{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T17:02:09Z","timestamp":1774890129555,"version":"3.50.1"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T00:00:00Z","timestamp":1750032000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T00:00:00Z","timestamp":1750032000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s10586-025-05220-4","type":"journal-article","created":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T12:19:52Z","timestamp":1750076392000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Dynamic colormap visualization integrated with Harris hawks optimization for enhanced lung CT segmentation and diagnostic precision"],"prefix":"10.1007","volume":"28","author":[{"given":"Osama","family":"Drogham","sequence":"first","affiliation":[]},{"given":"Mohammad H.","family":"Ryalat","sequence":"additional","affiliation":[]},{"given":"Nijad","family":"Al-Najdawi","sequence":"additional","affiliation":[]},{"given":"Rami S.","family":"Alkhawaldeh","sequence":"additional","affiliation":[]},{"given":"Jamil","family":"AlShaqsi","sequence":"additional","affiliation":[]},{"given":"Mohammed Azmi","family":"Al-Betar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,16]]},"reference":[{"issue":"1","key":"5220_CR1","doi-asserted-by":"publisher","first-page":"25","DOI":"10.3390\/fi11010025","volume":"11","author":"O Dorgham","year":"2019","unstructured":"Dorgham, O., Al-Mherat, I., Al-Shaer, J., Bani-Ahmad, S., Laycock, S.: Smart system for prediction of accurate surface electromyography signals using an artificial neural network. Future Internet 11(1), 25 (2019)","journal-title":"Future Internet"},{"key":"5220_CR2","volume-title":"Chronic Obstructive Pulmonary Disease (COPD)","author":"JA Barber\u00e0","year":"2016","unstructured":"Barber\u00e0, J.A., Blanco, I.: Chronic Obstructive Pulmonary Disease (COPD). CRC Press, Boca Raton (2016)"},{"key":"5220_CR3","volume-title":"Computer Vision","author":"G Stockman","year":"2001","unstructured":"Stockman, G., Shapiro, L.G.: Computer Vision. Prentice Hall PTR, Hoboken (2001)"},{"key":"5220_CR4","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.eswa.2019.01.047","volume":"125","author":"M Abd Elaziz","year":"2019","unstructured":"Abd Elaziz, M., Oliva, D., Ewees, A.A., Xiong, S.: Multi-level thresholding-based grey scale image segmentation using multi-objective multi-verse optimizer. Expert Syst. Appl. 125, 112\u2013129 (2019)","journal-title":"Expert Syst. Appl."},{"key":"5220_CR5","volume-title":"Information Visualization: Perception for Design","author":"C Ware","year":"2019","unstructured":"Ware, C.: Information Visualization: Perception for Design. Morgan Kaufmann, Burlington (2019)"},{"key":"5220_CR6","volume-title":"Designing Better Maps: A Guide for GIS Users","author":"C Brewer","year":"2016","unstructured":"Brewer, C.: Designing Better Maps: A Guide for GIS Users. ESRI Press, Redlands (2016)"},{"key":"5220_CR7","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1016\/j.future.2019.02.028","volume":"97","author":"AA Heidari","year":"2019","unstructured":"Heidari, A.A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., Chen, H.: Harris hawks optimization: algorithm and applications. Future Gener. Comput. Syst. 97, 849\u2013872 (2019)","journal-title":"Future Gener. Comput. Syst."},{"issue":"9","key":"5220_CR8","doi-asserted-by":"publisher","first-page":"6855","DOI":"10.1007\/s00521-022-08078-4","volume":"35","author":"MH Ryalat","year":"2023","unstructured":"Ryalat, M.H., Dorgham, O., Tedmori, S., Al-Rahamneh, Z., Al-Najdawi, N., Mirjalili, S.: Harris hawks optimization for COVID-19 diagnosis based on multi-threshold image segmentation. Neural Comput. Appl. 35(9), 6855\u20136873 (2023)","journal-title":"Neural Comput. Appl."},{"key":"5220_CR9","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1007\/s11548-012-0783-5","volume":"8","author":"M Fisher","year":"2013","unstructured":"Fisher, M., Dorgham, O., Laycock, S.D.: Fast reconstructed radiographs from octree-compressed volumetric data. Int. J. Comput. Assist. Radiol. Surg. 8, 313\u2013322 (2013)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"11","key":"5220_CR10","doi-asserted-by":"publisher","first-page":"1110","DOI":"10.1016\/j.compbiomed.2012.09.003","volume":"42","author":"SMB Netto","year":"2012","unstructured":"Netto, S.M.B., Silva, A.C., Nunes, R.A., Gattass, M.: Automatic segmentation of lung nodules with growing neural gas and support vector machine. Comput. Biol. Med. 42(11), 1110\u20131121 (2012)","journal-title":"Comput. Biol. Med."