Digital Commons Network^™

The Future Of Ai Regulation In Drug Development: A Comparative Analysis, Gabriela Lenarczyk, Timo Minssen, W. Nicholson Price Ii, Arti Rai

Articles

As artificial intelligence (AI) transforms drug development, regulatory frameworks are evolving to oversee its implementation, particularly at the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA). This paper makes three contributions to understanding emerging regulatory approaches. First, we offer a comparative analysis of how these agencies have responded to AI-driven advances, incorporating new US executive orders and the European Union (EU)’s AI Act. Second, we propose a novel analytical framework to understand regulatory divergence: the FDA’s flexible, dialog-driven model contrasts with the EMA’s structured, risk-tiered approach, reflecting broader institutional and political-economic differences. While the former encourages …

Improving Chatbot Interactions Through Ai-Driven Hate Speech Detection: Evolving To A Safer Digital Environment-Poster, Rata Gheorghita, Wellington Mariano

ICT

This project aims to explore how Machine Learning can contribute to a better digital interaction, mainly focusing on environments such as online chats, social media, and customer support as they are now an imperative part of daily communication. With this, concerns around hate speech in digital conversations is critical (Council of Europe, 2024). This study focus on the development of a Hate Speech Language Detection Chatbot using machine learning techniques. The key purpose of the chatbot is to monitor and detect harmful content in real time, reducing the need for manual intervention. The creation and implementation of such a tool …

Digital Evidence In Cybersecurity: Legal Constraints And Technical Hurdles, Steffany Butler

Master's Theses and Doctoral Dissertations

The increasing use of digital technologies such as Internet of Things devices, cloud computing, and networked information systems has made digital evidence essential to modern cybersecurity investigations. Digital evidence supports the reconstruction of cyber incidents, identification of responsible parties, and legal proceedings. However, its collection and preservation pose substantial technical and legal challenges. Rapid technological change, strong encryption, data volatility, cloud-based and distributed storage, and variations in jurisdictional privacy and evidentiary laws complicate the timely, reliable, and legally admissible acquisition of digital evidence. This study addresses how investigators can effectively collect and preserve digital evidence while maintaining data integrity, legal …

Assessing Iot Intrusion Detection Computational Costs When Using A Convolutional Neural Network, Mathew Nicho, Brian Cusack, Christopher D. Mcdermott, Shini Girija

All Works

IoT systems face vulnerabilities due to their data processing requirements and resource constraints. With 13 billion connected devices globally, this research investigates the economic viability of AI-based intrusion detection systems (IDSs), specifically analyzing the automation costs of implementing a Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) for classifying malicious sensor traffic. This study introduces an innovative framework that evaluates six distinct architectural components of CNN and LSTM: image input processing, convolutional layer operations, max pooling layer functionality, fully connected layer characteristics, softmax output activation, and class determination mechanisms. The framework employs six metrics: matrix size, feature vector number, …

A Spine-Specific Lexicon For The Sentiment Analysis Of Interviews With Adult Spinal Deformity Patients Correlate With Sf-36, Sf-36, And Odi Scores: A Pilot Study Of 25 Patients, Ross Gore, Michael M. Safaee, Christopher J. Lynch, Christopher P. Ames

VMASC Publications

Classic health-related quality of life (HRQOL) metrics are cumbersome, time-intensive, and subject to biases based on the patient’s native language, educational level, and cultural values. Natural language processing (NLP) converts text into quantitative metrics. Sentiment analysis enables subject matter experts to construct domain-specific lexicons that assign a value of either negative (−1) or positive (1) to certain words. The growth of telehealth provides opportunities to apply sentiment analysis to transcripts of adult spinal deformity patients’ visits to derive a novel and less biased HRQOL metric. In this study, we demonstrate the feasibility of constructing a spine-specific lexicon for sentiment analysis …

From Cyclones To Cybersecurity: A Call For Convergence In Risk And Crisis Communications Research, Ann Marie Reinhold, Ross J. Gore, Barry Ezell, Clemente I. Izurieta, Elizabeth A. Shanahan

VMASC Publications

Effective risk and crisis communication can improve health and safety and reduce harmful effects of hazards and disasters. A robust body of literature investigates mechanisms for improving risk and crisis communication. While effective risk and crisis communication strategies are equally desired across different hazard types (e.g., natural hazards, cyber security), the extent to which risk and crisis communication experts utilize the “lessons learned” from scientific domains outside their own is suspect. Therefore, we hypothesized that risk and crisis communication research is siloed according to academic disciplines at the detriment to the advancement of the field of risk communications research writ …

