{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T02:19:10Z","timestamp":1784081950683,"version":"3.55.0"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"28","license":[{"start":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T00:00:00Z","timestamp":1594166400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T00:00:00Z","timestamp":1594166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["CSC201906960047"],"award-info":[{"award-number":["CSC201906960047"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"name":"111Project","award":["B17035"],"award-info":[{"award-number":["B17035"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>In image-based medical decision-making, different modalities of medical images of a given organ of a patient are captured. Each of these images will represent a modality that will render the examined organ differently, leading to different observations of a given phenomenon (such as stroke). The accurate analysis of each of these modalities promotes the detection of more appropriate medical decisions. Multimodal medical imaging is a research field that consists in the development of robust algorithms that can enable the fusion of image information acquired by different sets of modalities. In this paper, a novel multimodal medical image fusion algorithm is proposed for a wide range of medical diagnostic problems. It is based on the application of a boundary measured pulse-coupled neural network fusion strategy and an energy attribute fusion strategy in a non-subsampled shearlet transform domain. Our algorithm was validated in dataset with modalities of several diseases, namely glioma, Alzheimer\u2019s, and metastatic bronchogenic carcinoma, which contain more than 100 image pairs. Qualitative and quantitative evaluation verifies that the proposed algorithm outperforms most of the current algorithms, providing important ideas for medical diagnosis.<\/jats:p>","DOI":"10.1007\/s00521-020-05173-2","type":"journal-article","created":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T21:02:36Z","timestamp":1594155756000},"page":"22995-23015","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":132,"title":["Multimodal medical image fusion algorithm in the era of big data"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0024-8009","authenticated-orcid":false,"given":"Wei","family":"Tan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2851-4260","authenticated-orcid":false,"given":"Prayag","family":"Tiwari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9128-068X","authenticated-orcid":false,"given":"Hari Mohan","family":"Pandey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Catarina","family":"Moreira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amit Kumar","family":"Jaiswal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,7,8]]},"reference":[{"key":"5173_CR1","unstructured":"Whole brain atlas. http:\/\/www.med.harvard.edu\/AANLIB\/"},{"key":"5173_CR2","doi-asserted-by":"publisher","unstructured":"Ahmed I, Din S, Jeon G, Piccialli F (2019) Exploring deep learning models for overhead view multiple object detection. IEEE Int Things J. https:\/\/doi.org\/10.1109\/JIOT.2019.2951365","DOI":"10.1109\/JIOT.2019.2951365"},{"issue":"3","key":"5173_CR3","doi-asserted-by":"publisher","first-page":"e4188","DOI":"10.1002\/cpe.4188","volume":"30","author":"F Amato","year":"2018","unstructured":"Amato F, Moscato V, Picariello A, Piccialli F, Sperl G (2018) Centrality in heterogeneous social networks for lurkers detection: an approach based on hypergraphs. Concurr Comput Pract Exp 30(3):e4188","journal-title":"Concurr Comput Pract Exp"},{"key":"5173_CR4","doi-asserted-by":"publisher","first-page":"40782","DOI":"10.1109\/ACCESS.2019.2908076","volume":"7","author":"C Asha","year":"2019","unstructured":"Asha C, Lal S, Gurupur VP, Saxena PP (2019) Multi-modal medical image fusion with adaptive weighted combination of nsst bands using chaotic grey wolf optimization. IEEE Access 7:40782\u201340796","journal-title":"IEEE Access"},{"key":"5173_CR5","doi-asserted-by":"crossref","unstructured":"Bebortta S, Senapati D, Rajput NK, Singh AK, Rathi VK, Pandey HM, Jaiswal AK, Qian J, Tiwari P (2020) Evidence of power-law behavior in cognitive IoT