{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T18:05:52Z","timestamp":1768154752251,"version":"3.49.0"},"reference-count":108,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T00:00:00Z","timestamp":1715212800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T00:00:00Z","timestamp":1715212800000},"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":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1007\/s13042-024-02134-2","type":"journal-article","created":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T16:01:47Z","timestamp":1715270507000},"page":"4021-4053","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Meta-DPSTL: meta learning-based differentially private self-taught learning"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6438-8222","authenticated-orcid":false,"given":"Upendra Pratap","family":"Singh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Indrajeet Kumar","family":"Sinha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Krishna Pratap","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shekhar","family":"Verma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,9]]},"reference":[{"key":"2134_CR1","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1007\/978-3-030-75855-4_9","volume-title":"Deep learning in data analytics","author":"A Adate","year":"2022","unstructured":"Adate A, Tripathy B (2022) A survey on deep learning methodologies of recent applications. Deep learning in data analytics. Springer, pp 145\u2013170"},{"issue":"7","key":"2134_CR2","doi-asserted-by":"crossref","first-page":"12741","DOI":"10.1111\/exsy.12741","volume":"38","author":"AJ Silva","year":"2021","unstructured":"Silva AJ, Cortez P, Pereira C, Pilastri A (2021) Business analytics in industry 4.0: a systematic review. Expert Syst 38(7):12741","journal-title":"Expert Syst"},{"key":"2134_CR3","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1016\/j.neucom.2021.05.103","volume":"470","author":"I Lauriola","year":"2022","unstructured":"Lauriola I, Lavelli A, Aiolli F (2022) An introduction to deep learning in natural language processing: models, techniques, and tools. Neurocomputing 470:443\u2013456","journal-title":"Neurocomputing"},{"key":"2134_CR4","doi-asserted-by":"crossref","DOI":"10.1017\/9781139061773","volume-title":"Transfer learning","author":"Q Yang","year":"2020","unstructured":"Yang Q, Zhang Y, Dai W, Pan SJ (2020) Transfer learning. Cambridge University Press"},{"key":"2134_CR5","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.neucom.2021.08.159","volume":"489","author":"X Yu","year":"2022","unstructured":"Yu X, Wang J, Hong Q-Q, Teku R, Wang S-H, Zhang Y-D (2022) Transfer learning for medical images analyses: a survey. Neurocomputing 489:230\u2013254","journal-title":"Neurocomputing"},{"issue":"1","key":"2134_CR6","doi-asserted-by":"crossref","first-page":"2014192","DOI":"10.1080\/08839514.2021.2014192","volume":"36","author":"A Alem","year":"2022","unstructured":"Alem A, Kumar S (2022) Transfer learning models for land cover and land use classification in remote sensing image. Appl Artif Intell 36(1):2014192","journal-title":"Appl Artif Intell"},{"key":"2134_CR7","first-page":"1","volume":"19","author":"Y Song","year":"2022","unstructured":"Song Y, Li J, Gao P, Li L, Tian T, Tian J (2022) Two-stage cross-modality transfer learning method for military-civilian sar ship recognition. IEEE Geosci Remote Sens Lett 19:1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"2134_CR8","doi-asserted-by":"crossref","unstructured":"Raina R, Battle A, Lee H, Packer B, Ng AY (2007) Self-taught learning: transfer learning from unlabeled data. In: Proceedings of the 24th International Conference on Machine Learning. pp 