},{"issue":"11","key":"5220_CR11","doi-asserted-by":"publisher","first-page":"1098","DOI":"10.1016\/j.compbiomed.2012.09.002","volume":"42","author":"D Cascio","year":"2012","unstructured":"Cascio, D., Magro, R., Fauci, F., Iacomi, M., Raso, G.: Automatic detection of lung nodules in CT datasets based on stable 3D mass\u2013spring models. Comput. Biol. Med. 42(11), 1098\u20131109 (2012)","journal-title":"Comput. Biol. Med."},{"issue":"1","key":"5220_CR12","first-page":"252","volume":"6","author":"S Akram","year":"2016","unstructured":"Akram, S., Javed, M.Y., Akram, M.U., Qamar, U., Hassan, A.: Pulmonary nodules detection and classification using hybrid features from computerized tomographic images. J. Med. Imaging Health Inform. 6(1), 252\u2013259 (2016)","journal-title":"J. Med. Imaging Health Inform."},{"issue":"8","key":"5220_CR13","doi-asserted-by":"publisher","first-page":"3453","DOI":"10.1118\/1.2948349","volume":"35","author":"J Pu","year":"2008","unstructured":"Pu, J., Zheng, B., Leader, J.K., Wang, X.-H., Gur, D.: An automated CT based lung nodule detection scheme using geometric analysis of signed distance field. Med. Phys. 35(8), 3453\u20133461 (2008)","journal-title":"Med. Phys."},{"issue":"3","key":"5220_CR14","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1016\/j.media.2010.02.004","volume":"14","author":"T Messay","year":"2010","unstructured":"Messay, T., Hardie, R.C., Rogers, S.K.: A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Med. Image Anal. 14(3), 390\u2013406 (2010)","journal-title":"Med. Image Anal."},{"issue":"10","key":"5220_CR15","doi-asserted-by":"publisher","first-page":"5630","DOI":"10.1118\/1.3633941","volume":"38","author":"M Tan","year":"2011","unstructured":"Tan, M., Deklerck, R., Jansen, B., Bister, M., Cornelis, J.: A novel computer-aided lung nodule detection system for CT images. Med. Phys. 38(10), 5630\u20135645 (2011)","journal-title":"Med. Phys."},{"issue":"1","key":"5220_CR16","volume":"2013","author":"A El-Baz","year":"2013","unstructured":"El-Baz, A., Elnakib, A., Abou El-Ghar, M., Gimel\u2019farb, G., Falk, R., Farag, A.: Automatic detection of 2D and 3D lung nodules in chest spiral CT scans. Int. J. Biomed. Imaging 2013(1), 517632 (2013)","journal-title":"Int. J. Biomed. Imaging"},{"key":"5220_CR17","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.compbiomed.2013.10.028","volume":"45","author":"HH Jo","year":"2014","unstructured":"Jo, H.H., Hong, H., Goo, J.M.: Pulmonary nodule registration in serial CT scans using global rib matching and nodule template matching. Comput. Biol. Med. 45, 87\u201397 (2014)","journal-title":"Comput. Biol. Med."},{"key":"5220_CR18","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1007\/978-3-030-33128-3_3","volume":"1213","author":"S Kido","year":"2020","unstructured":"Kido, S., Hirano, Y., Mabu, S.: Deep learning for pulmonary image analysis: classification, detection, and segmentation. Adv. Exp. Med. Biol. 1213, 47\u201358 (2020)","journal-title":"Adv. Exp. Med. Biol."},{"key":"5220_CR19","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1007\/978-3-030-33128-3_3","volume":"1213","author":"S Kido","year":"2020","unstructured":"Kido, S., Hirano, Y., Mabu, S.: Deep learning for pulmonary image analysis: classification, detection, and segmentation. Adv. Exp. Med. Biol. 1213, 47\u201358 (2020)","journal-title":"Adv. Exp. Med. Biol."},{"issue":"31","key":"5220_CR20","doi-asserted-by":"publisher","first-page":"22839","DOI":"10.1007\/s00521-021-06719-8","volume":"35","author":"S Gite","year":"2023","unstructured":"Gite, S., Mishra, A., Kotecha, K.: Enhanced lung image segmentation using deep learning. Neural Comput. Appl. 35(31), 22839\u201322853 (2023)","journal-title":"Neural Comput. Appl."