Are We Ready For Synchronous Conceptual Modeling In Augmented Reality? A Usability Study On Causal Maps With Hololens 2, Anish Shrestha, Philippe J. Giabbanelli

VMASC Publications

(1) Background: Participatory modeling requires combining individual views to create a shared conceptual model. While remote collaboration tools have enabled synchronous online modeling, they are limited to desktop settings. Augmented reality (AR) offers a new approach by potentially providing the sense of presence found in physical collaboration, which may better support participants in achieving the sense of presence found in physical locations, thus supporting them in negotiating meaning and building a shared model. (2) Methods: Building on prior works that developed technology, we performed a usability study with pairs of modelers to examine their ability at performing key conceptual modeling …

Quantum-Augmented Ai/Ml For O-Ran: Hierarchical Threat Detection For Synergistic Intelligence And Interpretability, Tan Le, Vanessa Le, Sachin Shetty

VMASC Publications

Open Radio Access Networks (O-RAN) enhance modularity and telemetry granularity but also widen the cybersecurity attack surface across disaggregated control, user and management planes. We propose a hierarchical defense framework with three coordinated layers—anomaly detection, intrusion confirmation, and multiattack classification—each aligned with O-RAN’s telemetry stack. Our approach integrates hybrid quantum computing and machine learning, leveraging amplitude- and entanglement-based feature encodings with deep and ensemble classifiers. We conduct extensive benchmarking across synthetic and real-world telemetry, evaluating encoding depth, architectural variants, and diagnostic fidelity. The framework consistently achieves near-perfect accuracy, high recall, and strong class separability. Multi-faceted evaluation across decision boundaries, probabilistic …

A Happy Medium?: Using Image Generators To Explore Solution-Focused Art Therapy’S Miracle Question, Daniel A. Hernried

Art Therapy | Master's Theses

This mixed methods, randomized, single-session study tested whether integrating text-to-image generations into Solution-Focused Brief Art Therapy alters therapeutic rapport and short-term outcomes relative to traditional artmaking materials. Participants were assigned by coin flip to create using either a text-to-image generator or convention media (23 per group), completing immediate and three-day follow-ups. Alliance was measured using DREAM (Dimensions of Regard, Empathy, and Authenticity Metric), and problems were rated pre/post; groups did not differ significantly on DREAM total or facets, and both modalities produced reliable pre-to-post reductions in problem severity. At the same time, process differences were pronounced: the AI condition showed …

Generative Ai And Finding The Law, Paul D. Callister

Faculty Works

Legal information science requires, among other things, principles and theories. The article states six principles or considerations that any discussion of generative AI large language models and their role in finding the law must include. The article concludes that law librarianship will increasingly become legal information science and require new paradigms. In addition to the six principles, the article applies ecological holistic media theory to understand the relationship of the legal community’s cognitive authority, institutions, techné (technology, medium and method), geopolitical factors, and the past and future to understand the changes in this information milieu. The article also explains generative …

Ai Lawyering Skills Trainers: Transforming Legal Education With Generative Ai, Alexandria Serra

Faculty Works

The integration of generative AI (GenAI) tools in legal education is not just an innovation—it's a transformative shift redefying how law students acquire and refine advocacy skills. This article examines AI’s critical role in modernizing legal education, emphasizing its potential to offer personalized, one-on-one coaching that enhances student learning and engagement. As AI reshapes the legal profession, law schools must evolve to prepare students for an AI-driven future. Serving as a practical guide, this article provides a step-by-step framework for educators and institutions to develop AI tools that simulate real-world courtroom scenarios and provide continuous, personalized feedback. It also highlights …

Improving Chinese Hate Speech Detection With Bert-Fasttext Fusion And Bert-Bilstm Fusion, Methini Ma

Chulalongkorn University Theses and Dissertations (Chula ETD)