759\u2013766","DOI":"10.1145\/1273496.1273592"},{"key":"2134_CR9","doi-asserted-by":"crossref","unstructured":"Xie L, Pan P, Lu Y, Jiang S (2015) Cross-modal self-taught learning for image retrieval. In: International Conference on Multimedia Modeling. Springer. pp 257\u2013268","DOI":"10.1007\/978-3-319-14445-0_23"},{"key":"2134_CR10","doi-asserted-by":"crossref","unstructured":"Sethi Y, Jain V, Singh KP, Ojha M (2017) Isomap based self-taught transfer learning for image classification. In: 2017 14th IEEE India Council International Conference (INDICON). IEEE. pp 1\u20136","DOI":"10.1109\/INDICON.2017.8487755"},{"key":"2134_CR11","doi-asserted-by":"crossref","unstructured":"Amft O (2011) Self-taught learning for activity spotting in on-body motion sensor data. In: 2011 15th Annual International Symposium on Wearable Computers. IEEE. pp 83\u201386","DOI":"10.1109\/ISWC.2011.37"},{"key":"2134_CR12","doi-asserted-by":"crossref","unstructured":"Jingjing H (2016) Self-taught learning features for human action recognition. In: 2016 3rd International Conference on Information Science and Control Engineering (ICISCE). IEEE. pp 611\u2013615","DOI":"10.1109\/ICISCE.2016.136"},{"key":"2134_CR13","unstructured":"Liu X (2019) Deep neural network based iterative self-taught learning on text mining. PhD thesis, University of Arkansas at Little Rock"},{"issue":"4\u20135","key":"2134_CR14","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1504\/IJKL.2007.016706","volume":"3","author":"A Laghos","year":"2007","unstructured":"Laghos A, Zaphiris P (2007) Social network analysis of self-taught e-learning communities. Int J Knowl Learn 3(4\u20135):465\u2013482","journal-title":"Int J Knowl Learn"},{"key":"2134_CR15","doi-asserted-by":"crossref","unstructured":"Fayyaz M, Hajizadeh-Saffar M, Sabokrou M, Hoseini M, Fathy M (2015) A novel approach for finger vein verification based on self-taught learning. In: 2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP). IEEE. pp 88\u201391","DOI":"10.1109\/IranianMVIP.2015.7397511"},{"key":"2134_CR16","doi-asserted-by":"crossref","unstructured":"Zhu T, Philip SY (2019) Applying differential privacy mechanism in artificial intelligence. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). IEEE. pp 1601\u20131609","DOI":"10.1109\/ICDCS.2019.00159"},{"key":"2134_CR17","doi-asserted-by":"crossref","unstructured":"Dankar FK, El\u00a0Emam K (2012) The application of differential privacy to health data. In: Proceedings of the 2012 Joint EDBT\/ICDT Workshops, pp 158\u2013166","DOI":"10.1145\/2320765.2320816"},{"key":"2134_CR18","doi-asserted-by":"crossref","unstructured":"Abadi M, Chu A, Goodfellow I, McMahan HB, Mironov I, Talwar K, Zhang L (2016) Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp 308\u2013318","DOI":"10.1145\/2976749.2978318"},{"issue":"7","key":"2134_CR19","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1145\/3433638","volume":"64","author":"J Domingo-Ferrer","year":"2021","unstructured":"Domingo-Ferrer J, S\u00e1nchez D, Blanco-Justicia A (2021) The limits of differential privacy (and its misuse in data release and machine learning). Commun ACM 64(7):33\u201335","journal-title":"Commun ACM"},{"key":"2134_CR20","doi-asserted-by":"crossref","unstructured":"Vu D, Slavkovic A (2009) Differential privacy for clinical trial data: Preliminary evaluations. In: 2009 IEEE International Conference on Data Mining Workshops. IEEE. pp 138\u2013143","DOI":"10.1109\/ICDMW.2009.52"},{"key":"2134_CR21","unstructured":"Vanschoren J (2018) Meta-learning: a survey. arXiv preprint arXiv:1810.03548"},{"key":"2134_CR22","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.neucom.2014.05.028","volume":"144","author":"J Gan","year":"2014","unstructured":"Gan J, Li L, Zhai Y, Liu Y (2014) Deep self-taught learning