applications. Neural Comput Appl pp 1\u201313","DOI":"10.1007\/s00521-020-04705-0"},{"issue":"4","key":"5173_CR6","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1109\/TCOM.1983.1095851","volume":"31","author":"P Burt","year":"1983","unstructured":"Burt P, Adelson E (1983) The Laplacian pyramid as a compact image code. IEEE Trans Commun 31(4):532\u2013540","journal-title":"IEEE Trans Commun"},{"key":"5173_CR7","doi-asserted-by":"publisher","unstructured":"Casolla G, Cuomo S, Di Cola VS, Piccialli F (2020) Exploring unsupervised learning techniques for the internet of things. IEEE Trans Ind Inform 16(4):2621\u20132628. https:\/\/doi.org\/10.1109\/TII.2019.2941142","DOI":"10.1109\/TII.2019.2941142"},{"issue":"2","key":"5173_CR8","doi-asserted-by":"publisher","first-page":"559","DOI":"10.3390\/app10020559","volume":"10","author":"V Chouhan","year":"2020","unstructured":"Chouhan V, Singh SK, Khamparia A, Gupta D, Moreira C, Damasevicius R, de Albuquerque VHC (2020) A novel transfer learning based approach for pneumonia detection in chest X-ray images. Appl Sci 10(2):559","journal-title":"Appl Sci"},{"issue":"12","key":"5173_CR9","doi-asserted-by":"publisher","first-page":"3347","DOI":"10.1109\/TBME.2013.2282461","volume":"60","author":"S Das","year":"2013","unstructured":"Das S, Kundu MK (2013) A neuro-fuzzy approach for medical image fusion. IEEE Trans Biomed Eng 60(12):3347\u20133353","journal-title":"IEEE Trans Biomed Eng"},{"key":"5173_CR10","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.neucom.2015.07.160","volume":"215","author":"J Du","year":"2016","unstructured":"Du J, Li W, Lu K, Xiao B (2016) An overview of multi-modal medical image fusion. Neurocomputing 215:3\u201320","journal-title":"Neurocomputing"},{"issue":"12","key":"5173_CR11","doi-asserted-by":"publisher","first-page":"5855","DOI":"10.1109\/TIP.2017.2745202","volume":"26","author":"J Du","year":"2017","unstructured":"Du J, Li W, Xiao B (2017) Anatomical-functional image fusion by information of interest in local Laplacian filtering domain. IEEE Trans Image Process 26(12):5855\u20135866","journal-title":"IEEE Trans Image Process"},{"key":"5173_CR12","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1016\/j.ins.2017.12.008","volume":"430","author":"J Du","year":"2018","unstructured":"Du J, Li W, Xiao B (2018) Fusion of anatomical and functional images using parallel saliency features. Inf Sci 430:567\u2013576","journal-title":"Inf Sci"},{"issue":"1","key":"5173_CR13","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.acha.2007.09.003","volume":"25","author":"G Easley","year":"2008","unstructured":"Easley G, Labate D, Lim WQ (2008) Sparse directional image representations using the discrete shearlet transform. Appl Comput Harmon Anal 25(1):25\u201346","journal-title":"Appl Comput Harmon Anal"},{"issue":"2","key":"5173_CR14","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1109\/TNN.2008.2005601","volume":"20","author":"PA Estevez","year":"2009","unstructured":"Estevez PA, Tesmer M, Perez CA, Zurada JM (2009) Normalized mutual information feature selection. IEEE Trans Neural Netw 20(2):189\u2013201","journal-title":"IEEE Trans Neural Netw"},{"key":"5173_CR15","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1016\/j.jclepro.2018.12.096","volume":"212","author":"SP Gochhayat","year":"2019","unstructured":"Gochhayat SP, Kaliyar P, Conti M, Prasath V, Gupta D, Khanna A (2019) LISA: lightweight context-aware IoT service architecture. J Clean Prod 212:1345\u20131356","journal-title":"J Clean Prod"},{"issue":"2","key":"5173_CR16","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.inffus.2011.08.002","volume":"14","author":"Y Han","year":"2013","unstructured":"Han Y, Cai Y, Cao Y, Xu X (2013) A new image fusion performance metric based on visual information fidelity. Inf Fusion 14(2):127\u2013135","journal-title":"Inf Fusion"},{"issue":"4","key":"5173_CR17","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1016\/j.patrec.2006.09.005","volume":"28","author":"W Huang","year":"2007","unstructured":"Huang W, Jing Z (2007) Evaluation of focus measures in multi-focus image fusion. Pattern Recognition Lett 28(4):493\u2013500","journal-title":"Pattern Recogntion