},{"key":"5220_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.smhl.2022.100304","volume":"26","author":"O Dorgham","year":"2022","unstructured":"Dorgham, O., Naser, M.A., Ryalat, M., Hyari, A., Al-Najdawi, N., Mirjalili, S.: U-NetCTS: U-Net deep neural network for fully automatic segmentation of 3D CT DICOM volume. Smart Health 26, 100304 (2022)","journal-title":"Smart Health"},{"key":"5220_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2020.100375","volume":"20","author":"O Dorgham","year":"2020","unstructured":"Dorgham, O., Ryalat, M.H., Naser, M.A.: Automatic body segmentation for accelerated rendering of digitally reconstructed radiograph images. Inform. Med. Unlocked 20, 100375 (2020). https:\/\/doi.org\/10.1016\/j.imu.2020.100375","journal-title":"Inform. Med. Unlocked"},{"key":"5220_CR23","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","volume":"35","author":"M Havaei","year":"2017","unstructured":"Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., Larochelle, H.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18\u201331 (2017)","journal-title":"Med. Image Anal."},{"issue":"2","key":"5220_CR24","first-page":"63","volume":"36","author":"HR Roth","year":"2018","unstructured":"Roth, H.R., Shen, C., Oda, H., Oda, M., Hayashi, Y., Misawa, K., Mori, K.: Deep learning and its application to medical image segmentation. Med. Imaging Technol. 36(2), 63\u201371 (2018)","journal-title":"Med. Imaging Technol."},{"key":"5220_CR25","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.media.2017.05.001","volume":"41","author":"Q Dou","year":"2017","unstructured":"Dou, Q., Yu, L., Chen, H., Jin, Y., Yang, X., Qin, J., Heng, P.-A.: 3D deeply supervised network for automated segmentation of volumetric medical images. Med. Image Anal. 41, 40\u201354 (2017)","journal-title":"Med. Image Anal."},{"issue":"5","key":"5220_CR26","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.1109\/TMI.2016.2528129","volume":"35","author":"Q Dou","year":"2016","unstructured":"Dou, Q., Chen, H., Yu, L., Zhao, L., Qin, J., Wang, D., Mok, V.C., Shi, L., Heng, P.-A.: Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks. IEEE Trans. Med. Imaging 35(5), 1182\u20131195 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"5220_CR27","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1007\/s10044-017-0653-4","volume":"22","author":"BN Narayanan","year":"2019","unstructured":"Narayanan, B.N., Hardie, R.C., Kebede, T.M., Sprague, M.J.: Optimized feature selection-based clustering approach for computer-aided detection of lung nodules in different modalities. Pattern Anal. Appl. 22, 559\u2013571 (2019)","journal-title":"Pattern Anal. Appl."},{"issue":"1","key":"5220_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cmpb.2009.07.006","volume":"98","author":"JRF Silva Sousa","year":"2010","unstructured":"Silva Sousa, J.R.F., Silva, A.C., Paiva, A.C., Nunes, R.A.: Methodology for automatic detection of lung nodules in computerized tomography images. Comput. Methods Programs Biomed. 98(1), 1\u201314 (2010)","journal-title":"Comput. Methods Programs Biomed."},{"issue":"10","key":"5220_CR29","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1093\/jamia\/ocy098","volume":"25","author":"R Gruetzemacher","year":"2018","unstructured":"Gruetzemacher, R., Gupta, A., Paradice, D.: 3D deep learning for detecting pulmonary nodules in CT scans. J. Am. Med. Inform. Assoc. 25(10), 1301\u20131310 (2018)","journal-title":"J. Am. Med. Inform. Assoc."},{"issue":"4","key":"5220_CR30","doi-asserted-by":"publisher","first-page":"1227","DOI":"10.1109\/JBHI.2017.2725903","volume":"22","author":"H Jiang","year":"2017","unstructured":"Jiang, H., Ma, H., Qian, W., Gao, M., Li, Y.: An automatic detection system of lung nodule based on multigroup patch-based deep learning network. IEEE J. Biomed. Health Inform. 