Artificial Intelligence and Robotics Commons^™

Enabling The Clinical Application Of Artificial Intelligence In Genomics: A Perspective Of The Amia Genomics And Translational Bioinformatics Workgroup, Nephi A Walton, Radha Nagarajan, Chen Wang, Murat Sincan, Robert R Freimuth, David B Everman, Derek C Walton, Scott P Mcgrath, Dominick J Lemas, Panayiotis V Benos, Alexander V Alekseyenko, Qianqian Song, Ece Gamsiz Uzun, Casey Overby Taylor, Alper Uzun, Thomas Nate Person, Nadav Rappoport, Zhongming Zhao, Marc S Williams

Faculty, Staff and Student Publications

OBJECTIVE: Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association-Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space.

PROCESS: A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members.

CONCLUSIONS: Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of …

Detection And Classification Of Sporadic E Using Convolutional Neural Networks, J. A. Ellis, Daniel J. Emmons, M. B. Cohen

Faculty Publications

In this work, convolutional neural networks (CNN) are developed to detect and characterize sporadic E (E_s), demonstrating an improvement over current methods. This includes a binary classification model to determine if Es is present, followed by a regression model to estimate the Es ordinary mode critical frequency (foEs), a proxy for the intensity, along with the height at which the Es layer occurs (hEs). Signal-to-noise ratio (SNR) and excess phase profiles from six Global Navigation Satellite System (GNSS) radio occultation (RO) missions during the years 2008–2022 are used as the inputs of the model. Intensity (foEs) and the …

Meet Scite, “Chatgpt For Research”, A New Artificial Intelligence (Ai) Tool Available Via Clemson Libraries, Jennifer Groff, Shelby Carroll

Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI

No abstract provided.

Hiccups And Hallucinations: Critically Engaging Ai In The Design Classroom, Drew Sisk

Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI

No abstract provided.

The Use And Misuse Of Generative Ai For Photos And Imagery, Erica B. Walker

Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI

No abstract provided.

Academic Ethics In Ai-Assisted Writing: A Writing Center-Informed Approach, John Falter

Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI

No abstract provided.

Navigating Anxiety And Activity: Generative Ai And Writing Support, Chelsea J. Murdock

Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI

No abstract provided.

Using Generative Artificial Intelligence For Engaged Student Learning, Janice G. Lanham, Charlotte Branyon

Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI

No abstract provided.

Ghostwriter To Co-Author: Helping Students Leverage Ai In The Classroom, Ishani Banerji

Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI

No abstract provided.

Optimizing Ideation And Digital Prepress Workflows With Ai Integration, Carl N. Blue

Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI

No abstract provided.

Requiring Students To Integrate Chatgptinto Course Assignments, Mark Small, Venera Balidemaj

Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI

No abstract provided.

Using Ai In The Teacher Preparation Programs And Social Studies Classrooms, Brandon Beck

Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI

No abstract provided.

Language Portraits: A Space To Explore Identities In A Graduate Course, Hazel Vega

Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI

No abstract provided.

Ai-Enhanced Education: Fostering Creativity, Efficiency, And Future-Ready Skills, Rodger Eugene Bishop

Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI

No abstract provided.

Critical Ai Engagement: Crafting Assignments That Encourage Productive Engagement With Ai, Carl Ehrett

Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI

No abstract provided.

Challenging Others When Posting Misinformation: A Uk Vs. Arab Cross-Cultural Comparison On The Perception Of Negative Consequences And Injunctive Norms, Muaadh Noman, Selin Gurgun, Keith Phalp, Preslav Nakov, Raian Ali

Natural Language Processing Faculty Publications

This study investigates the factors influencing the willingness to challenge misinformation on social media across two cultural contexts, the United Kingdom (UK) and Arab countries. A total of 462 participants completed an online survey (250 UK, 212 Arabs). The analysis revealed that three types of negative consequences (relationship cost, negative impact on the person being challenged, futility) and also injunctive norms influence the willingness to challenge misinformation. Cross-cultural comparisons using t-tests showed significant differences between the UK and the Arab countries in all factors except the injunctive norms. Multiple regression analyses identified differences between the UK and Arab participants concerning …

Texcot22, Md Ahmed Al Muzaddid, William J. Beksi

Computer Science and Engineering Datasets - Archive

The TexCot22 dataset is a set of cotton crop video sequences for training and testing multi-object tracking methods. Each tracking sequence is 10 to 20 seconds in length. The dataset contains of a total of 30 sequences of which 17 are for training and the remaining 13 are for testing. Among the training sequences, 2 of them consist of roughly 5,000 annotated images, which can be used to train a cotton boll detection model. The video sequences were captured at 4K resolution and at distinct frame rates (e.g., 10, 15, 30). There are typically 2 to 10 cotton bolls per …

