{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T13:26:11Z","timestamp":1770816371423,"version":"3.50.1"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2023,5,3]],"date-time":"2023-05-03T00:00:00Z","timestamp":1683072000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,5,3]],"date-time":"2023-05-03T00:00:00Z","timestamp":1683072000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100002386","name":"Cairo University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100002386","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Acute lymphocytic leukemia (ALL) is a common serious cancer in white blood cells (WBC) that advances quickly and produces abnormal cells in the bone marrow. Cancerous cells associated with ALL lead to impairment of body systems. Microscopic examination of ALL in a blood sample is applied manually by hematologists with many defects. Computer-aided leukemia image detection is used to avoid human visual recognition and to provide a more accurate diagnosis. This paper employs the ensemble strategy to detect ALL cells versus normal WBCs using three stages automatically. Firstly, image pre-processing is applied to handle the unbalanced database through the oversampling process. Secondly, deep spatial features are generated using a convolution neural network (CNN). At the same time, the gated recurrent unit (GRU)-bidirectional long short-term memory (BiLSTM) architecture is utilized to extract long-distance dependent information features or temporal features to obtain active feature learning. Thirdly, a softmax function and the multiclass support vector machine (MSVM) classifier are used for the classification mission. The proposed strategy has the resilience to classify the C-NMC 2019 database into two categories by using splitting the entire dataset into 90% as training and 10% as testing datasets. The main motivation of this paper is the novelty of the proposed framework for the purposeful and accurate diagnosis of ALL images. The proposed CNN-GRU-BiLSTM-MSVM is simply stacked by existing tools. However, the empirical results on C-NMC 2019 database show that the proposed framework is useful to the ALL image recognition problem compared to previous works. The DenseNet-201 model\u00a0yielded an F1-score of 96.23% and an accuracy of 96.29% using the MSVM classifier in the test dataset. The findings exhibited that the proposed strategy can be employed as a complementary diagnostic tool for ALL cells. Further, this proposed strategy will encourage researchers to augment the rare database, such as blood microscopic images by creating powerful applications in terms of combining machine learning with deep learning algorithms.<\/jats:p>","DOI":"10.1007\/s00521-023-08607-9","type":"journal-article","created":{"date-parts":[[2023,5,3]],"date-time":"2023-05-03T11:01:48Z","timestamp":1683111708000},"page":"17415-17427","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Refinement of ensemble strategy for acute lymphoblastic leukemia microscopic images using hybrid CNN-GRU-BiLSTM and MSVM classifier"],"prefix":"10.1007","volume":"35","author":[{"given":"Kamel K.","family":"Mohammed","sequence":"first","affiliation":[]},{"given":"Aboul Ella","family":"Hassanien","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6279-0883","authenticated-orcid":false,"given":"Heba M.","family":"Afify","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,3]]},"reference":[{"key":"8607_CR1","first-page":"596","volume":"2016","author":"M Saritha","year":"2016","unstructured":"Saritha M, Prakash BB, Sukesh K, Shrinivas B (2016) Detection of blood cancer in microscopic images of human blood samples: a review. Int Conf Electr Electron Optim Tech ICEEOT 2016:596\u2013600","journal-title":"Int Conf Electr Electron Optim Tech ICEEOT"},{"key":"8607_CR2","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1111\/j.1365-2354.2005.00513.x","volume":"14","author":"A Redaelli","year":"2005","unstructured":"Redaelli A, Laskin BL, Stephens JM, Botteman MF, Pashos CL (2005) A systematic literature review of the clinical and epidemiological burden of acute lymphoblastic leukaemia (ALL). Eur J Cancer Care Engl 14:53\u201362","journal-title":"Eur J Cancer Care Engl"},{"key":"8607_CR3","first-page":"579","volume":"46","author":"K