{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T11:11:28Z","timestamp":1768561888144,"version":"3.49.0"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T00:00:00Z","timestamp":1671580800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T00:00:00Z","timestamp":1671580800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100019397","name":"University Grants Commission - South Eastern Regional Office","doi-asserted-by":"publisher","award":["F.15-6(DEC-2018)\/2019(NET)"],"award-info":[{"award-number":["F.15-6(DEC-2018)\/2019(NET)"]}],"id":[{"id":"10.13039\/501100019397","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Evol. Intel."],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s12065-022-00800-4","type":"journal-article","created":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T05:02:31Z","timestamp":1671598951000},"page":"1163-1183","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Multi-resolution analysis and deep neural network architecture based hybrid feature extraction technique for plant disease identification and severity estimation"],"prefix":"10.1007","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2531-9638","authenticated-orcid":false,"given":"Kirti","family":"K.","sequence":"first","affiliation":[]},{"given":"Navin","family":"Rajpal","sequence":"additional","affiliation":[]},{"given":"Jyotsna","family":"Yadav","sequence":"additional","affiliation":[]},{"given":"Kalyan Kumar","family":"Mondal","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,21]]},"reference":[{"key":"800_CR1","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1094\/PDIS-04-20-0792-RE","volume":"105","author":"AM Baetsen-Young","year":"2021","unstructured":"Baetsen-Young AM, Swinton SM, Chilvers MI (2021) Economic impact of fluopyram-amended seed treatments to reduce soybean yield loss associated with sudden death syndrome. Plant Dis 105:78\u201386. https:\/\/doi.org\/10.1094\/PDIS-04-20-0792-RE","journal-title":"Plant Dis"},{"key":"800_CR2","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1007\/s11119-017-9543-4","volume":"19","author":"E Tona","year":"2018","unstructured":"Tona E, Calcante A, Oberti R (2018) The profitability of precision spraying on specialty crops: a technical\u2013economic analysis of protection equipment at increasing technological levels. Precis Agric 19:606\u2013629. https:\/\/doi.org\/10.1007\/s11119-017-9543-4","journal-title":"Precis Agric"},{"key":"800_CR3","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez MG, Miramontes-Varo V, Chocoteco JA, Vidal V (2020) Identification and classification of Botrytis Disease in Pomegranate with Machine Learning. Advances in Intelligent Systems and Computing. Springer, pp 582\u2013598","DOI":"10.1007\/978-3-030-52246-9_43"},{"key":"800_CR4","doi-asserted-by":"crossref","unstructured":"Agarwal M, Sinha A, Gupta SK et al (2020) Potato crop disease classification using convolutional neural network. Smart Innovation, Systems and Technologies. Springer, pp 391\u2013400","DOI":"10.1007\/978-981-13-8406-6_37"},{"key":"800_CR5","doi-asserted-by":"publisher","DOI":"10.1109\/CAST.2016.7915015","volume-title":"Monitoring and Controlling Rice Diseases using image Processing techniques","author":"AA Joshi","year":"2016","unstructured":"Joshi AA, Jadhav BD (2016) Monitoring and Controlling Rice Diseases using image Processing techniques. IEEE, Pune"},{"key":"800_CR6","doi-asserted-by":"crossref","unstructured":"Kolychikhina MS, Beloshapkina OO, Phiri C (2021) Change in potato productivity under the impact of viral diseases. In: IOP Conference Series: Earth and Environmental Science. IOP Publishing Ltd, p\u00a012035","DOI":"10.1088\/1755-1315\/663\/1\/012035"},{"key":"800_CR7","doi-asserted-by":"crossref","unstructured":"Osgouie KG, Azizi A (2010) Optimizing fuzzy logic controller for diabetes type I by genetic algorithm. In: 2010 The 2nd International Conference on Computer and Automation Engineering, ICCAE 2010. pp\u00a04\u20138","DOI":"10.1109\/ICCAE.2010.5451208"},{"key":"800_CR8","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1109\/MCE.2021.3063547","volume":"10","author":"NU Hassan","year":"2021","unstructured":"Hassan NU, Khan FZ, Bibi H et al (2021) A