Author

M. A. Tabak

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Ecology· 5

Signature Assays, Organisms & Methods

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Approach:deep learning· 4 Assay:imaging· 4 Research Focus:cell type annotation· 3 Research Focus:benchmarking· 2 Organism:multiple species· 2 Research Focus:clustering· 1 Research Focus:ecology & biodiversity· 1 Approach:experimental assay· 1

Frequent Co-Authors

K. C. VerCauteren· 4 shared R. S. Miller· 4 shared N. P. Snow· 3 shared R. K. Boughton· 3 shared R. K. Brook· 3 shared D. W. Wolfson· 2 shared E. A. Odell· 2 shared E. S. Newkirk· 2 shared

Activity by Year

2018-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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4 shared papersEcology

K. C. VerCauteren

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Author profile →

5 papers

Recent·Most cited

CameraTrapDetectoR: Automatically detect, classify, and count animals in camera trap images using artificial intelligence

M. A. Tabak, D. Falbel, T. Hamzeh, R. K. Brook, J. A. Goolsby, L. D. Zoromski, R. K. Boughton, N. P. Snow, K. C. VerCauteren, R. S. Miller

CameraTrapDetectoR uses deep learning to detect, classify, and count animals in camera trap images, achieving high accuracy across multiple taxonomic levels.

bioRxiv · Feb 7, 2022 · PreprintEcology

Automated classification of bat echolocation call recordings with artificial intelligence

M. A. Tabak, K. L. Murray, J. A. Lombardi, K. J. Bay

This paper develops a deep learning model to classify bat species from echolocation calls using computer vision, achieving higher accuracy than existing software.

bioRxiv · Jun 23, 2021Ecology

rapidPop: Rapid population assessments of wildlife using camera trap data in R Shiny Applications

M. A. Tabak, J. S. Lewis, P. E. Schlichting, N. P. Snow, K. C. VerCauteren, R. S. Miller

The paper introduces an R package for rapid population assessments using camera trap data, enabling occupancy modeling with Shiny applications to analyze density changes.

bioRxiv · Mar 30, 2020 · PreprintEcology

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

M. A. Tabak, M. S. Norouzzadeh, D. W. Wolfson, E. J. Newton, R. K. Boughton, J. S. Ivan, E. A. Odell, E. S. Newkirk, R. Y. Conrey, J. Stenglein, F. Iannarilli, J. Erb, R. K. Brook, A. J. Davis, J. Lewis, , R. S. Miller

This paper develops two deep learning models for camera trap image classification, achieving high accuracy and transferability across locations, and provides an accessible R package for biologists.

bioRxiv · Mar 18, 2020Ecology

Machine learning to classify animal species in camera trap images: applications in ecology

M. A. Tabak, M. S. Norouzzadeh, D. W. Wolfson, S. J. Sweeney, K. C. VerCauteren, N. P. Snow, J. M. Halseth, P. A. Di Salvo, J. S. Lewis, M. D. White, B. Teton, J. C. Beasley, P. E. Schlichting, R. K. Boughton, B. Wight, , R. S. Miller

This paper trains a machine learning model to classify animal species in camera trap images, achieving high accuracy and providing an R package for ecological use.

bioRxiv · Jul 9, 2018Ecology