Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Keyword
-
- Deep learning (2)
- Machine learning (2)
- Natural language processing (2)
- Simulation (2)
- Analytical data architecture (1)
-
- Attention (1)
- Autonomous vehicles (1)
- Avoid-ability (1)
- Biased competition (1)
- Binding (1)
- Biologically-inspired eep learning (1)
- CARLA simulator (1)
- Conceptual modeling (1)
- Conceptualization (1)
- Conflict Risk (1)
- Convolutional neural networks (1)
- Evaluation metrics (1)
- FFA (1)
- Face recognition (1)
- Feedback (1)
- Few-shot learning (1)
- Fleet management (1)
- Flight data (1)
- LiDAR (1)
- MRI (1)
- Machine learning methods (1)
- Medical image segmentation (1)
- Migration (1)
- Pedestrians (1)
- Point cloud (1)
- Publication
-
- Cybersecurity Undergraduate Research Showcase (10)
- Modeling, Simulation and Visualization Student Capstone Conference (5)
- MODVIS Workshop (4)
- I-GUIDE Forum (3)
- African Conference on Information Systems and Technology (2)
-
- Annual Symposium on Biomathematics and Ecology Education and Research (2)
- SDSU Data Science Symposium (2)
- Symposium of Student Scholars (2)
- UNO Student Research and Creative Activity Fair (2)
- AMNET XX Conferencia Internacional (1)
- Adventist Human-Subject Researchers Association (1)
- LSU Health New Orleans Symposium Series on Artificial Intelligence (1)
- National Training Aircraft Symposium (NTAS) (1)
- Research Day (1)
- Thinking Matters Symposium (1)
- File Type
Articles 1 - 30 of 38
Full-Text Articles in Physical Sciences and Mathematics
The Transformative Integration Of Artificial Intelligence With Cmmc And Nist 800-171 For Advanced Risk Management And Compliance, Mia Lunati
Cybersecurity Undergraduate Research Showcase
This paper explores the transformative potential of integrating Artificial Intelligence (AI) with established cybersecurity frameworks such as the Cybersecurity Maturity Model Certification (CMMC) and the National Institute of Standards and Technology (NIST) Special Publication 800-171. The thesis argues that the relationship between AI and these frameworks has the capacity to transform risk management in cybersecurity, where it could serve as a critical element in threat mitigation. In addition to addressing AI’s capabilities, this paper acknowledges the risks and limitations of these systems, highlighting the need for extensive research and monitoring when relying on AI. One must understand boundaries when integrating …
The Analysis And Impact Of Artificial Intelligence On Job Loss, Ava Baratz
The Analysis And Impact Of Artificial Intelligence On Job Loss, Ava Baratz
Cybersecurity Undergraduate Research Showcase
This paper illustrates the analysis and impact of Artificial Intelligence (AI) on job loss across various industries. This paper will discuss an overview of AI technology, a brief history of AI in industry, the positive impacts of AI, the negative impacts of AI on employment, AI considerations that contribute to job loss, the future outlook of AI, and employment loss mitigation strategies Various professional source articles and reputable blog posts will be used to finalize research on this topic.
Rising Threat - Deepfakes And National Security In The Age Of Digital Deception, Dougo Kone-Sow
Rising Threat - Deepfakes And National Security In The Age Of Digital Deception, Dougo Kone-Sow
Cybersecurity Undergraduate Research Showcase
This paper delves into the intricate landscape of deepfakes, exploring their genesis, capabilities, and far-reaching implications. The rise of deepfake technology presents an unprecedented threat to American national security, propagating disinformation and manipulation across various media formats. Notably, deepfakes have evolved from a historical backdrop of disinformation campaigns, merging with the advancements of artificial intelligence (AI) and machine learning to craft convincing but false multimedia content.
