{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T01:00:21Z","timestamp":1780966821142,"version":"3.54.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T00:00:00Z","timestamp":1694736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T00:00:00Z","timestamp":1694736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001777","name":"The University of Wollongong","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001777","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Computer vision technology for detecting objects in a complex environment often includes other key technologies, including pattern recognition, artificial intelligence, and digital image processing. It has been shown that Fast Convolutional Neural Networks (CNNs) with You Only Look Once (YOLO) is optimal for differentiating similar objects, constant motion, and low image quality. The proposed study aims to resolve these issues by implementing three different object detection algorithms\u2014You Only Look Once (YOLO), Single Stage Detector (SSD), and Faster Region-Based Convolutional Neural Networks (R-CNN). This paper compares three different deep-learning object detection methods to find the best possible combination of feature and accuracy. The R-CNN object detection techniques are performed better than single-stage detectors like Yolo (You Only Look Once) and Single Shot Detector (SSD) in term of accuracy, recall, precision and loss.<\/jats:p>","DOI":"10.1007\/s11042-023-16736-5","type":"journal-article","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T02:01:38Z","timestamp":1694743298000},"page":"30045-30072","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":148,"title":["An improved deep learning-based optimal object detection system from images"],"prefix":"10.1007","volume":"83","author":[{"given":"Satya Prakash","family":"Yadav","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muskan","family":"Jindal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Preeti","family":"Rani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Victor Hugo C.","family":"de Albuquerque","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caio","family":"dos Santos Nascimento","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5113-0639","authenticated-orcid":false,"given":"Manoj","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,9,15]]},"reference":[{"key":"16736_CR1","doi-asserted-by":"crossref","unstructured":"Fern\u00e1ndez A, Salmer\u00f3n A (2008) BayesChess: A computer chess program based on Bayesian networks. Pattern Recognit Lett 29(8) Art. no. 8, 2008","DOI":"10.1016\/j.patrec.2007.06.013"},{"key":"16736_CR2","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.physbeh.2018.10.017","volume":"198","author":"S Villafaina","year":"2019","unstructured":"Villafaina S, Collado-Mateo D, Cano-Plasencia R, Gusi N, Fuentes JP (2019) Electroencephalographic response of chess players in decision-making processes under time pressure. Physiol Behav 198:140\u2013143","journal-title":"Physiol Behav"},{"key":"16736_CR3","doi-asserted-by":"publisher","first-page":"2610","DOI":"10.1016\/j.procs.2020.04.283","volume":"171","author":"A Kumar","year":"2020","unstructured":"Kumar A, Srivastava S (2020) Object detection system based on convolution neural networks using single shot multi-box detector. Procedia Comput Sci 171:2610\u20132617","journal-title":"Procedia Comput Sci"},{"key":"16736_CR4","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.cviu.2019.01.006","volume":"182","author":"Y Jang","year":"2019","unstructured":"Jang Y, Gunes H, Patras I (2019) Registration-free face-ssd: Single shot analysis of smiles, facial attributes, and affect in the wild. Comput Vis Image Underst 182:17\u201329","journal-title":"Comput Vis Image Underst"},{"key":"16736_CR5","first-page":"3","volume-title":"Advances in Computer Games","author":"C Yi","year":"2021","unstructured":"Yi C, Kaneko T (2021) Improving counterfactual regret minimization agents training in card game