{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T19:05:37Z","timestamp":1763665537028,"version":"3.41.0"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-19629-3","type":"journal-article","created":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T08:02:55Z","timestamp":1719561775000},"page":"16683-16707","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep features and metaheuristics guided optimization-based method for breast cancer diagnosis"],"prefix":"10.1007","volume":"84","author":[{"given":"Emon","family":"Asad","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3445-7469","authenticated-orcid":false,"given":"Ayatullah Faruk","family":"Mollah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Subhadip","family":"Basu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tapabrata","family":"Chakraborti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,28]]},"reference":[{"issue":"3","key":"19629_CR1","first-page":"209","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer J Clin 71(3):209\u2013249","journal-title":"CA: A Cancer J Clin"},{"key":"19629_CR2","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.ejpb.2015.03.018","volume":"93","author":"E P\u00e9rez Herrero","year":"2015","unstructured":"P\u00e9rez Herrero E, Fern\u00e1ndezMedarde A (2015) Advanced targeted therapies in cancer: Drug nanocarriers, the future of chemotherapy. Eur J Pharm Biopharm 93:52\u201379","journal-title":"Eur J Pharm Biopharm"},{"issue":"1","key":"19629_CR3","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1186\/s12885-019-6311-z","volume":"19","author":"R Wang","year":"2019","unstructured":"Wang R, Zhu Y, Liu X, Liao X, He J, Niu L (2019) The Clinicopathological features and survival outcomes of patients with different metastatic sites in stage IV breast cancer. BMC Cancer 19(1):1091\u20131103","journal-title":"BMC Cancer"},{"issue":"7","key":"19629_CR4","doi-asserted-by":"publisher","first-page":"5479","DOI":"10.1007\/s00521-022-07895-x","volume":"35","author":"P Pramanik","year":"2023","unstructured":"Pramanik P, Mukhopadhyay S, Mirjalili S, Sarkar R (2023) Deep feature selection using local search embedded social ski-driver optimization algorithm for breast cancer detection in mammograms. Neural Comput Appl 35(7):5479\u20135499","journal-title":"Neural Comput Appl"},{"issue":"7","key":"19629_CR5","doi-asserted-by":"publisher","first-page":"1238","DOI":"10.3390\/diagnostics13071238","volume":"13","author":"K Jabeen","year":"2023","unstructured":"Jabeen K, Khan MA, Balili J, Alhaisoni M, Almujally NA, Alrashidi H, Tariq U, Cha JH (2023) BC2NetRF: Breast Cancer Classification from Mammogram Images Using Enhanced Deep Learning Features and Equilibrium-Jaya Controlled Regula Falsi-Based Features Selection. Diagnostics 13(7):1238\u20131260","journal-title":"Diagnostics"},{"key":"19629_CR6","unstructured":"Tan M, Le QV (2019) EfficientNet: rethinking model scaling for convolutional neural networks. In 36th international conference on machine learning PMLR, California, pp 6105\u20136114"},{"issue":"2","key":"19629_CR7","doi-asserted-by":"publisher","first-page":"557","DOI":"10.3390\/diagnostics12020557","volume":"12","author":"S Zahoor","year":"2022","unstructured":"Zahoor S, Shoaib U, Lali IU (2022) Breast Cancer Mammograms Classification Using Deep Neural Network and Entropy-Controlled Whale Optimization Algorithm. Diagnostics 12(2):557\u2013592","journal-title":"Diagnostics"},{"key":"19629_CR8","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) MobileNetV2: inverted residuals and linear bottlenecks. In The IEEE\u00a0Conf Comput Vis Pattern Recognit (CVPR), Salt Lake City, pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"19629_CR9","doi-asserted-by":"crossref","unstructured":"Zoph B, Vasudevan V, Shlens J, Le QV (2018) Learning transferable architectures for scalable