{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T15:57:53Z","timestamp":1780502273168,"version":"3.54.1"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"21-22","license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"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":["Soft Comput"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s00500-024-10305-0","type":"journal-article","created":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T12:50:16Z","timestamp":1732020616000},"page":"12991-13008","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Real-time mobile application for Arabic sign alphabet recognition using pre-trained CNN"],"prefix":"10.1007","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5445-0336","authenticated-orcid":false,"given":"Sarra","family":"Rouabhi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Redouane","family":"Tlemsani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nabil","family":"Neggaz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,19]]},"reference":[{"key":"10305_CR1","doi-asserted-by":"crossref","unstructured":"Ahmed S, Bons M (2020) Edge computed NILM: a phone-based implementation using MobileNet compressed by tensorflow lite. In: Proceedings of the 5th International Workshop on non-intrusive load monitoring, pp 44\u201348","DOI":"10.1145\/3427771.3427852"},{"key":"10305_CR2","first-page":"374","volume-title":"International MICCAI Brainlesion Workshop","author":"AS Akbar","year":"2020","unstructured":"Akbar AS, Fatichah C, Suciati N (2020) Modified MobileNet for patient survival prediction. International MICCAI Brainlesion Workshop. Springer International Publishing, Cham, pp 374\u2013387"},{"issue":"4","key":"10305_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuri.2021.100034","volume":"2","author":"K Ali","year":"2022","unstructured":"Ali K, Shaikh ZA, Khan AA, Laghari AA (2022) Multiclass skin cancer classification using EfficientNets\u2013a first step towards preventing skin cancer. Neurosci Inform 2(4):100034","journal-title":"Neurosci Inform"},{"issue":"5","key":"10305_CR4","volume":"2","author":"MA Almasre","year":"2016","unstructured":"Almasre MA, Al-Nuaim H (2016) A real-time letter recognition model for Arabic sign language using kinect and leap motion controller v2. Int J Adv Eng Manag Sci 2(5):239469","journal-title":"Int J Adv Eng Manag Sci"},{"issue":"5","key":"10305_CR5","doi-asserted-by":"publisher","first-page":"78","DOI":"10.3390\/computers11050078","volume":"11","author":"Z Alsaadi","year":"2022","unstructured":"Alsaadi Z, Alshamani E, Alrehaili M, Alrashdi AAD, Albelwi S, Elfaki AO (2022) A real time Arabic sign language alphabets (ArSLA) recognition model using deep learning architecture. Computers 11(5):78\u201398. https:\/\/doi.org\/10.3390\/computers11050078","journal-title":"Computers"},{"key":"10305_CR6","unstructured":"Barbu A, Mayo D, Alverio J, Luo W, Wang C, Gutfreund D, Katz B (2019) Objectnet: a large-scale bias-controlled dataset for pushing the limits of object recognition models.\u00a0In: Advances in neural information processing systems\u00a032 (NeurIPS 2019)"},{"key":"10305_CR7","unstructured":"Belissen V, Braffort A, Gouiff\u00e8s M (2020) Dicta-Sign-LSF-v2: remake of a continuous French sign language dialogue corpus and a first baseline for automatic sign language processing. In:\u00a0LREC 2020, 12th Conference on Language Resources and Evaluation"},{"issue":"18","key":"10305_CR8","doi-asserted-by":"publisher","first-page":"5151","DOI":"10.3390\/s20185151","volume":"20","author":"JJ Bird","year":"2020","unstructured":"Bird JJ, Ek\u00e1rt A, Faria DR (2020) British sign language recognition via late fusion of computer vision and leap motion with transfer learning