Fauziah","year":"2012","unstructured":"Fauziah K, Anton SP, Abdullah A (2012) Detection of leukemia in human blood sample based on microscopic images: a study. J Theor Appl Inf Technol 46:579\u2013586","journal-title":"J Theor Appl Inf Technol"},{"key":"8607_CR4","doi-asserted-by":"publisher","first-page":"10662","DOI":"10.3390\/app112210662","volume":"11","author":"MZ Ullah","year":"2021","unstructured":"Ullah MZ, Zheng Y, Song J, Aslam S, Xu C, Kiazolu GD, Wang L (2021) An attention-based convolutional neural network for acute lymphoblastic leukemia classification. Appl Sci 11:10662","journal-title":"Appl Sci"},{"issue":"1","key":"8607_CR5","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3322\/caac.21708","volume":"72","author":"RL Siegel","year":"2022","unstructured":"Siegel RL, Miller KD, Fuchs HE, Jemal A (2022) Cancer statistics, 2022. CA Cancer J Clin 72(1):7\u201333","journal-title":"CA Cancer J Clin"},{"key":"8607_CR6","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.1016\/S0889-8588(05)70185-4","volume":"14","author":"YO Huh","year":"2000","unstructured":"Huh YO, Ibrahim S (2000) Immunophenotypes in adult acute lymphocytic leukemia: Role of flow cytometry in diagnosis and monitoring of disease. Hematol Oncol Clin North Am 14:1251\u20131265","journal-title":"Hematol Oncol Clin North Am"},{"key":"8607_CR7","doi-asserted-by":"crossref","unstructured":"Sajana T, Maguluri LP, Syamala M and Kumari CU (2020). Classification of leukemia patients with different clinical presentation of blood cells. Mater Today 1\u20137","DOI":"10.1016\/j.matpr.2020.10.619"},{"key":"8607_CR8","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.bspc.2018.08.012","volume":"47","author":"S Mishra","year":"2019","unstructured":"Mishra S, Majhi B, Sa PK (2019) Texture feature based classification on microscopic blood smear for acute lymphoblastic leukemia detection. Biomed Signal Process Control 47:303\u2013311","journal-title":"Biomed Signal Process Control"},{"issue":"1","key":"8607_CR9","first-page":"1","volume":"1","author":"KD Gebremeskel","year":"2021","unstructured":"Gebremeskel KD, Kwa TC, Raj KH, Zewdie GA, Shenkute TY, Maleko WA (2021) Automatic early detection and classification of leukemia from microscopic blood image. Abyssinia J Eng Comput 1(1):1\u201310","journal-title":"Abyssinia J Eng Comput"},{"key":"8607_CR10","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.procs.2015.08.017","volume":"58","author":"P Viswanathan","year":"2015","unstructured":"Viswanathan P (2015) Fuzzy c means detection of leukemia based on morphological contour segmentation. Procedia Comput Sci 58:84\u201390","journal-title":"Procedia Comput Sci"},{"key":"8607_CR11","doi-asserted-by":"crossref","unstructured":"Ding Y, Yang Y, Cui Y (2019) Deep learning for classifying of white blood cancer. In: ISBI 2019 C-NMC challenge: classification in cancer cell imaging. Springer, pp 33\u201341","DOI":"10.1007\/978-981-15-0798-4_4"},{"key":"8607_CR12","doi-asserted-by":"crossref","unstructured":"Shi T, Wu L, Zhong C, Wang R, Zheng W (2019) Ensemble convolutional neural networks for cell classification in microscopic images. In: ISBI 2019 C-NMC challenge: classification in cancer cell imaging. Springer, pp 43\u201351","DOI":"10.1007\/978-981-15-0798-4_5"},{"key":"8607_CR13","doi-asserted-by":"crossref","unstructured":"Donida Labati R, Piuri V, Scotti F (2011) ALL-IDB: the acute lymphoblastic leukemia image database for image processing. In: Macq B, Schelkens P (eds) Proceedings of the 18th IEEE ICIP international conference on image processing, September 11\u201314. Brussels, Belgium. IEEE Publisher, pp 2045\u20138","DOI":"10.1109\/ICIP.2011.6115881"},{"key":"8607_CR14","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.artmed.2014.09.002","volume":"62","author":"L Putzu","year":"2014","unstructured":"Putzu L, Caocci G, Di Ruberto C (2014) Leucocyte classification for leukaemia detection using image processing techniques. Artif Intell Med 62:179\u2013191","journal-title":"Artif Intell Med"},{"key":"8607_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-59215-9","volume":"10","author":"AT Sahlol","year":"2020","unstructured":"Sahlol AT, Kollmannsberger P, Ewees AA (2020) Efficient classification of white blood cell leukemia with improved swarm optimization of deep