image recognition. In 2018 IEEE\/CVF IEEE Conf Comput Vis Pattern Recognit. Salt Lake City, pp 8697\u20138710","DOI":"10.1109\/CVPR.2018.00907"},{"issue":"12495","key":"19629_CR10","first-page":"1","volume":"9","author":"L Shen","year":"2019","unstructured":"Shen L, Margolies LR, Rothstein JH, Fluder E, McBride R, Sieh W (2019) Deep Learning to Improve Breast Cancer Detection on Screening Mammography. Sci Rep 9(12495):1\u201312","journal-title":"Sci Rep"},{"issue":"119643","key":"19629_CR11","first-page":"1","volume":"219","author":"R Pramanik","year":"2023","unstructured":"Pramanik R, Pramanik P, Sarkar R (2023) Breast cancer detection in thermograms using a hybrid of GA and GWO based deep feature selection method. Expert Syst Appl 219(119643):1\u201312","journal-title":"Expert Syst Appl"},{"key":"19629_CR12","unstructured":"O'Shea K, Nash R (2015) An introduction to convolutional neural networks.\u00a0arXiv preprint arXiv:1511.08458, pp 1\u201311"},{"key":"19629_CR13","doi-asserted-by":"publisher","first-page":"71194","DOI":"10.1109\/ACCESS.2021.3079204","volume":"9","author":"A Saber","year":"2021","unstructured":"Saber A, Sakr M, Aboseida OM, Keshk A, Chen H (2021) A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique. IEEE Access 9:71194\u201371209","journal-title":"IEEE Access"},{"issue":"112855","key":"19629_CR14","first-page":"1","volume":"139","author":"VK Singh","year":"2020","unstructured":"Singh VK, Rashwan HA, Romani S, Akram F, Pandey N, Sarker MMK, Saleh A, Arenas M, Arquez M, Puig D, Barrena JT (2020) Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network. Expert Syst Appl 139(112855):1\u201314","journal-title":"Expert Syst Appl"},{"issue":"5","key":"19629_CR15","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.3390\/diagnostics12051053","volume":"12","author":"MA Hassanien","year":"2022","unstructured":"Hassanien MA, Singh VK, Puig D, Nasser MA (2022) Predicting Breast Tumor Malignancy Using Deep ConvNeXt Radiomics and Quality-Based Score Pooling in Ultrasound Sequences. Diagnostics 12(5):1053","journal-title":"Diagnostics"},{"issue":"3","key":"19629_CR16","doi-asserted-by":"publisher","first-page":"807","DOI":"10.3390\/s22030807","volume":"22","author":"K Jabeen","year":"2022","unstructured":"Jabeen K, Khan MA, Alhaisoni M, Tariq U, Zhang YD, Hamza A, Mickus A, Dama\u0161evicius R (2022) Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion. Sensors 22(3):807","journal-title":"Sensors"},{"key":"19629_CR17","doi-asserted-by":"crossref","unstructured":"Larose DT, Larose CD (2014) k\u2010Nearest neighbor algorithm. In discovering knowledge in data: an introduction to data mining, second edition, John Wiley & Sons, Inc., pp 149\u2013164","DOI":"10.1002\/9781118874059"},{"issue":"2","key":"19629_CR18","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.acra.2011.09.014","volume":"19","author":"IC Moreira","year":"2012","unstructured":"Moreira IC, Amaral I, Domingues I, Cardoso A, Cardoso MJ, Cardoso JS (2012) INbreast: toward a full-field digital mammographic database. Acad Radiol 19(2):236\u2013248","journal-title":"Acad Radiol"},{"key":"19629_CR19","doi-asserted-by":"publisher","unstructured":"Suckling J, Parker J, Dance D, Astley S, Hutt I, Boggis C, Ricketts I, Stamatakis E, Cerneaz N, Kok S, Taylor P, Betal D, Savage J (2015) Mammographic image analysis society (MIAS) database v1.21. Apollo - University of Cambridge Repository. https:\/\/doi.org\/10.17863\/CAM.105113","DOI":"10.17863\/CAM.105113"},{"issue":"170177","key":"19629_CR20","first-page":"1","volume":"4","author":"RS Lee","year":"2017","unstructured":"Lee RS, Gimenez F, Hoogi A, Miyake KK, Gorovoy M, Rubin DL (2017) A curated mammography data set for use in computer-aided detection and diagnosis research. Scientific Data 4(170177):1\u20139","journal-title":"Scientific