Hate speech detection is an essential technique in the online environment, especially on social media platforms. This technique helps to create a safer space and reduce the risk of real-world harm. In Chinese, this task is particularly challenging because of unique linguistic structures and the frequent use of indirect expressions, sarcasm, homophones, character variants, and abbreviations. This study investigates how to improve Chinese hate speech detection by combining BERT with FastText and BERT with BiLSTM. There are six model variants that are configured: frozen BERT and fine-tuned BERT, each further extended with either FastText sentence embeddings or a clause-level BiLSTM. …

Efficient Task Scheduling In Cloud Infrastructures Using Dynamic Score-Based Allocation And Deep Q-Learning, Shadman Sakib

Graduate Theses/Dissertations

Cloud computing has grown rapidly in recent years, mainly due to the sharp increase in data transferred over the internet. This growth makes task scheduling a key and challenging part of cloud systems, as it helps distribute user requests across servers to minimize response time, prevent overloading, and ensure smooth user experience. This thesis proposes two novel approaches for dynamic task scheduling in cloud environments. First, a novel Score-Based Dynamic Load Balancing (SBDLB) strategy is developed, which leverages system parameters to allocate tasks efficiently across virtual machines (VMs) in data centers. SBDLB ensures balanced workload distribution by continuously evaluating VM …

Balancing Performance And Efficiency: An Autoencoder Approach To Api-Based Malware Detection, Selma Bouraoui

Graduate Theses/Dissertations

The constant evolution of malware presents a critical challenge to today's interconnected world. It poses an increasing threat on different scales, spanning from individuals, organizations to critical infrastructures such as government’s security. Hackers continuously develop new techniques to evade detection methods. When confronted with the high volume and variety of malware, conventional approaches tend to struggle to perform in robust, accurate and timely manner. This thesis explores the application of deep learning methods to improve malware detection and classification techniques. By analyzing API call sequences, the proposed approach leverages Autoencoders to compress high-dimensional malware data into more optimized representations that …

The Great Scrape: The Clash Between Scraping And Privacy, Daniel J. Solove, Woodrow Hartzog

Faculty Scholarship

Artificial intelligence (AI) systems depend on massive quantities of data, often gathered by “scraping”—the automated extraction of large amounts of data from the internet. A great deal of scraped data contains people’s personal information. This personal data provides the grist for AI tools such as facial recognition, deep fakes, and generative AI. Although scraping enables web searching, archiving of records, and meaningful scientific research, scraping for AI can also be objectionable and even harmful to individuals and society.

Organizations are scraping at an escalating pace and scale, even though many privacy laws are seemingly incongruous with the practice. In this …

An Integrated Machine Learning Approach For Identifying Emergency Rescue Messages On Social Media During Natural Disasters, Wael Khallouli, Jiang Li, Jingwei Huang, Ghaith Rabadi, Samuel Kovacic

School of Cybersecurity Faculty Publications

During large-scale disasters, emergency call centers are often overwhelmed by the large volume of rescue requests and calls for help. Consequently, people are turning to social media platforms to seek assistance. Rescue information posted on these platforms is extremely valuable for first responders to make informed rescue decisions. Therefore, the automatic identification of these requests from the vast amount of data posted on social media during crises is critical yet challenging. This work presents our ongoing research on applying deep learning techniques to extract actionable rescue information from social media during crises. We proposed a novel deep learning model that …

Connecting Worlds: Travelmate’S Bidding System For Personalized Travel Experiences, Suman Khadka

Williams Honors College, Honors Research Projects

TravelMate is a web platform that connects travelers with local guides through a personalized trip posting system along with real time bidding from freelancer guides. This platform utilizes Next.js for a responsive frontend, and Supabase for managing database and backend RESTful API, using PostgREST - a thin API layer on top of Postgres. This platform is ideal for anyone looking to explore new places with a freelancer guide and gain cultural insights from a local expert who understands the place deeply.

Silent Sabotage: Identifying And Preventing Cyber Attacks From Inside Actors, Autumn Groen

Williams Honors College, Honors Research Projects

Cyber-attacks are becoming increasingly common and damaging as technology advances each year. Many businesses cannot afford the latest security technologies, and even with the highest security measures, there can still be room for employee error or insider threats that are not taken into account. It is crucial to keep these factors in mind when securing a business network of any size or financial standing.This project will aim to simulate a business environment by first building a small network with three routers and a switch, and implementing some of the common best practices for network hardening from credible organizations like NIST …

