Open Access. Powered by Scholars. Published by Universities.®

Life Sciences Commons

Open Access. Powered by Scholars. Published by Universities.®

University of South Florida

Integrative Biology Faculty and Staff Publications

Series

Deep learning

Articles 1 - 2 of 2

Full-Text Articles in Life Sciences

Deep Learning For Supervised Classification Of Temporal Data In Ecology, César Capinha, Ana Ceia-Hasse, Andrew M. Kramer, Christiaan Meijer Jan 2021

Deep Learning For Supervised Classification Of Temporal Data In Ecology, César Capinha, Ana Ceia-Hasse, Andrew M. Kramer, Christiaan Meijer

Integrative Biology Faculty and Staff Publications

Temporal data is ubiquitous in ecology and ecologists often face the challenge of accurately differentiating these data into predefined classes, such as biological entities or ecological states. The usual approach consists of transforming the time series into user-defined features and then using these features as predictors in conventional statistical or machine learning models. Here we suggest the use of deep learning models as an alternative to this approach. Recent deep learning techniques can perform the classification directly from the time series, eliminating subjective and resource-consuming data transformation steps, and potentially improving classification results. We describe some of the deep learning …


Deep Learning For Supervised Classification Of Temporal Data In Ecology, César Capinha, Ana Ceia-Hasse, Andrew M. Kramer, Christiaan Meijer Jan 2021

Deep Learning For Supervised Classification Of Temporal Data In Ecology, César Capinha, Ana Ceia-Hasse, Andrew M. Kramer, Christiaan Meijer

Integrative Biology Faculty and Staff Publications

Temporal data is ubiquitous in ecology and ecologists often face the challenge of accurately differentiating these data into predefined classes, such as biological entities or ecological states. The usual approach consists of transforming the time series into user-defined features and then using these features as predictors in conventional statistical or machine learning models. Here we suggest the use of deep learning models as an alternative to this approach. Recent deep learning techniques can perform the classification directly from the time series, eliminating subjective and resource-consuming data transformation steps, and potentially improving classification results. We describe some of the deep learning …