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Journal articles on the topic 'Automated identification'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

Price, C. J., N. Snooke, and J. Landry. "Automated sneak identification." Engineering Applications of Artificial Intelligence 9, no. 4 (August 1996): 423–27. http://dx.doi.org/10.1016/0952-1976(96)00030-9.

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2

Gaston, Kevin J., and Mark A. O'Neill. "Automated species identification: why not?" Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 359, no. 1444 (April 29, 2004): 655–67. http://dx.doi.org/10.1098/rstb.2003.1442.

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Where possible, automation has been a common response of humankind to many activities that have to be repeated numerous times. The routine identification of specimens of previously described species has many of the characteristics of other activities that have been automated, and poses a major constraint on studies in many areas of both pure and applied biology. In this paper, we consider some of the reasons why automated species identification has not become widely employed, and whether it is a realistic option, addressing the notions that it is too difficult, too threatening, too different or too costly. Although recognizing that there are some very real technical obstacles yet to be overcome, we argue that progress in the development of automated species identification is extremely encouraging that such an approach has the potential to make a valuable contribution to reducing the burden of routine identifications. Vision and enterprise are perhaps more limiting at present than practical constraints on what might possibly be achieved.

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3

Gulliver, Austin F., and Joachim G. Stadel. "Automated spectral line identification." Publications of the Astronomical Society of the Pacific 102 (May 1990): 587. http://dx.doi.org/10.1086/132674.

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4

Watson, Anna T., Mark A. O'Neill, and Ian J. Kitching. "Automated identification of live moths (Macrolepidoptera) using digital automated identification System (DAISY)." Systematics and Biodiversity 1, no. 3 (February 2004): 287–300. http://dx.doi.org/10.1017/s1477200003001208.

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5

Burckhardt, Irene. "Zu einer schnelleren und besseren individualisierten Diagnostik." BIOspektrum 25, no. 6 (October 2019): 620–23. http://dx.doi.org/10.1007/s12268-019-0204-1.

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Abstract Total lab automation automates inoculation, incubation and analysis of human specimens. It enables the laboratory to shorten incubation times and significantly reduce the time to report. It improves the quality of microbiologic analysis by increasing the number of grown organisms. In the future it will enable automatic reading and fully automated identification and susceptibility testing of bacteria.

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6

Tauran, Patricia M., Irda Handayani, and Nurhayana Sennang. "IDENTIFIKASI BAKTERI AEROB GRAM NEGATIF DAN GRAM POSITIF MENGGUNAKAN METODE KONVENSIONAL DAN OTOMATIK." INDONESIAN JOURNAL OF CLINICAL PATHOLOGY AND MEDICAL LABORATORY 19, no. 2 (March 21, 2018): 105. http://dx.doi.org/10.24293/ijcpml.v19i2.1065.

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Choosing the method of bacteria identification is crucial to obtain accurate and quick results. This study will analyze the identificationresults of Gram negative and Gram positive from aerobic bacteria by examination using conventional and automatic methods at Dr.Wahidin Sudirohusodo Hospital Laboratory. A total of 85 samples consisting of 66 Gram negative bacteria and 19 Gram positive bacteriawere identified using conventional and automated methods. In this study, there was some correspondent identification result betweenthe conventional as well as the automated methods, namely 31.5% for Gram negative bacteria and 30.8% for Gram positive bacteria.However, the non-correspondent identification result between conventional and automated methods was found greater, namely, 68.5%for Gram negative bacteria and 69.2% for Gram positive bacteria. The non-correspondent identification result was due to the developmentof bacterial taxonomy and the differences of numbers and types of the biochemical tests between conventional and automatic methods.Bacteria identification using automated method is more accurate and faster than the conventional method, so it is recommended usingthis particularly for the laboratory and educational referral center.

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7

Chesmore, David. "Automated bioacoustic identification of species." Anais da Academia Brasileira de Ciências 76, no. 2 (June 2004): 436–40. http://dx.doi.org/10.1590/s0001-37652004000200037.

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Research into the automated identification of animals by bioacoustics is becoming more widespread mainly due to difficulties in carrying out manual surveys. This paper describes automated recognition of insects (Orthoptera) using time domain signal coding and artificial neural networks. Results of field recordings made in the UK in 2002 are presented which show that it is possible to accurately recognize 4 British Orthoptera species in natural conditions under high levels of interference. Work is under way to increase the number of species recognized.

