Dr Dmitry Konovalov ~ Senior Lecturer
Information Technology
- About
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- Teaching
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- BU3102: Multidisciplinary Project (Level 3; TSV)
- CP2406: Programming III (Level 2; TSV)
- CP2410: Algorithms and Data Structures (Level 2; TSV)
- CP3102: Multidisciplinary Project (Level 3; TSV)
- CP3403: Data Mining (Level 3; CNS & TSV)
- CP3406: Mobile Computing (Level 3; TSV)
- CP3407: Advanced Software Engineering (Level 3; TSV)
- CP5047: ICT Project 2: Implementation and Commissioning (Level 5; CNS & TSV)
- CP5307: Advanced Mobile Technology (Level 5; TSV)
- CP5634: Data Mining (Level 5; CNS & TSV)
- Interests
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- Professional
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- 2022: Kaggle Competitions Expert https://www.kaggle.com/dmitrykonovalov
- Research
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- Computer vision with Convolutional Neural Networks
- Application of Deep Learning AI to Northern Australia Beef Industry, Aquaculture, Australian Great Barrier Reef monitoring
- Experience
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- 2002 to present - Senior Lecturer, James Cook University (Townsville)
- Research Disciplines
- Socio-Economic Objectives
I am currently focusing on Deep Learning AI applications to Australian industries.
2022: 2nd place finish in NASA-sponsored Mars Spectrometry 2: Gas Chromatography Challenge: https://drivendata.co/blog/mars-2-gcms-challenge-winners
- Publications
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These are the most recent publications associated with this author. To see a detailed profile of all publications stored at JCU, visit ResearchOnline@JCU. Hover over Altmetrics badges to see social impact.
- Journal Articles
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- Kophamel S, Ward L, Konovalov D, Mendez D, Ariel E, Cassidy N, Bell I, Balastegui Martínez M and Munns S (2022) Field?based adipose tissue quantification in sea turtles using bioelectrical impedance spectroscopy validated with CT scans and deep learning. Ecology and Evolution, 12.
- Mattone C, Bradley M, Barnett A, Konovalov D and Sheaves M (2022) Environmental conditions constrain nursery habitat value in Australian sub-tropical estuaries. Marine Environmental Research, 175.
- Josi D, Heg D, Takeyama T, Bonfils D, Konovalov D, Frommen J, Kohda M and Taborsky M (2021) Age‐ and sex‐dependent variation in relatedness corresponds to reproductive skew, territory inheritance and workload in cooperatively breeding cichlids. Evolution, 75 (11). pp. 2881-2897
- Dexter B, King R, Parisi A, Harrison S, Konovalov D and Downs N (2020) Keratinocyte skin cancer risks for working school teachers: scenarios and implications of the timing of scheduled duty periods in Queensland, Australia. Journal of Photochemistry and Photobiology B: Biology, 213.
- Konovalov D, Swinhoe N, Efremova D, Birtles R, Kusetic M, Hillcoat S, Curnock M, Williams G and Sheaves M (2020) Automatic sorting of Dwarf Minke Whale underwater images. Information, 11 (4).
- Saleh A, Laradji I, Konovalov D, Bradley M, Vazquez D and Sheaves M (2020) A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis. Scientific Reports, 10.