decision support benchmark for forecasting the consumption of Agriculture stocks. IEEE Consum Electron Mag 10:45\u201352. https:\/\/doi.org\/10.1109\/MCE.2021.3063547","journal-title":"IEEE Consum Electron Mag"},{"key":"800_CR9","doi-asserted-by":"crossref","unstructured":"Azizi A, Osgouie KG, Rashidnejhad S, Cheragh M (2013) Modeling of melatonin behavior in major depression: a fuzzy logic modeling. In: Applied Mechanics and Materials, pp 317\u2013321","DOI":"10.4028\/www.scientific.net\/AMM.367.317"},{"key":"800_CR10","doi-asserted-by":"crossref","unstructured":"Ashkzari A, Azizi A (2014) Introducing genetic algorithm as an intelligent optimization technique. Applied mechanics and materials. Trans Tech Publications Ltd, pp 793\u2013797","DOI":"10.4028\/www.scientific.net\/AMM.568-570.793"},{"key":"800_CR11","doi-asserted-by":"publisher","unstructured":"Azizi A (2017) Introducing a novel hybrid artificial intelligence algorithm to optimize network of industrial applications in modern manufacturing. https:\/\/doi.org\/10.1155\/2017\/8728209. Complexity 2017:","DOI":"10.1155\/2017\/8728209"},{"key":"800_CR12","doi-asserted-by":"crossref","unstructured":"Azizi A (2019) Hybrid artificial intelligence optimization technique. SpringerBriefs in Applied Sciences and Technology. Springer Verlag, pp 27\u201347","DOI":"10.1007\/978-981-13-2640-0_4"},{"key":"800_CR13","doi-asserted-by":"publisher","unstructured":"Azizi A (2020) A case study on computer-based analysis of the Stochastic Stability of mechanical structures driven by White and Colored noise: utilizing Artificial Intelligence Techniques to Design an effective active suspension system. https:\/\/doi.org\/10.1155\/2020\/7179801. Complexity 2020:","DOI":"10.1155\/2020\/7179801"},{"key":"800_CR14","doi-asserted-by":"publisher","unstructured":"Azizi A (2020) Applications of Artificial Intelligence Techniques to enhance sustainability of industry 4.0: design of an Artificial neural network model as dynamic Behavior optimizer of robotic arms. https:\/\/doi.org\/10.1155\/2020\/8564140. Complexity 2020:","DOI":"10.1155\/2020\/8564140"},{"key":"800_CR15","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.biosystemseng.2018.05.013","volume":"172","author":"JGA Barbedo","year":"2018","unstructured":"Barbedo JGA (2018) Factors influencing the use of deep learning for plant disease recognition. Biosyst Eng 172:84\u201391. https:\/\/doi.org\/10.1016\/j.biosystemseng.2018.05.013","journal-title":"Biosyst Eng"},{"key":"800_CR16","doi-asserted-by":"crossref","unstructured":"Abou El-Maged LM, Darwish A, Hassanien AE (2021) Artificial Intelligence-Based Plant Diseases Classification. pp\u00a045\u201361","DOI":"10.1007\/978-3-030-59338-4_3"},{"key":"800_CR17","doi-asserted-by":"publisher","unstructured":"Gao L, Lin X (2019) Fully automatic segmentation method for medicinal plant leaf images in complex background. Comput Electron Agric 164. https:\/\/doi.org\/10.1016\/j.compag.2019.104924","DOI":"10.1016\/j.compag.2019.104924"},{"key":"800_CR18","doi-asserted-by":"publisher","unstructured":"Douarre C, Crispim-Junior CF, Gelibert A et al (2019) Novel data augmentation strategies to boost supervised segmentation of plant disease. Comput Electron Agric 165. https:\/\/doi.org\/10.1016\/j.compag.2019.104967","DOI":"10.1016\/j.compag.2019.104967"},{"key":"800_CR19","doi-asserted-by":"publisher","unstructured":"Hu G, Wu H, Zhang Y, Wan M (2019) A low shot learning method for tea leaf\u2019s disease identification. Comput Electron Agric 163. https:\/\/doi.org\/10.1016\/j.compag.2019.104852","DOI":"10.1016\/j.compag.2019.104852"},{"key":"800_CR20","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/j.compag.2017.09.012","volume":"142","author":"J Lu","year":"2017","unstructured":"Lu J, Hu J, Zhao G et al (2017) An in-field automatic wheat disease diagnosis system. Comput Electron Agric 142:369\u2013379. https:\/\/doi.org\/10.1016\/j.compag.2017.09.012","journal-title":"Comput Electron Agric"},{"key":"800_CR21","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.compag.2017.04.008","volume":"138","author":"H Ali","year":"2017","unstructured":"Ali H, Lali MI, Nawaz MZ et al (2017) Symptom based automated detection of citrus