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8

Balodis, Jānis, Ansis Zariņš, Diāna Haritonova, and Inese Janpaule. "PARAMETERS FOR AUTOMATED STAR IDENTIFICATION." Geodesy and cartography 40, no. 4 (December 16, 2014): 163–70. http://dx.doi.org/10.3846/20296991.2014.987457.

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The determination of parameters for identifying stars sensed by charge-coupled device (CCD) is discussed. Numerical experiments are summarized which support the parameter space bound estimation practicality of the proposed star pattern recognition and identification by matching with coordinate list in star catalogue. The parameter analysis performed to apply them for proper identification algorithm which is developed and used at the Institute of Geodesy and Geoinformatics. This algorithm is applied for identification of large volume star sets.

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9

Shu, Wei. "Automated personal identification by palmprint." Optical Engineering 37, no. 8 (August 1, 1998): 2359. http://dx.doi.org/10.1117/1.601756.

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10

Shen, W., and T. Tan. "Automated biometrics-based personal identification." Proceedings of the National Academy of Sciences 96, no. 20 (September 28, 1999): 11065–66. http://dx.doi.org/10.1073/pnas.96.20.11065.

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11

Young, D. "Automated identification of Fos expression." Biostatistics 2, no. 3 (September 1, 2001): 351–64. http://dx.doi.org/10.1093/biostatistics/2.3.351.

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12

Duchstein, Patrick, Oliver Hochrein, and Dirk Zahn. "Automated Motif Identification in Solids." Zeitschrift für anorganische und allgemeine Chemie 634, no. 11 (September 2008): 2035. http://dx.doi.org/10.1002/zaac.200870054.

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13

Mehnen, Jörn, Lawrence Tinsley, and Rajkumar Roy. "Automated in-service damage identification." CIRP Annals 63, no. 1 (2014): 33–36. http://dx.doi.org/10.1016/j.cirp.2014.03.016.

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14

Handegard, Nils Olav, and Kresimir Williams. "Automated tracking of fish in trawls using the DIDSON (Dual frequency IDentification SONar)." ICES Journal of Marine Science 65, no. 4 (March 11, 2008): 636–44. http://dx.doi.org/10.1093/icesjms/fsn029.

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Abstract Handegard, N. O., and Williams, K. 2008. Automated tracking of fish in trawls using the DIDSON (Dual frequency IDentification SONar). – ICES Journal of Marine Science. 65: 636–644. An application for the automated tracking of dual-frequency, identification sonar (DIDSON) data was developed and tested on fish observations taken in midwater trawls. The process incorporates target detection, multiple target tracking, and the extraction of behaviour information such as target speed and direction from the track data. The automatic tracker was evaluated using three test datasets with different target sizes, observation ranges, and densities. The targets in the datasets were tracked manually and with the automated tracker, using the manual-tracking results as the standard for estimating the performance of the automated tracking process. In the first and third dataset, where the targets were smaller and less dense, the automated tracking performed well, correctly identifying 74% and 57% of targets, respectively, and associating targets into tracks with <10% error compared with the manually tracked data. In the second dataset, where targets were dense and appeared large owing to the shorter observation range, 45% of targets were correctly identified, and the track error rate was 21%. Target speed and direction, derived from the tracking data, agreed well between the manual and automatic methods for all three test cases. Automated tracking represents a useful technique for processing DIDSON data, and a valuable alternative to time-consuming, manual data-processing, when used in appropriate conditions.

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15

Stager, C. E., and J. R. Davis. "Automated systems for identification of microorganisms." Clinical Microbiology Reviews 5, no. 3 (July 1992): 302–27. http://dx.doi.org/10.1128/cmr.5.3.302.

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Automated instruments for the identification of microorganisms were introduced into clinical microbiology laboratories in the 1970s. During the past two decades, the capabilities and performance characteristics of automated identification systems have steadily progressed and improved. This article explores the development of the various automated identification systems available in the United States and reviews their performance for identification of microorganisms. Observations regarding deficiencies and suggested improvements for these systems are provided.

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16

Bilal, Huda. "Automated Identification and Monitoring of Fruit Fly." Current Research in Agriculture and Farming 2, no. 3 (June 30, 2021): 41–44. http://dx.doi.org/10.18782/2582-7146.144.

