{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T17:11:26Z","timestamp":1772644286524,"version":"3.50.1"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T00:00:00Z","timestamp":1648512000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T00:00:00Z","timestamp":1648512000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s11042-022-12942-9","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T07:15:50Z","timestamp":1648538150000},"page":"27783-27798","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["A deep learning approach to intelligent fruit identification and family classification"],"prefix":"10.1007","volume":"81","author":[{"given":"Nehad M.","family":"Ibrahim","sequence":"first","affiliation":[]},{"given":"Dalia Goda Ibrahim","family":"Gabr","sequence":"additional","affiliation":[]},{"given":"Atta-ur","family":"Rahman","sequence":"additional","affiliation":[]},{"given":"Sujata","family":"Dash","sequence":"additional","affiliation":[]},{"given":"Anand","family":"Nayyar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,29]]},"reference":[{"key":"12942_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-01943-x","author":"M Ahmad","year":"2020","unstructured":"Ahmad M, Qadir MA, Rahman A, Zagrouba R, Alhaidari F et al (2020) Enhanced query processing over semantic cache for cloud based relational databases. J Ambient Intell Humaniz Comput. https:\/\/doi.org\/10.1007\/s12652-020-01943-x","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"12942_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-02448-3","author":"F Alhaidari","year":"2020","unstructured":"Alhaidari F, Rahman A, Zagrouba R (2020) Cloud of things: architecture, applications and challenges. J Ambient Intell Humaniz Comput. https:\/\/doi.org\/10.1007\/s12652-020-02448-3","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"1","key":"12942_CR3","doi-asserted-by":"publisher","first-page":"149","DOI":"10.32604\/cmc.2021.015976","volume":"68","author":"SM Alotaibi","year":"2021","unstructured":"Alotaibi SM, Rahman A, Basheer MI, Khan MA (2021) Ensemble machine learning based identification of pediatric epilepsy. Comput Mater Continua 68(1):149\u2013165","journal-title":"Comput Mater Continua"},{"key":"12942_CR4","doi-asserted-by":"crossref","unstructured":"Biswas S, Dash S, Acharya S (2018) Firefly algorithm based multilingual named entity recognition for Indian languages. In: Proc Luhach A, Singh D, Hsiung PA, Hawari K, Lingras P, Singh P (eds) Advanced Informatics for Computing Research. ICAICR Communications in Computer and Information Science, vol 955. Springer, Singapore, pp 540\u2013552","DOI":"10.1007\/978-981-13-3140-4_49"},{"issue":"1","key":"12942_CR5","doi-asserted-by":"publisher","first-page":"77","DOI":"10.3233\/HIS-160226","volume":"13","author":"S Dash","year":"2016","unstructured":"Dash S, Behera R (2016) Sampling based hybrid algorithms for imbalanced data classification. Int J Hybrid Intell Syst 13(1):77\u201386. https:\/\/doi.org\/10.3233\/HIS-160226","journal-title":"Int J Hybrid Intell Syst"},{"issue":"1","key":"12942_CR6","first-page":"1","volume":"6","author":"S Dash","year":"2016","unstructured":"Dash S, Patra BN (2016) Genetic diagnosis of cancer by evolutionary fuzzy-based neural network ensemble. Int J Appl Res Bioinf 6(1):1\u201320","journal-title":"Int J Appl Res Bioinf"},{"issue":"2","key":"12942_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/IJSIR.2019040101","volume":"10","author":"S Dash","year":"2019","unstructured":"Dash S, Thulasiram R, Thulasiram P (2019) Modified firefly based meta-search algorithm for feature selection: a predictive model for medical data. Int J Swarm Intell 10(2):1\u201320","journal-title":"Int J Swarm Intell"},{"key":"12942_CR8","doi-asserted-by":"crossref","unstructured":"Dash S, Abraham A, Luhach AK, Mizera-Pietraszko J, Rodrigues JJPC (2019) Hybrid chaotic firefly decision-making model for Parkinson\u2019s disease