to american sign language. Sensors 20(18):5151","journal-title":"Sensors"},{"key":"10305_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.aquaeng.2020.102117","volume":"91","author":"K Cai","year":"2020","unstructured":"Cai K, Miao X, Wang W, Pang H, Liu Y, Song J (2020) A modified YOLOv3 model for fish detection based on MobileNetv1 as backbone. Aquacult Eng 91:102117","journal-title":"Aquacult Eng"},{"key":"10305_CR10","doi-asserted-by":"crossref","unstructured":"Cenggoro, T. W. (2020). Incorporating the knowledge distillation to improve the efficientnet transfer learning capability. In\u00a02020 International Conference on Data Science and its Applications (ICoDSA)\u00a0(pp. 1\u20135). IEEE.","DOI":"10.1109\/ICoDSA50139.2020.9212994"},{"issue":"3","key":"10305_CR11","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1148\/radiol.2017171115","volume":"286","author":"MC Chen","year":"2018","unstructured":"Chen MC, Ball RL, Yang L, Moradzadeh N, Chapman BE, Larson DB, Lungren MP (2018) Deep learning to classify radiology free-text reports. Radiology 286(3):845\u2013852","journal-title":"Radiology"},{"issue":"4","key":"10305_CR12","doi-asserted-by":"publisher","first-page":"1292","DOI":"10.1109\/JBHI.2020.3009383","volume":"25","author":"X Chen","year":"2020","unstructured":"Chen X, Li Y, Hu R et al (2020) Hand gesture recognition based on surface electromyography using convolutional neural network with transfer learning method. IEEE J Biomed Health Inform 25(4):1292\u20131304","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"10305_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12864-019-6413-7","volume":"21","author":"D Chicco","year":"2020","unstructured":"Chicco D, Jurman G (2020) The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom 21(1):1\u201313","journal-title":"BMC Genom"},{"issue":"1","key":"10305_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13040-021-00244-z","volume":"14","author":"D Chicco","year":"2021","unstructured":"Chicco D, T\u00f6tsch N, Jurman G (2021) The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation. BioData Min 14(1):1\u201322","journal-title":"BioData Min"},{"issue":"1","key":"10305_CR15","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1111\/modl.12630","volume":"104","author":"K Csiz\u00e9r","year":"2020","unstructured":"Csiz\u00e9r K, Kontra EH (2020) Foreign language learning characteristics of deaf and severely hard-of-hearing students. Mod Lang J 104(1):233\u2013249","journal-title":"Mod Lang J"},{"key":"10305_CR16","doi-asserted-by":"publisher","unstructured":"Cuxac C (2001) Les langues des signes: analyseurs de la facult\u00e9 de langage. Acquisition et Interaction en Langue \u00c9trang\u00e8re (15):11\u201336. https:\/\/doi.org\/10.4000\/aile.536","DOI":"10.4000\/aile.536"},{"issue":"18","key":"10305_CR17","doi-asserted-by":"publisher","first-page":"8067","DOI":"10.1109\/JSEN.2019.2917525","volume":"19","author":"M Deriche","year":"2019","unstructured":"Deriche M, Aliyu SO, Mohandes M (2019) An intelligent arabic sign language recognition system using a pair of LMCs with GMM based classification. IEEE Sens J 19(18):8067\u20138078","journal-title":"IEEE Sens J"},{"key":"10305_CR18","doi-asserted-by":"crossref","unstructured":"Duarte A, Palaskar S, Ventura L, Ghadiyaram D, DeHaan K, Metze F, Giro-i-Nieto X (2021) How2sign: a large-scale multimodal dataset for continuous American sign language. In:\u00a0Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition, pp 2735\u20132744)","DOI":"10.1109\/CVPR46437.2021.00276"},{"issue":"1","key":"10305_CR19","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/2291\/1\/012008","volume":"2291","author":"G Edel","year":"2022","unstructured":"Edel G, Kapustin V (2022) Exploring of the MobileNet V1 and MobileNet V2 models on NVIDIA Jetson Nano microcomputer. J Phys Conf Ser 2291(1):012008","journal-title":"J Phys Conf Ser"},{"key":"10305_CR20","doi-asserted-by":"publisher","first-page":"5601","DOI":"10.1007\/s10639-020-10184-6","volume":"25","author":"SM Elatawy","year":"2020","unstructured":"Elatawy SM, Hawa DM, Ewees AA, Saad AM (2020) Recognition system for alphabet Arabic sign language using neutrosophic and fuzzy c-means. Educ Inf Technol 25:5601\u20135616","journal-title":"Educ Inf Technol"},{"key":"10305_CR21","doi-asserted-by":"crossref","unstructured":"El-Bendary N, Zawbaa HM, Daoud MS, Hassanien AE, Nakamatsu K (2010) Arslat: Arabic sign language alphabets translator. In:\u00a02010 International Conference on computer information systems and industrial management applications (CISIM), pp 590\u2013595. IEEE","DOI":"10.1109\/CISIM.2010.5643519"},{"issue":"1","key":"10305_CR22","doi-asserted-by":"publisher","first-page":"012085","DOI":"10.1088\/1742-6596\/1858\/1\/012085","volume":"1858","author":"U Fadlilah","year":"2021","unstructured":"Fadlilah U, Handaga B (2021) The development of android for Indonesian sign language using tensorflow lite and CNN: an initial study. J Phys Conf Ser 1858(1):012085","journal-title":"J Phys Conf Ser"},{"key":"10305_CR23","unstructured":"Flach P, Kull M (2015) Precision-recall-gain curves: PR analysis done right. In: Advances in neural information processing systems (NIPS 2015), vol  28, pp 838\u2013846. https:\/\/dl.acm.org\/doi\/10.5555\/2969239.2969333"},{"issue":"6","key":"10305_CR24","first-page":"990","volume":"36","author":"D Fleurion","year":"2021","unstructured":"Fleurion D, Verdun S, Ridoux I, Scemama C, Bouillevaux I, Ciosi A, Drion B (2021) Transposition and normalization of the mini-mental state examination in French sign language. Arch Clin Neuropsychol 36(6):990\u20131002","journal-title":"Arch Clin Neuropsychol"},{"key":"10305_CR25","volume-title":"Deep learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press"},{"issue":"20","key":"10305_CR26","doi-asserted-by":"publisher","first-page":"4876","DOI":"10.7150\/jca.28769","volume":"10","author":"Q Guan","year":"2019","unstructured":"Guan Q, Wang Y, Ping B, Li D, Du J, Qin Y, Xiang J (2019) Deep convolutional neural network VGG-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: a pilot study. J Cancer 10(20):4876","journal-title":"J Cancer"},{"key":"10305_CR27","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.procs.2020.07.015","volume":"175","author":"S Han","year":"2020","unstructured":"Han S, Jeong J (2020) An weighted CNN ensemble model with small amount of data for bearing fault diagnosis. Proc Comput Sci 175:88\u201395","journal-title":"Proc Comput Sci"},{"key":"10305_CR28","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.aiopen.2021.08.002","volume":"2","author":"X Han","year":"2021","unstructured":"Han X, Zhang Z, Ding N, Gu Y, Liu X, Huo Y, Zhu J (2021) Pre-trained models: PAST, present and future. AI Open 2:225\u2013250","journal-title":"AI Open"},{"key":"10305_CR29","doi-asserted-by":"crossref","unstructured":"Hoang VT, Jo KH (2021) Practical analysis on architecture of EfficientNet. In: 2021 14th International Conference on Human System Interaction (HSI), pp 1\u20134. IEEE.","DOI":"10.1109\/HSI52170.2021.9538782"},{"key":"10305_CR30","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications.\u00a0arXiv preprint arXiv:1704.04861."