Data"},{"issue":"5","key":"19629_CR21","doi-asserted-by":"publisher","first-page":"1696","DOI":"10.1002\/ima.22889","volume":"33","author":"S Thawkar","year":"2023","unstructured":"Thawkar S, Katta V, Parashar AR, Singh LK, Khanna M (2023) Breast cancer: A hybrid method for feature selection and classification in digital mammography. Int J Imaging Syst Technol 33(5):1696\u20131712","journal-title":"Int J Imaging Syst Technol"},{"issue":"113525","key":"19629_CR22","first-page":"1","volume":"221","author":"LK Singh","year":"2023","unstructured":"Singh LK, Khanna M, Singh R (2023) A novel enhanced hybrid clinical decision support system for accurate breast cancer prediction. Measurement 221(113525):1","journal-title":"Measurement"},{"issue":"5","key":"19629_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.heliyon.2024.e26799","volume":"10","author":"M Khanna","year":"2024","unstructured":"Khanna M, Singh LK, Shrivastava K, Singh R (2024) An enhanced and efficient approach for feature selection for chronic human disease prediction: A breast cancer study. Heliyon 10(5):1\u201321","journal-title":"Heliyon"},{"key":"19629_CR24","doi-asserted-by":"crossref","unstructured":"Singh LK, Khanna M, Singh R (2024) An enhanced soft-computing based strategy for efficient feature selection for timely breast cancer prediction: Wisconsin Diagnostic Breast Cancer dataset case. Multimedia Tools and Applications (in press)","DOI":"10.1007\/s11042-024-18473-9"},{"key":"19629_CR25","doi-asserted-by":"publisher","first-page":"105584","DOI":"10.1016\/j.cmpb.2020.105584","volume":"196","author":"MA Al-antari","year":"2020","unstructured":"Al-antari MA, Han SM, Kim TS (2020) Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms. Comput Methods Prog Biomed 196:105584","journal-title":"Comput Methods Prog Biomed"},{"key":"19629_CR26","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2017) YYOLO9000: better, faster, stronger. In IEEE Conf Comput Vis Pattern Recognit, Honolulu, pp 6517\u20136525","DOI":"10.1109\/CVPR.2017.690"},{"key":"19629_CR27","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In IEEE Conf Comput Vis Pattern Recognit, Las Vegas, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"19629_CR28","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V (2017)\u00a0Inception-v4, inception-resnet and the impact of residual connections on learning. In Proceedings of the AAAI Conf Artif Intell, San Francisco, 31(1):4278\u20134284","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"19629_CR29","doi-asserted-by":"publisher","first-page":"165724","DOI":"10.1109\/ACCESS.2019.2953318","volume":"7","author":"HN Khan","year":"2019","unstructured":"Khan HN, Shahid AR, Raza B, Dar AH, Alquhayz H (2019) Multi-View Feature Fusion Based Four Views Model for Mammogram Classification Using Convolutional Neural Network. IEEE Access 7:165724\u2013165733","journal-title":"IEEE Access"},{"key":"19629_CR30","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. In 3rd International Conference on Learning Representations, San Diego, pp 1\u201314"},{"key":"19629_CR31","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In IEEE Conf Comput Vis Patt Recognit,\u00a0Boston, pp 1\u20139\u00a0","DOI":"10.1109\/CVPR.2015.7298594"},{"issue":"106045","key":"19629_CR32","first-page":"1","volume":"204","author":"RS Cauce","year":"2021","unstructured":"Cauce RS, Mart\u00edn JP, Luque M (2021) Multi-input convolutional neural network for breast cancer detection using thermal images and clinical data. Comput Methods Programs Biomed 204(106045):1\u20139","journal-title":"Comput Methods Programs Biomed"},{"key":"19629_CR33","doi-asserted-by":"publisher","first-page":"105437","DOI":"10.1016\/j.compbiomed.2022.105437","volume":"145","author":"S Chattopadhyay","year":"2022","unstructured":"Chattopadhyay S, Dey A, Singh PK, Sarkar R (2022) DRDA-Net: Dense residual