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The fruit fly is an invasive pest that causes significant loss in horticultural crop production by damaging the fruit quality. For the integrated management of fruit fly, monitoring is a major component. Automated pest monitoring is most promising and advanced for monitoring and accurate assessment of the status of the fruit fly. The need of the hour is to automate and monitor pest populations using advanced technologies to save time and to examine the real-time situation of the field. The purpose of this paper is to discuss the approaches and sensors which detect and monitor fruit flies automatically.The review focuses on image-based sensors for identifying fruit flies, analyzing the wing beat biometric signature with optoelectronics sensors, direct counting with photo interruption sensors and E-Traps that can provide real-time field information, and presenting the various integrated systems available.With a real-time imaging system, all information about the fruit fly population and infection rate is recorded and maintained in a database. All of this has resulted in enhanced monitoring systems for fruit fly integrated pest management.

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17

Benfield, Mark, Philippe Grosjean, Phil Culverhouse, Xabier Irigolen, Michael Sieracki, Angel Lopez-Urrutia, Hans Dam, et al. "RAPID: Research on Automated Plankton Identification." Oceanography 20, no. 2 (June 1, 2007): 172–87. http://dx.doi.org/10.5670/oceanog.2007.63.

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18

Ugege, Peter E., and Omokafe A. Ugbogu. "Automated Binary based Tree Species Identification." International Journal of Computer Applications 11, no. 6 (December 10, 2010): 30–33. http://dx.doi.org/10.5120/1585-2125.

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19

Zadeh, Ali Taei, Shahnorbanun Sahran, and Muriati Mukhtar. "Automated Service Identification Methods: A Review." International Journal on Advanced Science, Engineering and Information Technology 6, no. 6 (December 9, 2016): 1053. http://dx.doi.org/10.18517/ijaseit.6.6.1481.

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20

Hinuma, Yoyo, Takashi Kamachi, and Nobutsugu Hamamoto. "Automated Identification of Facet Pair Orientations." MATERIALS TRANSACTIONS 61, no. 8 (July 1, 2020): 1430–33. http://dx.doi.org/10.2320/matertrans.mt-mn2019004.

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21

Kumar, Ajay, and Chenye Wu. "Automated human identification using ear imaging." Pattern Recognition 45, no. 3 (March 2012): 956–68. http://dx.doi.org/10.1016/j.patcog.2011.06.005.

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22

Louage, F., and T. P. Dobrowiecki. "An Automated Measurement and Identification Environment." IFAC Proceedings Volumes 27, no. 8 (July 1994): 749–54. http://dx.doi.org/10.1016/s1474-6670(17)47799-8.

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23

Etzkorn, Letha H., and Carl G. Davis. "Automated object-oriented reusable component identification." Knowledge-Based Systems 9, no. 8 (December 1996): 517–24. http://dx.doi.org/10.1016/s0950-7051(96)00003-2.

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24

Fahmy, Gamal. "Toward an automated dental identification system." Journal of Electronic Imaging 14, no. 4 (October 1, 2005): 043018. http://dx.doi.org/10.1117/1.2135310.

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25

Zhang, Z., N. Carriero, D. Zheng, J. Karro, P. M. Harrison, and M. Gerstein. "PseudoPipe: an automated pseudogene identification pipeline." Bioinformatics 22, no. 12 (March 30, 2006): 1437–39. http://dx.doi.org/10.1093/bioinformatics/btl116.

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26

Clark, Adam J., Andrew MacKenzie, Amy McGovern, Valliappa Lakshmanan, and Rodger A. Brown. "An Automated, Multiparameter Dryline Identification Algorithm." Weather and Forecasting 30, no. 6 (December 1, 2015): 1781–94. http://dx.doi.org/10.1175/waf-d-15-0070.1.

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Abstract Moisture boundaries, or drylines, are common over the southern U.S. high plains and are one of the most important airmass boundaries for convective initiation over this region. In favorable environments, drylines can initiate storms that produce strong and violent tornadoes, large hail, lightning, and heavy rainfall. Despite their importance, there are few studies documenting climatological dryline location and frequency, or performing systematic dryline forecast evaluation, which likely stems from difficulties in objectively identifying drylines over large datasets. Previous studies have employed tedious manual identification procedures. This study aims to streamline dryline identification by developing an automated, multiparameter algorithm, which applies image-processing and pattern recognition techniques to various meteorological fields and their gradients to identify drylines. The algorithm is applied to five years of high-resolution 24-h forecasts from Weather Research and Forecasting (WRF) Model simulations valid April–June 2007–11. Manually identified dryline positions, which were available from a previous study using the same dataset, are used as truth to evaluate the algorithm performance. Generally, the algorithm performed very well. High probability of detection (POD) scores indicated that the majority of drylines were identified by the method. However, a relatively high false alarm ratio (FAR) was also found, indicating that a large number of nondryline features were also identified. Preliminary use of random forests (a machine learning technique) significantly decreased the FAR, while minimally impacting the POD. The algorithm lays the groundwork for applications including model evaluation and operational forecasting, and should enable efficient analysis of drylines from very large datasets.