- Sheaves M, Bradley M, Herrera C, Mattone C, Lennard C, Sheaves J and Konovalov D (2020) Optimizing video sampling for juvenile fish surveys: using deep learning and evaluation of assumptions to produce critical fisheries parameters. Fish and Fisheries, 21 (6). pp. 1259-1276
- Conference Papers
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- Konovalov D, Swinhoe N, Efremova D, Birtles R, Kusetic M, Adams K, Hillcoat S, Curnock M, Williams G, Sobtzick S and Sheaves M (2020) Minke whale detection in underwater imagery using classification CNNs. Proceedings of Global Oceans 2020. In: Global Oceans 2020: Singapore – U.S. Gulf Coast, 5-30 October 2020, Biloxi, MS, USA
- Efremova D, Sankupellay M and Konovalov D (2019) Data-efficient classification of birdcall through Convolutional Neural Networks transfer learning. Proceedings of the International Conference on Digital Image Computing. In: DICTA 2019: International Conference on Digital Image Computing: Techniques and Applications, 2-4 December 2019, Perth, WA, Australia
- Konovalov D, Saleh A, Efremova D, Domingos J and Jerry D (2019) Automatic weight estimation of harvested fish from images. Proceedings of the International Conference on Digital Image Computing. In: DICTA 2019: International Conference on Digital Image Computing: Techniques and Applications, 2-4 December 2019, Perth, WA, Australia
- Konovalov D, Saleh A, Bradley M, Sankupellay M, Marini S and Sheaves M (2019) Underwater fish detection with weak multi-domain supervision. Proceedings of the International Joint Conference on Neural Networks. In: 2019 IJCNN: International Joint Conference on Neural Networks, 14-19 July 2019, Budapest, Hungary
- Lapico A, Sankupellay M, Cianciullo L, Myers T, Konovalov D, Jerry D, Toole P, Jones D and Zenger K (2019) Using image processing to automatically measure pearl oyster size for selective breeding. Proceedings of the International Conference on Digital Image Computing. In: DICTA 2019: International Conference on Digital Image Computing: Techniques and Applications, 2-4 December 2019, Perth, WA, Australia
- More
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ResearchOnline@JCU stores 42+ research outputs authored by Dr Dmitry Konovalov from 2004 onwards.
- Current Funding
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Current and recent Research Funding to JCU is shown by funding source and project.
Great Barrier Reef Foundation - Reef Trust Partnership
Integrated reef fish monitoring - Nursery Seascapes
- Indicative Funding
- $102,345 over 2 years (administered by Australian Institute of Marine Science)
- Summary
- A 2 year monitoring program to understand the abundance, diversity, and assemblage composition of Great Barrier Reef Fishes. Within this program, JCU Marine Data Tech will be working with project partners conducting bi-annual surveys of reef fishes in nursery seascapes in the central GBR. Data will be collected using stereo Remote Underwater Video Systems and processed using Artificial Intelligence computing.
- Investigators
- Michael Bradley, Dmitry Konovalov and Marcus Sheaves (College of Science & Engineering)
- Keywords
- Reef Fish; Seascape; Great Barrier Reef; Fisheries; Monitoring; Nursery grounds
MAKO Tidal Turbines Pty Ltd - Contract Research
Barney Point Turbine Monitoring
- Indicative Funding
- $27,457
- Summary
- Monitoring surface video, underwater video, underwater acoustic and sidescan data streams using AI (January-April 2019) (including monthly regular reporting, final reporting and feasibility analysis), to assess whether the tidal turbine impacts fish and other aquatic organisms during its operations.
- Investigators
- Marcus Sheaves, Carlo Mattone and Dmitry Konovalov (College of Science & Engineering)
- Keywords
- Artificial Intelligence; Impact Assessment; Tidal turbine monitoring
- Supervision
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Advisory Accreditation: I can be on your Advisory Panel as a Primary or Secondary Advisor.
These Higher Degree Research projects are either current or by students who have completed their studies within the past 5 years at JCU. Linked titles show theses available within ResearchOnline@JCU.
- Completed
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- Developing deep learning methods for aquaculture applications (2020, Masters , Primary Advisor)
- Collaboration
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The map shows research collaborations by institution from the past 7 years.
Note: Map points are indicative of the countries or states that institutions are associated with.- 5+ collaborations
- 4 collaborations
- 3 collaborations
- 2 collaborations
- 1 collaboration
- Indicates the Tropics (Torrid Zone)
Connect with me
- Phone
- Location
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- 17.047, Faculty of Science & Engineering (Townsville campus)
- Advisory Accreditation
- Primary Advisor
- Find me on…
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My research areas
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