Examining the capabilities of deepfakes reveals their potential for misuse, evidenced by instances targeting individuals, companies, and even influencing political events like the 2020 U.S. elections. The paper highlights the direct threats posed by …
Integrating Ai Into Uavs, Huong Quach
Integrating Ai Into Uavs, Huong Quach
Cybersecurity Undergraduate Research Showcase
This research project explores the application of Deep Learning (DL) techniques, specifically Convolutional Neural Networks (CNNs), to develop a smoke detection algorithm for deployment on mobile platforms, such as drones and self-driving vehicles. The project focuses on enhancing the decision-making capabilities of these platforms in emergency response situations. The methodology involves three phases: algorithm development, algorithm implementation, and testing and optimization. The developed CNN model, based on ResNet50 architecture, is trained on a dataset of fire, smoke, and neutral images obtained from the web. The algorithm is implemented on the Jetson Nano platform to provide responsive support for first responders. …
New Paths Of Attacks: Revealing The Adaptive Integration Of Artificial Intelligence In Evolving Cyber Threats Targeting Social Media Users And Their Data, Larry Teasley
Cybersecurity Undergraduate Research Showcase
The intersection between artificial intelligence tools and social media has opened doors to numerous opportunities and risks. This research delves into the escalating threat landscape in a society heavily dependent on social media. Despite the efforts by social media companies and cybersecurity professionals to mitigate cyber-attacks, the constant advancements of new technologies render social media platforms increasingly vulnerable. Malicious actors exploit generative AI to collect user data, enhancing cyber threats on social media. Notably, generative AI amplifies phishing attacks, disseminates false information, and propagates propaganda, posing substantial challenges to platform security. Ease access to large language models (LLMs) further complicates …
Knowing Just Enough To Be Dangerous: The Sociological Effects Of Censoring Public Ai, David Hopkins
Knowing Just Enough To Be Dangerous: The Sociological Effects Of Censoring Public Ai, David Hopkins
Cybersecurity Undergraduate Research Showcase
This paper will present the capabilities and security concerns of public AI, also called generative AI, and look at the societal and sociological effects of implementing regulations of this technology.
Machine Learning In Minecraft: Proof Of Concept For Object Detection Oriented Autonomous Bots In Minecraft, John Merkin
Machine Learning In Minecraft: Proof Of Concept For Object Detection Oriented Autonomous Bots In Minecraft, John Merkin
Symposium of Student Scholars
Machine learning provides new methods of problem solving through applied pattern recognition. An interesting challenge is to utilize machine learning in the automation of tasks and behaviors in virtual environments. Minecraft is an open-world, sandbox style game giving players nearly limitless freedom to alter a procedurally generated world. In the survival game mode, the player must collect resources to craft tools and build structures. The collection of resources can be tedious, so this project seeks to automate the standard initial task of collecting wood. By combining a convolutional neural network with API, a bot can collect resources while remaining scalable …
Toxic Comment Classification Project, Brandon Solon
Toxic Comment Classification Project, Brandon Solon
Symposium of Student Scholars
The digital landscape has blossomed thanks to the surge of online platforms, boosting the variety and volume of user-created content. But it's not without its shadows; cyberbullying and hate speech have also proliferated, making web spaces less safe. At our project centerstage, we work on creating a machine learning model skilled at spotting toxic comments with precision - this way contributing towards an internet society free from fear or discomfort. We put well-documented datasets to good use along with careful preprocessing maneuvers while trialing diverse machina-learning protocols as part of constructing solid classification architecture for usages beyond current limitations within …
Large Language Model Use Cases For Instruction, Plus A Primer On Prompt Engineering, Roy Haggerty, Justin Cochran
Large Language Model Use Cases For Instruction, Plus A Primer On Prompt Engineering, Roy Haggerty, Justin Cochran
LSU Health New Orleans Symposium Series on Artificial Intelligence
AMA Credit Designation Statement: The Louisiana State University School of Medicine, New Orleans designates this live activity for a maximum of 1.0 AMA PRA Category 1 Credit™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
NCPD Credit Designation Statement: Nursing participants may earn 1.0 NCPD contact hours. Each nursing participant must be present for the entire session for which NCPD contact hours are requested and must complete an evaluation of the session to receive credit.