diagnosis. Int J Distrib Sens Netw 16(12):1\u201318","DOI":"10.1177\/1550147719895210"},{"key":"12942_CR9","doi-asserted-by":"crossref","unstructured":"Dash S, Biswas S, Banerjee D, Rahman A (2019) Edge and fog computing in healthcare \u2013 a review. Scalable Comput 20(2):191\u2013206","DOI":"10.12694\/scpe.v20i2.1504"},{"key":"12942_CR10","doi-asserted-by":"crossref","unstructured":"Dileep MR (2019) AyurLeaf: a deep learning approach for classification of medicinal plants. In: Proc TENCON 2019\u20132019 IEEE Region 10 Conference (TENCON), pp 319\u2013323","DOI":"10.1109\/TENCON.2019.8929394"},{"key":"12942_CR11","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.compag.2017.07.024","volume":"141","author":"O Grillo","year":"2017","unstructured":"Grillo O, Blangiforti S, Venora G (2017) Wheat landraces identification through glumes image analysis. Comput Electron Agric 141:223\u2013231","journal-title":"Comput Electron Agric"},{"issue":"1","key":"12942_CR12","first-page":"88","volume":"19","author":"BP Gyires-T\u00f3th","year":"2019","unstructured":"Gyires-T\u00f3th BP, Osv\u00e1th M, Papp D, Szucs G (2019) Deep learning for plant classification and content-based image retrieval. Cybern Inf Technol 19(1):88\u2013100","journal-title":"Cybern Inf Technol"},{"key":"12942_CR13","unstructured":"Haupt J, Kahl S, Kowerko D, Eibl M (2018)Large-scale plant classification using deep convolutional neural networks. In: Proc CEUR Workshop, vol 2125, pp 1\u20137"},{"key":"12942_CR14","doi-asserted-by":"crossref","unstructured":"He K, Thang X, Ren S, Sun J (2015) Deep residual learning for image recognition. In: Proc IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"12942_CR15","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proc IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"12942_CR16","doi-asserted-by":"publisher","unstructured":"Hossain MS, Al-Hammadi M, Muhammad G (2019) Automatic fruit classification using deep learning for industrial applications. In: IEEE Trans Ind Inform 15(2):1027\u20131034. https:\/\/doi.org\/10.1109\/TII.2018.2875149","DOI":"10.1109\/TII.2018.2875149"},{"key":"12942_CR17","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Maaten LV, Weinberger KQ (2016) Densely connected convolutional Networks. In: Proc IEEE conference on computer vision and pattern recognition, vol 1, no 2, p 3","DOI":"10.1109\/CVPR.2017.243"},{"issue":"1","key":"12942_CR18","first-page":"51","volume":"2","author":"BK Jana","year":"2012","unstructured":"Jana BK, Mukherjee SK (2012) Diversity of cypselar features of seven species of the genus crepis L. in compositae. Indian J Fundam Appl Life Sci 2(1):51\u201358","journal-title":"Indian J Fundam Appl Life Sci"},{"key":"12942_CR19","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.compag.2017.04.013","volume":"138","author":"A Johannes","year":"2017","unstructured":"Johannes A, Picon A, Alvarez-Gila A, Echazarra J, Rodriguez-Vaamonde S et al (2017) Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case. Comput Electron Agric 138:200\u2013209","journal-title":"Comput Electron Agric"},{"key":"12942_CR20","doi-asserted-by":"publisher","first-page":"941","DOI":"10.3389\/fpls.2019.00941","volume":"10","author":"B Justine","year":"2019","unstructured":"Justine B, Samuel F, J\u00e9r\u00f4me T, Pierre-Luc S (2019) Convolutional neural networks for the automatic identification of plant diseases. Front Plant Sci 10:941\u2013956","journal-title":"Front Plant Sci"},{"key":"12942_CR21","doi-asserted-by":"crossref","unstructured":"Kaya A, Keceli AS, Catal C, Yalic HY, Temucin H et al (2019) Analysis of transfer learning for deep neural network-based plant classification models. Comput Electron Agric 158(1):20\u201329","DOI":"10.1016\/j.compag.2019.01.041"},{"issue":"1","key":"12942_CR22","doi-asserted-by":"publisher","first-page":"139","DOI":"10.32604\/cmc.2020.011416","volume":"65","author":"MA Khan","year":"2020","unstructured":"Khan MA, Abbas