diseases using color histogram and textural descriptors. Comput Electron Agric 138:92\u2013104. https:\/\/doi.org\/10.1016\/j.compag.2017.04.008","journal-title":"Comput Electron Agric"},{"key":"800_CR22","doi-asserted-by":"publisher","unstructured":"Chen J, Chen J, Zhang D et al (2020) Using deep transfer learning for image-based plant disease identification. Comput Electron Agric 173. https:\/\/doi.org\/10.1016\/j.compag.2020.105393","DOI":"10.1016\/j.compag.2020.105393"},{"key":"800_CR23","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1504\/IJCVR.2019.098012","volume":"9","author":"E Alehegn","year":"2019","unstructured":"Alehegn E (2019) Ethiopian maize diseases recognition and classification using support vector machine. Int J Comput Vis Robot 9:90\u2013109. https:\/\/doi.org\/10.1504\/IJCVR.2019.098012","journal-title":"Int J Comput Vis Robot"},{"key":"800_CR24","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.compind.2019.02.003","volume":"108","author":"S Coulibaly","year":"2019","unstructured":"Coulibaly S, Kamsu-Foguem B, Kamissoko D, Traore D (2019) Deep neural networks with transfer learning in millet crop images. Comput Ind 108:115\u2013120. https:\/\/doi.org\/10.1016\/j.compind.2019.02.003","journal-title":"Comput Ind"},{"key":"800_CR25","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.compag.2017.03.016","volume":"137","author":"MA Ebrahimi","year":"2017","unstructured":"Ebrahimi MA, Khoshtaghaza MH, Minaei S, Jamshidi B (2017) Vision-based pest detection based on SVM classification method. Comput Electron Agric 137:52\u201358. https:\/\/doi.org\/10.1016\/j.compag.2017.03.016","journal-title":"Comput Electron Agric"},{"key":"800_CR26","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.compag.2018.08.048","volume":"154","author":"J Ma","year":"2018","unstructured":"Ma J, Du K, Zheng F et al (2018) A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Comput Electron Agric 154:18\u201324. https:\/\/doi.org\/10.1016\/j.compag.2018.08.048","journal-title":"Comput Electron Agric"},{"key":"800_CR27","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1016\/j.compag.2018.03.032","volume":"161","author":"EC Too","year":"2019","unstructured":"Too EC, Yujian L, Njuki S, Yingchun L (2019) A comparative study of fine-tuning deep learning models for plant disease identification. Comput Electron Agric 161:272\u2013279. https:\/\/doi.org\/10.1016\/j.compag.2018.03.032","journal-title":"Comput Electron Agric"},{"key":"800_CR28","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/j.compag.2018.01.009","volume":"145","author":"KP Ferentinos","year":"2018","unstructured":"Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311\u2013318. https:\/\/doi.org\/10.1016\/j.compag.2018.01.009","journal-title":"Comput Electron Agric"},{"key":"800_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2019.104948","author":"K KC","year":"2019","unstructured":"KC K, Yin Z, Wu M, Wu Z (2019) Depthwise separable convolution architectures for plant disease classification. Comput Electron Agric. https:\/\/doi.org\/10.1016\/j.compag.2019.104948. 165:","journal-title":"Comput Electron Agric"},{"key":"800_CR30","doi-asserted-by":"crossref","unstructured":"Ouhami M, Es-Saady Y, Hajji M, El et al (2020) Deep transfer learning models for tomato disease detection. Lecture notes in Computer Science (including subseries lecture notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer, pp 65\u201373","DOI":"10.1007\/978-3-030-51935-3_7"},{"key":"800_CR31","doi-asserted-by":"crossref","unstructured":"Bhattacharya S, Mukherjee A, Phadikar S (2020) A Deep Learning Approach for the classification of Rice Leaf Diseases. Advances in Intelligent Systems and Computing. Springer, pp 61\u201369","DOI":"10.1007\/978-981-15-2021-1_8"},{"key":"800_CR32","unstructured":"GitHub - spMohanty \/PlantVillage-Dataset: Dataset of diseased plant leaf images and corresponding labels. https:\/\/github.com\/spMohanty\/PlantVillage-Dataset. Accessed 11 Dec 2020"},{"key":"800_CR33","unstructured":"Mondal KK (2016) Emerging Phytobacterial Diseases in India:. Research Status and Challenges Kalyan K Mondal"},{"key":"800_CR34","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.pmpp.2014.03.005","volume":"86","author":"KK