22(4), 1227\u20131237 (2017)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"2","key":"5220_CR31","doi-asserted-by":"publisher","first-page":"020901","DOI":"10.1117\/1.JMI.6.2.020901","volume":"6","author":"F Shaukat","year":"2019","unstructured":"Shaukat, F., Raja, G., Frangi, A.F.: Computer-aided detection of lung nodules: a review. J. Med. Imaging 6(2), 020901 (2019)","journal-title":"J. Med. Imaging"},{"issue":"1","key":"5220_CR32","doi-asserted-by":"publisher","first-page":"29","DOI":"10.3390\/diagnostics9010029","volume":"9","author":"LM Pehrson","year":"2019","unstructured":"Pehrson, L.M., Nielsen, M.B., Ammitzb\u00f8l Lauridsen, C.: Automatic pulmonary nodule detection applying deep learning or machine learning algorithms to the LIDC-IDRI database: a systematic review. Diagnostics 9(1), 29 (2019)","journal-title":"Diagnostics"},{"key":"5220_CR33","doi-asserted-by":"crossref","unstructured":"Kumar, A.S., Kumar, A., Bajaj, V., Singh, G.K.: Fractional-order Darwinian swarm intelligence inspired multilevel thresholding for mammogram segmentation. In: 2018 International Conference on Communication and Signal Processing (ICCSP), 2018, pp. 0160\u20130164. IEEE (2018)","DOI":"10.1109\/ICCSP.2018.8524302"},{"key":"5220_CR34","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1007\/s00500-017-2794-1","volume":"23","author":"D Oliva","year":"2019","unstructured":"Oliva, D., Hinojosa, S., Osuna-Enciso, V., Cuevas, E., P\u00e9rez-Cisneros, M., Sanchez-Ante, G.: Image segmentation by minimum cross entropy using evolutionary methods. Soft Comput. 23, 431\u2013450 (2019)","journal-title":"Soft Comput."},{"issue":"285\u2013296","key":"5220_CR35","first-page":"23","volume":"11","author":"N Otsu","year":"1975","unstructured":"Otsu, N., et al.: A threshold selection method from gray-level histograms. Automatica 11(285\u2013296), 23\u201327 (1975)","journal-title":"Automatica"},{"issue":"3","key":"5220_CR36","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/0734-189X(85)90125-2","volume":"29","author":"JN Kapur","year":"1985","unstructured":"Kapur, J.N., Sahoo, P.K., Wong, A.K.: A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vis. Graph. Image Process. 29(3), 273\u2013285 (1985)","journal-title":"Comput. Vis. Graph. Image Process."},{"issue":"1","key":"5220_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s40998-019-00251-1","volume":"44","author":"S Pare","year":"2020","unstructured":"Pare, S., Kumar, A., Singh, G.K., Bajaj, V.: Image segmentation using multilevel thresholding: a research review. Iran. J. Sci. Technol. Trans. Electr. Eng. 44(1), 1\u201329 (2020)","journal-title":"Iran. J. Sci. Technol. Trans. Electr. Eng."},{"key":"5220_CR38","doi-asserted-by":"crossref","unstructured":"Pare, S., Bhandari, A., Kumar, A., Singh, G.K.: R\u00e9nyi\u2019s entropy and bat algorithm based color image multilevel thresholding. In: Machine Intelligence and Signal Analysis, pp. 71\u201384. Springer, Berlin (2019)","DOI":"10.1007\/978-981-13-0923-6_7"},{"key":"5220_CR39","doi-asserted-by":"publisher","first-page":"23003","DOI":"10.1007\/s11042-019-7515-6","volume":"78","author":"M Ahmadi","year":"2019","unstructured":"Ahmadi, M., Kazemi, K., Aarabi, A., Niknam, T., Helfroush, M.S.: Image segmentation using multilevel thresholding based on modified bird mating optimization. Multimed. Tools Appl. 78, 23003\u201323027 (2019)","journal-title":"Multimed. Tools Appl."},{"key":"5220_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.04.002","volume":"97","author":"SJ Mousavirad","year":"2020","unstructured":"Mousavirad, S.J., Ebrahimpour-Komleh, H.: Human mental search-based multilevel thresholding for image segmentation. Appl. Soft Comput. 97, 105427 (2020)","journal-title":"Appl. Soft Comput."},{"key":"5220_CR41","doi-asserted-by":"publisher","first-page":"11258","DOI":"10.1109\/ACCESS.2019.2891673","volume":"7","author":"H Liang","year":"2019","unstructured":"Liang, H., Jia, H., Xing, Z., Ma, J., Peng, X.: Modified grasshopper algorithm-based multilevel thresholding for color image segmentation. IEEE Access 7, 11258\u201311295 (2019)","journal-title":"IEEE Access"},{"issue":"5","key":"5220_CR42","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1016\/j.jksuci.2018.04.007","volume":"33","author":"KB Resma","year":"2021","unstructured":"Resma, K.B., Nair, M.S.: Multilevel thresholding for image segmentation using krill herd optimization algorithm. J. King Saud Univ. Comput. Inf. Sci. 33(5), 528\u2013541 (2021)","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"5220_CR43","doi-asserted-by":"crossref","unstructured":"Zhou, C., Tian, L., Zhao, H., Zhao, K.: A method of two-dimensional Otsu image threshold segmentation based on improved firefly