{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T21:59:48Z","timestamp":1777586388871,"version":"3.51.4"},"reference-count":11,"publisher":"Walter de Gruyter GmbH","issue":"4","license":[{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/3.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>We propose a method for content-based retrieving solar magnetograms. We use the SDO Helioseismic and Magnetic Imager output collected with SunPy PyTorch libraries. We create a mathematical representation of the magnetic field regions of the Sun in the form of a vector. Thanks to this solution we can compare short vectors instead of comparing full-disk images. In order to decrease the retrieval time, we used a fully-connected autoencoder, which reduced the 256-element descriptor to a 32-element semantic hash. The performed experiments and comparisons proved the efficiency of the proposed approach. Our approach has the highest precision value in comparison with other state-of-the-art methods. The presented method can be used not only for solar image retrieval but also for classification tasks.<\/jats:p>","DOI":"10.2478\/jaiscr-2022-0020","type":"journal-article","created":{"date-parts":[[2022,10,29]],"date-time":"2022-10-29T13:20:20Z","timestamp":1667049620000},"page":"299-306","source":"Crossref","is-referenced-by-count":8,"title":["Semantic Hashing for Fast Solar Magnetogram Retrieval"],"prefix":"10.2478","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3097-985X","authenticated-orcid":false,"given":"Rafa\u0142","family":"Grycuk","sequence":"first","affiliation":[{"name":"Department of Intelligent Computer Systems , Cz\u0119stochowa University of Technology , al. Armii Krajowej 36 , Cz\u0119stochowa , Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9592-262X","authenticated-orcid":false,"given":"Rafa\u0142","family":"Scherer","sequence":"additional","affiliation":[{"name":"Department of Intelligent Computer Systems , Cz\u0119stochowa University of Technology , al. Armii Krajowej 36 , Cz\u0119stochowa , Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4386-7715","authenticated-orcid":false,"given":"Alina","family":"Marchlewska","sequence":"additional","affiliation":[{"name":"Institute of Information Technologies , University of Social Sciences ul. Sienkiewicza 9 , \u0141\u00f3d\u017a , Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3336-5853","authenticated-orcid":false,"given":"Christian","family":"Napoli","sequence":"additional","affiliation":[{"name":"Department of Computer, Control and Management Engineering , Sapienza University of Rome , Via Ariosto 25, Roma 00185 , Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2022,10,29]]},"reference":[{"key":"2026042814174489149_j_jaiscr-2022-0020_ref_001","doi-asserted-by":"crossref","unstructured":"[1] R. Salakhutdinov and G. Hinton, Semantic hashing, International Journal of Approximate Reasoning, vol. 50, no. 7, pp. 969\u2013978, 2009.10.1016\/j.ijar.2008.11.006","DOI":"10.1016\/j.ijar.2008.11.006"},{"key":"2026042814174489149_j_jaiscr-2022-0020_ref_002","doi-asserted-by":"crossref","unstructured":"[2] The SunPy Community et al., The sunpy project: Open source development and status of the version 1.0 core package, The Astrophysical Journal, vol. 890, pp. 1\u201312, 2020. [Online]. Available: https:\/\/iopscience.iop.org\/article\/10.3847\/1538-4357\/ab4f7a10.3847\/1538-4357\/ab4f7a","DOI":"10.3847\/1538-4357\/ab4f7a"},{"key":"2026042814174489149_j_jaiscr-2022-0020_ref_003","doi-asserted-by":"crossref","unstructured":"[3] Stuart Mumford, Nabil Freij et al., Sunpy: A python package for solar physics, Journal of Open Source Software, vol. 5, no. 46, p. 1832, 2020. [Online]. Available: https:\/\/doi.org\/10.21105\/joss.01832","DOI":"10.21105\/joss.02073"},{"key":"2026042814174489149_j_jaiscr-2022-0020_ref_004","doi-asserted-by":"crossref","unstructured":"[4] C. Brunner, A. K\u02ddo, and S. Fodor, An autoencoder-enhanced stacking neural network model for increasing the performance of intrusion detection, Journal of Artificial Intelligence and Soft Computing Research, vol. 12, no. 2, pp. 149\u2013163, 2022.10.2478\/jaiscr-2022-0010","DOI":"10.2478\/jaiscr-2022-0010"},{"key":"2026042814174489149_j_jaiscr-2022-0020_ref_005","doi-asserted-by":"crossref","unstructured":"[5] R. Grycuk, T. Galkowski, R. Scherer, and L. Rutkowski, A novel method for solar image retrieval based on the parzen kernel estimate of the function derivative and convolutional autoencoder, in 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022, pp. 1\u20137.10.1109\/IJCNN55064.2022.9892753","DOI":"10.1109\/IJCNN55064.2022.9892753"},{"key":"2026042814174489149_j_jaiscr-2022-0020_ref_006","doi-asserted-by":"crossref","unstructured":"[6] P. Najgebauer, R. Scherer, and L. Rutkowski, Fully convolutional network for removing dct artefacts from images, in 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020, pp. 1\u20138.10.1109\/IJCNN48605.2020.9207249","DOI":"10.1109\/IJCNN48605.2020.9207249"},{"key":"2026042814174489149_j_jaiscr-2022-0020_ref_007","doi-asserted-by":"crossref","unstructured":"[7] M. Buckland and F. Gey, The relationship between recall and precision, Journal of the American society for information science, vol. 45, no. 1, p. 12, 1994.10.1002\/(SICI)1097-4571(199401)45:1<12::AID-ASI2>3.0.CO;2-L","DOI":"10.1002\/(SICI)1097-4571(199401)45:1<12::AID-ASI2>3.0.CO;2-L"},{"key":"2026042814174489149_j_jaiscr-2022-0020_ref_008","doi-asserted-by":"crossref","unstructured":"[8] K. M. Ting, Precision and recall, in Encyclopedia of machine learning. Springer, 2011, pp. 781\u2013781.10.1007\/978-0-387-30164-8_652","DOI":"10.1007\/978-0-387-30164-8_652"},{"key":"2026042814174489149_j_jaiscr-2022-0020_ref_009","doi-asserted-by":"crossref","unstructured":"[9] J. M. Banda and R. A. Angryk, Regional content-based image retrieval for solar images: Traditional versus modern methods, Astronomy and computing, vol. 13, pp. 108\u2013116, 2015.10.1016\/j.ascom.2015.09.005","DOI":"10.1016\/j.ascom.2015.09.005"},{"key":"2026042814174489149_j_jaiscr-2022-0020_ref_010","doi-asserted-by":"crossref","unstructured":"[10] J. M. Banda and R. A. Angryk, Large-scale region-based multimedia retrieval for solar images, in International Conference on Artificial Intelligence and Soft Computing. Springer, 2014, pp. 649\u2013661.10.1007\/978-3-319-07173-2_55","DOI":"10.1007\/978-3-319-07173-2_55"},{"key":"2026042814174489149_j_jaiscr-2022-0020_ref_011","doi-asserted-by":"crossref","unstructured":"[11] R. Grycuk and R. Scherer, Grid-based concise hash for solar images, in International Conference on Computational Science. 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