Novel Techniques In Imaging Congenital Heart Disease: Jacc Scientific Statement, Ritu Sachdeva, Aimee K Armstrong, Rima Arnaout, Lars Grosse-Wortmann, B Kelly Han, Luc Mertens, Ryan A Moore, Laura J Olivieri, Anitha Parthiban, Andrew J Powell

Faculty, Staff and Students Publications

Recent years have witnessed exponential growth in cardiac imaging technologies, allowing better visualization of complex cardiac anatomy and improved assessment of physiology. These advances have become increasingly important as more complex surgical and catheter-based procedures are evolving to address the needs of a growing congenital heart disease population. This state-of-the-art review presents advances in echocardiography, cardiac magnetic resonance, cardiac computed tomography, invasive angiography, 3-dimensional modeling, and digital twin technology. The paper also highlights the integration of artificial intelligence with imaging technology. While some techniques are in their infancy and need further refinement, others have found their way into clinical workflow …

Creative Technologies: A Conversation With Roy Magnuson, Roy Magnuson, Maureen Russell

Faculty Publications - Music

[In lieu of an abstract, the introduction is provided.] Today I am speaking with Roy Magnuson, Associate Professor Creative Technologies in the School of Music at Illinois State University (ISU). (see Figure 1) His music has been performed throughout the United States and Europe at venues such as the World Saxophone Congress, WASBE, CBDNA, the RED NOTE New Music Festival, and the Robb Composers’ Symposium. Magnuson is also the creator of the virtual reality composition software solsticeVR and the conducting software RibbonsVR. He is a member of ASCAP, and his music is recorded on Albany Records and NAXOS.

Racism Detection In Tweets, Ndjeuha Gihane

All Graduate Projects

Since the advent of social networks in 1997, businesses and people’s lives have changed in a good way. From promoting companies to reaching out to friends and family, social networking has become a major element in our lives. X is a very popular platform that is used by many people, including celebrities and politicians, to communicate with their audience. Like other platforms, X is not spared by the racism contained in the tweets. We should be able to catch those racist comments on any social media and block the accounts of those responsible for them. To do so, we have …

Crafting Effective Prompts: Leveraging Generative Ai In Libraries, April Sheppard, Kristin Flachsbart

Staff and Faculty Scholarship

Discover how strategic prompt design can help you harness the power of generative artificial intelligence (AI) in your library. Through a series of examples, the presenters will demonstrate the impact that well-crafted prompts can have on the quality and relevance of AI-generated outputs.

A Comparison Of Machine Learning Surrogate Models Of Street-Scale Flooding In Norfolk, Virginia, Diana Mcspadden, Steven Goldenberg, Binata Roy, Malachi Schram, Jonathan L. Goodall, Heather Richter

Community & Environmental Health Faculty Publications

Low-lying coastal cities, exemplified by Norfolk, Virginia, face the challenge of street flooding caused by rainfall and tides, which strain transportation and sewer systems and can lead to personal and property damage. While high-fidelity, physics-based simulations provide accurate predictions of urban pluvial flooding, their computational complexity renders them unsuitable for real-time applications. Using data from Norfolk rainfall events between 2016 and 2018, this study compares the performance of a previous surrogate model based on a random forest algorithm with two deep learning models: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The comparison of deep learning to the random …

Usage And Knowledge Of Online Tools And Generative Ai: A Survey Of Students, Rahul R. Divekar Phd, Lisette Gonzalez, Sophia Guerra, Natasha Boos

Department of Experience Design (XD) Faculty Publications

Artificial Intelligence (AI) tools like ChatGPT are poised to transform student and educator workflows in higher education. However, there is less documentation on the range of tools students in higher education use, how they use them and in coordination with other online tools for learning, and their expertise using AI tools. We present a mixed-method analysis of a survey conducted at a doctoral-granting university in the United States investigating the adoption of AI tools in the context of other technologies. The findings include how the students used GenAI tools in light of other on-line technologies, their perception of expertise on …

Developing Policies For The Ethical Use Of Artificial Intelligence In Higher Education And Libraries, April Sheppard, Matthew Mayton

Staff and Faculty Scholarship

This presentation will provide sample artificial intelligence policy language from various higher education institutions and academic libraries. Topics covered will include the acceptable use of AI in the classroom, the role of faculty in making AI-related decisions, syllabus statements, AI use and detection, AI literacy, and library policies regarding AI. Participants will be able to compare and contrast policies to help them develop their own policies that work for their unique organization.