cheat using ordered abstraction. Advances in Computer Games. Springer International Publishing, Cham, pp 3\u201313"},{"key":"16736_CR6","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.future.2018.09.068","volume":"92","author":"Y Sakai","year":"2019","unstructured":"Sakai Y, Lu H, Tan J-K, Kim H (2019) Recognition of surrounding environment from electric wheelchair videos based on modified YOLOv2. Future Gener Comput Syst 92:157\u2013161","journal-title":"Future Gener Comput Syst"},{"key":"16736_CR7","doi-asserted-by":"publisher","first-page":"107131","DOI":"10.1016\/j.patcog.2019.107131","volume":"105","author":"J Yuan","year":"2020","unstructured":"Yuan J et al (2020) Gated CNN: Integrating multi-scale feature layers for object detection. Pattern Recognit 105:107131","journal-title":"Pattern Recognit"},{"key":"16736_CR8","doi-asserted-by":"publisher","first-page":"107226","DOI":"10.1016\/j.compeleceng.2021.107226","volume":"93","author":"I Ahmed","year":"2021","unstructured":"Ahmed I, Ahmad M, Ahmad A, Jeon G (2021) IoT-based crowd monitoring system: Using SSD with transfer learning. Comput Electr Eng 93:107226","journal-title":"Comput Electr Eng"},{"key":"16736_CR9","doi-asserted-by":"publisher","first-page":"115987","DOI":"10.1016\/j.image.2020.115987","volume":"89","author":"H Pan","year":"2020","unstructured":"Pan H, Jiang J, Chen G (2020) TDFSSD: Top-down feature fusion single shot MultiBox detector. Signal Process Image Commun 89:115987","journal-title":"Signal Process Image Commun"},{"issue":"4","key":"16736_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/IJEHMC.309436","volume":"13","author":"P Rani","year":"2022","unstructured":"Rani P, Verma S, Yadav SP, Rai BK, Naruka MS, Kumar D (2022) Simulation of the Lightweight Blockchain Technique Based on Privacy and Security for Healthcare Data for the Cloud System. Int J E-Health Med Commun 13(4):1\u201315. https:\/\/doi.org\/10.4018\/IJEHMC.309436","journal-title":"Int J E-Health Med Commun"},{"key":"16736_CR11","doi-asserted-by":"publisher","first-page":"10854","DOI":"10.1016\/j.compeleceng.2022.108543","volume":"105","author":"P Rani","year":"2023","unstructured":"Rani P, Sharma R (2023) Intelligent transportation system for internet of vehicles based vehicular networks for smart cities. Comput Electr Eng 105:10854. https:\/\/doi.org\/10.1016\/j.compeleceng.2022.108543","journal-title":"Comput Electr Eng"},{"key":"16736_CR12","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1016\/j.ins.2023.01.004","volume":"626","author":"Q Wang","year":"2023","unstructured":"Wang Q et al (2023) Deep convolutional cross-connected kernel mapping support vector machine based on SelectDropout. Inf Sci 626:694\u2013709","journal-title":"Inf Sci"},{"key":"16736_CR13","doi-asserted-by":"publisher","first-page":"102949","DOI":"10.1016\/j.dsp.2020.102949","volume":"110","author":"L Ding","year":"2021","unstructured":"Ding L, Xu X, Cao Y, Zhai G, Yang F, Qian L (2021) Detection and tracking of infrared small target by jointly using SSD and pipeline filter. Digit Signal Process 110:102949","journal-title":"Digit Signal Process"},{"issue":"24","key":"16736_CR14","doi-asserted-by":"publisher","first-page":"33377","DOI":"10.1007\/s11042-021-11419-5","volume":"80","author":"Z Halim","year":"2021","unstructured":"Halim Z, Zouq A (2021) On identification of big-five personality traits through choice of images in a real-world setting. Multimed Tools Appl 80(24):33377\u201333408","journal-title":"Multimed Tools Appl"},{"issue":"6","key":"16736_CR15","doi-asserted-by":"publisher","first-page":"1747","DOI":"10.1016\/j.cja.2020.02.024","volume":"33","author":"LI Yundong","year":"2020","unstructured":"Yundong LI et al (2020) Multi-block SSD based on small object detection for UAV railway scene surveillance. Chin J Aeronaut 33(6):1747\u20131755","journal-title":"Chin J Aeronaut"},{"key":"16736_CR16","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.cviu.2013.10.008","volume":"118","author":"S