Validation and verification of the OPI 2.0 System

Richard Abelson1,2, Keith J Lane3, John Rodriguez3, Patrick Johnston3, Endri Angjeli3, George Ousler3, Douglas Montgomery11School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 2Statistics and Data Corporation, Tempe, AZ, 3Ora, Inc, Andover, MA, USAPurpose: The Ocular Protection Index (OPI) 2.0 Sys

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Development of a Machine Learning-Based Cysticidal Assay and Identification of an Amebicidal and Cysticidal Marine Microbial Metabolite against Acanthamoeba

ABSTRACT Traditional Grooming and Care cysticidal assays for Acanthamoeba species revolve around treating cysts with compounds and manually observing the culture for evidence of excystation.This method is time-consuming, labor-intensive, and low throughput.We adapted and trained a YOLOv3 machine learning, object detection neural network to recogniz

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FEA-Swin: Foreground Enhancement Attention Swin Transformer Network for Accurate UAV-Based Dense Object Detection

UAV-based object detection has recently attracted a lot of attention due to its diverse applications.Most of the existing convolution neural network based object detection models can perform well in common object detection cases.However, due to the fact that objects in UAV images are spatially distributed in a very dense manner, these methods have

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