User Interface For Custom Car Infotainment Systems, Dylan Miller

Williams Honors College, Honors Research Projects

The infotainment system is often considered one of the most functional and luxurious aspects of modern cars, containing functions that are useful to drivers in ways that range from convenient to safety-enhancing. However, modern infotainment systems can have some drawbacks such as making it more difficult to repair the vehicles they are in, helping to artificially limit the lifespan of the vehicles they are in, and not being present in most vehicles more than 15 years old. The software described in this paper, OpenQarUI, seeks to be a part of a solution to these problems. It is a piece of …

Advancing Generative Ai In 3d And 4d Spaces, Hasan Iqbal

Wayne State University Dissertations

Generative Artificial Intelligence (AI) has transformed how we synthesize, edit, and manipulate complex data, opening new possibilities for immersive digital content. This dissertation, titled “Advancing Generative AI in 3D and 4D Spaces,” investigates the capabilities and limitations of state-of-the-art generative models in static 3D scenes and time-aware 4D environments. In the 3D setting, scene editing is hindered by multi-view inconsistency and the high computational cost of per-scene retraining. To address these issues, the work introduces Free-Editor, a training free approach that utilizes an Edit Transformer to propagate a single edited view across all camera perspectives without additional optimisation, delivering prompt …

Artificial Intelligence And Digital Technologies In Finance: A Comprehensive Review, Soudeh Pazouki, Mohamad Jamshidi, Mirarmia Jalali, Arya Tafreshi

Finance Faculty Publications

This study explores the transformative impact of artificial intelligence (AI) and digital technologies on the financial technology (FinTech) industry, highlighting their role in fostering business growth, operational efficiency, and enhanced customer engagement. AI-driven strategies have unlocked new avenues for streamlining workflows, boosting productivity, and expanding financial inclusion by reaching underrepresented populations. However, these advancements also pose challenges, including navigating complex regulatory frameworks and adapting to the rapidly evolving technological landscape. This paper delves into the macroeconomic effects of AI, examining its influence on labor markets, consumer behavior, and organizational success. Furthermore, the paper discusses blockchain applications and their potential to …

Fares On Fairness: Using A Total Error Framework To Examine The Role Of Measurement And Representation In Training Data On Model Fairness And Bias, Patrick Oliver Schenk, Christoph Kern, Trent D. Buskirk

Data Science Faculty Publications

Data-driven decisions, often based on predictions from machine learning (ML) models are becoming ubiquitous. For these decisions to be just, the underlying ML models must be fair, i.e., work equally well for all parts of the population such as groups defined by gender or age. What are the logical next steps if, however, a trained model is accurate but not fair? How can we guide the whole data pipeline such that we avoid training unfair models based on inadequate data, recognizing possible sources of unfairness early on? How can the concepts of data-based sources of unfairness that exist in the …

Designing Ai To Foster Acceptance: Do Freedom To Choose And Social Proof Impact Ai Attitudes Among British And Arab Populations?, Sameha Alshakhsi, Mohamed Basel Almourad, Areej Babkir, Dena Al-Thani, Ala Yankouskaya, Christian Montag, Raian Ali

All Works

This study examines the impact of two key AI modalities–freedom of choice (FoC) and social proof (SP)–on public attitudes toward AI, focusing on cultural differences between UK and Arab participants. FoC refers to the option of selecting a non-AI, possibly human, alternative, while SP means knowing that others have used AI without issues. Four scenarios were designed, combining the presence or absence of these modalities. The context was a customer service chatbot for a telecommunications company, familiar to all participants. A total of 639 participants (316 British and 323 Arab) were introduced to the modalities and then the scenarios in …

Data-Driven Public Transport Planning For Dublin: A Clustering And Forecasting Approach, Magdalena Burtinik Urueta, Mirae Yu

ICT

Dublin has been experiencing severe traffic congestion due to rapid economic and population growth, with residents losing an average of 158 hours per year in traffic during rush hour (Europe Data, 2025). A 2022 European Commission study found that 76% of Irish people use a car as their primary mode of transport on a typical day—an 8% increase from 2019, compared to the EU average of 47% (MacCarthaigh, 2022).