Physics-Informed Neural Networks For Agent-Based Epidemiological Model Calibration, Alvan C. Arulandu, Padmanabhan Seshaiyer
Physics-Informed Neural Networks For Agent-Based Epidemiological Model Calibration, Alvan C. Arulandu, Padmanabhan Seshaiyer
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Disease Informed Neural Network And Mathematical Modeling Of Covid-19 With Human Intervention, Jeremis Morales-Morales, Alonso Gabriel Ogueda, Carmen Caiseda, Padmanabhan Seshaiyer
Disease Informed Neural Network And Mathematical Modeling Of Covid-19 With Human Intervention, Jeremis Morales-Morales, Alonso Gabriel Ogueda, Carmen Caiseda, Padmanabhan Seshaiyer
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Graph Transformer Network For Flood Forecasting With Heterogeneous Covariates, Jimeng Shi, Vitalii Stebliankin, Zhaonan Wang, Shaowen Wang, Giri Narasimhan
Graph Transformer Network For Flood Forecasting With Heterogeneous Covariates, Jimeng Shi, Vitalii Stebliankin, Zhaonan Wang, Shaowen Wang, Giri Narasimhan
I-GUIDE Forum
Floods can be very destructive causing heavy damage to life, property, and livelihoods. Global climate change and the consequent sea-level rise have increased the occurrence of extreme weather events, resulting in elevated and frequent flood risk. Therefore, accurate and timely flood forecasting in coastal river systems is critical to facilitate good flood management. However, the computational tools currently used are either slow or inaccurate. In this paper, we propose a Flood prediction tool using Graph Transformer Network (FloodGTN) for river systems. More specifically, FloodGTN learns the spatio-temporal dependencies of water levels at different monitoring stations using Graph Neural Networks (GNNs) …
Curriculum Design Of Artificial Intelligence And Sustainability In Secondary School, Jinyi Cai, Mei-Po Kwan, Chunyu Hou, Dong Liu, Yeung Yam
Curriculum Design Of Artificial Intelligence And Sustainability In Secondary School, Jinyi Cai, Mei-Po Kwan, Chunyu Hou, Dong Liu, Yeung Yam
I-GUIDE Forum
Artificial Intelligence is revolutionizing numerous sectors with its transformative power, while at the same time, there is an increasing sense of urgency to address sustainability challenges. Despite the significance of both areas, secondary school curriculums still lack comprehensive integration of AI and sustainability education. This paper presents a curriculum designed to bridge this gap. The curriculum integrates progressive objectives, computational thinking competencies and system thinking components across five modules—awareness, knowledge, interaction, empowerment and ethics—to cater to varying learner levels. System thinking components help students understand sustainability in a holistic manner. Computational thinking competencies aim to cultivate computational thinkers to guide …
Reducing Uncertainty In Sea-Level Rise Prediction: A Spatial-Variability-Aware Approach, Subhankar Ghosh, Shuai An, Arun Sharma, Jayant Gupta, Shashi Shekhar, Aneesh Subramanian
Reducing Uncertainty In Sea-Level Rise Prediction: A Spatial-Variability-Aware Approach, Subhankar Ghosh, Shuai An, Arun Sharma, Jayant Gupta, Shashi Shekhar, Aneesh Subramanian
I-GUIDE Forum
Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important because sea-level rise affects millions of people in coastal communities and beyond due to climate change's impacts on polar ice sheets and the ocean. This problem is challenging due to spatial variability and unknowns such as possible tipping points (e.g., collapse of Greenland or West Antarctic ice-shelf), climate feedback loops (e.g., clouds, permafrost thawing), future policy decisions, and human actions. Most existing climate modeling approaches use the same set of weights globally, during either regression or …
Codesigning A Big Data Analytic Tool For Girl Child Learner Drop Out From Eastern Cape Province -South Africa, Nobert Rangarirai Jere, Nosipho Carol Mavuso, Nelly Sharpley
Codesigning A Big Data Analytic Tool For Girl Child Learner Drop Out From Eastern Cape Province -South Africa, Nobert Rangarirai Jere, Nosipho Carol Mavuso, Nelly Sharpley
African Conference on Information Systems and Technology
Developing sustainable solutions is critical for adoption of digital solutions. As the high number of learners dropping out of school continues to increase, it is critical to find innovative ways of predicting and preventing high drop out. Current literature has documented a number of factors that influence learner drop out. Innovative ideas, techniques and activities have been undertaken to motivate learners to stay at school. It is unfortunate that most of the initiatives have not helped to avoid drop out of learners. The study is based on a mixed approached that was used targeting female learns from Oliver Tambo District …
A Social Profile-Based E-Learning Model, Xola Ntlangula
A Social Profile-Based E-Learning Model, Xola Ntlangula
African Conference on Information Systems and Technology
Many High Education Institutions (HEIs) have migrated to blended or complete online learning to cater for less interruption with learning. As such, there is a growing demand for personalized e-learning to accommodate the diversity of students' needs. Personalization can be achieved using recommendation systems powered by artificial intelligence. Although using student data to personalize learning is not a new concept, collecting and identifying appropriate data is necessary to determine the best recommendations for students. By reviewing the existing data collection capabilities of the e-learning platforms deployed by public universities in South Africa, we were able to establish the readiness of …
Rede Neural Para A Predição De Óbito Utilizando Biomarcadores De Pacientes Em Hemodiálise No Sistema Único De Saúde., Isadora Badalotti-Teloken
Rede Neural Para A Predição De Óbito Utilizando Biomarcadores De Pacientes Em Hemodiálise No Sistema Único De Saúde., Isadora Badalotti-Teloken
AMNET XX Conferencia Internacional
No abstract provided.