for facial beauty prediction. Neurocomputing 144:295\u2013303","journal-title":"Neurocomputing"},{"key":"2134_CR23","doi-asserted-by":"crossref","unstructured":"Zhou K, Liu Y, Song J, Yan L, Zou F, Shen F (2015) Deep self-taught hashing for image retrieval. In: Proceedings of the 23rd ACM International Conference on Multimedia, pp 1215\u20131218","DOI":"10.1145\/2733373.2806320"},{"key":"2134_CR24","doi-asserted-by":"crossref","unstructured":"Jie Z, Wei Y, Jin X, Feng J, Liu W (2017) Deep self-taught learning for weakly supervised object localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1377\u20131385","DOI":"10.1109\/CVPR.2017.457"},{"key":"2134_CR25","doi-asserted-by":"crossref","unstructured":"Jie Z, Wei Y, Jin X, Feng J, Liu W (2017) Deep self-taught learning for weakly supervised object localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1377\u20131385","DOI":"10.1109\/CVPR.2017.457"},{"key":"2134_CR26","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.neunet.2016.12.008","volume":"88","author":"J Xu","year":"2017","unstructured":"Xu J, Xu B, Wang P, Zheng S, Tian G, Zhao J (2017) Self-taught convolutional neural networks for short text clustering. Neural Netw 88:22\u201331","journal-title":"Neural Netw"},{"key":"2134_CR27","doi-asserted-by":"crossref","unstructured":"Zhang D, Wang J, Cai D, Lu J (2010) Self-taught hashing for fast similarity search. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 18\u201325","DOI":"10.1145\/1835449.1835455"},{"issue":"10","key":"2134_CR28","doi-asserted-by":"crossref","first-page":"2964","DOI":"10.1016\/j.patcog.2015.02.012","volume":"48","author":"J Kuen","year":"2015","unstructured":"Kuen J, Lim KM, Lee CP (2015) Self-taught learning of a deep invariant representation for visual tracking via temporal slowness principle. Pattern Recognit 48(10):2964\u20132982","journal-title":"Pattern Recognit"},{"issue":"8","key":"2134_CR29","doi-asserted-by":"crossref","first-page":"3135","DOI":"10.1007\/s00521-019-04152-6","volume":"32","author":"AS Qureshi","year":"2020","unstructured":"Qureshi AS, Khan A, Shamim N, Durad MH (2020) Intrusion detection using deep sparse auto-encoder and self-taught learning. Neural Comput Appl 32(8):3135\u20133147","journal-title":"Neural Comput Appl"},{"key":"2134_CR30","doi-asserted-by":"crossref","unstructured":"Roscher R, R\u00f6mer C, Waske B, Pl\u00fcmer L (2015) Landcover classification with self-taught learning on archetypal dictionaries. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE. pp 2358\u20132361","DOI":"10.1109\/IGARSS.2015.7326282"},{"key":"2134_CR31","doi-asserted-by":"crossref","first-page":"725","DOI":"10.5194\/isprs-archives-XLIII-B2-2020-725-2020","volume":"43","author":"Y Yang","year":"2020","unstructured":"Yang Y, Zhu D, Ren F, Cheng C (2020) A novel self-taught learning framework using spatial pyramid matching for scene classification. Int Arch Photogramm, Remote Sens Spatial Inf Sci 43:725\u2013729","journal-title":"Int Arch Photogramm, Remote Sens Spatial Inf Sci"},{"key":"2134_CR32","unstructured":"Bettge A, Roscher R, Wenzel S (2017) Deep self-taught learning for remote sensing image classification. arXiv preprint arXiv:1710.07096"},{"issue":"5","key":"2134_CR33","doi-asserted-by":"crossref","first-page":"2693","DOI":"10.1109\/TGRS.2017.2651639","volume":"55","author":"R Kemker","year":"2017","unstructured":"Kemker R, Kanan C (2017) Self-taught feature learning for hyperspectral image classification. IEEE Trans Geosci Remote Sens 55(5):2693\u20132705","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"2134_CR34","doi-asserted-by":"crossref","unstructured":"Roscher R, Wenzel S, Waske B (2016) Discriminative archetypal self-taught learning for multispectral landcover classification. In: 