Lett"},{"key":"5173_CR18","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1016\/j.measurement.2019.05.076","volume":"145","author":"AK Jaiswal","year":"2019","unstructured":"Jaiswal AK, Tiwari P, Kumar S, Gupta D, Khanna A, Rodrigues JJ (2019) Identifying pneumonia in chest x-rays: a deep learning approach. Measurement 145:511\u2013518","journal-title":"Measurement"},{"key":"5173_CR19","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.infrared.2014.04.003","volume":"65","author":"W Kong","year":"2014","unstructured":"Kong W, Zhang L, Lei Y (2014) Novel fusion method for visible light and infrared images based on NSST-SF-PCNN. Infrared Phys Technol 65:103\u2013112","journal-title":"Infrared Phys Technol"},{"issue":"1","key":"5173_CR20","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1186\/s40537-019-0268-2","volume":"6","author":"S Kumar","year":"2019","unstructured":"Kumar S, Tiwari P, Zymbler M (2019) Internet of things is a revolutionary approach for future technology enhancement: a review. J Big Data 6(1):111","journal-title":"J Big Data"},{"key":"5173_CR21","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.inffus.2016.05.004","volume":"33","author":"S Li","year":"2017","unstructured":"Li S, Kang X, Fang L, Hu J, Yin H (2017) Pixel-level image fusion: a survey of the state of the art. Inf Fusion 33:100\u2013112","journal-title":"Inf Fusion"},{"key":"5173_CR22","doi-asserted-by":"publisher","first-page":"56367","DOI":"10.1109\/ACCESS.2019.2900376","volume":"7","author":"S Liu","year":"2019","unstructured":"Liu S, Wang J, Lu Y, Li H, Zhao J, Zhu Z (2019) Multi-focus image fusion based on adaptive dual-channel spiking cortical model in non-subsampled shearlet domain. IEEE Access 7:56367\u201356388","journal-title":"IEEE Access"},{"key":"5173_CR23","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/j.bspc.2017.10.001","volume":"40","author":"X Liu","year":"2018","unstructured":"Liu X, Mei W, Du H (2018) Multi-modality medical image fusion based on image decomposition framework and nonsubsampled shearlet transform. Biomed Signal Process Control 40:343\u2013350","journal-title":"Biomed Signal Process Control"},{"key":"5173_CR24","doi-asserted-by":"crossref","unstructured":"Liu Y, Chen X, Cheng J, Peng H (2017) A medical image fusion method based on convolutional neural networks. In: 2017 20th international conference on information fusion (Fusion), pp 1\u20137. IEEE","DOI":"10.23919\/ICIF.2017.8009769"},{"issue":"3","key":"5173_CR25","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1109\/LSP.2019.2895749","volume":"26","author":"Y Liu","year":"2019","unstructured":"Liu Y, Chen X, Ward RK, Wang ZJ (2019) Medical image fusion via convolutional sparsity based morphological component analysis. IEEE Signal Process Lett 26(3):485\u2013489","journal-title":"IEEE Signal Process Lett"},{"key":"5173_CR26","doi-asserted-by":"publisher","first-page":"46278","DOI":"10.1109\/ACCESS.2019.2902252","volume":"7","author":"PK Mallick","year":"2019","unstructured":"Mallick PK, Ryu SH, Satapathy SK, Mishra S, Nguyen GN (2019) Brain MRI image classification for cancer detection using deep wavelet autoencoder-based deep neural network. IEEE Access 7:46278\u201346287","journal-title":"IEEE Access"},{"issue":"9","key":"5173_CR27","doi-asserted-by":"publisher","first-page":"1447","DOI":"10.1049\/iet-ipr.2018.6556","volume":"13","author":"RR Nair","year":"2019","unstructured":"Nair RR, Singh T (2019) Multi-sensor medical image fusion using pyramid-based dwt: a multi-resolution approach. IET Image Proc 13(9):1447\u20131459","journal-title":"IET Image Proc"},{"key":"5173_CR28","unstructured":"Piccialli F, Bessis N, Jung JJ (2020) Data science challenges in industry 4.0. IEEE Trans Ind Inform"},{"key":"5173_CR29","doi-asserted-by":"publisher","unstructured":"Piccialli F, Casolla G, Cuomo S, Giampaolo F, di Cola VS (2020) Decision making in iot environment through unsupervised learning. IEEE Intell Syst 35(1):27\u201335. https:\/\/doi.org\/10.1109\/MIS.2019.2944783","DOI":"10.1109\/MIS.2019.2944783"},{"key":"5173_CR30","doi-asserted-by":"crossref","unstructured":"Piccialli F, Cuomo S, di Cola VS, Casolla G (2019) A machine learning approach for iot cultural data. J Ambient Intell Human Comput pp 1\u201312","DOI":"10.1007\/s12652-019-01452-6"},{"key":"5173_CR31","doi-asserted-by":"crossref","unstructured":"Piccialli