Bennett","year":"2014","unstructured":"Bennett S, Lasenby J (2014) ChESS\u2013Quick and robust detection of chess-board features. Comput Vis Image Underst 118:197\u2013210","journal-title":"Comput Vis Image Underst"},{"key":"16736_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/3365392","volume":"2022","author":"P Rani","year":"2022","unstructured":"Rani P, Singh PN, Verma S, Ali N, Shukla PK, Alhassan M (2022) An Implementation of Modified Blowfish Technique with Honey Bee Behavior Optimization for Load Balancing in Cloud System Environment. Wirel Commun Mob Comput 2022:1\u201314. https:\/\/doi.org\/10.1155\/2022\/3365392","journal-title":"Wirel Commun Mob Comput"},{"key":"16736_CR18","doi-asserted-by":"crossref","unstructured":"Li C, Chen G (2020) Research on Chinese Chess Detection and Recognition Based on Convolutional Neural Network. In: Recent Trends in Intelligent Computing, Communication and Devices: Proceedings of ICCD 2018, Springer, pp. 467\u2013473","DOI":"10.1007\/978-981-13-9406-5_57"},{"key":"16736_CR19","doi-asserted-by":"crossref","unstructured":"Czyzewski MA, Laskowski A, Wasik S (2020) Chessboard and chess piece recognition with the support of neural networks. Found Comput Decis Sci 45(4), Art. no. 4","DOI":"10.2478\/fcds-2020-0014"},{"key":"16736_CR20","doi-asserted-by":"publisher","first-page":"102827","DOI":"10.1016\/j.cviu.2019.102827","volume":"189","author":"J Yi","year":"2019","unstructured":"Yi J, Wu P, Metaxas DN (2019) ASSD: Attentive single shot multibox detector. Comput Vis Image Underst 189:102827","journal-title":"Comput Vis Image Underst"},{"key":"16736_CR21","doi-asserted-by":"crossref","unstructured":"Adarsh P, Rathi P, Kumar M (2020) YOLO v3-Tiny: object detection and recognition using one stage improved model. In: 2020 6th international conference on advanced computing and communication systems (ICACCS).IEEE, pp 687\u2013694","DOI":"10.1109\/ICACCS48705.2020.9074315"},{"key":"16736_CR22","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. In: Advances in Neural Information Processing Systems, Curran Associates, Inc.. Accessed: May 09, 2023. [Online]. Available: https:\/\/proceedings.neurips.cc\/paper\/2015\/hash\/14bfa6bb14875e45bba028a21ed38046-Abstract.html"},{"key":"16736_CR23","unstructured":"Preeti R, Sharma R (2022) An experimental study of IEEE 802.11 n Devices for Vehicular Networks with Various Propagation Loss Models.\" International Conference on Signal Processing and Integrated Networks. Singapore: Springer Nature Singapore, 2022."},{"key":"16736_CR24","doi-asserted-by":"publisher","unstructured":"Ansari G, Rani P, Kumar V (2023) A novel technique of mixed gas identification based on the group method of data handling (GMDH) on time-dependent MOX gas sensor data. In: Mahapatra RP, Peddoju SK, Roy S, Parwekar P (eds.) Proceedings of International Conference on Recent Trends in Computing. Lecture Notes in Networks and Systems, vol. 600. Springer Nature Singapore, Singapore, pp. 641\u2013654. https:\/\/doi.org\/10.1007\/978-981-19-8825-7_55","DOI":"10.1007\/978-981-19-8825-7_55"},{"key":"16736_CR25","doi-asserted-by":"publisher","unstructured":"Li X, Li Y, Shen C, Dick\u00a0A, Hengel\u00a0AVD(2013) Contextual hypergraph modeling for salient object detection. In: 2013 IEEE International Conference on Computer Vision, Sydney, pp. 3328\u20133335. https:\/\/doi.org\/10.1109\/ICCV.2013.413","DOI":"10.1109\/ICCV.2013.413"},{"key":"16736_CR26","doi-asserted-by":"crossref","unstructured":"Cheng M-M, Mitra NJ, Huang X, Torr PH, Hu S-M (2014) Global contrast based salient region detection. IEEE Trans Pattern Anal Mach Intell 37(3), Art. no. 3","DOI":"10.1109\/TPAMI.2014.2345401"},{"key":"16736_CR27","doi-asserted-by":"publisher","unstructured":"Jiang H, Wang J, Yuan Z, Wu Y, Zheng N, Li  S (2013) Salient object detection: a discriminative regional feature integration approach. