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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Using Unmanned Aircraft Systems To Investigate The Detectability Of Burmese Pythons In South Florida, Joseph Cerreta Ph.D., William Austin Ed.D., David Thirtyacre Ph.D., Scott S. Burgess Ph.D., Peter Miller
Using Unmanned Aircraft Systems To Investigate The Detectability Of Burmese Pythons In South Florida, Joseph Cerreta Ph.D., William Austin Ed.D., David Thirtyacre Ph.D., Scott S. Burgess Ph.D., Peter Miller
Journal of Aviation/Aerospace Education & Research
Burmese pythons are an invasive, non-native species of snake to southern Florida and attempts at eradicating the snakes had yielded mixed results. The current rate of detection had been reported as 0.05%. The purpose of this research project was to determine if a UAS equipped with a near-infrared (NIR) camera could be used to detect pythons at a higher rate when compared to a RGB camera. The approach involved collecting 55 images from RGB and NIR cameras, over carcass pythons at flying heights of 3, 6, 9, 12, and 15 meters. A likelihood ratio consisting of a true positive rate …
A Deep Bilstm Machine Learning Method For Flight Delay Prediction Classification, Desmond B. Bisandu Phd, Irene Moulitsas Phd
A Deep Bilstm Machine Learning Method For Flight Delay Prediction Classification, Desmond B. Bisandu Phd, Irene Moulitsas Phd
Journal of Aviation/Aerospace Education & Research
This paper proposes a classification approach for flight delays using Bidirectional Long Short-Term Memory (BiLSTM) and Long Short-Term Memory (LSTM) models. Flight delays are a major issue in the airline industry, causing inconvenience to passengers and financial losses to airlines. The BiLSTM and LSTM models, powerful deep learning techniques, have shown promising results in a classification task. In this study, we collected a dataset from the United States (US) Bureau of Transportation Statistics (BTS) of flight on-time performance information and used it to train and test the BiLSTM and LSTM models. We set three criteria for selecting highly important features …