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Asteroidal Sets And Dominating Targets In Graphs, Oleksiy Al-Saadi
Asteroidal Sets And Dominating Targets In Graphs, Oleksiy Al-Saadi
Department of Computer Science and Engineering: Dissertations, Theses, and Student Research
The focus of this PhD thesis is on various distance and domination properties in graphs. In particular, we prove strong results about the interactions between asteroidal sets and dominating targets. Our results add to or extend a plethora of results on these properties within the literature. We define the class of strict dominating pair graphs and show structural and algorithmic properties of this class. Notably, we prove that such graphs have diameter 3, 4, or contain an asteroidal quadruple. Then, we design an algorithm to to efficiently recognize chordal hereditary dominating pair graphs. We provide new results that describe the …
Algorithmic Approaches For Object Tracking And Facial Detection Using Drones, Kareem Shahatta, Peter Savarese, Gina Egitto, Jongwook Kim
Algorithmic Approaches For Object Tracking And Facial Detection Using Drones, Kareem Shahatta, Peter Savarese, Gina Egitto, Jongwook Kim
Computer Science Student Work
Drones are unmanned aerial vehicles that have a variety of uses in many fields such as package delivery and search operations. Tello is a small, programmable drone designed for educational purposes. We developed algorithms using DJI Tello Py, an open-source Application Programming Interface, to command the movements of Tello for tracking a target object (i.e., human). Our algorithms utilize digital image processing techniques on Tello's live video stream to optimize the number of movements Tello needs to reach its target. Our poster presentation will explain our approaches to implement object-tracking and facial detection for Tello, discuss lessons we learned, and …
The Impact Of Artificial Intelligence And Machine Learning On Organizations Cybersecurity, Mustafa Abdulhussein
The Impact Of Artificial Intelligence And Machine Learning On Organizations Cybersecurity, Mustafa Abdulhussein
Doctoral Dissertations and Projects
As internet technology proliferate in volume and complexity, the ever-evolving landscape of malicious cyberattacks presents unprecedented security risks in cyberspace. Cybersecurity challenges have been further exacerbated by the continuous growth in the prevalence and sophistication of cyber-attacks. These threats have the capacity to disrupt business operations, erase critical data, and inflict reputational damage, constituting an existential threat to businesses, critical services, and infrastructure. The escalating threat is further compounded by the malicious use of artificial intelligence (AI) and machine learning (ML), which have increasingly become tools in the cybercriminal arsenal. In this dynamic landscape, the emergence of offensive AI introduces …
Persistent Relative Homology For Topological Data Analysis, Christian J. Lentz
Persistent Relative Homology For Topological Data Analysis, Christian J. Lentz
Mathematics, Statistics, and Computer Science Honors Projects
A central problem in data-driven scientific inquiry is how to interpret structure in noisy, high-dimensional data. Topological data analysis (TDA) provides a solution via the language of persistent homology, which encodes features of interest as holes within a filtration of the data. The recently presented U-Match Decomposition places the standard persistence computation in a flexible form, allowing for straight-forward extensions of the algorithm to variations of persistent homology. We describe U-Match Decomposition in the context of persistent homology, and extend it to an algorithm for persistent relative homology, providing proofs for the correctness and stability of the presented algorithm.
Machine Learning As A Tool For Early Detection: A Focus On Late-Stage Colorectal Cancer Across Socioeconomic Spectrums, Hadiza Galadima, Rexford Anson-Dwamena, Ashley Johnson, Ghalib Bello, Georges Adunlin, James Blando
Machine Learning As A Tool For Early Detection: A Focus On Late-Stage Colorectal Cancer Across Socioeconomic Spectrums, Hadiza Galadima, Rexford Anson-Dwamena, Ashley Johnson, Ghalib Bello, Georges Adunlin, James Blando
Community & Environmental Health Faculty Publications
Purpose: To assess the efficacy of various machine learning (ML) algorithms in predicting late-stage colorectal cancer (CRC) diagnoses against the backdrop of socio-economic and regional healthcare disparities. Methods: An innovative theoretical framework was developed to integrate individual- and census tract-level social determinants of health (SDOH) with sociodemographic factors. A comparative analysis of the ML models was conducted using key performance metrics such as AUC-ROC to evaluate their predictive accuracy. Spatio-temporal analysis was used to identify disparities in late-stage CRC diagnosis probabilities. Results: Gradient boosting emerged as the superior model, with the top predictors for late-stage CRC diagnosis being anatomic site, …
External Support Structures In Fused Deposition Modeling 3d Printing, Adam Mystkowski
External Support Structures In Fused Deposition Modeling 3d Printing, Adam Mystkowski
Undergraduate Honors Theses
3D Printing, sometimes also referred to as Additive Manufacturing, is a technology that has garnered a lot of attention in the past several years. While the technology has attracted a significant community of hobbyists, the benefits of the technology have also been recognized in manufacturing. While there are many different types of 3D Printing techniques, the most common type is Fused Deposition Modeling (FDM), mainly due to its relatively low cost compared to other types. However, the technique also present a significant limitation: it tends to struggle with models that have overhanging parts. When a layer is extruded by an …
Using Feature Selection Enhancement To Evaluate Attack Detection In The Internet Of Things Environment, Khawlah Harahsheh, Rami Al-Naimat, Chung-Hao Chen
Using Feature Selection Enhancement To Evaluate Attack Detection In The Internet Of Things Environment, Khawlah Harahsheh, Rami Al-Naimat, Chung-Hao Chen
Electrical & Computer Engineering Faculty Publications
The rapid evolution of technology has given rise to a connected world where billions of devices interact seamlessly, forming what is known as the Internet of Things (IoT). While the IoT offers incredible convenience and efficiency, it presents a significant challenge to cybersecurity and is characterized by various power, capacity, and computational process limitations. Machine learning techniques, particularly those encompassing supervised classification techniques, offer a systematic approach to training models using labeled datasets. These techniques enable intrusion detection systems (IDSs) to discern patterns indicative of potential attacks amidst the vast amounts of IoT data. Our investigation delves into various aspects …