{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T16:31:08Z","timestamp":1784046668769,"version":"3.55.0"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,4,22]],"date-time":"2021-04-22T00:00:00Z","timestamp":1619049600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,4,22]],"date-time":"2021-04-22T00:00:00Z","timestamp":1619049600000},"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":["Appl Intell"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s10489-021-02375-6","type":"journal-article","created":{"date-parts":[[2021,4,22]],"date-time":"2021-04-22T01:02:29Z","timestamp":1619053349000},"page":"55-70","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Dynamic Kernel CNN-LR model for people counting"],"prefix":"10.1007","volume":"52","author":[{"given":"Ankit","family":"Tomar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1008-0804","authenticated-orcid":false,"given":"Santosh","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bhaskar","family":"Pant","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Umesh Kumar","family":"Tiwari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,4,22]]},"reference":[{"key":"2375_CR1","doi-asserted-by":"publisher","unstructured":"He L, Wen S, Wang L, Li F (2020) Vehicle theft recognition from surveillance video based on spatiotemporal attention. Applied Intelligence. https:\/\/doi.org\/10.1007\/s10489-020-01933-8","DOI":"10.1007\/s10489-020-01933-8"},{"key":"2375_CR2","doi-asserted-by":"publisher","unstructured":"Albi G, Bellomo N, Fermo L, Ha SY, Kim J, Pareschi L, Poyato D, Soler J (2019) Vehicular traffic, crowds, and swarms: From kinetic theory and multiscale methods to applications and research perspectives. Mathematical Models and Methods in Applied Sciences. https:\/\/doi.org\/10.1142\/S0218202519500374","DOI":"10.1142\/S0218202519500374"},{"issue":"3","key":"2375_CR3","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1109\/TCSVT.2014.2358029","volume":"25","author":"T Li","year":"2015","unstructured":"Li T, Chang H, Wang M, Ni B, Hong R, Yan S (2015) Crowded Scene Analysis: A Survey. IEEE Trans Circ Syst Video Technol 25(3):367\u2013386. https:\/\/doi.org\/10.1109\/TCSVT.2014.2358029","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"2375_CR4","doi-asserted-by":"publisher","unstructured":"Yogameena B, Nagananthini C (2017) Computer Vision based crowd disaster avoidance system: a survey. International Journal of Disaster Risk Reduction. https:\/\/doi.org\/10.1016\/j.ijdrr.2017.02.021","DOI":"10.1016\/j.ijdrr.2017.02.021"},{"key":"2375_CR5","doi-asserted-by":"publisher","unstructured":"Bai H, Wen S, Gary Chan S-H (2019) Crowd counting on images with scale variation and isolated clusters. Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019. https:\/\/doi.org\/10.1109\/ICCVW.2019.00009","DOI":"10.1109\/ICCVW.2019.00009"},{"issue":"5","key":"2375_CR6","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1080\/15481603.2017.1323377","volume":"54","author":"X Yu","year":"2017","unstructured":"Yu X, Wu X, Luo C, Ren P (2017) Deep learning in remote sensing scene classification: a data augmentation enhanced convolutional neural network framework. GIScience Remote Sens 54(5):741\u2013758. https:\/\/doi.org\/10.1080\/15481603.2017.1323377","journal-title":"GIScience Remote Sens"},{"key":"2375_CR7","doi-asserted-by":"publisher","unstructured":"Satyanarayana P, Sai Priya K, Sai Chandu MV, Sahithi M (2018) Automated raspberry pi controlled people counting system for pilgrim crowd management. In: Dash S, Naidu P, Bayindir R, Das S (eds) Artificial intelligence and evolutionary computations in engineering systems. Advances in intelligent systems and computing. https:\/\/doi.org\/10.1007\/978-981-10-7868-2-41, vol 