F, Cuomo S, Giampaolo F, Casolla G, di Cola VS (2020) Path prediction in iot systems through Markov chain algorithm. Fut Gen Comput Syst","DOI":"10.1016\/j.future.2020.03.053"},{"key":"5173_CR32","doi-asserted-by":"publisher","unstructured":"Piccialli F, Yoshimura Y, Benedusi P, Ratti C, Cuomo S (2020) Lessons learned from longitudinal modeling of mobile-equipped visitors in a complex museum. Neural Comput Appl 32:7785\u20137801. https:\/\/doi.org\/10.1007\/s00521-019-04099-8","DOI":"10.1007\/s00521-019-04099-8"},{"key":"5173_CR33","doi-asserted-by":"crossref","unstructured":"Piella G, Heijmans H (2003) A new quality metric for image fusion. In: Proceedings 2003 international conference on image processing (Cat No 03CH37429), vol 3, pp III-173. IEEE","DOI":"10.1109\/ICIP.2003.1247209"},{"key":"5173_CR34","doi-asserted-by":"publisher","first-page":"163947","DOI":"10.1016\/j.ijleo.2019.163947","volume":"205","author":"S Polinati","year":"2020","unstructured":"Polinati S, Dhuli R (2020) Multimodal medical image fusion using empirical wavelet de-composition and local energy maxima. Optik 205:163947","journal-title":"Optik"},{"key":"5173_CR35","doi-asserted-by":"crossref","unstructured":"Qian J, Tiwari P, Gochhayat SP, Pandey HM (2020) A noble double dictionary based ecg compression technique for ioth. IEEE Intern Things J","DOI":"10.1109\/JIOT.2020.2974678"},{"key":"5173_CR36","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.infrared.2018.05.002","volume":"91","author":"S Rong","year":"2018","unstructured":"Rong S, Zhou H, Zhao D, Cheng K, Qian K, Qin H (2018) Infrared x pattern noise reduction method based on shearlet transform. Infrared Phys Technol 91:243\u2013249","journal-title":"Infrared Phys Technol"},{"key":"5173_CR37","doi-asserted-by":"publisher","first-page":"42540","DOI":"10.1109\/ACCESS.2020.2977299","volume":"8","author":"W Tan","year":"2020","unstructured":"Tan W, Xiang P, Zhang J, Zhou H, Qin H (2020) Remote sensing image fusion via boundary measured dual-channel pcnn in multi-scale morphological gradient domain. IEEE Access 8:42540\u201342549","journal-title":"IEEE Access"},{"key":"5173_CR38","doi-asserted-by":"crossref","unstructured":"Tan W, Zhang J, Xiang P, Zhou H, Thit\u00f8n W (2020) Infrared and visible image fusion via nsst and pcnn in multiscale morphological gradient domain. In: Optics, photonics and digital technologies for imaging applications VI, vol 11353, p 113531E. International society for optics and photonics","DOI":"10.1117\/12.2551830"},{"issue":"35","key":"5173_CR39","doi-asserted-by":"publisher","first-page":"10092","DOI":"10.1364\/AO.57.010092","volume":"57","author":"W Tan","year":"2018","unstructured":"Tan W, Zhou H, Rong S, Qian K, Yu Y (2018) Fusion of multi-focus images via a Gaussian curvature filter and synthetic focusing degree criterion. Appl Opt 57(35):10092\u201310101","journal-title":"Appl Opt"},{"issue":"12","key":"5173_CR40","doi-asserted-by":"publisher","first-page":"3064","DOI":"10.1364\/AO.58.003064","volume":"58","author":"W Tan","year":"2019","unstructured":"Tan W, Zhou H, Song J, Li H, Yu Y, Du J (2019) Infrared and visible image perceptive fusion through multi-level Gaussian curvature filtering image decomposition. Appl Opt 58(12):3064\u20133073","journal-title":"Appl Opt"},{"key":"5173_CR41","doi-asserted-by":"crossref","unstructured":"Tan W, Zhou Hx, Yu Y, Du J, Qin H, Ma Z, Zheng R (2017) Multi-focus image fusion using spatial frequency and discrete wavelet transform. In: AOPC 2017: Optical sensing and imaging technology and applications, vol 10462, p 104624\u00a0K. International society for optics and photonics","DOI":"10.1117\/12.2285561"},{"key":"5173_CR42","doi-asserted-by":"crossref","unstructured":"Tiwari P, Melucci M (2018) Towards a quantum-inspired framework for binary classification. In: Proceedings of the 27th ACM international conference on information and knowledge management, pp 1815\u20131818","DOI":"10.1145\/3269206.3269304"},{"key":"5173_CR43","first-page":"10051","volume":"33","author":"P Tiwari","year":"2019","unstructured":"Tiwari P, Melucci M (2019) Binary classifier inspired by quantum theory. Proc AAAI Conf Artif Intell 