},{"issue":"12","key":"10305_CR31","doi-asserted-by":"publisher","first-page":"9859","DOI":"10.1007\/s13369-020-04758-2","volume":"45","author":"X Jiang","year":"2020","unstructured":"Jiang X, Satapathy SC, Yang L et al (2020a) A survey on artificial intelligence in Chinese sign language recognition. Arab J Sci Eng 45(12):9859\u20139894","journal-title":"Arab J Sci Eng"},{"key":"10305_CR32","doi-asserted-by":"publisher","first-page":"15697","DOI":"10.1007\/s11042-019-08345-y","volume":"79","author":"X Jiang","year":"2020","unstructured":"Jiang X, Lu M, Wang SH (2020b) An eight-layer convolutional neural network with stochastic pooling, batch normalization and dropout for fingerspelling recognition of Chinese sign language. Multimedia Tools Appli 79:15697\u201315715","journal-title":"Multimedia Tools Appli"},{"issue":"10","key":"10305_CR33","doi-asserted-by":"publisher","first-page":"278","DOI":"10.3390\/a14100278","volume":"14","author":"A Kallipolitis","year":"2021","unstructured":"Kallipolitis A, Revelos K, Maglogiannis I (2021) Ensembling EfficientNets for the classification and interpretation of histopathology images. Algorithms 14(10):278","journal-title":"Algorithms"},{"issue":"1","key":"10305_CR34","first-page":"3685614","volume":"2020","author":"MM Kamruzzaman","year":"2020","unstructured":"Kamruzzaman MM (2020) Arabic sign language recognition and generating Arabic speech using convolutional neural network. Wirel Commun Mobile Comput 2020(1):3685614","journal-title":"Wirel Commun Mobile Comput"},{"key":"10305_CR35","doi-asserted-by":"publisher","first-page":"2351","DOI":"10.1007\/s10462-021-10066-4","volume":"55","author":"NE Khalifa","year":"2022","unstructured":"Khalifa NE, Loey M, Mirjalili S (2022) A comprehensive survey of recent trends in deep learning for digital images augmentation. Artif Intell Rev 55(3):2351\u20132377. https:\/\/doi.org\/10.1007\/s10462-021-10066-4","journal-title":"Artif Intell Rev"},{"key":"10305_CR36","doi-asserted-by":"publisher","unstructured":"Koonce B, Koonce BE (2021) EfficientNet.\u00a0In: Convolutional neural networks with swift for tensorflow: image recognition and dataset categorization. Apress, New York, NY, USA, pp 109\u2013123. https:\/\/doi.org\/10.1007\/978-1-4842-6168-2","DOI":"10.1007\/978-1-4842-6168-2"},{"key":"10305_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2019.103777","volume":"23","author":"G Latif","year":"2019","unstructured":"Latif G, Mohammad N, Alghazo J, AlKhalaf R, AlKhalaf R (2019) ArASL: Arabic alphabets sign language dataset. Data Brief 23:103777","journal-title":"Data Brief"},{"key":"10305_CR38","doi-asserted-by":"crossref","unstructured":"Lincy RB, Gayathri R (2020) Off-Line Tamil handwritten character recognition based on convolutional neural network with VGG16 and VGG19 model. In:\u00a0International Conference on Automation, signal processing, instrumentation and control, pp 1935\u20131945. Singapore: Springer Nature Singapore.","DOI":"10.1007\/978-981-15-8221-9_180"},{"key":"10305_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106691","volume":"96","author":"G Marques","year":"2020","unstructured":"Marques G, Agarwal D, De la Torre D\u00edez I (2020) Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network. Appl Soft Comput 96:106691","journal-title":"Appl Soft Comput"},{"key":"10305_CR40","doi-asserted-by":"crossref","unstructured":"Mohandes M, Deriche M (2005) Image based Arabic sign language recognition. In: 8th International Symposium on Signal Processing and its Applications, ISSPA 2005, pp 86\u201389","DOI":"10.1109\/ISSPA.2005.1580202"},{"key":"10305_CR41","doi-asserted-by":"crossref","unstructured":"Mulim W, Revikasha MF, Hanafiah N (2021) Waste classification using EfficientNet-B0. In: 2021 1st International Conference on computer science and artificial intelligence (ICCSAI), Vol. 1, pp 253\u2013257. IEEE","DOI":"10.1109\/ICCSAI53272.2021.9609756"},{"key":"10305_CR42","doi-asserted-by":"publisher","first-page":"4101","DOI":"10.1007\/s12652-020-01790-w","volume":"12","author":"M