668. Springer, Singapore","DOI":"10.1007\/978-981-10-7868-2-41"},{"key":"2375_CR8","doi-asserted-by":"publisher","unstructured":"Ouyang W, Wang X (2013) Single-Pedestrian Detection aided by Multi-Pedestrian detection. Proceedings of the IEEE computer society conference on computer vision and pattern recognition. https:\/\/doi.org\/10.1109\/CVPR.2013.411","DOI":"10.1109\/CVPR.2013.411"},{"key":"2375_CR9","doi-asserted-by":"crossref","unstructured":"Zhang Y, Zhou D, Chen S, Gao S, Ma Y (2016) Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 589\u2013597","DOI":"10.1109\/CVPR.2016.70"},{"key":"2375_CR10","doi-asserted-by":"publisher","unstructured":"Chen K, Loy CC, Gong S, Xiang T (2012) Feature mining for localised crowd counting. BMVC 2012 - Electronic Proceedings of the British Machine Vision Conference 2012. https:\/\/doi.org\/10.5244\/C.26.21","DOI":"10.5244\/C.26.21"},{"key":"2375_CR11","doi-asserted-by":"publisher","first-page":"4670","DOI":"10.1007\/s10489-020-01818-w","volume":"50","author":"Z-F Xu","year":"2020","unstructured":"Xu Z-F, Jia R-S, Sun H-M, Liu Q-M, Cui Z (2020) Light-YOLOv3: fast method for detecting green mangoes in complex scenes using picking robots. Appl Intell 50:4670\u20134687. https:\/\/doi.org\/10.1007\/s10489-020-01818-w","journal-title":"Appl Intell"},{"key":"2375_CR12","doi-asserted-by":"publisher","unstructured":"Frontoni E, Paolanti M, Pietrini R (2019) People Counting in Crowded Environment and Re-identification. In: Rosin P., Lai Y. K., Shao L., Liu Y (eds) RGB-D image analysis and processing. Advances in computer vision and pattern recognition. https:\/\/doi.org\/10.1007\/978-3-030-28603-3-18. Springer, Cham","DOI":"10.1007\/978-3-030-28603-3-18"},{"key":"2375_CR13","doi-asserted-by":"publisher","unstructured":"Wang L, Yin B, Guo A, Ma H, Cao J (2018) Skip-Connection Convolutional Neural Network for Still Image Crowd Counting. Applied Intelligence. https:\/\/doi.org\/10.1007\/s10489-018-1150-1","DOI":"10.1007\/s10489-018-1150-1"},{"key":"2375_CR14","doi-asserted-by":"publisher","unstructured":"Szabo P (2018) Urbanization And mental health: a developing world perspective. Current Opinion in Psychiatry. https:\/\/doi.org\/10.1097\/YCO.0000000000000414","DOI":"10.1097\/YCO.0000000000000414"},{"key":"2375_CR15","doi-asserted-by":"publisher","unstructured":"Shahriare Satu Md, Tania Akter Md, Sadrul Arifen Md, Mia M. d., Raza Mia Md (2017) Predicting accidental locations of Dhaka-Aricha highway in Bangladesh using different data mining techniques. International Journal of Computer Applications. https:\/\/doi.org\/10.5120\/ijca2017914096","DOI":"10.5120\/ijca2017914096"},{"key":"2375_CR16","doi-asserted-by":"publisher","unstructured":"Haque S, SadiMd M, RafiMd EH, IslamMd M, Hasan K (2020) Real-Time Crowd Detection to Prevent Stampede. https:\/\/doi.org\/10.1007\/978-981-13-7564-4-56","DOI":"10.1007\/978-981-13-7564-4-56"},{"key":"2375_CR17","doi-asserted-by":"publisher","unstructured":"Felemban EA, Rehman FU, Biabani SAA, Ahmad A, Naseer A, Majid ARMA, Hussain OK, Qamar AM, Falemban R, Zanjir F (2020) Digital Revolution for Hajj Crowd Management: A Technology Survey. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2020.3037396","DOI":"10.1109\/ACCESS.2020.3037396"},{"key":"2375_CR18","doi-asserted-by":"publisher","unstructured":"Kok VJ, Lim MK, SengChan C (2016) Crowd Behavior analysis: a review where physics meets biology. Neurocomputing. https:\/\/doi.org\/10.1016\/j.neucom.2015.11.021","DOI":"10.1016\/j.neucom.2015.11.021"},{"key":"2375_CR19","doi-asserted-by":"publisher","unstructured":"Hsu CL, Lin JCC (2016) An Empirical examination of consumer adoption of internet of things services: Network externalities and concern for information privacy perspectives. Computers in Human