33:10051\u201310052","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"5173_CR44","doi-asserted-by":"publisher","first-page":"42354","DOI":"10.1109\/ACCESS.2019.2904624","volume":"7","author":"P Tiwari","year":"2019","unstructured":"Tiwari P, Melucci M (2019) Towards a quantum-inspired binary classifier. IEEE Access 7:42354\u201342372","journal-title":"IEEE Access"},{"key":"5173_CR45","doi-asserted-by":"publisher","first-page":"1036","DOI":"10.1016\/j.cogsys.2018.08.022","volume":"52","author":"P Tiwari","year":"2018","unstructured":"Tiwari P, Qian J, Li Q, Wang B, Gupta D, Khanna A, Rodrigues JJ, de Al-buquerque VHC (2018) Detection of subtype blood cells using deep learning. Cogn Syst Res 52:1036\u20131044","journal-title":"Cogn Syst Res"},{"issue":"11","key":"5173_CR46","doi-asserted-by":"publisher","first-page":"1101","DOI":"10.1007\/s00521-016-2633-9","volume":"29","author":"Z Wang","year":"2018","unstructured":"Wang Z, Wang S, Guo L (2018) Novel multi-focus image fusion based on PCNN and random walks. Neural Comput Appl 29(11):1101\u20131114","journal-title":"Neural Comput Appl"},{"issue":"11","key":"5173_CR47","doi-asserted-by":"publisher","first-page":"2622","DOI":"10.1109\/TBME.2018.2811243","volume":"65","author":"H Yin","year":"2018","unstructured":"Yin H (2018) Tensor sparse representation for 3-D medical image fusion using weighted average rule. IEEE Trans Biomed Eng 65(11):2622\u20132633","journal-title":"IEEE Trans Biomed Eng"},{"issue":"1","key":"5173_CR48","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1109\/TIM.2018.2838778","volume":"68","author":"M Yin","year":"2018","unstructured":"Yin M, Liu X, Liu Y, Chen X (2018) Medical image fusion with parameter-adaptive pulse coupled neural network in nonsubsampled shearlet transform domain. IEEE Trans Instrum Meas 68(1):49\u201364","journal-title":"IEEE Trans Instrum Meas"},{"issue":"7","key":"5173_CR49","doi-asserted-by":"publisher","first-page":"1334","DOI":"10.1016\/j.sigpro.2009.01.012","volume":"89","author":"Q Zhang","year":"2009","unstructured":"Zhang Q, Guo BL (2009) Multifocus image fusion using the nonsubsampled contourlet transform. Signal Process 89(7):1334\u20131346","journal-title":"Signal Process"},{"key":"5173_CR50","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.inffus.2016.09.006","volume":"35","author":"Y Zhang","year":"2017","unstructured":"Zhang Y, Bai X, Wang T (2017) Boundary finding based multi-focus image fusion through multi-scale morphological focus-measure. Inf Fusion 35:81\u2013101","journal-title":"Inf Fusion"},{"key":"5173_CR51","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.inffus.2015.11.003","volume":"30","author":"Z Zhou","year":"2016","unstructured":"Zhou Z, Wang B, Li S, Dong M (2016) Perceptual fusion of infrared and visible images through a hybrid multi-scale decomposition with Gaussian and bilateral filters. Inf Fusion 30:15\u201326","journal-title":"Inf Fusion"},{"key":"5173_CR52","doi-asserted-by":"publisher","first-page":"20811","DOI":"10.1109\/ACCESS.2019.2898111","volume":"7","author":"Z Zhu","year":"2019","unstructured":"Zhu Z, Zheng M, Qi G, Wang D, Xiang Y (2019) A phase congruency and local Laplacian energy based multi-modality medical image fusion method in NSCT domain. IEEE Access 7:20811\u201320824","journal-title":"IEEE Access"},{"key":"5173_CR53","doi-asserted-by":"crossref","unstructured":"Pandey HM, Windridge D (2019) A comprehensive classification of deep learning libraries. In: Third international congress on information and communication technology. Springer, Singapore","DOI":"10.1007\/978-981-13-1165-9_40"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05173-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05173-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05173-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T06:03:20Z","timestamp":1759125800000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05173-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,8]]},"references-count":53,"journal-issue":{"issue":"28","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["5173"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05173-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,8]]},"assertion":[{"value":"11 March 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}