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27

Suresh Ramanan, S., and Anatoliy A. Khapugin. "Computational Botany:Methods for Automated Species Identification." Current Science 115, no. 2 (July 1, 2018): 349. http://dx.doi.org/10.18520/cs/v115/i2/349-350.

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28

Tenenholtz, Neil A., and Monica J. Wood. "Automated De-Identification: Embracing the Imperfect." Radiology: Artificial Intelligence 2, no. 6 (November 1, 2020): e200230. http://dx.doi.org/10.1148/ryai.2020200230.

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29

Smith, A. W., J. A. Slavin, C. M. Jackman, R. C. Fear, G. ‐K Poh, G. A. DiBraccio, J. M. Jasinski, and L. Trenchi. "Automated force‐free flux rope identification." Journal of Geophysical Research: Space Physics 122, no. 1 (January 2017): 780–91. http://dx.doi.org/10.1002/2016ja022994.

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30

Breitzman, Anthony. "Automated identification of technologically similar organizations." Journal of the American Society for Information Science and Technology 56, no. 10 (2005): 1015–23. http://dx.doi.org/10.1002/asi.20191.

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31

Stager, C. E., and J. R. Davis. "Automated systems for identification of microorganisms." Clinical Microbiology Reviews 5, no. 3 (1992): 302–27. http://dx.doi.org/10.1128/cmr.5.3.302-327.1992.

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32

Koh, T. H., L. S. Yong Ng, J. L. Foon Ho, L. H. Sng, G. C. Y. Wang, and R. Valentine Tzer Pin Lin. "Automated Identification Systems and Burkholderia pseudomallei." Journal of Clinical Microbiology 41, no. 4 (April 1, 2003): 1809. http://dx.doi.org/10.1128/jcm.41.4.1809.2003.

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33

Morris, Victoria. "Automated Language Identification of Bibliographic Resources." Cataloging & Classification Quarterly 58, no. 1 (December 20, 2019): 1–27. http://dx.doi.org/10.1080/01639374.2019.1700201.

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34

Brown, Duncan P., Nandini Krishnamurthy, and Kimmen Sjölander. "Automated Protein Subfamily Identification and Classification." PLoS Computational Biology 3, no. 8 (August 17, 2007): e160. http://dx.doi.org/10.1371/journal.pcbi.0030160.

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35

Jurkiewicz, J., and J. Nawrocki. "Automated events identification in use cases." Information and Software Technology 58 (February 2015): 110–22. http://dx.doi.org/10.1016/j.infsof.2014.09.011.

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36

Darwin, David, Mohamed Nagib Abou-Zeid, and Kirk W. Ketcham. "Automated crack identification for cement paste." Cement and Concrete Research 25, no. 3 (April 1995): 605–16. http://dx.doi.org/10.1016/0008-8846(95)00049-i.

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37

Shen, Xuening, Minde Cheng, Qingyun Shi, and Guisheng Qiu. "A new automated fingerprint identification system." Journal of Computer Science and Technology 4, no. 4 (October 1989): 289–94. http://dx.doi.org/10.1007/bf02943110.

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38

Kerpez, Kenneth J. "Automated loop identification on DSL lines." International Journal of Communication Systems 22, no. 12 (December 2009): 1479–93. http://dx.doi.org/10.1002/dac.1029.

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39

Govender, N., and L. Coetzee. "Automated vehicle identification through metallurgical fingerprints." SAIEE Africa Research Journal 96, no. 1 (March 2005): 11–16. http://dx.doi.org/10.23919/saiee.2005.9488143.

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40

He, Min, Peng Liang, Jun Li, Yang Zhang, and Yongjian Liu. "Fully automated precise operational modal identification." Engineering Structures 234 (May 2021): 111988. http://dx.doi.org/10.1016/j.engstruct.2021.111988.

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41

Arun Anand, Arun Anand, and Bahram Javidi Bahram Javidi. "Digital holographic microscopy for automated 3D cell identification: an overview (Invited Paper)." Chinese Optics Letters 12, no. 6 (2014): 060012–60017. http://dx.doi.org/10.3788/col201412.060012.