algorithm. In: 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2015, pp. 1420\u20131424. IEEE (2015)","DOI":"10.1109\/CYBER.2015.7288151"},{"key":"5220_CR44","doi-asserted-by":"crossref","unstructured":"Moreland, K.: Diverging color maps for scientific visualization. In: International Symposium on Visual Computing, 2009, pp. 92\u2013103. Springer (2009)","DOI":"10.1007\/978-3-642-10520-3_9"},{"key":"5220_CR45","unstructured":"Mittelst\u00e4dt, S., Bernard, J., Schreck, T., Steiger, M., Kohlhammer, J., Keim, D.A.: Revisiting perceptually optimized color mapping for high-dimensional data analysis. In: Eurographics Conference on Visualization, 2014 (2014)"},{"key":"5220_CR46","doi-asserted-by":"crossref","unstructured":"Tominski, C., Fuchs, G., Schumann, H.: Task-driven color coding. In: 2008 12th International Conference Information Visualisation, 2008, pp. 373\u2013380. IEEE (2008)","DOI":"10.1109\/IV.2008.24"},{"key":"5220_CR47","volume":"7","author":"S Ahuja","year":"2022","unstructured":"Ahuja, S., Panigrahi, B.K., Gandhi, T.K.: Enhanced performance of Dark-Nets for brain tumor classification and segmentation using colormap-based superpixel techniques. Mach. Learn. Appl. 7, 100212 (2022)","journal-title":"Mach. Learn. Appl."},{"key":"5220_CR48","unstructured":"yt-project.org: Applying a Colormap to your Rendering. https:\/\/yt-project.org\/doc\/visualizing\/colormaps\/index.html"},{"issue":"6","key":"5220_CR49","doi-asserted-by":"publisher","DOI":"10.1063\/1.5113654","volume":"11","author":"N Ali","year":"2019","unstructured":"Ali, N., Hamilton, N., Calaf, M., Cal, R.B.: Classification of the Reynolds stress anisotropy tensor in very large thermally stratified wind farms using colormap image segmentation. J. Renew. Sustain. Energy 11(6), 063305 (2019)","journal-title":"J. Renew. Sustain. Energy"},{"issue":"1","key":"5220_CR50","first-page":"216","volume":"9","author":"H Gezici","year":"2022","unstructured":"Gezici, H., Livatyal\u0131, H.: Chaotic Harris hawks optimization algorithm. J. Comput. Des. Eng. 9(1), 216\u2013245 (2022)","journal-title":"J. Comput. Des. Eng."},{"key":"5220_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113428","volume":"155","author":"E Rodr\u00edguez-Esparza","year":"2020","unstructured":"Rodr\u00edguez-Esparza, E., Zanella-Calzada, L.A., Oliva, D., Heidari, A.A., Zaldivar, D., P\u00e9rez-Cisneros, M., Foong, L.K.: An efficient Harris hawks-inspired image segmentation method. Expert Syst. Appl. 155, 113428 (2020). https:\/\/doi.org\/10.1016\/j.eswa.2020.113428","journal-title":"Expert Syst. Appl."},{"issue":"6","key":"5220_CR52","doi-asserted-by":"publisher","first-page":"8423","DOI":"10.1007\/s11042-020-10035-z","volume":"80","author":"DRIM Setiadi","year":"2021","unstructured":"Setiadi, D.R.I.M.: PSNR vs SSIM: imperceptibility quality assessment for image steganography. Multimed. Tools Appl. 80(6), 8423\u20138444 (2021)","journal-title":"Multimed. Tools Appl."},{"issue":"1","key":"5220_CR53","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/s13278-020-00660-9","volume":"10","author":"V Verma","year":"2020","unstructured":"Verma, V., Aggarwal, R.K.: A comparative analysis of similarity measures akin to the Jaccard index in collaborative recommendations: empirical and theoretical perspective. Soc. Netw. Anal. Min. 10(1), 43 (2020)","journal-title":"Soc. Netw. Anal. Min."}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05220-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-025-05220-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05220-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T22:26:23Z","timestamp":1757197583000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-025-05220-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,16]]},"references-count":53,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["5220"],"URL":"https:\/\/doi.org\/10.1007\/s10586-025-05220-4","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,16]]},"assertion":[{"value":"22 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 February 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there is no conflict of interest regarding the publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"377"}}