Utilizing Few-Shot Meta Learning Algorithms For Medical Image Segmentation, Nick Littlefield
Utilizing Few-Shot Meta Learning Algorithms For Medical Image Segmentation, Nick Littlefield
Thinking Matters Symposium
Deep learning models can be difficult to train because they require large amounts of data, which we usually do not have or are too expensive to get or annotate. To overcome this problem, we can use few-shot meta-learning, which allows us to train deep learning models with little data. Using a few examples, meta-learning, or learning-to-learn, aims to use the experience learned during training to generalize to unknown tasks. Medical imaging is an industry where it is particularly useful, as there is limited publicly available data due to patient privacy concerns and annotating costs.
This project examines how meta-learning performs …
What Effects Do Large Language Models Have On Cybersecurity, Josiah Marshall
What Effects Do Large Language Models Have On Cybersecurity, Josiah Marshall
Cybersecurity Undergraduate Research Showcase
Large Language Models (LLMs) are artificial intelligence (AI) tools that can process, summarize, and translate texts and predict future words in a sentence, letting the LLM generate sentences similar to how humans talk and write. One concern that needs to be flagged is that, often, the content generated by different LLMs is inaccurate. LLMs are trained on code that can be used to detect data breaches, detect ransomware, and even pinpoint organizational vulnerabilities in advance of a cyberattack. LLMs are new but have unbelievable potential with their ability to generate code that brings awareness to cyber analysts and IT professionals. …
"Church On My Couch": Predicting The Future Impact Of Online Ministry Based On The Impact During Covid-19, Samukeliso Mabarani, Sikhumbuzo Dube
"Church On My Couch": Predicting The Future Impact Of Online Ministry Based On The Impact During Covid-19, Samukeliso Mabarani, Sikhumbuzo Dube
Adventist Human-Subject Researchers Association
With “everything from home” as the new norm, “how does the use of digital platforms impact Adventist education, community engagement, and spiritual outreach?” Using a quantitative approach, we draw insights from online ministry during Covid-19 and use the insights to predict the future impact of online ministry statistically.
The Model 2.0 And Friends: An Interim Report, Garrison W. Cottrell, Martha Gahl, Shubham Kulkarni, Shashank Venkatramani, Yash Shah, Keyu Long, Xuzhe Zhi, Shivaank Agarwal, Cody Li, Jingyuan He, Thomas Fischer
The Model 2.0 And Friends: An Interim Report, Garrison W. Cottrell, Martha Gahl, Shubham Kulkarni, Shashank Venkatramani, Yash Shah, Keyu Long, Xuzhe Zhi, Shivaank Agarwal, Cody Li, Jingyuan He, Thomas Fischer
MODVIS Workshop
Last year, I reported on preliminary results of an anatomically-inspired deep learning model of the visual system and its role in explaining the face inversion effect. This year, I will report on new results and some variations on network architectures that we have explored, mainly as a way to generate discussion and get feedback. This is by no means a polished, final presentation!