2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS). IEEE. pp 1\u20135","DOI":"10.1109\/PRRS.2016.7867022"},{"key":"2134_CR35","doi-asserted-by":"crossref","unstructured":"Zhao Z, Zhang X, Chen C, Li W, Peng S, Wang J, Yang X, Zhang L, Zeng Z (2019) Semi-supervised self-taught deep learning for finger bones segmentation. In: 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). IEEE. pp 1\u20134","DOI":"10.1109\/BHI.2019.8834460"},{"key":"2134_CR36","doi-asserted-by":"crossref","unstructured":"Hou C-A, Yang M-C, Wang Y-CF (2014) Domain adaptive self-taught learning for heterogeneous face recognition. In: 2014 22nd International Conference on Pattern Recognition. IEEE. pp 3068\u20133073","DOI":"10.1109\/ICPR.2014.529"},{"key":"2134_CR37","doi-asserted-by":"crossref","unstructured":"Hu H, Phan N, Geller J, Vo H, Manasi B, Huang X, Di\u00a0Lorio S, Dinh T, Chun SA (2018) Deep self-taught learning for detecting drug abuse risk behavior in tweets. In: International Conference on Computational Social Networks. Springer. pp 330\u2013342","DOI":"10.1007\/978-3-030-04648-4_28"},{"issue":"10","key":"2134_CR38","doi-asserted-by":"crossref","first-page":"2279","DOI":"10.3390\/s17102279","volume":"17","author":"P He","year":"2017","unstructured":"He P, Jia P, Qiao S, Duan S (2017) Self-taught learning based on sparse autoencoder for e-nose in wound infection detection. Sensors 17(10):2279","journal-title":"Sensors"},{"key":"2134_CR39","doi-asserted-by":"crossref","unstructured":"Liu W, Ye X, Phan H, Hu H (2019) Scalable self-taught deep-embedded learning framework for drug abuse spatial behaviors detection. In: International Conference on Computational Data and Social Networks. Springer. pp 223\u2013228","DOI":"10.1007\/978-3-030-34980-6_26"},{"key":"2134_CR40","doi-asserted-by":"crossref","unstructured":"Huang K, Xu Z, King I, Lyu MR, Campbell C (2009) Supervised self-taught learning: Actively transferring knowledge from unlabeled data. In: 2009 International Joint Conference on Neural Networks. IEEE. pp 1272\u20131277","DOI":"10.1109\/IJCNN.2009.5178647"},{"key":"2134_CR41","doi-asserted-by":"crossref","unstructured":"Fang M, Zhu X, Li B, Ding W, Wu X (2012) Self-taught active learning from crowds. In: 2012 IEEE 12th International Conference on Data Mining. IEEE. pp 858\u2013863","DOI":"10.1109\/ICDM.2012.64"},{"key":"2134_CR42","unstructured":"Wang H, Nie F, Huang H (2013) Robust and discriminative self-taught learning. In: International Conference on Machine Learning. PMLR. pp 298\u2013306"},{"issue":"1","key":"2134_CR43","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1109\/TII.2019.2926778","volume":"16","author":"X Zhang","year":"2019","unstructured":"Zhang X, Liu Q, Wang D, Zhao L, Gu N, Maybank S (2019) Self-taught semisupervised dictionary learning with nonnegative constraint. IEEE Trans Ind Inform 16(1):532\u2013543","journal-title":"IEEE Trans Ind Inform"},{"key":"2134_CR44","doi-asserted-by":"crossref","unstructured":"Markov K, Matsui T (2012) Nonnegative matrix factorization based self-taught learning with application to music genre classification. In: 2012 IEEE International Workshop on Machine Learning for Signal Processing. IEEE. pp 1\u20135","DOI":"10.1109\/MLSP.2012.6349719"},{"key":"2134_CR45","unstructured":"Upadhyay R, Phlypo R, Saini R, Liwicki M (2021) Sharing to learn and learning to share-fitting together meta-learning, multi-task learning, and transfer learning: a meta review. arXiv preprint arXiv:2111.12146"},{"key":"2134_CR46","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108111","volume":"120","author":"R Singh","year":"2021","unstructured":"Singh R, Bharti V, Purohit V, Kumar A, Singh AK, Singh SK (2021) Metamed: few-shot medical image classification using