Author

Richard J. Povinelli

Marquette UniversityORCID 0000-0001-8439-0146Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Approach:deep learning· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1

Frequent Co-Authors

Floyd E. DowelliD· 2 shared George F. CorlissiD· 2 shared Maggy T. Sikulu-LordiD· 2 shared Masabho P. MilaliiD· 2 shared Samson S. KiwareiD· 2 shared Fredros O. OkumuiD· 1 shared J. D. CharlwoodiD· 1 shared Marta F. MaiaiD· 1 shared

Activity by Year

2019-2020 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersBioinformatics

Floyd E. DowellORCID

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2 papers

Recent·Most cited

BioinformaticsPLoS ONEPeer-reviewedJun 18, 2020

An Autoencoder and Artificial Neural Network-based Method to Estimate Parity Status of Wild Mosquitoes from Near-infrared Spectra

Masabho P. Milali, Samson S. Kiware, Nicodem James Govella…·Richard J. Povinelli

This paper develops an autoencoder and ANN model to estimate parity status of wild mosquitoes using NIR spectra, achieving high accuracy across multiple datasets.

26 citations

deep learning

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BioinformaticsPLoS ONEPeer-reviewedAug 14, 2019

Age Grading An. Gambiae and An. Arabiensis Using Near Infrared Spectra and Artificial Neural Networks

Masabho P. Milali, Maggy T. Sikulu-Lord, Samson S. Kiware…·Richard J. Povinelli

This study compares artificial neural networks and partial least squares regression for age grading malaria mosquitoes using NIRS, finding higher accuracy with direct binary classifiers.

44 citations

imagingstatistical model

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