Behavior. https:\/\/doi.org\/10.1016\/j.chb.2016.04.023","DOI":"10.1016\/j.chb.2016.04.023"},{"key":"2375_CR20","doi-asserted-by":"publisher","first-page":"2818","DOI":"10.1007\/s10489-020-01688-2","volume":"50","author":"Q Ji","year":"2020","unstructured":"Ji Q, Zhu T, Bao D (2020) A hybrid model of convolutional neural networks and deep regression forests for crowd counting. Appl Intell 50:2818\u20132832. https:\/\/doi.org\/10.1007\/s10489-020-01688-2","journal-title":"Appl Intell"},{"key":"2375_CR21","doi-asserted-by":"publisher","unstructured":"Paul V, Jones MJ (2004) Robust Real-Time Face Detection. International Journal of Computer Vision. https:\/\/doi.org\/10.1023\/B:VISI.0000013087.49260.fb","DOI":"10.1023\/B:VISI.0000013087.49260.fb"},{"key":"2375_CR22","doi-asserted-by":"crossref","unstructured":"Sindagi VA, Patel VM (2018) A Survey of Recent Advances in CNN-Based Single Image Crowd Counting and Density Estimation, pp 1\u201316","DOI":"10.1016\/j.patrec.2017.07.007"},{"key":"2375_CR23","doi-asserted-by":"publisher","unstructured":"Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005. https:\/\/doi.org\/10.1109\/CVPR.2005.177","DOI":"10.1109\/CVPR.2005.177"},{"issue":"3","key":"2375_CR24","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1109\/JSTARS.2016.2602439","volume":"10","author":"D Konstantinidis","year":"2017","unstructured":"Konstantinidis D, Stathaki T, Argyriou V, Grammalidis N (2017) Building Detection Using Enhanced HOG\u2013LBP Features and Region Refinement Processes. IEEE J Sel Top Appl Earth Observ Remote Sens 10(3):888\u2013905. https:\/\/doi.org\/10.1109\/JSTARS.2016.2602439","journal-title":"IEEE J Sel Top Appl Earth Observ Remote Sens"},{"key":"2375_CR25","doi-asserted-by":"publisher","unstructured":"Lin Z, Davis LS (2010) Shape-Based Human detection and segmentation via hierarchical Part-Template matching. IEEE transactions on pattern analysis and machine intelligence. https:\/\/doi.org\/10.1109\/TPAMI.2009.204","DOI":"10.1109\/TPAMI.2009.204"},{"key":"2375_CR26","doi-asserted-by":"publisher","unstructured":"Meshgi K, Maeda S-I, Oba S, Skibbe H, Li Y-z, Ishii S (2016) An Occlusion-Aware Particle Filter Tracker to Handle Complex and Persistent Occlusions. Computer Vision and Image Understanding. https:\/\/doi.org\/10.1016\/j.cviu.2016.05.011","DOI":"10.1016\/j.cviu.2016.05.011"},{"key":"2375_CR27","doi-asserted-by":"publisher","unstructured":"Choudhury SK, Padhy RP, Sa PK, Bakshi S, Sa PK, Bakshi S (2019) Human Detection Using Orientation Shape Histogram and Co ocurrence Textures. Multimedia Tools and Applications. https:\/\/doi.org\/10.1007\/s11042-018-6866-8","DOI":"10.1007\/s11042-018-6866-8"},{"issue":"4","key":"2375_CR28","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1109\/TPAMI.2011.155","volume":"34","author":"P Dollar","year":"2012","unstructured":"Dollar P, Wojek C, Schiele B, Perona P (2012) Pedestrian Detection: An Evaluation of the State of the Art. IEEE Trans Pattern Anal Mach Intell 34(4):743\u2013761. https:\/\/doi.org\/10.1109\/TPAMI.2011.155","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2375_CR29","doi-asserted-by":"publisher","unstructured":"Xiong F, Shi X, Yeung D (2017) Spatiotemporal modeling for crowd counting in videos, 2017 IEEE international conference on computer vision (ICCV), Venice, pp 5161\u20135169. https:\/\/doi.org\/10.1109\/ICCV.2017.551","DOI":"10.1109\/ICCV.2017.551"},{"key":"2375_CR30","doi-asserted-by":"publisher","unstructured":"Bolei X, Qiu G (2016) Crowd density estimation based on rich features and random projection forest. 