dual-shuffle attention network for breast cancer classification using histopathological images. Comput Biol Med 145:105437","journal-title":"Comput Biol Med"},{"issue":"2","key":"19629_CR34","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1049\/ipr2.12035","volume":"15","author":"H Li","year":"2021","unstructured":"Li H, Niu J, Li D, Zhang C (2021) Classification of breast mass in two-view mammograms via deep learning. IET Image Proc 15(2):454\u2013467","journal-title":"IET Image Proc"},{"key":"19629_CR35","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1016\/j.matcom.2022.05.038","volume":"202","author":"R Karthiga","year":"2022","unstructured":"Karthiga R, Narasimhan K, Amirtharajan R (2022) Diagnosis of breast cancer for modern mammography using artificial intelligence. Math Comput Simul 202:316\u2013330","journal-title":"Math Comput Simul"},{"key":"19629_CR36","doi-asserted-by":"publisher","unstructured":"Kushwah VS, Saxena A, Pahariya JS, Goyal SK (2021) Support Vector Machine Technique to Prognosis Breast Cancer. In\u00a0Sharma TK, Ahn CW, Verma OP, Panigrahi BK (eds) Soft Comput: Theor Appl Adv Intell Syst Comput, vol 1381. Springer, Singapore.\u00a0https:\/\/doi.org\/10.1007\/978-981-16-1696-9_31","DOI":"10.1007\/978-981-16-1696-9_31"},{"key":"19629_CR37","unstructured":"Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) SSqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. arXiv preprint arXiv:1602.07360, pp 1\u201313\u00a0"},{"issue":"104692","key":"19629_CR38","first-page":"1","volume":"83","author":"A Bhattacharya","year":"2023","unstructured":"Bhattacharya A, Saha B, Chattopadhyay S, Sarkar R (2023) Deep feature selection using adaptive \u03b2-Hill Climbing aided whale optimization algorithm for lung and colon cancer detection. Biomed Signal Process Control 83(104692):1\u201320","journal-title":"Biomed Signal Process Control"},{"key":"19629_CR39","unstructured":"Scuccimarra EA (2023) DDSM Mammography. [online]. Available: https:\/\/www.kaggle.com\/datasets\/skooch\/ddsm-mammography. Accessed 15 Jun 2023"},{"key":"19629_CR40","doi-asserted-by":"crossref","unstructured":"Luong MT, Pham H, Manning CD (2015) Effective approaches to attention-based neural machine translation. In Proc 2015 Conf Empir Methods Nat Lang Process,\u00a0Lisbon, pp. 1412\u20131421.","DOI":"10.18653\/v1\/D15-1166"},{"key":"19629_CR41","unstructured":"Lin M, Chen Q, Yan S (2014) Network in network. arXiv preprint arXiv:1312.4400, pp 1\u201310"},{"key":"19629_CR42","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","volume":"95","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S, Lewis A (2016) The Whale Optimization Algorithm. Adv Eng Softw 95:51\u201367","journal-title":"Adv Eng Softw"},{"issue":"105349","key":"19629_CR43","first-page":"1","volume":"144","author":"R Kundu","year":"2022","unstructured":"Kundu R, Chattopadhyay S, Cuevas E, Sarkar R (2022) AltWOA: Altruistic Whale Optimization Algorithm for Feature Selection on Microarray Datasets. Comput Biol Med 144(105349):1\u201321","journal-title":"Comput Biol Med"},{"key":"19629_CR44","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili S, Mirjalili SM, Lewis A (2014) Grey Wolf Optimizer. Adv Eng Softw 69:46\u201361","journal-title":"Adv Eng Softw"},{"key":"19629_CR45","doi-asserted-by":"publisher","unstructured":"Asad E, Mollah AF (2023) A gray wolf optimization-inspired hybrid method for disease identification. In Proc 4th Int Conf Commun Devices Comput. Lecture notes in electrical engineering, vol 1046. Springer, Singapore. https:\/\/doi.org\/10.1007\/978-981-99-2710-4_2","DOI":"10.1007\/978-981-99-2710-4_2"},{"issue":"8955","key":"19629_CR46","first-page":"1","volume":"14","author":"Z Pang","year":"2024","unstructured":"Pang Z, Wang Y, Yang F (2024) Application of optimized Kalman filtering in target tracking based on improved