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Sadaiappan B, Balakrishnan P, C.R. V, Vijayan NT, Subramanian M, Gauns MU. Applications of Machine Learning in Chemical and Biological Oceanography. ACS OMEGA 2023; 8:15831-15853. [PMID: 37179641 PMCID: PMC10173431 DOI: 10.1021/acsomega.2c06441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 02/22/2023] [Indexed: 05/15/2023]
Abstract
Machine learning (ML) refers to computer algorithms that predict a meaningful output or categorize complex systems based on a large amount of data. ML is applied in various areas including natural science, engineering, space exploration, and even gaming development. This review focuses on the use of machine learning in the field of chemical and biological oceanography. In the prediction of global fixed nitrogen levels, partial carbon dioxide pressure, and other chemical properties, the application of ML is a promising tool. Machine learning is also utilized in the field of biological oceanography to detect planktonic forms from various images (i.e., microscopy, FlowCAM, and video recorders), spectrometers, and other signal processing techniques. Moreover, ML successfully classified the mammals using their acoustics, detecting endangered mammalian and fish species in a specific environment. Most importantly, using environmental data, the ML proved to be an effective method for predicting hypoxic conditions and harmful algal bloom events, an essential measurement in terms of environmental monitoring. Furthermore, machine learning was used to construct a number of databases for various species that will be useful to other researchers, and the creation of new algorithms will help the marine research community better comprehend the chemistry and biology of the ocean.
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Affiliation(s)
- Balamurugan Sadaiappan
- Department
of Biology, United Arab Emirates University, Al Ain 971, UAE
- Plankton
Laboratory, Biological Oceanography Division, CSIR-National Institute of Oceanography, Dona Paula, Goa 403004, India
| | - Preethiya Balakrishnan
- Faraday-Fleming
Laboratory, London W148TL, United Kingdom
- University
of London, London WC1E 7HU, United
Kingdom
| | - Vishal C.R.
- Plankton
Laboratory, Biological Oceanography Division, CSIR-National Institute of Oceanography, Dona Paula, Goa 403004, India
| | - Neethu T. Vijayan
- Plankton
Laboratory, Biological Oceanography Division, CSIR-National Institute of Oceanography, Dona Paula, Goa 403004, India
| | - Mahendran Subramanian
- Faraday-Fleming
Laboratory, London W148TL, United Kingdom
- Department
of Computing, Imperial College, London SW7 2AZ, United Kingdom
| | - Mangesh U. Gauns
- Plankton
Laboratory, Biological Oceanography Division, CSIR-National Institute of Oceanography, Dona Paula, Goa 403004, India
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Sosa-Trejo D, Bandera A, González M, Hernández-León S. Vision-based techniques for automatic marine plankton classification. Artif Intell Rev 2023. [DOI: 10.1007/s10462-023-10456-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
Abstract
AbstractPlankton are an important component of life on Earth. Since the 19th century, scientists have attempted to quantify species distributions using many techniques, such as direct counting, sizing, and classification with microscopes. Since then, extraordinary work has been performed regarding the development of plankton imaging systems, producing a massive backlog of images that await classification. Automatic image processing and classification approaches are opening new avenues for avoiding time-consuming manual procedures. While some algorithms have been adapted from many other applications for use with plankton, other exciting techniques have been developed exclusively for this issue. Achieving higher accuracy than that of human taxonomists is not yet possible, but an expeditious analysis is essential for discovering the world beyond plankton. Recent studies have shown the imminent development of real-time, in situ plankton image classification systems, which have only been slowed down by the complex implementations of algorithms on low-power processing hardware. This article compiles the techniques that have been proposed for classifying marine plankton, focusing on automatic methods that utilize image processing, from the beginnings of this field to the present day.
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Zhang J, Li C, Yin Y, Zhang J, Grzegorzek M. Applications of artificial neural networks in microorganism image analysis: a comprehensive review from conventional multilayer perceptron to popular convolutional neural network and potential visual transformer. Artif Intell Rev 2022; 56:1013-1070. [PMID: 35528112 PMCID: PMC9066147 DOI: 10.1007/s10462-022-10192-7] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Microorganisms are widely distributed in the human daily living environment. They play an essential role in environmental pollution control, disease prevention and treatment, and food and drug production. The analysis of microorganisms is essential for making full use of different microorganisms. The conventional analysis methods are laborious and time-consuming. Therefore, the automatic image analysis based on artificial neural networks is introduced to optimize it. However, the automatic microorganism image analysis faces many challenges, such as the requirement of a robust algorithm caused by various application occasions, insignificant features and easy under-segmentation caused by the image characteristic, and various analysis tasks. Therefore, we conduct this review to comprehensively discuss the characteristics of microorganism image analysis based on artificial neural networks. In this review, the background and motivation are introduced first. Then, the development of artificial neural networks and representative networks are presented. After that, the papers related to microorganism image analysis based on classical and deep neural networks are reviewed from the perspectives of different tasks. In the end, the methodology analysis and potential direction are discussed.