2016 IEEE winter conference on applications of computer vision, WACV 2016. https:\/\/doi.org\/10.1109\/WACV.2016.7477682","DOI":"10.1109\/WACV.2016.7477682"},{"key":"2375_CR31","doi-asserted-by":"publisher","unstructured":"Pham V, Kozakaya T, Yamaguchi O, Okada A (2015) COUNT Forest: CO-Voting Uncertain Number of Targets Using Random Forest for Crowd Density Estimation, 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, pp 3253\u20133261. https:\/\/doi.org\/10.1109\/ICCV.2015.372","DOI":"10.1109\/ICCV.2015.372"},{"key":"2375_CR32","doi-asserted-by":"publisher","unstructured":"Li Y, Zhang X, Chen D (2018) CSRNEt: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https:\/\/doi.org\/10.1109\/CVPR.2018.00120","DOI":"10.1109\/CVPR.2018.00120"},{"key":"2375_CR33","doi-asserted-by":"publisher","unstructured":"Kumagai S, Hotta K, Kurita T (2018) Mixture of counting CNNs. Mach Vis Appl 29, 1119\u20131126. https:\/\/doi.org\/10.1007\/s00138-018-0955-6","DOI":"10.1007\/s00138-018-0955-6"},{"key":"2375_CR34","doi-asserted-by":"publisher","unstructured":"Chan AB, Liang Z-SJ, Vasconcelos N (2008) Privacy preserving crowd monitoring: Counting peoplewithout people models or tracking, 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, pp 1\u20137. https:\/\/doi.org\/10.1109\/CVPR.2008.4587569","DOI":"10.1109\/CVPR.2008.4587569"},{"key":"2375_CR35","doi-asserted-by":"publisher","unstructured":"Ryan D, Denman S, Fookes C, Sridharan S (2009) Crowd counting using multiple local features, 2009 digital image computing: Techniques and applications, Melbourne, pp 81\u201388. https:\/\/doi.org\/10.1109\/DICTA.2009.22","DOI":"10.1109\/DICTA.2009.22"},{"key":"2375_CR36","doi-asserted-by":"crossref","unstructured":"Zhang Y, Zhou D, Chen S, Gao S, Ma Y (2016) Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 589\u2013597","DOI":"10.1109\/CVPR.2016.70"},{"key":"2375_CR37","doi-asserted-by":"publisher","unstructured":"Everingham SM, Eslami A, Van Gool L, Williams CKI, Winn J, Zisserman A (2015) The Pascal Visual Object Classes Challenge: A Retrospective.international Journal of Computer Vision. https:\/\/doi.org\/10.1007\/s11263-014-0733-5","DOI":"10.1007\/s11263-014-0733-5"},{"key":"2375_CR38","doi-asserted-by":"publisher","unstructured":"Chang X, Nie Feiping, Wang S, Yang Y, Zhou X, Zhang C (2016) Compound Rank-k Projections for Bi linear Analysis. IEEE Transactions on Neural Networks and Learning Systems. https:\/\/doi.org\/10.1109\/TNNLS.2015.2441735","DOI":"10.1109\/TNNLS.2015.2441735"},{"key":"2375_CR39","doi-asserted-by":"publisher","unstructured":"Wang T, Li G, Lei J, Li S, Xu S (2017) Crowd Counting Based on MMCNN in Still Images. Lecture Notes in Computer Science (Including Sub series Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https:\/\/doi.org\/10.1007\/978-3-319-59126-1-39","DOI":"10.1007\/978-3-319-59126-1-39"},{"issue":"5","key":"2375_CR40","doi-asserted-by":"publisher","first-page":"1728","DOI":"10.1109\/TITS.2018.2829987","volume":"20","author":"Q Zhou","year":"2019","unstructured":"Zhou Q, Zhang J, Che L, Shan H, Wang JZ (2019) Crowd Counting With Limited Labeling Through Submodular Frame Selection. IEEE Trans Intell Transp Syst 20(5):1728\u20131738. https:\/\/doi.org\/10.1109\/TITS.2018.2829987","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"2375_CR41","doi-asserted-by":"publisher","unstructured":"Miao Y, Han J, Gao Y, Zhang B (2019) ST-CNN: Spatial-Temporal Convolutional Neural Network for Crowd Counting in Videos. Pattern Recognition Letters, vol 125, Elsevier, pp 113\u201318. https:\/\/doi.org\/10.1016\/j.patrec.2019.04.012","DOI":"10.1016\/j.patrec.2019.04.012"},{"key":"2375_CR42","doi-asserted-by":"publisher","unstructured":"Xua M, Ge Z, Jiang X, Cui G, Lv P, Zhou B, Xub C (2019) Depth information guided crowd counting for complex crowd scenes. Pattern Recognition Letters. https:\/\/doi.org\/10.1016\/j.patrec.2019.02.026","DOI":"10.1016\/j.patrec.2019.02.026"},{"key":"2375_CR43","doi-asserted-by":"publisher","unstructured":"Wu X, Xu B, Zheng Y, Ye H, Yang J, He J (2020) Fast Video Crowd Counting with a Temporal Aware