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42

MacLeod, Norman, and David Steart. "Automated leaf physiognomic character identification from digital images." Paleobiology 41, no. 4 (September 2015): 528–53. http://dx.doi.org/10.1017/pab.2015.13.

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AbstractResearch into the relationship between leaf form and climate over the last century has revealed that, in many species, the sizes and shapes of leaf characters exhibit highly structured and predictable patterns of variation in response to the local climate. Several procedures have been developed that quantify covariation between the relative abundance of plant character states and the states of climate variables as a means of estimating paleoclimate parameters. One of the most widely used of these is the Climate Leaf Analysis Multivariate Program (CLAMP). The consistency, accuracy and reliability with which leaf characters can be identified and assigned to CLAMP character-state categories is critical to the accuracy of all CLAMP analyses. Here we report results of a series of performance tests for an image-based, fully automated at the point of use, leaf character scoring system that can be used to generate CLAMP leaf character state data for: leaf bases (acute, cordate and round), leaf apices (acute, attenuate), leaf shapes (ovate, elliptical and obovate), leaf lobing (unlobed, lobed), and leaf aspect ratios (length/width). This image-based system returned jackknifed identification accuracy ratios of between 87% and 100%. These results demonstrate that automated image-based identification systems have the potential to improve paleoenvironmental inferences via the provision of accurate, consistent and rapid CLAMP leaf-character identifications. More generally, our results provide strong support for the feasibility of using fully automated, image-based morphometric procedures to address the general problem of morphological character-state identification.

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43

Lidstone, Daniel E., Louise M. Porcher, Jessica DeBerardinis, Janet S. Dufek, and Mohamed B. Trabia. "Concurrent Validity of an Automated Footprint Detection Algorithm to Measure Plantar Contact Area During Walking." Journal of the American Podiatric Medical Association 109, no. 6 (November 1, 2019): 416–25. http://dx.doi.org/10.7547/17-118.

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Background: Monitoring footprints during walking can lead to better identification of foot structure and abnormalities. Current techniques for footprint measurements are either static or dynamic, with low resolution. This work presents an approach to monitor the plantar contact area when walking using high-speed videography. Methods: Footprint images were collected by asking the participants to walk across a custom-built acrylic walkway with a high-resolution digital camera placed directly underneath the walkway. This study proposes an automated footprint identification algorithm (Automatic Identification Algorithm) to measure the footprint throughout the stance phase of walking. This algorithm used coloration of the plantar tissue that was in contact with the acrylic walkway to distinguish the plantar contact area from other regions of the foot that were not in contact. Results: The intraclass correlation coefficient (ICC) demonstrated strong agreement between the proposed automated approach and the gold standard manual method (ICC = 0.939). Strong agreement between the two methods also was found for each phase of stance (ICC > 0.78). Conclusions: The proposed automated footprint detection technique identified the plantar contact area during walking with strong agreement with a manual gold standard method. This is the first study to demonstrate the concurrent validity of an automated identification algorithm to measure the plantar contact area during walking.

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44

Hadjiiski, Lubomir, Jordan Liu, Heang-Ping Chan, Chuan Zhou, Jun Wei, Aamer Chughtai, Jean Kuriakose, Prachi Agarwal, and Ella Kazerooni. "Best-Quality Vessel Identification Using Vessel Quality Measure in Multiple-Phase Coronary CT Angiography." Computational and Mathematical Methods in Medicine 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/1835297.

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The detection of stenotic plaques strongly depends on the quality of the coronary arterial tree imaged with coronary CT angiography (cCTA). However, it is time consuming for the radiologist to select the best-quality vessels from the multiple-phase cCTA for interpretation in clinical practice. We are developing an automated method for selection of the best-quality vessels from coronary arterial trees in multiple-phase cCTA to facilitate radiologist’s reading or computerized analysis. Our automated method consists of vessel segmentation, vessel registration, corresponding vessel branch matching, vessel quality measure (VQM) estimation, and automatic selection of best branches based on VQM. For every branch, the VQM was calculated as the average radial gradient. An observer preference study was conducted to visually compare the quality of the selected vessels. 167 corresponding branch pairs were evaluated by two radiologists. The agreement between the first radiologist and the automated selection was 76% with kappa of 0.49. The agreement between the second radiologist and the automated selection was also 76% with kappa of 0.45. The agreement between the two radiologists was 81% with kappa of 0.57. The observer preference study demonstrated the feasibility of the proposed automated method for the selection of the best-quality vessels from multiple cCTA phases.

