted in March 2024. The panel invited three qualified health care professionals: Evan Carey, Susan Persky and John Torous. The panelists are all active in multiple aspects of AI in health care but represent a focus on the key areas of policy …

Comparing Unidirectional, Bidirectional, And Word2vec Models For Discovering Vulnerabilities In Compiled Lifted Code, Gary Mccully, John Hastings, Shengjie Xu, Adam Fortier

Research & Publications

Ransomware and other forms of malware cause significant financial and operational damage to organizations by exploiting long-standing and often difficult-to-detect software vulnerabilities. To detect vulnerabilities such as buffer overflows in compiled code, this research investigates the application of unidirectional transformer-based embeddings, specifically GPT-2. Using a dataset of LLVM functions, we trained a GPT-2 model to generate embeddings, which were subsequently used to build LSTM neural networks to differentiate between vulnerable and non-vulnerable code. Our study reveals that embeddings from the GPT-2 model significantly outperform those from bidirectional models of BERT and RoBERTa, achieving an accuracy of 92.5\% and an F1-score …

Ai 101: What It Can (And Can't) Do For You, April Sheppard

Staff and Faculty Scholarship

In this presentation, April defines AI, describes how it works, reviews some pros and cons, and finally discusses what AI can actually accomplish in its current iteration.

Chalkboards To Chatbots: Helping Faculty Harness Ai For The Future Of Higher Education, Justin C. Grace

Regis University Student Publications (comprehensive collection)

Integrating artificial intelligence (AI) into nursing education presented significant opportunities yet posed challenges due to varied faculty readiness. This Doctor of Nursing Practice (DNP) quality improvement (QI) project evaluated an educational intervention aimed at enhancing nursing faculty's AI proficiency and confidence at Regis University’s Rueckert-Hartman College for Health Professions. Using a mixed-methods, pre- and post-intervention design, validated surveys assessed changes in faculty perceptions, knowledge, and skills related to AI. The intervention included a digital toolkit with nine instructional videos demonstrating practical AI applications using FreedAI’s large language model, ChatGPT, supported by voiceover narration and closed captioning. Data analysis involved descriptive …

The Impacts Of Artificial Intelligence In Radiology, Misty Farmer, Wendy Trzyna

Theses, Dissertations and Capstones

Introduction: There has been significant growth in the use of Artificial Intelligence (AI) in the healthcare industry, especially in Medical Imaging. Radiology has been the clear frontrunner in the adoption of AI in medicine, due in part to the massive amount of digital data available for use in Deep Learning (DL) AI integration has the potential to solve multiple challenges in radiology, address workload issues and transform the field.

Purpose of the Study: The purpose of the research was to evaluate the impact of implementing Artificial Intelligence in radiology to determine if these technologies have had an impact …

Analysis Of Computational Approaches To Cognitive Diagnosis, Andrew Toussaint

Masters Theses & Specialist Projects

Access to good education is crucial to the well-being of individuals as well as communities. Recent technological advancements in the field of computer science show promise of generating precise descriptions of student cognitive states regarding specified knowledge concepts through a process called cognitive diagnosis. This can facilitate the creation of more targeted lesson plans and more personalized educational software. Experiments were conducted to evaluate the performance of four computerized cognitive diagnosis models. The models include three existing models: Item Response Theory, Neural Cognitive Diagnosis, Knowledge Association Neural Cognitive Diagnosis, and a proposed model, Concept Agnostic Knowledge Evaluation, which was used …

Artificial Intelligence In Health Care: Business Opportunities And Ethical Challenges, Ankita Srivastava, Marco Marabelli, Jeffrey Moriarty

Philosophy Faculty Publications

Artificial intelligence (AI), and in particular generative AI (GAI), are making incredible progress toward automation. The pervasive and invasive nature of these technologies are affecting every industry, and health care is no exception. This paper summarizes insights derived from a panel that the Hoffman Center for Business Ethics and the Center for Health and Business at Bentley University hosted in March 2024. The panel invited three qualified health care professionals: Evan Carey, Susan Persky and John Torous. The panelists are all active in multiple aspects of AI in health care but represent a focus on the key areas of policy …

Navigating Artificial Intelligence: How Traditional Midwestern Four-Year Higher Education Institution Distance Learning Programs Are Addressing Artificial Intelligence, Eric Samaritoni

Theses, Dissertations and Capstones

AI's rapid evolution and integration into society has had and will continue to influence how distance education programs teach their students profoundly. This study aimed to explore the opinions of higher education distance education Provost administrators, program managers, directors, department chairs, and other subject matter experts on the perceived impact of AI in the classroom. This study used a qualitative, phenomenological approach to examine how AI impacts students, faculty, program delivery, institutional policies, and university budgets. Semistructured interviews were conducted with 13 faculty members meeting the criteria to answer research questions based on their experience or observations. The study used …