features. Sci Rep 10:1\u201311","journal-title":"Sci Rep"},{"issue":"11","key":"8607_CR16","doi-asserted-by":"publisher","first-page":"E903","DOI":"10.1016\/j.clml.2021.06.025","volume":"21","author":"K Dese","year":"2021","unstructured":"Dese K et al (2021) Accurate machine-learning-based classification of leukemia from blood smear images. Clin Lymphoma Myeloma Leuk 21(11):E903\u2013E914","journal-title":"Clin Lymphoma Myeloma Leuk"},{"key":"8607_CR17","unstructured":"Gupta A, Gupta R, Gehlot S, Mourya S. (2019) Classification of normal vs malignant cells in B-ALL white blood cancer microscopic images. In: IEEE international symposium on biomedical imaging (ISBI)-2019 challenges internet"},{"key":"8607_CR18","doi-asserted-by":"crossref","unstructured":"Liu Y, Long F (2019) Acute lymphoblastic leukemia cells image analysis with deep bagging ensemble learning. ISBI 2019 C-NMC challenge: classification in cancer cell imaging, pp 113\u2013121","DOI":"10.1007\/978-981-15-0798-4_12"},{"key":"8607_CR19","doi-asserted-by":"crossref","unstructured":"Prellberg J, Kramer O (2019) Acute lymphoblastic leukemia classification from microscopic images using convolutional neural networks. In: ISBI 2019 C-NMC challenge: classification in cancer cell imaging, pp 53\u201361","DOI":"10.1007\/978-981-15-0798-4_6"},{"key":"8607_CR20","doi-asserted-by":"crossref","unstructured":"Mondal C, Hasan MK, Jawad MT, Dutta A, Islam MR, Awal MA, Ahmad M, Alyami SA, Ali Moni M (2021) Acute lymphoblastic leukemia detection from microscopic images using weighted ensemble of convolutional neural networks, pp 1\u201331","DOI":"10.20944\/preprints202105.0429.v1"},{"key":"8607_CR21","doi-asserted-by":"crossref","unstructured":"Bin Y, Yang Y, Shen F, et al (2019) Describing video with attention-based bidirectional LSTM. IEEE Trans Cyber 7:1\u201311","DOI":"10.1109\/TCYB.2018.2831447"},{"key":"8607_CR22","doi-asserted-by":"crossref","unstructured":"Dey R, Salem FM (2017) Gate-variants of gated recurrent unit (GRU) neural networks. In: Proceedings of the 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS), Boston, MA, USA, 6\u20139 August 2017, pp 1597\u20131600","DOI":"10.1109\/MWSCAS.2017.8053243"},{"key":"8607_CR23","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1186\/s12859-022-04558-5","volume":"22","author":"YM Chen","year":"2021","unstructured":"Chen YM, Chou FI, Ho WH, Tsai JT (2021) Classifying microscopic images as acute lymphoblastic leukemia by Resnet ensemble model and Taguchi method. BMC Bioinf 22:615","journal-title":"BMC Bioinf"},{"key":"8607_CR24","doi-asserted-by":"publisher","first-page":"100794","DOI":"10.1016\/j.imu.2021.100794","volume":"27","author":"C Mondal","year":"2021","unstructured":"Mondal C, Hasan K, Ahmad M, Awal A, Jawad T, Dutta A, Islam R, Moni MA (2021) Ensemble of convolutional neural networks to diagnose acute lymphoblastic leukemia from microscopic images. Inform Med Unlock 27:100794","journal-title":"Inform Med Unlock"},{"key":"8607_CR25","doi-asserted-by":"crossref","unstructured":"Marzahl C, Aubreville M, Voigt J, Maier A (2019) Classification of leukemic b-lymphoblast cells from blood smear microscopic images with an attention-based deep learning method and advanced augmentation techniques. In: Gupta A, Gupta R (eds) ISBI 2019 C-NMC challenge: classification in cancer cell imaging. Springer Nature Singapore Pte Ltd, pp 13\u201322","DOI":"10.1007\/978-981-15-0798-4_2"},{"key":"8607_CR26","doi-asserted-by":"crossref","unstructured":"Kulhalli R, Savadikar C, Garware B (2019) Toward automated classification of b-acute lymphoblastic leukemia. In: Gupta A, Gupta R (eds) ISBI 2019 C-NMC challenge: classification in cancer cell imaging. Springer Nature Singapore Pte Ltd, pp 63\u201372","DOI":"10.1007\/978-981-15-0798-4_7"},{"key":"8607_CR27","doi-asserted-by":"crossref","unstructured":"Verma E, Singh V (2019) ISBI challenge 2019: convolution neural networks for B-ALL cell classification. In: Gupta A, Gupta R (eds) ISBI 2019 C-NMC challenge: classification in cancer cell imaging. Springer Nature Singapore Pte Ltd, pp 131\u2013139","DOI":"10.1007\/978-981-15-0798-4_14"},{"key":"8607_CR28","doi-asserted-by":"crossref","unstructured":"Pan Y, Liu M, Xia Y, Shen D (2019) Neighborhood-correction algorithm for classification of normal and malignant