Network. Neurocomputing, vol 403, Elsevier, pp 13\u201320. https:\/\/doi.org\/10.1016\/j.neucom.2020.04.071","DOI":"10.1016\/j.neucom.2020.04.071"},{"key":"2375_CR44","doi-asserted-by":"publisher","unstructured":"Li Y, Khoshelham K, Sarvi M (2019) Direct Generation of level of service maps from images using convolutional and long Short-Term memory networks. Journal of Intelligent Transportation Systems, Technology, Planning, and Operations. https:\/\/doi.org\/10.1080\/15472450.2018.1563865","DOI":"10.1080\/15472450.2018.1563865"},{"key":"2375_CR45","doi-asserted-by":"publisher","unstructured":"Kong X, Zhao M, Zhou H, Zhang C (2020) Weakly Supervised Crowd-Wise Attention For Robust Crowd Counting. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, pp 2722\u20132726, https:\/\/doi.org\/10.1109\/ICASSP40776.2020.9054258","DOI":"10.1109\/ICASSP40776.2020.9054258"},{"key":"2375_CR46","doi-asserted-by":"publisher","unstructured":"Zhang L, Shi M, Chen Q (2018) Crowd counting via Scale-Adaptive convolution neural network. 2018 IEEE winter conference on applications of computer vision (WACV), Lake Tahoe. 1113\u20131121. https:\/\/doi.org\/10.1109\/WACV.2018.00127","DOI":"10.1109\/WACV.2018.00127"},{"key":"2375_CR47","doi-asserted-by":"crossref","unstructured":"Marsden M, McGuinness K, Little S, O\u2019Connor N (2017) Fully convolutional crowd counting on highly congested scenes. VISIGRAPP 2017 - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, vol 5, pp 27\u201333","DOI":"10.5220\/0006097300270033"},{"key":"2375_CR48","doi-asserted-by":"publisher","unstructured":"Ding X, Lin Z, He F, Wang Y (2018) A Deeply-Recursive convolutional network for crowd counting. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp 1942\u201346. https:\/\/doi.org\/10.1109\/ICASSP.2018.8461772","DOI":"10.1109\/ICASSP.2018.8461772"},{"key":"2375_CR49","doi-asserted-by":"publisher","unstructured":"Sam DB, Surya S, Babu RV (2017) Switching Convolutional Neural Network for Crowd Counting. 2017 IEEE Conference onComputer Vision and Pattern Recognition (CVPR), Honolulu, pp 4031\u20134039. https:\/\/doi.org\/10.1109\/CVPR.2017.429","DOI":"10.1109\/CVPR.2017.429"},{"key":"2375_CR50","doi-asserted-by":"publisher","unstructured":"Zhang C, Li H, Wang X, Yang X (2015) Cross-scene crowd counting via deep convolutional neural networks. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, pp 833\u2013841. https:\/\/doi.org\/10.1109\/CVPR.2015.7298684","DOI":"10.1109\/CVPR.2015.7298684"},{"key":"2375_CR51","doi-asserted-by":"publisher","unstructured":"Sheng B, Shen C, Lin G, Li J (2018) Crowd Counting via Weighted VLAD on aDense Attribute Feature Map. IEEE Transactions on Circuits and Systems forVideo Technology. https:\/\/doi.org\/10.1109\/TCSVT.2016.2637379","DOI":"10.1109\/TCSVT.2016.2637379"},{"key":"2375_CR52","doi-asserted-by":"publisher","unstructured":"Cabada RZ, Rangel HR, Estrada MLB, Lopez HMC (2020) Hyperparameter Optimization in CNN for Learning-Centered emotion recognition for intelligent tutoring systems. Soft Computing. https:\/\/doi.org\/10.1007\/s00500-019-04387-4","DOI":"10.1007\/s00500-019-04387-4"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02375-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-02375-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02375-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T06:28:49Z","timestamp":1642141729000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-02375-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,22]]},"references-count":52,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["2375"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-02375-6","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,22]]},"assertion":[{"value":"23 March 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}