We look forward to the group’s suggestions for these projects.
Automated Delineation Of Visual Area Boundaries And Eccentricities By A Cnn Using Functional, Anatomical, And Diffusion-Weighted Mri Data, Noah C. Benson, Bogeng Song, Toshikazu Miyata, Hiromasa Takemura, Jonathan Winawer
Automated Delineation Of Visual Area Boundaries And Eccentricities By A Cnn Using Functional, Anatomical, And Diffusion-Weighted Mri Data, Noah C. Benson, Bogeng Song, Toshikazu Miyata, Hiromasa Takemura, Jonathan Winawer
MODVIS Workshop
Delineating visual field maps and iso-eccentricities from fMRI data is an important but time-consuming task for many neuroimaging studies on the human visual cortex because the traditional methods of doing so using retinotopic mapping experiments require substantial expertise as well as scanner, computer, and human time. Automated methods based on gray-matter anatomy or a combination of anatomy and functional mapping can reduce these requirements but are less accurate than experts. Convolutional Neural Networks (CNNs) are powerful tools for automated medical image segmentation. We hypothesize that CNNs can define visual area boundaries with high accuracy. We trained U-Net CNNs with ResNet18 …
How Object Segmentation And Perceptual Grouping Emerge In Noisy Variational Autoencoders, Ben Lonnqvist, Zhengqing Wu, Michael H. Herzog
How Object Segmentation And Perceptual Grouping Emerge In Noisy Variational Autoencoders, Ben Lonnqvist, Zhengqing Wu, Michael H. Herzog
MODVIS Workshop
Many animals and humans can recognize and segment objects from their backgrounds. Whether object segmentation is necessary for object recognition has long been a topic of debate. Deep neural networks (DNNs) excel at object recognition, but not at segmentation tasks - this has led to the belief that object recognition and segmentation are separate mechanisms in visual processing. Here, however, we show evidence that in variational autoencoders (VAEs), segmentation and faithful representation of data can be interlinked. VAEs are encoder-decoder models that learn to represent independent generative factors of the data as a distribution in a very small bottleneck layer; …
A Dynamical Model Of Binding In Visual Cortex During Incremental Grouping And Search, Daniel Schmid, Daniel A. Braun, Heiko Neumann
A Dynamical Model Of Binding In Visual Cortex During Incremental Grouping And Search, Daniel Schmid, Daniel A. Braun, Heiko Neumann
MODVIS Workshop
Binding of visual information is crucial for several perceptual tasks. To incrementally group an object, elements in a space-feature neighborhood need to be bound together starting from an attended location (Roelfsema, TICS, 2005). To perform visual search, candidate locations and cued features must be evaluated conjunctively to retrieve a target (Treisman&Gormican, Psychol Rev, 1988). Despite different requirements on binding, both tasks are solved by the same neural substrate. In a model of perceptual decision-making, we give a mechanistic explanation for how this can be achieved. The architecture consists of a visual cortex module and a higher-order thalamic module. While the …
Integrating Ai Into Culinary Medicine: A Revolution In Nutrition And Home Cooking, Emeka Ikeakanam, Evan Curry, Terrence Mchugh, Jason Walker
Integrating Ai Into Culinary Medicine: A Revolution In Nutrition And Home Cooking, Emeka Ikeakanam, Evan Curry, Terrence Mchugh, Jason Walker
Research Day
Introduction
With the growing popularity of the emerging field of culinary medicine, there is a growing understanding of the culinary barriers needed to be overcome to adopt healthier eating habits. Lack of confidence, low skills, and lack of time are some of the most common barriers that prevent individuals from cooking at home. However, integrating AI can offer personalized support for home cooking and help individuals overcome these barriers. AI-powered meal planning and recipe suggestions can guide healthy and nutritious food choices that cater to their dietary needs and preferences. Additionally, AI can modify recipes to accommodate individual health conditions …
Visual Art In The Age Of Ai, Roshnica Gurung
Visual Art In The Age Of Ai, Roshnica Gurung
Cybersecurity Undergraduate Research Showcase
Artists and researchers have been deeply interested in using AI programs that generate art for quite some time now. As a result, there have been many advancements in making AI more accessible and easier to use for the public. This is because AI is not just for business anymore. Nowadays an individual without a college degree with even the slightest interest in art can go on a website like Stable Diffusion and create an artistic image using a text prompt in a quick couple minutes. The only limit is your imagination- and your internet’s stability. This accessibility was a huge …