S, Atta A, Ditta A, Alquhayz H et al (2020) Intelligent cloud based heart disease prediction Ssystem empowered with supervised machine learning. Comput Mater Continua 65(1):139\u2013151","journal-title":"Comput Mater Continua"},{"key":"12942_CR23","unstructured":"Kingma DP, Ba JL (2015) ADAM: a method for stochastic optimization. In: Proc International Conference on Learning Representations (ICRL 2015), pp 1\u201315"},{"key":"12942_CR24","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Proc. 25th International Conference on Neural Information Processing Systems (NIPS\u201912), vol 1. Curran Associates Inc. USA, pp 1097\u20131105"},{"key":"12942_CR25","unstructured":"Le TL, Dng DN, Vu H, Nguyen TN (2015) Mica at LifeCLEF 2015: multi-organ plant Identification. In: Proc Working Notes of CLEF 2015 Conference"},{"key":"12942_CR26","doi-asserted-by":"publisher","unstructured":"Lee JW, Yoon YC (2019)Fine-grained plant identification using wide and deep learning model 1. In: Proc International Conference on Platform Technology and Service (PlatCon 2019), pp 1\u20135. https:\/\/doi.org\/10.1109\/PlatCon.2019.8669407","DOI":"10.1109\/PlatCon.2019.8669407"},{"issue":"1","key":"12942_CR27","first-page":"1","volume":"121","author":"M Mahmud","year":"2020","unstructured":"Mahmud M, Rahman A, Lee M, Choi J (2020)Evolutionary-based image encryption using RNA codons truth table. Opt Laser Technol 121(1):1\u20138","journal-title":"Opt Laser Technol"},{"issue":"3","key":"12942_CR28","doi-asserted-by":"publisher","first-page":"177","DOI":"10.14311\/NNW.2020.30.013","volume":"30","author":"MT Naseem","year":"2020","unstructured":"Naseem MT, Qureshi IM, Rahman A, Muzaffar MZ (2020) Robust and fragile watermarking for medical images using redundant residue number system and chaos. Neural Netw World 30(3):177\u2013192","journal-title":"Neural Netw World"},{"key":"12942_CR29","doi-asserted-by":"publisher","unstructured":"Panda M, Dash S (2019) A Framework for testing object oriented programs using hybrid nature inspired algorithms. In: Proc A. K. Luhach et al (Eds.): ICAICR 2018, CCIS 955. Springer Nature, Singapore, pp 1\u20139. https:\/\/doi.org\/10.1007\/978-981-13-3140-4","DOI":"10.1007\/978-981-13-3140-4"},{"issue":"2","key":"12942_CR30","doi-asserted-by":"publisher","first-page":"8","DOI":"10.21172\/1.72.502","volume":"7","author":"BN Patra","year":"2016","unstructured":"Patra BN, Dash S (2016) A FRGSNN hybrid feature selection combining FRGS filter and GSNN wrapper. Int J Latest Trends Eng Technol 7(2):8\u201315","journal-title":"Int J Latest Trends Eng Technol"},{"key":"12942_CR31","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.compag.2018.04.002","volume":"161","author":"A Picon","year":"2019","unstructured":"Picon A, Alvarez-Gila A, Seitz M, Ortiz-Barredo A, Echazarra J et al (2019) Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Comput Electron Agric 161:280\u2013290","journal-title":"Comput Electron Agric"},{"issue":"1","key":"12942_CR32","doi-asserted-by":"publisher","first-page":"553","DOI":"10.3233\/JIFS-162405","volume":"37","author":"A Rahman","year":"2019","unstructured":"Rahman A (2019) Optimum information embedding in digital watermarking. J Intell Fuzzy Syst 37(1):553\u2013564","journal-title":"J Intell Fuzzy Syst"},{"issue":"1","key":"12942_CR33","doi-asserted-by":"publisher","first-page":"1545","DOI":"10.3233\/JIFS-18579","volume":"37","author":"A Rahman","year":"2019","unstructured":"Rahman A (2019) Memetic computing based numerical solution to Troesch problem. J Intell Fuzzy Syst 37(1):1545\u20131554","journal-title":"J Intell Fuzzy Syst"},{"key":"12942_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-02174-w","author":"A Rahman","year":"2020","unstructured":"Rahman A (2020)GRBF-NN based ambient aware realtime adaptive communication in DVB-S2. J Ambient Intell Humaniz Comput. https:\/\/doi.org\/10.1007\/s12652-020-02174-w","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"18","key":"12942_CR35","first-page":"95","volume":"10","author":"A