Towards A Transparency-Based, Value-Sensitive Design Solution For Bias In Self-Driving Cars: An Ethical Violation Assessment And Risk Analysis Framework On Consumer-Held Values, Nada Ahmad Madkour

Master's Theses and Doctoral Dissertations

Background: The rapid growth of automated systems and artificial intelligence (AI), particularly, self-driving cars (SDCs), has attracted significant investments and can potentially contribute to humanity’s flourishing. However, before widespread adoption, it is important to address ethical violations such as bias in AI, highlighted by many real-world cases of bias in AI leading to unfair outcomes in tools like facial recognition, hiring software, and pedestrian detection. Bias in AI can lead to potentially fatal outcomes in SDCs, emphasizing the need for a thorough examination of bias in SDCs.

Purpose: To enhance AI ethics by providing tools to support transparency and value- …

Survey Of Memory Consolidation Techniques For Video Question Answering, Matthew Couts, Pha Nguyen, Khoa Luu

Inquiry: The University of Arkansas Undergraduate Research Journal

Video Question Answering (VideoQA) is a field of research focused on developing models that can engage in natural conversations with humans about the content of videos. Currently, the most successful approaches involve analyzing videos frame-by-frame, which is computationally and memory-intensive. To imitate human memory, the Atkinson-Shiffrin memory model can formulate the machine’s video understanding capability through Vision-Language Models. Reducing the number of frames processed by the model is a crucial operation in this approach category and can be handled by a memory consolidation algorithm. The memory consolidation algorithm should be able to determine the keyframes to transfer from short-term to …

The Measure Of Efficiency And Effectiveness When Using Artificial Intelligence (Ai) In Radiology, Jordan Watts

Theses, Dissertations and Capstones

Introduction: The use of artificial intelligence in radiology has helped radiologists identify patterns and abnormalities in medical images to diagnose and treat patients. Deep learning and machine learning algorithms have been used to assist physicians in detecting features that are not noticeable to the human eye. The FDA has approved almost 400 AI algorithms for radiology and estimated that the market for AI in medical imaging would grow from $21.48 billion in 2018 to $264.85 billion in 2028.

Purpose of the Study: The purpose of this research was to evaluate the use of artificial intelligence in radiology to determine its …

Machine Intelligence With Associative Memory And Event-Driven Transaction History, Rao Mikkilineni, W. Patrick Kelly

Barowsky School of Business | Faculty Scholarship

Digital machine intelligence has evolved from its inception in the form of computation of numbers to AI, which is centered around performing cognitive tasks that humans can perform, such as predictive reasoning or complex calculations. The state of the art includes tasks that are easily described by a list of formal, mathematical rules or a sequence of event-driven actions such as modeling, simulation, business workflows, interaction with devices, etc., and also tasks that are easy to do “intuitively”, but are hard to describe formally or as a sequence of event-driven actions such as recognizing spoken words or faces. While these …

Bert-Based Detection Of Ai-Generated Text For Content Verification, Soham Biren Katlariwala

2024 REYES Proceedings

With advancements in AI-driven natural language generation, distinguishing between AI-generated and human-written text has become imperative for ensuring content authenticity across industries. This study explores the effectiveness of Bidirectional Encoder Representations from Transformers (BERT) in addressing this classification challenge. Utilizing a diverse dataset and robust preprocessing techniques, BERT achieved a peak F1-score of 0.94364, outperforming traditional models such as Logistic Regression and Support Vector Machines. The results underscore the potential of transformer-based models in addressing real-world con- tent verification problems. Future enhancements include fine-tuning and expanding datasets for greater generalizability.

Predicting Compressive Strength Of Concrete Incorporating Fly Ash, Blast Furnace Slag, And Superplasticizer Using Machine Learning Techniques, Muhammad Faisal Yaqub

2024 REYES Proceedings

Concrete is the second most essential element in the construction industry, and its strength requirements vary based on the specific conditions of each project. However, determining the compressive strength of concrete involves laboratory tests, which wastes a lot of time and money. Researchers have developed machine learning models that predict the compressive strength of cement-based concrete having various mixes. In this research, the compressive strength of concrete incorporating fly ash, blast furnace slag, and superplasticizer is predicted using different machine learning models, namely, Linear Regression, Random Forest Regression, Decision Tree Regression, Extreme Gradient Boosting, Light Gradient Boosting, AdaBoost, and CatBoost …