Deep learning for internet of underwater things and ocean data analytics
Jahanbakht, Mohammad (2022) Deep learning for internet of underwater things and ocean data analytics. PhD thesis, James Cook University.
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Abstract
The Internet of Underwater Things (IoUT) is an emerging technological ecosystem developed for connecting objects in maritime and underwater environments. IoUT technologies are empowered by an extreme number of deployed sensors and actuators. In this thesis, multiple IoUT sensory data are augmented with machine intelligence for forecasting purposes.
| Item ID: | 76919 |
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| Item Type: | Thesis (PhD) |
| Keywords: | Atmospheric modeling, Big Data, Big marine data, Deep learning, Deep neural networks, Distributed databases, Edge computing, eReefs modelling suite, Finite element analysis, Fish image segmentation, Forecasting, Great Barrier Reef, Internet of things, Internet of underwater things, Machine learning, Next-frame prediction, Numerical models, Ocean temperature, Partial differential equation, Physics-informed neural network, Predictive models, Real-time video processing, Sensors, Temperature distribution, Time series analysis, Tools, Total nitrogen forecasting, Tutorials, U-Net convolutional neural networks |
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| Copyright Information: | Copyright © 2022 Mohammad Jahanbakht. |
| Additional Information: | Five publications arising from this thesis are stored in ResearchOnline@JCU, at the time of processing. Please see the Related URLs. The publications are: [Chapter 2] Jahanbakht, Mohammad, Xiang, Wei, Hanzo, Lajos, and Rahimi Azghadi, Mostafa (2021) Internet of underwater things and big marine data analytics — a comprehensive survey. IEEE Communications Surveys & Tutorials, 23 (2). pp. 904-956. [Chapter 3] Jahanbakht, Mohammad, Xiang, Wei, and Rahimi Azghadi, Mostafa (2022) Sea surface temperature forecasting with ensemble of stacked deep neural networks. IEEE Geoscience and Remote Sensing Letters, 19. 1502605. [Chapter 4] Jahanbakht, Mohammad, Xiang, Wei, Robson, Barbara, and Rahimi Azghadi, Mostafa (2022) Nitrogen prediction in the Great Barrier Reef using finite element analysis with deep neural networks. Environmental Modelling & Software, 150. 105311. [Chapter 5] Jahanbakht, Mohammad, Xiang, Wei, and Azghadi, Mostafa Rahimi (2022) Sediment Prediction in the Great Barrier Reef using Vision Transformer with finite element analysis. Neural Networks, 152. pp. 311-321. [Chapter 6] Jahanbakht, Mohammad, Xiang, Wei, Waltham, Nathan J., and Rahimiazghadi, Mostafa (2022) Distributed Deep Learning in the Cloud and Energy-efficient Real-time Image Processing at the Edge for Fish Segmentation in Underwater Videos Segmentation in Underwater Videos. IEEE Access, 10. pp. 117796-117807. |
| Date Deposited: | 30 Nov 2022 04:15 |
| FoR Codes: | 46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460502 Data mining and knowledge discovery @ 30% 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 30% 41 ENVIRONMENTAL SCIENCES > 4104 Environmental management > 410402 Environmental assessment and monitoring @ 40% |
| SEO Codes: | 18 ENVIRONMENTAL MANAGEMENT > 1805 Marine systems and management > 180505 Measurement and assessment of marine water quality and condition @ 40% 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 30% 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220408 Information systems @ 30% |
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