Rahman","year":"2017","unstructured":"Rahman A, Dash S (2017) Big data analysis for teacher recommendation using data mining techniques. Int J Control Theory Appl 10(18):95\u2013105","journal-title":"Int J Control Theory Appl"},{"key":"12942_CR36","doi-asserted-by":"publisher","unstructured":"Rahman A, Sultan K, Aldhafferi N, Alqahtani A, Mahmud M (2018) Reversible and fragile watermarking for medical images. Comput Math Methods Med Article ID 3461382, 7 pages. https:\/\/doi.org\/10.1155\/2018\/3461382","DOI":"10.1155\/2018\/3461382"},{"key":"12942_CR37","doi-asserted-by":"publisher","unstructured":"Rahman A, Sultan K, Musleh D, Aldhafferi N, Alqahtani A, Mahmud M (2018) Robust and fragile medical image watermarking: a joint venture of coding and chaos theories. J Healthc Eng Article ID 8137436, 11 pages. https:\/\/doi.org\/10.1155\/2018\/8137436","DOI":"10.1155\/2018\/8137436"},{"key":"12942_CR38","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/s13677-019-0144-9","volume":"8","author":"A Rahman","year":"2019","unstructured":"Rahman A, Dash S, Luhach AK, Chilamkurti N, Baek S et al (2019) A neuro-fuzzy approach for user behavior classification and prediction. J Cloud Comput 8:17","journal-title":"J Cloud Comput"},{"key":"12942_CR39","doi-asserted-by":"publisher","DOI":"10.1007\/s11235-020-00700-x","author":"A Rahman","year":"2020","unstructured":"Rahman A, Dash S, Luhach AK (2020) Dynamic MODCOD and power allocation in DVB-S2: a hybrid intelligent approach. Telecommun Syst. https:\/\/doi.org\/10.1007\/s11235-020-00700-x","journal-title":"Telecommun Syst"},{"key":"12942_CR40","doi-asserted-by":"crossref","unstructured":"Rahman A, Sultan K, Naseer I, Majeed R, Musleh D et al (2021) Supervised machine learning-based prediction of COVID-19. Comput Mater Continua 69(1):21\u201334","DOI":"10.32604\/cmc.2021.013453"},{"issue":"2","key":"12942_CR41","doi-asserted-by":"publisher","first-page":"125","DOI":"10.3233\/AIS-200554","volume":"12","author":"A Rehman","year":"2020","unstructured":"Rehman A, Athar A, Khan MA, Abbas S, Rahman A et al (2020) Modelling, simulation, and optimization of diabetes type II prediction using deep extreme learning machine. J Ambient Intell Smart Environ 12(2):125\u2013138","journal-title":"J Ambient Intell Smart Environ"},{"key":"12942_CR42","unstructured":"Reyes AK, Caicedo JC, Camargo JE (2015)Fine-tuning deep convolutional networks for plant cecognition. In: Proc Working Notes of CLEF"},{"issue":"1","key":"12942_CR43","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.inpa.2017.09.002","volume":"5","author":"S Sabzi","year":"2018","unstructured":"Sabzi S, Abbaspour-Gilandeh Y, Garc\u00eda-Mateos G (2018) A new approach for visual identification of orange varieties using neural networks and metaheuristic algorithms. Inform Process Agric 5(1):162\u2013172. https:\/\/doi.org\/10.1016\/j.inpa.2017.09.002","journal-title":"Inform Process Agric"},{"key":"12942_CR44","doi-asserted-by":"publisher","unstructured":"Shi Y, Wei Z, Ling H, Wang Z, Shen J, Li P. Person retrieval in surveillance videos via deep attribute mining and reasoning. In: IEEE Trans Multimed. https:\/\/doi.org\/10.1109\/TMM.2020.3042068","DOI":"10.1109\/TMM.2020.3042068"},{"key":"12942_CR45","unstructured":"Singh R, Dash S, Biswas S, Deka B (2020) Mobile technology solution for COVID-19. In: Proc. Fadi Al-Turjaman et al. (eds) Emerging Technologies for battling COVID-19 applications and innovations. Springer, Berlin, ISBN: 978-030-60038-9"},{"key":"12942_CR46","unstructured":"Sungbin C (2015) Plant identification with deep convolutional neural network: SNUMedinfo at LifeCLEF plant identification task 2015. In: Proc Working Notes of CLEF"},{"key":"12942_CR47","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2015) Rethinking the incpetion architecture of computer vision. In: Proc IEEE Conference on Computer