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, pp 2083\u20132090. https:\/\/doi.org\/10.1109\/CVPR.2013.271","DOI":"10.1109\/CVPR.2013.271"},{"key":"16736_CR28","doi-asserted-by":"publisher","unstructured":"Hou Q, Cheng M-M, Hu X, Borji A, Tu Z, Torr PH (2017) Deeply supervised salient object detection with short connections. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3203\u20133212. https:\/\/doi.org\/10.48550\/arXiv.1611.04849","DOI":"10.48550\/arXiv.1611.04849"},{"key":"16736_CR29","doi-asserted-by":"publisher","unstructured":"Wang X, Shrivastava\u00a0A, Gupta A (2017) A-Fast-RCNN: hard positive generation via adversary for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, pp 3039\u20133048. https:\/\/doi.org\/10.1109\/CVPR.2017.324","DOI":"10.1109\/CVPR.2017.324"},{"key":"16736_CR30","doi-asserted-by":"publisher","first-page":"1706","DOI":"10.1016\/j.procs.2018.05.144","volume":"132","author":"AR Pathak","year":"2018","unstructured":"Pathak AR, Pandey M, Rautaray S (2018) Application of deep learning for object detection. Procedia Computer Science 132:1706\u20131717. https:\/\/doi.org\/10.1016\/j.procs.2018.05.144","journal-title":"Procedia Computer Science"},{"key":"16736_CR31","doi-asserted-by":"publisher","unstructured":"Kumar A, Singh N, Kumar P, Vijayvergia A, Kumar K (2017) A novel superpixel based color spatial feature for salient object detection. In: 2017 Conference on Information and Communication Technology (CICT). IEEE, Gwalior, pp. 1\u20135. https:\/\/doi.org\/10.1109\/INFOCOMTECH.2017.8340630","DOI":"10.1109\/INFOCOMTECH.2017.8340630"},{"issue":"6","key":"16736_CR32","doi-asserted-by":"publisher","first-page":"7383","DOI":"10.1007\/s11042-017-4642-9","volume":"77","author":"K Kumar","year":"2018","unstructured":"Kumar K, Shrimankar DD, Singh N (2018) Eratosthenes sieve based key-frame extraction technique for event summarization in videos. Multimed Tools Appl 77(6):7383\u20137404. https:\/\/doi.org\/10.1007\/s11042-017-4642-9","journal-title":"Multimed Tools Appl"},{"issue":"2","key":"16736_CR33","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1109\/TMM.2017.2741423","volume":"20","author":"K Kumar","year":"2018","unstructured":"Kumar K, Shrimankar DD (2018) F-DES: Fast and Deep Event Summarization. IEEE Trans Multimed 20(2):323\u2013334. https:\/\/doi.org\/10.1109\/TMM.2017.2741423","journal-title":"IEEE Trans Multimed"},{"key":"16736_CR34","doi-asserted-by":"publisher","unstructured":"Sharma S, Kumar K, Singh N (2017) D-FES: Deep facial expression recognition system. In: 2017 Conference on Information and Communication Technology (CICT). IEEE, Gwalior, India, pp. 1\u20136. https:\/\/doi.org\/10.1109\/INFOCOMTECH.2017.8340635","DOI":"10.1109\/INFOCOMTECH.2017.8340635"},{"issue":"5","key":"16736_CR35","doi-asserted-by":"publisher","first-page":"3798","DOI":"10.1080\/03772063.2020.1780164","volume":"68","author":"S Sharma","year":"2022","unstructured":"Sharma S, Kumar K, Singh N (2022) Deep Eigen Space Based ASL Recognition System. IETE J Res 68(5):3798\u20133808. https:\/\/doi.org\/10.1080\/03772063.2020.1780164","journal-title":"IETE J Res"},{"key":"16736_CR36","doi-asserted-by":"publisher","unstructured":"Kumar K, Shrimankar DD, Singh N (2019) Key-Lectures: Keyframes Extraction in Video Lectures. In: Tanveer M, Pachori RB (eds.) Machine Intelligence and Signal Analysis. Advances in Intelligent Systems and Computing, vol. 748. Springer Singapore, Singapore, pp. 453\u2013459. https:\/\/doi.org\/10.1007\/978-981-13-0923-6_39","DOI":"10.1007\/978-981-13-0923-6_39"},{"issue":"7","key":"16736_CR37","doi-asserted-by":"publisher","first-page":"11079","DOI":"10.1007\/s11042-020-10157-4","volume":"80","author":"K Kumar","year":"2021","unstructured":"Kumar K (2021) Text query based summarized event searching interface system using deep learning over cloud. Multimed Tools Appl 80(7):11079\u201311094. https:\/\/doi.org\/10.1007\/s11042-020-10157-4","journal-title":"Multimed Tools Appl"},{"issue":"17","key":"16736_CR38","doi-asserted-by":"publisher","first-page":"26319","DOI":"10.1007\/s11042-021-10768-5","volume":"80","author":"S Sharma","year":"2021","unstructured":"Sharma