Mondal","year":"2014","unstructured":"Mondal KK, Meena BR, Junaid A et al (2014) Pathotyping and genetic screening of type III effectors in indian strains of Xanthomonas oryzae pv. Oryzae causing bacterial leaf blight of rice. Physiol Mol Plant Pathol 86:98\u2013106. https:\/\/doi.org\/10.1016\/j.pmpp.2014.03.005","journal-title":"Physiol Mol Plant Pathol"},{"key":"800_CR35","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.cropro.2005.04.013","volume":"25","author":"NVL Rajarajeswari","year":"2006","unstructured":"Rajarajeswari NVL, Muralidharan K (2006) Assessments of farm yield and district production loss from bacterial leaf blight epidemics in rice. Crop Prot 25:244\u2013252. https:\/\/doi.org\/10.1016\/j.cropro.2005.04.013","journal-title":"Crop Prot"},{"key":"800_CR36","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-018-7003-4","author":"S AlZu\u2019bi","year":"2018","unstructured":"AlZu\u2019bi S, Jararweh Y, Al-Zoubi H et al (2018) Multi-orientation geometric medical volumes segmentation using 3D multiresolution analysis. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-018-7003-4","journal-title":"Multimed Tools Appl"},{"key":"800_CR37","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1007\/s41870-018-0134-z","volume":"11","author":"K Singh","year":"2019","unstructured":"Singh K, Kumar S, Kaur P (2019) Support vector machine classifier based detection of fungal rust disease in pea plant (Pisam sativam). Int J Inf Technol 11:485\u2013492. https:\/\/doi.org\/10.1007\/s41870-018-0134-z","journal-title":"Int J Inf Technol"},{"key":"800_CR38","doi-asserted-by":"publisher","first-page":"5265","DOI":"10.3233\/JIFS-169810","volume":"35","author":"J Yadav","year":"2018","unstructured":"Yadav J, Rajpal N, Mehta R (2018) A new illumination normalization framework via homomorphic filtering and reflectance ratio in DWT domain for face recognition. J Intell Fuzzy Syst 35:5265\u20135277. https:\/\/doi.org\/10.3233\/JIFS-169810","journal-title":"J Intell Fuzzy Syst"},{"key":"800_CR39","doi-asserted-by":"publisher","first-page":"9067","DOI":"10.1007\/s13369-019-03729-6","volume":"44","author":"J Yadav","year":"2019","unstructured":"Yadav J, rajpal N, Mehta R (2019) An Improved Illumination normalization and robust feature extraction technique for Face Recognition under varying illuminations. Arab J Sci Eng 44:9067\u20139086. https:\/\/doi.org\/10.1007\/s13369-019-03729-6","journal-title":"Arab J Sci Eng"},{"key":"800_CR40","doi-asserted-by":"crossref","unstructured":"Daubechies I (1992) Ten Lectures on Wavelets. Society for Industrial and Applied Mathematics","DOI":"10.1137\/1.9781611970104"},{"key":"800_CR41","first-page":"32","volume-title":"Communications in Computer and Information Science","author":"A Deng","year":"2011","unstructured":"Deng A, Wu J, Yang S (2011) An image fusion algorithm based on discrete wavelet transform and canny operator. Communications in Computer and Information Science. Springer, Berlin, Heidelberg, pp 32\u201338"},{"key":"800_CR42","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1007\/s11633-014-0858-6","volume":"12","author":"C Raja","year":"2015","unstructured":"Raja C, Gangatharan N (2015) Appropriate sub-band selection in wavelet packet decomposition for automated glaucoma diagnoses. Int J Autom Comput 12:393\u2013401. https:\/\/doi.org\/10.1007\/s11633-014-0858-6","journal-title":"Int J Autom Comput"},{"key":"800_CR43","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1016\/j.pisc.2016.05.010","volume":"8","author":"K Keerthi Vasan","year":"2016","unstructured":"Keerthi Vasan K, Surendiran B (2016) Dimensionality reduction using principal component analysis for network intrusion detection. Perspect Sci 8:510\u2013512. https:\/\/doi.org\/10.1016\/j.pisc.2016.05.010","journal-title":"Perspect Sci"},{"key":"800_CR44","doi-asserted-by":"publisher","first-page":"1954","DOI":"10.1002\/ima.22608","volume":"31","author":"Z Guo","year":"2021","unstructured":"Guo Z, Xu L, Si Y, Razmjooy N (2021) Novel computer-aided lung cancer detection based on convolutional neural network-based and feature-based classifiers using metaheuristics. Int J Imaging Syst Technol 