Successfully Navigating The Disruption Ai Will Bring To Survey Research, David M. Rothschild, Trent D. Buskirk, Stephanie Eckman, D. Sunshine Hillygus, Frauke Kreuter, David Lazer

Information Technology & Decision Sciences Faculty Publications

Surveys are a core methodological tool in government, industry, and academia, providing essential data for theory development and evidence-based decision-making. As artificial intelligence continues its rapid advancement, it stands to fundamentally transform the entire survey lifecycle - from design and administration to analytics and reporting. Previous transitions to new technologies, such as telephone, internet, and non-probability surveys, led to divisions within the survey research community with real consequences for both the trajectory of research and trust in the industry. We believe the survey community should take proactive steps now to avoid similar challenges with AI integration. Specifically, our paper examines …

The Influence Of Generative Artificial Intelligence On Leadership: An Exploration Of Technology Professionals' Perceptions Regarding Leadership, Adaptation, And Organizational Culture In The Digital Age, Paul Thomas Herdman

Theses, Dissertations and Capstones

The emergence of generative artificial intelligence (GenAI) has introduced new complexities for organizational leadership, requiring technology professionals to adapt in real time to evolving digital tools, strategic demands, and cultural dynamics. Although prior research has examined AI’s broad influence on business processes, few studies have explored how technology leaders experience and interpret the leadership challenges and opportunities arising from GenAI. This qualitative dissertation addressed that gap by investigating the perceptions of senior technology professionals regarding GenAI’s influence on leadership roles, competencies, decision making, and organizational culture. Using Lanigan’s (1977) phenomenological method of human science, this study explored the lived experiences …

Fusion-Based Utilization And Synthesis Of Efficient Detections, Ethan C. Rogers, Parker H. Liberatore, Thomas G. James

Endeavors: Mississippi State Undergraduate Research Journal

This study aimed to develop hardware and software for an object detection fusion system, using three different sensors. The system was built and studied with the motivating application of autonomous drones searching for and detecting people in a search-and-rescue scenario. The system’s performance was compared to that of individual sensors deployed for the same task. The focus of the research was to prove the competence and benefits of a decision-level fusion method as it was applied to a lightweight object detection architecture, and the driving motivators behind the study were simplicity in implementation and good computational performance. In short, the …

Modeling Trust And Deception In Multi-Agent Reinforcement Learning Using The Werewolf Game, Pathikkumar Dharmeshbhai Patel

Computer Science and Engineering Theses - Archive

This thesis explores the emergence of trust, deception, and adaptive strategy in multi-agent reinforcement learning (MARL) environments using the social deduction game Werewolf as a simulation framework. In this environment, agents operate with hidden roles, incomplete information, and the need to reason about others’ intentions- mirroring the complexities of real-world social interactions. We present and evaluate two agent architectures: Agent vA, a symbolic, heuristic-based agent with probabilistic trust modeling and scalable memory structures; and Agent vB, a modular Q-learning agent that learns phase-specific policies through reinforcement. Agent vA relies on symbolic reasoning, bounded belief updates, and generalizable heuristics, while Agent …

Technology-Facilitated Abuse (Tfa): Analyzing Trends, Tactics, And Victim Responses On Reddit, Solomon G. Dandekar

Computer Science and Engineering Theses - Archive

The increasing integration of technology into daily life has provided numerous benefits but also significant risks, particularly when exploited by malicious actors in cases of technology facilitated abuse (TFA). Per- petrators can misuse technology to monitor, control, and intimidate their partners, random strangers, etc. exacerbating cycles of abuse. From location tracking and cellphone surveillance to smart device manipula- tion, spyware, and doxing, digital tools have become powerful instruments for coercion and control. This research project investigates the role of technology in stalking and harassment by analyzing discussions on a relevant subreddit where victims share their experiences, strategies for coping, and …

A Deep Reinforcement Learning Framework For Sequential Art Creation, Asmin Pothula

Computer Science and Engineering Theses - Archive

Most computational art systems rely on generative models that produce a complete artwork in a single pass, without capturing the gradual, decision-driven process through which human artists construct visual pieces. Prior research in sequential, stroke-based image generation, including differentiable neural painters and model-based reinforcement learning agents, has explored step-by-step creation, but these systems typically aim to reconstruct the input image within the same visual representation space, closely matching brushstrokes, textures, or colors to the target. In contrast, this thesis investigates sequential art creation in a different artistic representation, where the final artwork does not share the same visual form as …