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45

Jonker, Richard, René Groben, Glen Tarran, Linda Medlin, Malcolm Wilkins, Laura García, Laura Zabala, and Lynne Boddy. "Automated identification and characterisation of microbial populations using flow cytometry: the AIMS project." Scientia Marina 64, no. 2 (June 30, 2000): 225–34. http://dx.doi.org/10.3989/scimar.2000.64n2225.

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46

Lee, Han Soo, Su Dae Kim, Yong Hyun Kim, Jung Min Kim, and Sung Shin Kim. "Automated Precipitate Identification for Sludge Volume Indicator." Advanced Materials Research 488-489 (March 2012): 1636–40. http://dx.doi.org/10.4028/www.scientific.net/amr.488-489.1636.

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Nowadays, since sewage disposal plant monitoring system is changed to combine automating control system tendency, automatic measurement method is widely applicable water temperature, pH, BOD, COD, suspended solid, etc. And its tendency is accumulated objective data. However, in the case of leaking suspended solid concentration measurement, it is literally not a long way from managing settling procedure. Because it has an effect on SS concentration of leaked water only when settled ability is disturbed. In other words, it means that research about solid-liquid separation activated sludge on automated monitoring system currently lacks, and needs research in this field. Considering that sedimentation process in the biological treatment process is one of the most important part, currently performed way, namely, SVI measured and judged by experienced specialists in standard procedure has weaknesses which objectivity cannot be guaranteed. Therefore, for maintaining the consistency of judging criteria and ensuring continuity of knowledge, objectified in the settling process is needed to build diagnostic systems. This paper proposed the method using image processing techniques over time due to settling of the sludge is analyzed for changes in height, also due to changes in sedimentation rate by analyzing the performance of the sludge settling, then send administrator for the system to pass the current state of the sludge.

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47

Salido, Jesús, Carlos Sánchez, Jesús Ruiz-Santaquiteria, Gabriel Cristóbal, Saul Blanco, and Gloria Bueno. "A Low-Cost Automated Digital Microscopy Platform for Automatic Identification of Diatoms." Applied Sciences 10, no. 17 (August 31, 2020): 6033. http://dx.doi.org/10.3390/app10176033.

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Currently, microalgae (i.e., diatoms) constitute a generally accepted bioindicator of water quality and therefore provide an index of the status of biological ecosystems. Diatom detection for specimen counting and sample classification are two difficult time-consuming tasks for the few existing expert diatomists. To mitigate this challenge, in this work, we propose a fully operative low-cost automated microscope, integrating algorithms for: (1) stage and focus control, (2) image acquisition (slide scanning, stitching, contrast enhancement), and (3) diatom detection and a prospective specimen classification (among 80 taxa). Deep learning algorithms have been applied to overcome the difficult selection of image descriptors imposed by classical machine learning strategies. With respect to the mentioned strategies, the best results were obtained by deep neural networks with a maximum precision of 86% (with the YOLO network) for detection and 99.51% for classification, among 80 different species (with the AlexNet network). All the developed operational modules are integrated and controlled by the user from the developed graphical user interface running in the main controller. With the developed operative platform, it is noteworthy that this work provides a quite useful toolbox for phycologists in their daily challenging tasks to identify and classify diatoms.

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48

Avestruz, Camille, Nan Li, Hanjue Zhu, Matthew Lightman, Thomas E. Collett, and Wentao Luo. "Automated Lensing Learner: Automated Strong Lensing Identification with a Computer Vision Technique." Astrophysical Journal 877, no. 1 (May 24, 2019): 58. http://dx.doi.org/10.3847/1538-4357/ab16d9.

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49

Carranza-Rojas, Jose, Alexis Joly, Pierre Bonnet, Hervé Goëau, and Erick Mata-Montero. "Automated Herbarium Specimen Identification using Deep Learning." Proceedings of TDWG 1 (August 16, 2017): e20302. http://dx.doi.org/10.3897/tdwgproceedings.1.20302.

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50

Januszkiewicz, Ƚukasz, Paolo Di Barba, and Sƚawomir Hausman. "Automated identification of human-body model parameters." International Journal of Applied Electromagnetics and Mechanics 51, s1 (April 7, 2016): S41—S47. http://dx.doi.org/10.3233/jae-2009.

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