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Articles 1 - 7 of 7
Full-Text Articles in Physical Sciences and Mathematics
Using Pose Estimation Software To Predict Actions In Sabre Fencing, Micah Edwin Peters Ii
Using Pose Estimation Software To Predict Actions In Sabre Fencing, Micah Edwin Peters Ii
Honors College Theses
Fencing is a combat sport that uses three different swords: epee, foil, and sabre. Due to its fast-paced nature and employment of right of way, sabre fencing is often considered the most difficult of the three to learn. Computer vision and pose estimation software can be used to lower the barrier of entry to sabre fencing by identifying the different actions in sabre fencing. This project focuses on using open-source software to design a program that can identify the sabre parries as well as the main sabre movements. This program could be used to help newer fencers and spectators better …
Predictive Machine Learning And Its Future In Professional Basketball, Zachary Harmon
Predictive Machine Learning And Its Future In Professional Basketball, Zachary Harmon
Honors College Theses
Artificial Intelligence (AI) is an ever-evolving field, transforming various aspects of contemporary life. From language models to immersive gaming experiences, AI technologies have become integral to our daily existence. Among the most promising arenas for AI integration is the world of sports. This research delves into the application of machine learning models to predict NBA game outcomes, shedding light on the profound impact of machine learning in the realm of professional basketball. Beyond the scope of game prediction, this study explores the broader implications, such as optimizing the selection of televised games, assisting players in showcasing their skills, and much …
Programming An Autonomous Robot, Maxwell Brueggeman
Programming An Autonomous Robot, Maxwell Brueggeman
Honors College Theses
Ravaged by hurricanes, Florida needed help restoring its natural beauty and returning its wildlife to their homes. This was the task for the IEEE SoutheastCon 2023 Hardware Competition. Florida’s restoration was simulated by returning various ducks and pillars that lay strewn across a game board to their proper places. Ducks needed to return to their pond, pillars needed to be stacked to create statues, and food needed to be placed in the manatee and alligator aquariums. Competing teams were challenged to create an autonomous robot capable of performing these tasks. During the first semester, sensor selection was tackled. Research was …
Data And Algorithmic Modeling Approaches To Count Data, Andraya Hack
Data And Algorithmic Modeling Approaches To Count Data, Andraya Hack
Honors College Theses
Various techniques are used to create predictions based on count data. This type of data takes the form of a non-negative integers such as the number of claims an insurance policy holder may make. These predictions can allow people to prepare for likely outcomes. Thus, it is important to know how accurate the predictions are. Traditional statistical approaches for predicting count data include Poisson regression as well as negative binomial regression. Both methods also have a zero-inflated version that can be used when the data has an overabundance of zeros. Another procedure is to use computer algorithms, also known as …
Data-Driven Models For Remaining Useful Life Estimation Of Aircraft Engines And Hard Disk Drives, Austin Coursey
Data-Driven Models For Remaining Useful Life Estimation Of Aircraft Engines And Hard Disk Drives, Austin Coursey
Honors College Theses
Failure of physical devices can cause inconvenience, loss of money, and sometimes even deaths. To improve the reliability of these devices, we need to know the remaining useful life (RUL) of a device at a given point in time. Data-driven approaches use data from a physical device to build a model that can estimate the RUL. They have shown great performance and are often simpler than traditional model-based approaches. Typical statistical and machine learning approaches are often not suited for sequential data prediction. Recurrent Neural Networks are designed to work with sequential data but suffer from the vanishing gradient problem …
Benchmarking Clustering And Classification Tasks Using K-Means, Fuzzy C-Means And Feedforward Neural Networks Optimized By Pso, Adam Pickens, Adam Pickens
Benchmarking Clustering And Classification Tasks Using K-Means, Fuzzy C-Means And Feedforward Neural Networks Optimized By Pso, Adam Pickens, Adam Pickens
Honors College Theses
Clustering is a widely used unsupervised learning technique across data mining and machine learning applications and finds frequent use in diverse fields ranging from astronomy, medical imaging, search and optimization, geology, geophysics and sentiment analysis to name a few. It is therefore important to verify the effectiveness of the clustering algorithms in question and to make reasonably strong arguments for the acceptance of the end results generated by the validity indices that measure the compactness and separability of clusters. This work aims to explore the successes and limitations of popular clustering mechanisms such as K-Means and Fuzzy C-Means by comparing …
Prediction Of Days-On-Market For Single-Family Homes In The Housing Market Of Savannah, Keagan Galbraith
Prediction Of Days-On-Market For Single-Family Homes In The Housing Market Of Savannah, Keagan Galbraith
Honors College Theses
The number of days that a home stays on the housing market (Days-On-Market—DOM) provides crucial information about the real estate market’s behavior that affects the buyer’s/seller’s decision (at the micro-level) and indicates the level of risk associated with real estate investments and identifies the housing bubbles (at the macro level). Housing data has a mixture of simple and complex attributes. A complex attribute in contrast with a simple attribute, has an array of values for a real estate property, which creates a major challenge in prediction of DOM. DOM is a binary attribute with values of “short” (£ six months) …