A Deep Learning Approach to Predict Dissolved Oxygen in Aquaculture-Reference-Cited by-同舟云学术

A Deep Learning Approach to Predict Dissolved Oxygen in Aquaculture

Published:2022-08-24 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference on Advanced Robotics and Intelligent Systems (ARIS)
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Author:

Khabusi Simon Peter¹,Huang Yo-Ping¹

Affiliation:

1. National Taipei University of Technology,Department of Electrical Engineering,Taipei,Taiwan,10608

Funder

Ministry of Science and Technology, Taiwan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9910419/9910440/09910453.pdf?arnumber=9910453

Reference23 articles.

1. A hybrid time series matching algorithm based on feature-points and DTW;xi;Proc Int Symp Comput Intell Design,2016

2. Modeling of dissolved oxygen concentration and its hysteresis behavior in rivers using wavelet Transform-based hybrid models;khani;Intl Jour Machine Learning,2016

3. Neural network modeling of dissolved oxygen in the Gruža reservoir, Serbia

4. A method for predicting dissolved oxygen in aquaculture water in an aquaponics system;qin;Comput Electron Agric,2018

5. Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China

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1. Attention-Based Mechanism for Fish Disease Classification in Aquaculture;2023 International Conference on System Science and Engineering (ICSSE);2023-07-27

2. A Comparative Study of LSTM, GRU, BiLSTM and BiGRU to Predict Dissolved Oxygen;2023 20th International Joint Conference on Computer Science and Software Engineering (JCSSE);2023-06-28

3. A CNN-OSELM Multi-Layer Fusion Network With Attention Mechanism for Fish Disease Recognition in Aquaculture;IEEE Access;2023