This project proposes a data-driven approach to identifying current transport accessibility gaps and forecasting future population growth across Dublin to support sustainable infrastructure development. Using Ireland’s Census data, an unsupervised method …

Development Of A Deep Learning Model For Synthetic Vs. Real Image Classification Synthetic Vs. Real Image Classification-Poster, Bernardo Gandara, Ignacio Varela

ICT

This project develops a deep learning model to classify images as either AI-generated or real, addressing the growing challenge of synthetic media detection. Using the DeepGuardDB dataset and guided by the CRISP-DM methodology, we implemented and compared three Convolutional Neural Networks (CNNs) architectures via transfer learning. The best-performing model was further optimised using hyperparameter tuning and fine-tuning techniques The resulting model achieved strong accuracy and generalisation, making it a promising candidate for real-time deployment and practical use across diverse industries.

Detecting Fake News Using Ai, Gustavo Lambert, Lucas Schultz

ICT

This is a document that presents a strategic analysis of the project “Detecting Fake News using AI”. Developed as part of the BSc (Hons) in Computing in IT at CCT College Dublin. The goal is to analyse the potential advantages, exploiting the viability and the impact of applying Artificial intelligence to check, verify and alert about misinformation found and to answer the question “How can Artificial Intelligence be leveraged to accurately detect and combat fake news while ensuring data privacy and compliance with regulations?” and “To what extent can AI-driven misinformation detection help reduce the spread of fake news on …

Development Of A Deep Learning Model For Synthetic Vs. Real Image Classification Synthetic Vs. Real Image Classification, Bernardo Gandara, Ignacio Varela

ICT

The advancement of generative AI technologies has made it increasingly difficult to distinguish synthetic images from authentic ones. This capstone project addresses the challenge by developing a binary image classification model using deep learning techniques to differentiate AI-generated images from real photographs. Guided by the CRISP-DM methodology, we employed the DeepGuardDB dataset, consisting of 13,000 balanced image samples, evenly split between real and synthetic sources. We implemented and compared three Convolutional Neural Network (CNN) architectures through transfer learning, standardising input pipelines and integrating custom classification heads. Following a performance evaluation across multiple metrics, the best-performing model was selected for further …

Digital Health Using Data Science, Padmapriya Velupillai Mekandan, Paramita Basak Upama, Masud Rabbani, Amity Ali, Sheikh Iqbal Ahamed

Computer Science Faculty Research and Publications

The digitization of healthcare has led to an unprecedented growth in health-related data, offering new opportunities to transform clinical decision-making, disease prediction, and patient engagement. However, extracting actionable insights from diverse data sources such as electronic health records, wearable devices, and mobile health apps requires a fusion of domain knowledge in healthcare and technical expertise in data science. This paper presents a structured, interdisciplinary framework for digital health that emphasizes practical strategies for data acquisition, preprocessing, feature engineering, machine learning, and ethical data use. The model promotes a holistic understanding of digital health challenges and opportunities, preparing future professionals to …

A New Deepfake Detection Method With No-Reference Image Quality Assessment To Resist Image Degradation, Jiajun Jiang, Wen-Chao Yang, Chung-Hao Chen, Timothy Young

Electrical & Computer Engineering Faculty Publications

Deepfake technology, which utilizes advanced AI models such as Generative Adversarial Networks (GANs), has led to the proliferation of highly convincing manipulated media, posing significant challenges for detection. Existing detection methods often struggle with the low-quality or compressed press, which is prevalent on social media platforms. This paper proposes a novel Deepfake detection framework that leverages No-Reference Image Quality Assessment (NRIQA) techniques, specifically, BRISQUE, NIQE, and PIQUE, to extract quality-related features from facial images. These features are then classified using a Support Vector Machine (SVM) with various kernel functions. We evaluate our method under both intra-dataset and cross-dataset settings. For …

Multitec: A Data-Driven Multimodal Short Video Detection Framework For Healthcare Misinformation On Tiktok, Lanyu Shang, Yang Zhang, Yawen Deng, Dong Wang

Computer Science Faculty Works

With the prevalence of social media and short video sharing platforms (e.g., TikTok, YouTube Shorts), the proliferation of healthcare misinformation has become a widespread and concerning issue that threatens public health and undermines trust in mass media. This paper focuses on an important problem of detecting multimodal healthcare misinformation in short videos on TikTok. Our objective is to accurately identify misleading healthcare information that is jointly conveyed by the visual, audio, and textual content within the TikTok short videos. Three critical challenges exist in solving our problem: i) how to effectively extract information from distractive and manipulated visual content in …