Vision and Pattern Recognition, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"12942_CR48","doi-asserted-by":"publisher","unstructured":"Tan JW, Chang SW, Binti KA, Yap HJ, Yong KT (2018) Deep learning for plant species classification using leaf vein morphometric. IEEE\/ACM Trans Comput Biol Bioinform 5963(c). https:\/\/doi.org\/10.1109\/TCBB.2018.2848653","DOI":"10.1109\/TCBB.2018.2848653"},{"issue":"1","key":"12942_CR49","doi-asserted-by":"publisher","first-page":"77","DOI":"10.3233\/HIS-160226","volume":"13","author":"S Dash","year":"2016","unstructured":"Dash S, Behera R (2016) Sampling based hybrid algorithms for imbalanced data classification. Int J Hybrid Intell Syst 13(1):77\u201386. https:\/\/doi.org\/10.3233\/HIS-160226","journal-title":"Int J Hybrid Intell Syst"},{"key":"12942_CR50","doi-asserted-by":"publisher","first-page":"2722","DOI":"10.1109\/TIP.2021.3053459","volume":"30","author":"Z Xiong","year":"2021","unstructured":"Xiong Z, Yuan Y, Wang Q (2021) Adaptively Selecting Key Local Features for RGB-D Scene Recognition,\u201c. IEEE Trans Image Process 30:2722\u20132733. https:\/\/doi.org\/10.1109\/TIP.2021.3053459","journal-title":"IEEE Trans Image Process"},{"issue":"6","key":"12942_CR51","doi-asserted-by":"publisher","first-page":"2397","DOI":"10.32604\/cmc.2021.014042","volume":"66","author":"R Zagrouba","year":"2021","unstructured":"Zagrouba R, Khan MA, Rahman A, Saleem MA, Mushtaq MF et al (2021) Modelling and simulation of COVID-19 outbreak prediction using supervised machine learning. Comput Mater Continua 66(6):2397\u20132407","journal-title":"Comput Mater Continua"},{"key":"12942_CR52","doi-asserted-by":"publisher","first-page":"42111","DOI":"10.1109\/ACCESS.2021.3063181","volume":"9","author":"G Zaman","year":"2021","unstructured":"Zaman G, Mahdin H, Hussain K, Rahman A, Abawajy J, Mostafa SA (2021) \"An Ontological Framework for Information Extraction From Diverse Scientific Sources,\u201c. IEEE Access 9:42111\u201342124","journal-title":"IEEE Access"},{"key":"12942_CR53","doi-asserted-by":"publisher","unstructured":"Zhang H, He G, Peng J, Kuang Z, Fan J (2018) Deep learning of path-based tree classifiers for large-scale plant species identification. In: Proc IEEE 1st Conference on Multimedia Information Processing and Retrieval, MIPR, pp 25\u201330. https:\/\/doi.org\/10.1109\/MIPR.2018.00013","DOI":"10.1109\/MIPR.2018.00013"},{"key":"12942_CR54","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.neucom.2021.03.103","volume":"449","author":"C Zhang","year":"2021","unstructured":"Zhang C, Wang Q, Li X (2021) Towards a unified framework for license plate detection, tracking, and recognition in real-world traffic videos. Neurocomputing 449:189\u2013206","journal-title":"Neurocomputing"},{"key":"12942_CR55","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.compag.2017.07.028","volume":"141","author":"Y Zheng","year":"2017","unstructured":"Zheng Y, Zhu Q, Huang M, Guo Y, Qin J (2017) Maize and weed classification using color indices with support vector data description in outdoor fields. Comput Electron Agric 141:215\u2013222","journal-title":"Comput Electron Agric"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12942-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-12942-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12942-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T07:28:04Z","timestamp":1658215684000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-12942-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,29]]},"references-count":55,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["12942"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-12942-9","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,29]]},"assertion":[{"value":"29 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 May 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 March 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}