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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Enhancing The Performance Of The Mtcnn For The Classification Of Cancer Pathology Reports: From Data Annotation To Model Deployment, Kevin De Angeli
Enhancing The Performance Of The Mtcnn For The Classification Of Cancer Pathology Reports: From Data Annotation To Model Deployment, Kevin De Angeli
Doctoral Dissertations
Information contained in electronic health records (EHR) combined with the latest advances in machine learning (ML) have the potential to revolutionize the medical sciences. In particular, information contained in cancer pathology reports is essential to investigate cancer trends across the country. Unfortunately, large parts of information in EHRs are stored in the form of unstructured, free-text which limit their usability and research potential. To overcome this accessibility barrier, cancer registries depend on expert personnel who read, interpret, and extract relevant information. Naturally, as the number of stored pathology reports increases every day, depending on human experts presents scalability challenges. Recently, …
Transition Metal Computational Catalysis: Mechanistic Approaches And Development Of Novel Performance Metrics, Brett Anthony Smith
Transition Metal Computational Catalysis: Mechanistic Approaches And Development Of Novel Performance Metrics, Brett Anthony Smith
Doctoral Dissertations
Computational catalysis is an ever-growing field, thanks in part to the incredible progression of computational power and the efficiency offered by our current methodologies. Additionally, the accuracy of computation and the emergence of new methods that can decompose energetics and sterics into quantitative descriptors has allowed for researchers to begin to identify important structure-function relationships that predict the properties of unexplored subspaces within the overall chemical space. Catalytic descriptors have been used frequently in data driven high-throughput computational screenings. With the use of machine learning, a large portion of the chemical space an be predicted in matter of minutes or …
Better Understanding Genomic Architecture With The Use Of Applied Statistics And Explainable Artificial Intelligence, Jonathon C. Romero
Better Understanding Genomic Architecture With The Use Of Applied Statistics And Explainable Artificial Intelligence, Jonathon C. Romero
Doctoral Dissertations
With the continuous improvements in biological data collection, new techniques are needed to better understand the complex relationships in genomic and other biological data sets. Explainable Artificial Intelligence (X-AI) techniques like Iterative Random Forest (iRF) excel at finding interactions within data, such as genomic epistasis. Here, the introduction of new methods to mine for these complex interactions is shown in a variety of scenarios. The application of iRF as a method for Genomic Wide Epistasis Studies shows that the method is robust in finding interacting sets of features in synthetic data, without requiring the exponentially increasing computation time of many …
The Bracelet: An American Sign Language (Asl) Interpreting Wearable Device, Samuel Aba, Ahmadre Darrisaw, Pei Lin, Thomas Leonard
The Bracelet: An American Sign Language (Asl) Interpreting Wearable Device, Samuel Aba, Ahmadre Darrisaw, Pei Lin, Thomas Leonard
Chancellor’s Honors Program Projects
No abstract provided.