Lidar Buoy Detection For Autonomous Marine Vessel Using Pointnet Classification, Christopher Adolphi, Dorothy Dorie Parry, Yaohang Li, Masha Sosonkina, Ahmet Saglam, Yiannis E. Papelis
Lidar Buoy Detection For Autonomous Marine Vessel Using Pointnet Classification, Christopher Adolphi, Dorothy Dorie Parry, Yaohang Li, Masha Sosonkina, Ahmet Saglam, Yiannis E. Papelis
Modeling, Simulation and Visualization Student Capstone Conference
Maritime autonomy, specifically the use of autonomous and semi-autonomous maritime vessels, is a key enabling technology supporting a set of diverse and critical research areas, including coastal and environmental resilience, assessment of waterway health, ecosystem/asset monitoring and maritime port security. Critical to the safe, efficient and reliable operation of an autonomous maritime vessel is its ability to perceive on-the-fly the external environment through onboard sensors. In this paper, buoy detection for LiDAR images is explored by using several tools and techniques: machine learning methods, Unity Game Engine (herein referred to as Unity) simulation, and traditional image processing. The Unity Game …
Behind Derogatory Migrants' Terms For Venezuelan Migrants: Xenophobia And Sexism Identification With Twitter Data And Nlp, Joseph Martínez, Melissa Miller-Felton, Jose Padilla, Erika Frydenlund
Behind Derogatory Migrants' Terms For Venezuelan Migrants: Xenophobia And Sexism Identification With Twitter Data And Nlp, Joseph Martínez, Melissa Miller-Felton, Jose Padilla, Erika Frydenlund
Modeling, Simulation and Visualization Student Capstone Conference
The sudden arrival of many migrants can present new challenges for host communities and create negative attitudes that reflect that tension. In the case of Colombia, with the influx of over 2.5 million Venezuelan migrants, such tensions arose. Our research objective is to investigate how those sentiments arise in social media. We focused on monitoring derogatory terms for Venezuelans, specifically veneco and veneca. Using a dataset of 5.7 million tweets from Colombian users between 2015 and 2021, we determined the proportion of tweets containing those terms. We observed a high prevalence of xenophobic and defamatory language correlated with the …
Statistical Approach To Quantifying Interceptability Of Interaction Scenarios For Testing Autonomous Surface Vessels, Benjamin E. Hargis, Yiannis E. Papelis
Statistical Approach To Quantifying Interceptability Of Interaction Scenarios For Testing Autonomous Surface Vessels, Benjamin E. Hargis, Yiannis E. Papelis
Modeling, Simulation and Visualization Student Capstone Conference
This paper presents a probabilistic approach to quantifying interceptability of an interaction scenario designed to test collision avoidance of autonomous navigation algorithms. Interceptability is one of many measures to determine the complexity or difficulty of an interaction scenario. This approach uses a combined probability model of capability and intent to create a predicted position probability map for the system under test. Then, intercept-ability is quantified by determining the overlap between the system under test probability map and the intruder’s capability model. The approach is general; however, a demonstration is provided using kinematic capability models and an odometry-based intent model.
Towards Nlp-Based Conceptual Modeling Frameworks, David Shuttleworth, Jose Padilla
Towards Nlp-Based Conceptual Modeling Frameworks, David Shuttleworth, Jose Padilla
Modeling, Simulation and Visualization Student Capstone Conference
This paper presents preliminary research using Natural Language Processing (NLP) to support the development of conceptual modeling frameworks. NLP-based frameworks are intended to lower the barrier of entry for non-modelers to develop models and to facilitate communication across disciplines considering simulations in research efforts. NLP drives conceptual modeling in two ways. Firstly, it attempts to automate the generation of conceptual models and simulation specifications, derived from non-modelers’ narratives, while standardizing the conceptual modeling process and outcome. Secondly, as the process is automated, it is simpler to replicate and be followed by modelers and non-modelers. This allows for using a common …