Mustafa","year":"2021","unstructured":"Mustafa M (2021) A study on Arabic sign language recognition for differently abled using advanced machine learning classifiers. J Ambient Intell Humaniz Comput 12(3):4101\u20134115. https:\/\/doi.org\/10.1007\/s12652-020-01790-w","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"10305_CR43","doi-asserted-by":"crossref","unstructured":"Naglot D, Kulkarni M (2016) Real time sign language recognition using the leap motion controller. In:\u00a02016 International Conference on inventive computation technologies (ICICT), Vol. 3, pp. 1\u20135. IEEE","DOI":"10.1109\/INVENTIVE.2016.7830097"},{"issue":"3","key":"10305_CR44","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1139\/t04-008","volume":"41","author":"CW Ng","year":"2004","unstructured":"Ng CW, Lee KM, Tang DK (2004) Three-dimensional numerical investigations of new Austrian tunnelling method (NATM) twin tunnel interactions. Can Geotech J 41(3):523\u2013539","journal-title":"Can Geotech J"},{"key":"10305_CR45","unstructured":"Organization WH, et al (2019) Deafness and hearing loss [www document]. URL https:\/\/www.who. int\/news-room\/fact-sheets\/detail\/deafness-and-hearing-loss. Accessed 9 Aug 2019) (2019)"},{"key":"10305_CR46","unstructured":"Pratama ATM, Pratama AR (2021) Rancang Bangun Aplikasi Android \u201cKuliah Apa?\u201d Berbasis Flutter dan TensorFlow Lite.\u00a0Automata 2(1)"},{"key":"10305_CR47","doi-asserted-by":"crossref","unstructured":"Qassim H, Verma A, Feinzimer D (2018) Compressed residual-VGG16 CNN model for big data places image recognition. In:\u00a02018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), pp 169\u2013175. IEEE","DOI":"10.1109\/CCWC.2018.8301729"},{"issue":"10","key":"10305_CR48","doi-asserted-by":"publisher","first-page":"1872","DOI":"10.1007\/s11431-020-1647-3","volume":"63","author":"X Qiu","year":"2020","unstructured":"Qiu X, Sun T, Xu Y, Shao Y, Dai N, Huang X (2020) Pre-trained models for natural language processing: a survey. SCIENCE CHINA Technol Sci 63(10):1872\u20131897","journal-title":"SCIENCE CHINA Technol Sci"},{"key":"10305_CR49","doi-asserted-by":"crossref","unstructured":"Rahman MM, Islam MS, Rahman MH, Sassi R, Rivolta MW, Aktaruzzaman M (2019) A new benchmark on American sign language recognition using convolutional neural network. In:\u00a02019 International Conference on Sustainable Technologies for Industry 4.0 (STI), pp. 1\u20136. IEEE","DOI":"10.1109\/STI47673.2019.9067974"},{"issue":"1","key":"10305_CR50","doi-asserted-by":"publisher","first-page":"8","DOI":"10.26480\/aim.01.2023.08.14","volume":"7","author":"KMH Rawf","year":"2022","unstructured":"Rawf KMH, Mohammed AA, Abdulrahman AO, Abdalla PA, Ghafoor KJ (2022) A comparative technique using 2D CNN and transfer learning to detect and classify Arabic-Script-based sign language. Acta Inform Malaysia 7(1):8\u201314","journal-title":"Acta Inform Malaysia"},{"issue":"8","key":"10305_CR51","doi-asserted-by":"publisher","first-page":"1511","DOI":"10.1177\/0363546508316020","volume":"36","author":"YG Rhee","year":"2008","unstructured":"Rhee YG, Cho NS, Lim CT, Yi JW, Vishvanathan T (2008) Bridging the gap in immobile massive rotator cuff tears: augmentation using the tenotomized biceps. Am J Sports Med 36(8):1511\u20131518","journal-title":"Am J Sports Med"},{"key":"10305_CR52","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018).Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"issue":"17","key":"10305_CR53","doi-asserted-by":"publisher","first-page":"5856","DOI":"10.3390\/s21175856","volume":"21","author":"J Shin","year":"2021","unstructured":"Shin J, Matsuoka A, Hasan MAM, Srizon AY (2021) American sign language alphabet recognition by extracting feature from hand pose estimation. Sensors 21(17):5856","journal-title":"Sensors"},{"key":"10305_CR54","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition.\u00a0arXiv preprint arXiv:1409.1556."},{"key":"10305_CR55","doi-asserted-by":"publisher","unstructured":"Sutton-Spence R, Woll B (2004) British sign language.\u00a0In: Davies A, Elder C (eds) The handbook of applied linguistics. Blackwell Publishing Ltd., pp 165\u2013186. https:\/\/doi.org\/10.1002\/9780470757000","DOI":"10.1002\/9780470757000"},{"key":"10305_CR56","unstructured":"Tan, M., & Le, Q. (2019, May). Efficientnet: Rethinking model scaling for convolutional neural networks. In:\u00a0International Conference on machine learning, pp 6105\u20136114. PMLR"},{"key":"10305_CR57","unstructured":"Tan M, Le Q (2021) Efficientnetv2: smaller models and faster training. In: International Conference on machine learning, pp 10096\u201310106. PMLR"},{"key":"10305_CR58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-020-0114-9","volume":"1","author":"D Theckedath","year":"2020","unstructured":"Theckedath D, Sedamkar RR (2020) Detecting affect states using VGG16, ResNet50 and SE-ResNet50 networks. SN Comput Sci 1:1\u20137","journal-title":"SN Comput Sci"},{"key":"10305_CR59","doi-asserted-by":"crossref","unstructured":"Wanjaya I, Goncharenko I, Gu Y (2022) Comparison of image-based and skeleton-based ML methods in the task of alphabetical sign language recognition. In:\u00a02022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech), pp. 316\u2013317. IEEE","DOI":"10.1109\/LifeTech53646.2022.9754896"},{"key":"10305_CR60","doi-asserted-by":"crossref","unstructured":"Yacouby R, Axman D (2020) Probabilistic extension of precision, recall, and f1 score for more thorough evaluation of classification models. In: Proceedings of the First Workshop on evaluation and comparison of NLP systems, pp 79\u201391","DOI":"10.18653\/v1\/2020.eval4nlp-1.9"},{"issue":"22","key":"10305_CR61","doi-asserted-by":"publisher","first-page":"11035","DOI":"10.3390\/app112211035","volume":"11","author":"SL Yi","year":"2021","unstructured":"Yi SL, Yang XL, Wang TW, She FR, Xiong X, He JF (2021) Diabetic retinopathy diagnosis based on RA-EfficientNet. Appl Sci 11(22):11035","journal-title":"Appl Sci"},{"issue":"1","key":"10305_CR62","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","volume":"109","author":"F Zhuang","year":"2020","unstructured":"Zhuang F, Qi Z, Duan K, Xi D, Zhu Y, Zhu H, He Q (2020) A comprehensive survey on transfer learning. Proc IEEE 109(1):43\u201376","journal-title":"Proc IEEE"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-024-10305-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-024-10305-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-024-10305-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T07:32:12Z","timestamp":1733902332000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-024-10305-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11]]},"references-count":62,"journal-issue":{"issue":"21-22","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["10305"],"URL":"https:\/\/doi.org\/10.1007\/s00500-024-10305-0","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11]]},"assertion":[{"value":"24 September 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 November 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"(a) All authors have participated in conception and design, or analysis and interpretation of the data. (b) Drafting the article or revising it critically for important intellectual content. (c) Approval of the final version. (d) This article includes the generation and analysis of a new dataset, the ArASL\u2009+\u2009database,","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}