31:1954\u20131969. https:\/\/doi.org\/10.1002\/ima.22608","journal-title":"Int J Imaging Syst Technol"},{"key":"800_CR45","doi-asserted-by":"crossref","unstructured":"Azizi A, Seifipour N(2009) Modeling of dermal wound healing-remodeling phase by neural networks. In: 2009 International Association of Computer Science and Information Technology - Spring Conference, IACSIT-SC 2009. pp\u00a0447\u2013450","DOI":"10.1109\/IACSIT-SC.2009.121"},{"key":"800_CR46","doi-asserted-by":"publisher","unstructured":"Umair MB, Iqbal Z, Faraz MA et al (2022) A Network Intrusion Detection System using Hybrid Multilayer Deep Learning Model. https:\/\/doi.org\/10.1089\/BIG.2021.0268. Big data","DOI":"10.1089\/BIG.2021.0268"},{"key":"800_CR47","doi-asserted-by":"publisher","first-page":"3543","DOI":"10.1007\/s40747-022-00694-w","volume":"8","author":"R Ranjbarzadeh","year":"2022","unstructured":"Ranjbarzadeh R, Dorosti S, Jafarzadeh Ghoushchi S et al (2022) Nerve optic segmentation in CT images using a deep learning model and a texture descriptor. Complex Intell Syst 8:3543\u20133557. https:\/\/doi.org\/10.1007\/s40747-022-00694-w","journal-title":"Complex Intell Syst"},{"key":"800_CR48","doi-asserted-by":"publisher","DOI":"10.2316\/J.2020.203-0189","author":"Z Yin","year":"2020","unstructured":"Yin Z, Razmjooy N, PEMFC IDENTIFICATION USING DEEP LEARNING DEVELOPED BY IMPROVED DEER HUNTING OPTIMIZATION ALGORITHM (2020) Int J Power Energy Syst. https:\/\/doi.org\/10.2316\/J.2020.203-0189. 2020 40:","journal-title":"Int J Power Energy Syst"},{"key":"800_CR49","doi-asserted-by":"crossref","unstructured":"Azizi A, Entessari F, Osgouie KG, Rashnoodi AR (2014) Introducing neural networks as a computational intelligent technique. In: Applied Mechanics and Materials, pp 369\u2013374","DOI":"10.4028\/www.scientific.net\/AMM.464.369"},{"key":"800_CR50","doi-asserted-by":"publisher","unstructured":"Tian Q, Wu Y, Ren X, Razmjooy N (2021) A new optimized sequential method for lung tumor diagnosis based on deep learning and converged search and rescue algorithm. Biomed Signal Process Control 68. https:\/\/doi.org\/10.1016\/j.bspc.2021.102761","DOI":"10.1016\/j.bspc.2021.102761"},{"key":"800_CR51","doi-asserted-by":"publisher","first-page":"27783","DOI":"10.1007\/s11042-022-12942-9","volume":"81","author":"NM Ibrahim","year":"2022","unstructured":"Ibrahim NM, Gabr DGI, Rahman A, ur et al (2022) A deep learning approach to intelligent fruit identification and family classification. Multimed Tools Appl 81:27783\u201327798. https:\/\/doi.org\/10.1007\/s11042-022-12942-9","journal-title":"Multimed Tools Appl"},{"key":"800_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1504\/ijarge.2020.10030235","volume":"16","author":"A Kaur","year":"2020","unstructured":"Kaur A, Singh S, Nayyar A, Singh P (2020) Classification of wheat seeds using image Processing and fuzzy clustered Random Forest. Int J Agric Resour Gov Ecol 16:1. https:\/\/doi.org\/10.1504\/ijarge.2020.10030235","journal-title":"Int J Agric Resour Gov Ecol"}],"container-title":["Evolutionary Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12065-022-00800-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12065-022-00800-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12065-022-00800-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T11:31:46Z","timestamp":1710847906000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12065-022-00800-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,21]]},"references-count":52,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["800"],"URL":"https:\/\/doi.org\/10.1007\/s12065-022-00800-4","relation":{},"ISSN":["1864-5909","1864-5917"],"issn-type":[{"value":"1864-5909","type":"print"},{"value":"1864-5917","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,21]]},"assertion":[{"value":"12 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 September 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 November 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 December 2022","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 writing process and the article\u2019s content do not give grounds for raising the issue of a conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}