S, Kumar K (2021) ASL-3DCNN: American sign language recognition technique using 3-D convolutional neural networks. Multimed Tools Appl 80(17):26319\u201326331. https:\/\/doi.org\/10.1007\/s11042-021-10768-5","journal-title":"Multimed Tools Appl"},{"key":"16736_CR39","doi-asserted-by":"publisher","unstructured":"Abhay A, et al (2017) An automated hierarchical framework for player recognition in sports image. Proceedings of the international conference on video and image processing. https:\/\/doi.org\/10.1145\/3177404.3177432","DOI":"10.1145\/3177404.3177432"},{"issue":"5","key":"16736_CR40","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/MCE.2020.3003127","volume":"10","author":"RK Koppanati","year":"2021","unstructured":"Koppanati RK, Kumar K (2021) P-MEC: Polynomial Congruence-Based Multimedia Encryption Technique Over Cloud. IEEE Consum Electron Mag 10(5):41\u201346. https:\/\/doi.org\/10.1109\/MCE.2020.3003127","journal-title":"IEEE Consum Electron Mag"},{"key":"16736_CR41","doi-asserted-by":"publisher","unstructured":"Kumar K, Kumar A, Bahuguna A (2017) D-CAD: Deep and crowded anomaly detection. Proceedings of the 7th international conference on computer and communication technology. https:\/\/doi.org\/10.1145\/3154979.3154998","DOI":"10.1145\/3154979.3154998"},{"issue":"3","key":"16736_CR42","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1007\/s10115-020-01538-0","volume":"63","author":"J Hu","year":"2021","unstructured":"Hu J, Shi C-JR, Zhang J (2021) Saliency-based YOLO for single target detection. Knowl Inf Syst 63(3):717\u2013732. https:\/\/doi.org\/10.1007\/s10115-020-01538-0","journal-title":"Knowl Inf Syst"},{"issue":"3","key":"16736_CR43","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/s00138-020-01065-6","volume":"31","author":"G Srivastava","year":"2020","unstructured":"Srivastava G, Srivastava R (2020) User-interactive salient object detection using YOLOv2, lazy snapping, and gabor filters. Mach Vis Appl 31(3):17. https:\/\/doi.org\/10.1007\/s00138-020-01065-6","journal-title":"Mach Vis Appl"},{"key":"16736_CR44","doi-asserted-by":"publisher","unstructured":"Redmon, Joseph, and Ali Farhadi. \"Yolov3: An incremental improvement.\" arXiv preprint arXiv:1804.02767 (2018).\nhttps:\/\/doi.org\/10.48550\/ARXIV.1804.02767","DOI":"10.48550\/ARXIV.1804.02767"},{"key":"16736_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2021.3065438","volume":"70","author":"Y Cai","year":"2021","unstructured":"Cai Y et al (2021) YOLOv4-5D: An Effective and Efficient Object Detector for Autonomous Driving. IEEE Trans Instrum Meas 70:1\u201313. https:\/\/doi.org\/10.1109\/TIM.2021.3065438","journal-title":"IEEE Trans Instrum Meas"},{"key":"16736_CR46","doi-asserted-by":"publisher","unstructured":"Agyemang IO, et al. (2021) On salient concrete crack detection via improved Yolov5. In: 2021 18th International computer conference on wavelet active media technology and information processing (ICCWAMTIP). IEEE. https:\/\/doi.org\/10.1109\/ICCWAMTIP53232.2021.9674153","DOI":"10.1109\/ICCWAMTIP53232.2021.9674153"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16736-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-16736-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16736-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T08:38:59Z","timestamp":1709800739000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-16736-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,15]]},"references-count":46,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["16736"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-16736-5","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,15]]},"assertion":[{"value":"22 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 August 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 August 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 September 2023","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 have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}