cells. In: Gupta A, Gupta R (eds) ISBI 2019 C-NMC challenge: classification in cancer cell imaging. Springer Nature Singapore Pte Ltd, pp 73\u201382","DOI":"10.1007\/978-981-15-0798-4_8"},{"key":"8607_CR29","first-page":"03295","volume":"2003","author":"S Goswami","year":"2020","unstructured":"Goswami S, Mehta S, Sahrawat D, Gupta A, Gupta R (2020) Heterogeneity loss to handle intersubject and intrasubject variability in cancer 2003:03295","journal-title":"Heterogeneity loss to handle intersubject and intrasubject variability in cancer"},{"key":"8607_CR30","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.3390\/diagnostics10121064","volume":"10","author":"PH Kasani","year":"2020","unstructured":"Kasani PH, Park SW, Jang JW (2020) An aggregated-based deep learning method for leukemic B-lymphoblast classification. Diagnostics 10:1064","journal-title":"Diagnostics"},{"key":"8607_CR31","unstructured":"Joshi MD, Karode AH, Suralkar S (2013) White blood cells segmentation and classification to detect acute leukemia. Int J Emerg Trends Technol Comput Sci (IJETTCS) 2:147\u2013151"},{"key":"8607_CR32","doi-asserted-by":"publisher","first-page":"49","DOI":"10.4103\/2228-7477.150428","volume":"5","author":"MM Amin","year":"2015","unstructured":"Amin MM, Kermani S, Talebi A, Oghli MG (2015) Recognition of acute lymphoblastic leukemia cells in microscopic images using k-means clustering and support vector machine classifier. J Med Signals Sens 5:49","journal-title":"J Med Signals Sens"},{"key":"8607_CR33","doi-asserted-by":"crossref","unstructured":"Singhal V, Singh P (2014) Local binary pattern for automatic detection of acute lymphoblastic leukemia. In: Proceedings of 2014 twentieth national conference on communications (NCC), Kanpur, India, 28 February\u20132 March 2014, pp 1\u20135","DOI":"10.1109\/NCC.2014.6811261"},{"key":"8607_CR34","first-page":"3136","volume":"4","author":"T Karthikeyan","year":"2017","unstructured":"Karthikeyan T, Poornima N (2017) Microscopic image segmentation using fuzzy c means for leukemia diagnosis. Int J Adv Res Sci Eng Technol 4:3136\u20133142","journal-title":"Int J Adv Res Sci Eng Technol"},{"key":"8607_CR35","unstructured":"C-NMC 2019 dataset: https:\/\/wiki.cancerimagingarchive.net\/pages\/viewpage.action?pageId=52758223"},{"key":"8607_CR36","doi-asserted-by":"publisher","first-page":"100709","DOI":"10.1016\/j.imu.2021.100709","volume":"26","author":"MK Hasan","year":"2021","unstructured":"Hasan MK, Jawad MT, Hasan KN, Partha SB, Al Masba MM, Saha S, Moni MA (2021) COVID-19 identification from volumetric chest CT scans using a progressively resized 3D-CNN incorporating segmentation, augmentation, and class-rebalancing. Inf Med Unlocked. 26:100709","journal-title":"Inf Med Unlocked."},{"key":"8607_CR37","doi-asserted-by":"crossref","unstructured":"Donahue J, et al (2015) Long-term recurrent convolutional networks for visual recognition and description. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2625\u20132634","DOI":"10.1109\/CVPR.2015.7298878"},{"key":"8607_CR38","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.cviu.2017.10.011","volume":"166","author":"Z Li","year":"2018","unstructured":"Li Z, Gavrilyuk K, Gavves E, Jain M, Snoek CG (2018) Videolstm convolves attends and flows for action recognition. Comput Vis Image Underst 166:41\u201350","journal-title":"Comput Vis Image Underst"},{"key":"8607_CR39","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1109\/ACCESS.2017.2778011","volume":"6","author":"A Ullah","year":"2018","unstructured":"Ullah A, Ahmad J, Muhammad K, Sajjad M, Baik SW (2018) Action recognition in video sequences using deep Bi-directional LSTM with CNN features. IEEE Access 6:1155\u20131166","journal-title":"IEEE Access"},{"key":"8607_CR40","unstructured":"Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modelling. arXiv preprint arXiv:1412.3555"},{"key":"8607_CR41","doi-asserted-by":"crossref","unstructured":"Ranjit S, Shrestha S, Subedi S, Shakya S (2018) Comparison of algorithms in foreign exchange rate prediction. In: Proceedings of the 2018 IEEE 3rd international conference on computing, communication and security (ICCCS). IEEE, pp 9\u201313","DOI":"10.1109\/CCCS.2018.8586826"},{"key":"8607_CR42","doi-asserted-by":"crossref","unstructured":"He K, Zhang