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Affiliation(s)
- Jinghua Zhang
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Institute for Medical Informatics, University of Luebeck, Luebeck, Germany
| | - Chen Li
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Yimin Yin
- School of Mathematics and Statistics, Hunan First Normal University, Changsha, China
| | - Jiawei Zhang
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Marcin Grzegorzek
- Institute for Medical Informatics, University of Luebeck, Luebeck, Germany
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Vaughan L, Zamyadi A, Ajjampur S, Almutaram H, Freguia S. A review of microscopic cell imaging and neural network recognition for synergistic cyanobacteria identification and enumeration. ANAL SCI 2022; 38:261-279. [PMID: 35286640 PMCID: PMC8938360 DOI: 10.1007/s44211-021-00013-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 10/07/2021] [Indexed: 11/30/2022]
Abstract
Real-time cyanobacteria/algal monitoring is a valuable tool for early detection of harmful algal blooms, water treatment efficacy evaluation, and assists tailored water quality risk assessments by considering taxonomy and cell counts. This review evaluates and proposes a synergistic approach using neural network image recognition and microscopic imaging devices by first evaluating published literature for both imaging microscopes and image recognition. Quantitative phase imaging was considered the most promising of the investigated imaging techniques due to the provision of enhanced information relative to alternatives. This information provides significant value to image recognition neural networks, such as the convolutional neural networks discussed within this review. Considering published literature, a cyanobacteria monitoring system and corresponding image processing workflow using in situ sample collection buoys and on-shore sample processing was proposed. This system can be implemented using commercially available equipment to facilitate accurate, real-time water quality monitoring.
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Affiliation(s)
- Liam Vaughan
- Water Research Australia, Level 2, 250 Victoria Square, Adelaide, SA, 5000, Australia.
| | - Arash Zamyadi
- Water Research Australia, Level 2, 250 Victoria Square, Adelaide, SA, 5000, Australia
- Department of Chemical Engineering, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Suraj Ajjampur
- Water Research Australia, Level 2, 250 Victoria Square, Adelaide, SA, 5000, Australia
| | - Husein Almutaram
- Department of Civil and Mineral Engineering, University of Toronto, Toronto, ON, M5S 1A4, Canada
| | - Stefano Freguia
- Department of Chemical Engineering, The University of Melbourne, Parkville, VIC, 3010, Australia
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C 2DAN: An Improved Deep Adaptation Network with Domain Confusion and Classifier Adaptation. SENSORS 2020; 20:s20123606. [PMID: 32604859 PMCID: PMC7349586 DOI: 10.3390/s20123606] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 06/20/2020] [Accepted: 06/23/2020] [Indexed: 11/17/2022]
Abstract
Deep neural networks have been successfully applied in domain adaptation which uses the labeled data of source domain to supplement useful information for target domain. Deep Adaptation Network (DAN) is one of these efficient frameworks, it utilizes Multi-Kernel Maximum Mean Discrepancy (MK-MMD) to align the feature distribution in a reproducing kernel Hilbert space. However, DAN does not perform very well in feature level transfer, and the assumption that source and target domain share classifiers is too strict in different adaptation scenarios. In this paper, we further improve the adaptability of DAN by incorporating Domain Confusion (DC) and Classifier Adaptation (CA). To achieve this, we propose a novel domain adaptation method named C2DAN. Our approach first enables Domain Confusion (DC) by using a domain discriminator for adversarial training. For Classifier Adaptation (CA), a residual block is added to the source domain classifier in order to learn the difference between source classifier and target classifier. Beyond validating our framework on the standard domain adaptation dataset office-31, we also introduce and evaluate on the Comprehensive Cars (CompCars) dataset, and the experiment results demonstrate the effectiveness of the proposed framework C2DAN.
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