gradient-based meta-learning. Pattern Recognit 120:108111","journal-title":"Pattern Recognit"},{"key":"2134_CR47","unstructured":"Dumoulin V, Houlsby N, Evci U, Zhai X, Goroshin R, Gelly S, Larochelle H (2021) Comparing transfer and meta learning approaches on a unified few-shot classification benchmark. arXiv preprint arXiv:2104.02638"},{"issue":"6","key":"2134_CR48","doi-asserted-by":"crossref","first-page":"4483","DOI":"10.1007\/s10462-021-10004-4","volume":"54","author":"M Huisman","year":"2021","unstructured":"Huisman M, Van Rijn JN, Plaat A (2021) A survey of deep meta-learning. Artif Intell Rev 54(6):4483\u20134541","journal-title":"Artif Intell Rev"},{"key":"2134_CR49","volume":"168","author":"F Zhou","year":"2021","unstructured":"Zhou F, Cao C, Zhong T, Geng J (2021) Learning meta-knowledge for few-shot image emotion recognition. Expert Syst Appl 168:114274","journal-title":"Expert Syst Appl"},{"key":"2134_CR50","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.115011","volume":"178","author":"N Moniz","year":"2021","unstructured":"Moniz N, Cerqueira V (2021) Automated imbalanced classification via meta-learning. Expert Syst Appl 178:115011","journal-title":"Expert Syst Appl"},{"key":"2134_CR51","doi-asserted-by":"crossref","unstructured":"Dwork C (2008) Differential privacy: a survey of results. In: International Conference on Theory and Applications of Models of Computation. Springer. pp 1\u201319","DOI":"10.1007\/978-3-540-79228-4_1"},{"key":"2134_CR52","doi-asserted-by":"crossref","first-page":"132665","DOI":"10.1109\/ACCESS.2020.3010287","volume":"8","author":"ME Chowdhury","year":"2020","unstructured":"Chowdhury ME, Rahman T, Khandakar A, Mazhar R, Kadir MA, Mahbub ZB, Islam KR, Khan MS, Iqbal A, Al Emadi N et al (2020) Can ai help in screening viral and COVID-19 pneumonia? IEEE Access 8:132665\u2013132676","journal-title":"IEEE Access"},{"key":"2134_CR53","doi-asserted-by":"publisher","DOI":"10.29012\/jpc.749","author":"C Dwork","year":"2020","unstructured":"Dwork C, Karr A, Nissim K, Vilhuber L (2020) On privacy in the age of COVID-19. J Priv Confid. https:\/\/doi.org\/10.29012\/jpc.749","journal-title":"J Priv Confid"},{"key":"2134_CR54","doi-asserted-by":"crossref","unstructured":"Machanavajjhala A, He X, Hay M (2017) Differential privacy in the wild: a tutorial on current practices & open challenges. In: Proceedings of the 2017 ACM International Conference on Management of Data, pp 1727\u20131730","DOI":"10.1145\/3035918.3054779"},{"key":"2134_CR55","doi-asserted-by":"crossref","first-page":"3093","DOI":"10.1109\/TIFS.2020.2983254","volume":"15","author":"Q Wang","year":"2020","unstructured":"Wang Q, Li Z, Zou Q, Zhao L, Wang S (2020) Deep domain adaptation with differential privacy. IEEE Trans Inf Forensics Secur 15:3093\u20133106","journal-title":"IEEE Trans Inf Forensics Secur"},{"issue":"10","key":"2134_CR56","doi-asserted-by":"crossref","first-page":"9530","DOI":"10.1109\/JIOT.2020.2991416","volume":"7","author":"R Hu","year":"2020","unstructured":"Hu R, Guo Y, Li H, Pei Q, Gong Y (2020) Personalized federated learning with differential privacy. IEEE Internet Things J 7(10):9530\u20139539","journal-title":"IEEE Internet Things J"},{"key":"2134_CR57","doi-asserted-by":"crossref","unstructured":"Ha T, Dang TK, Dang TT, Truong TA, Nguyen MT (2019) Differential privacy in deep learning: an overview. In: 2019 International Conference on Advanced Computing and Applications (ACOMP). IEEE. pp. 97\u2013102","DOI":"10.1109\/ACOMP.2019.00022"},{"issue":"1","key":"2134_CR58","first-page":"35","volume":"6","author":"FK Dankar","year":"2013","unstructured":"Dankar FK, El Emam K (2013) Practicing differential privacy in health care: a review. Trans Data Priv 6(1):35\u201367","journal-title":"Trans