Gray Wolf algorithm. Sci Rep 14(8955):1\u20139","journal-title":"Sci Rep"},{"issue":"24","key":"19629_CR47","doi-asserted-by":"publisher","first-page":"13489","DOI":"10.1007\/s00500-019-03887-7","volume":"23","author":"MA Al-Betar","year":"2019","unstructured":"Al-Betar MA, Aljarah I, Awadallah MA, Faris H, Mirjalili S (2019) Adaptive beta-hill climbing for optimization. Soft Comput 23(24):13489\u201313512","journal-title":"Soft Comput"},{"key":"19629_CR48","unstructured":"Heath M, Bowyer K, Kopans D, Moore R, Kegelmeyer WP (2001) The digital database for screening mammography. In Proc Fifth Int Work Digit Mammography. pp 212\u2013218"},{"issue":"3","key":"19629_CR49","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1007\/s00521-013-1525-5","volume":"25","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili S, Mirjalili SM, Yang XS (2014) Binary bat algorithm. Neural Comput Appl 25(3):663\u2013681","journal-title":"Neural Comput Appl"},{"key":"19629_CR50","doi-asserted-by":"publisher","first-page":"105190","DOI":"10.1016\/j.knosys.2019.105190","volume":"191","author":"A Faramarzi","year":"2020","unstructured":"Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S (2020) Equilibrium optimizer: A novel optimization algorithm. Knowl Based Syst 191:105190","journal-title":"Knowl Based Syst"},{"issue":"12259","key":"19629_CR51","first-page":"1","volume":"12","author":"A Baccouche","year":"2022","unstructured":"Baccouche A, Zapirain B, Elmaghraby AS (2022) An integrated framework for breast mass classifcation and diagnosis using stacked ensemble of residual neural networks. Scientifc Reports 12(12259):1\u201317","journal-title":"Scientifc Reports"},{"issue":"8860011","key":"19629_CR52","first-page":"1","volume":"22","author":"Q Zhang","year":"2020","unstructured":"Zhang Q, Li Y, Zhao G, Man P, Lin Y, Wang M (2020) A Novel Algorithm for Breast Mass Classification in Digital Mammography Based on Feature Fusion. J Healthcare Eng 22(8860011):1\u201311","journal-title":"J Healthcare Eng"},{"key":"19629_CR53","doi-asserted-by":"crossref","unstructured":"Arias R, Narv\u00e1ez F, Franco H (2019) Evaluation of learning approaches based on convolutional neural networks for mammogram classification. In Int Conf Smart Technol Syst Appl. Quito, pp 273\u2013287","DOI":"10.1007\/978-3-030-46785-2_22"},{"key":"19629_CR54","doi-asserted-by":"crossref","unstructured":"Falconi LG, Perez M, Aguilar WG, Conci A (2020) Transfer learning and fine tuning in breast mammogram abnormalities classification on CBIS-DDSM database. Adv Sci Technol Eng Sys J 5(2):154\u2013165","DOI":"10.25046\/aj050220"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19629-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-19629-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19629-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T03:15:29Z","timestamp":1748056529000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-19629-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,28]]},"references-count":54,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["19629"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-19629-3","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2024,6,28]]},"assertion":[{"value":"20 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 May 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 June 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2024","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 declare that they have no conflict of interests and no financial competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"All ethical and professional conduct principles have been followed during this work.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"All authors have consented to publish this article.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent of Publication"}}]}}