Z, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of computer vision and pattern recognition (CVPR), pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"8607_CR43","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: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"8607_CR44","unstructured":"Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5 MB model size. Preprint, submitted November 4, 2016"},{"key":"8607_CR45","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, van der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of computer vision and pattern recognition (CVPR), pp 2261\u20132269","DOI":"10.1109\/CVPR.2017.243"},{"key":"8607_CR46","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) MobileNetV2: inverted residuals and linear bottlenecks. In: 2018 IEEE\/CVF conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"8607_CR47","unstructured":"Jozefowicz R, Zaremba W, Sutskever I (2015) An empirical exploration of recurrent network architectures. In: Proceedings of international conference on machine learning, pp 2342\u20132350"},{"key":"8607_CR48","doi-asserted-by":"crossref","unstructured":"Kim J, Moon N (2019) BiLSTM model based on multivariate time series data in multiple field for forecasting trading area. J Ambient Intell Hum Comput 1\u201310","DOI":"10.1007\/s12652-019-01398-9"},{"key":"8607_CR49","doi-asserted-by":"publisher","first-page":"106664","DOI":"10.1016\/j.asoc.2020.106664","volume":"96","author":"D Pradhan","year":"2020","unstructured":"Pradhan D, Sahoo B, Misra BB, Padhy S (2020) A multiclass SVM classifier with teaching learning based feature subset selection for enzyme subclass classification. Appl Soft Comput 96:106664","journal-title":"Appl Soft Comput"},{"key":"8607_CR50","doi-asserted-by":"publisher","first-page":"1512","DOI":"10.18520\/cs\/v115\/i8\/1512-1518","volume":"115","author":"P Mirmohammadi","year":"2018","unstructured":"Mirmohammadi P, Rasooli A, Ashtiyani M, Amin MM, Deevband MR (2018) Automatic recognition of acute lymphoblastic leukemia using multi-SVM classifier. Curr Sci 115:1512\u20131518","journal-title":"Curr Sci"},{"key":"8607_CR51","doi-asserted-by":"publisher","first-page":"103793","DOI":"10.1016\/j.medengphy.2022.103793","volume":"103","author":"R Gupta","year":"2022","unstructured":"Gupta R, Gehlot S, Gupta A (2022) C-NMC: B-lineage acute lymphoblastic leukaemia: a blood cancer dataset. Med Eng Phys 103:103793","journal-title":"Med Eng Phys"},{"key":"8607_CR52","first-page":"488","volume":"4","author":"L Faivdullah","year":"2015","unstructured":"Faivdullah L, Azahar F, Htike ZZ, Naing WYN (2015) Leukemia detection from blood smears. J Med Bioeng 4:488\u2013491","journal-title":"J Med Bioeng"},{"key":"8607_CR53","doi-asserted-by":"publisher","first-page":"2562","DOI":"10.1038\/s41598-023-29160-4","volume":"13","author":"P Manescu","year":"2023","unstructured":"Manescu P, Narayanan P, Bendkowski C et al (2023) Detection of acute promyelocytic leukemia in peripheral blood and bone marrow with annotation-free deep learning. Sci Rep 13:2562","journal-title":"Sci Rep"},{"key":"8607_CR54","doi-asserted-by":"publisher","first-page":"348","DOI":"10.3390\/life13020348","volume":"13","author":"TG Devi","year":"2023","unstructured":"Devi TG, Patil N, Rai S, Philipose CS (2023) Gaussian blurring technique for detecting and classifying acute lymphoblastic leukemia cancer cells from microscopic biopsy images. Life 13:348","journal-title":"Life"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08607-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08607-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08607-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T19:17:58Z","timestamp":1689189478000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08607-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,3]]},"references-count":54,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["8607"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08607-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,3]]},"assertion":[{"value":"8 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 April 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 May 2023","order":3,"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 interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}