Data Priv"},{"key":"2134_CR59","doi-asserted-by":"crossref","unstructured":"Yang Y, Zhang Z, Miklau G, Winslett M, Xiao X (2012) Differential privacy in data publication and analysis. In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, pp 601\u2013606","DOI":"10.1145\/2213836.2213910"},{"key":"2134_CR60","unstructured":"Ji Z, Lipton ZC, Elkan C (2014) Differential privacy and machine learning: a survey and review. arXiv preprint arXiv:1412.7584"},{"issue":"1","key":"2134_CR61","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-021-93030-0","volume":"11","author":"A Ziller","year":"2021","unstructured":"Ziller A, Usynin D, Braren R, Makowski M, Rueckert D, Kaissis G (2021) Medical imaging deep learning with differential privacy. Sci Rep 11(1):1\u20138","journal-title":"Sci Rep"},{"issue":"4","key":"2134_CR62","doi-asserted-by":"crossref","first-page":"27468","DOI":"10.2196\/27468","volume":"23","author":"M Ghaderzadeh","year":"2021","unstructured":"Ghaderzadeh M, Asadi F, Jafari R, Bashash D, Abolghasemi H, Aria M et al (2021) Deep convolutional neural network-based computer-aided detection system for COVID-19 using multiple lung scans: Design and implementation study. J Med Internet Res 23(4):27468","journal-title":"J Med Internet Res"},{"key":"2134_CR63","doi-asserted-by":"crossref","unstructured":"Hospedales T, Antoniou A, Micaelli P, Storkey A (2020) Meta-learning in neural networks: a survey. arXiv preprint arXiv:2004.05439","DOI":"10.1109\/TPAMI.2021.3079209"},{"key":"2134_CR64","doi-asserted-by":"crossref","unstructured":"Chen S, Kahla M, Jia R, Qi G-J (2020) Knowledge-enriched distributional model inversion attacks. arXiv preprint arXiv:2010.04092","DOI":"10.1109\/ICCV48922.2021.01587"},{"key":"2134_CR65","doi-asserted-by":"crossref","first-page":"124988","DOI":"10.1109\/ACCESS.2019.2938759","volume":"7","author":"C Park","year":"2019","unstructured":"Park C, Hong D, Seo C (2019) An attack-based evaluation method for differentially private learning against model inversion attack. IEEE Access 7:124988\u2013124999","journal-title":"IEEE Access"},{"key":"2134_CR66","doi-asserted-by":"crossref","unstructured":"Cohen G, Afshar S, Tapson J, Van\u00a0Schaik A (2017) Emnist: extending mnist to handwritten letters. In: 2017 International Joint Conference on Neural Networks (IJCNN). IEEE. pp 2921\u20132926","DOI":"10.1109\/IJCNN.2017.7966217"},{"issue":"2","key":"2134_CR67","first-page":"651","volume":"2","author":"D Kermany","year":"2018","unstructured":"Kermany D, Zhang K, Goldbaum M (2018) Labeled optical coherence tomography (oct) and chest x-ray images for classification. Mendeley Data 2(2):651","journal-title":"Mendeley Data"},{"issue":"3","key":"2134_CR68","first-page":"28","volume":"128","author":"A Abdiansah","year":"2015","unstructured":"Abdiansah A, Wardoyo R (2015) Time complexity analysis of support vector machines (svm) in libsvm. Int J Comput Appl 128(3):28\u201334","journal-title":"Int J Comput Appl"},{"issue":"8","key":"2134_CR69","doi-asserted-by":"crossref","first-page":"1592","DOI":"10.1162\/neco_a_01207","volume":"31","author":"N Bulso","year":"2019","unstructured":"Bulso N, Marsili M, Roudi Y (2019) On the complexity of logistic regression models. Neural Comput 31(8):1592\u20131623","journal-title":"Neural Comput"},{"key":"2134_CR70","doi-asserted-by":"crossref","unstructured":"Liu F, Ma J, Zhao R, Wang Q (2018) Online dictionary self-taught learning for hyperspectral image classification. In: 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE. pp 1\u20135","DOI":"10.1109\/I2MTC.2018.8409676"},{"key":"2134_CR71","doi-asserted-by":"crossref","unstructured":"Chen X, Li B, Shamsabardeh M, Proietti R, Zhu Z, Yoo SB (2018) On real-time and self-taught anomaly detection in optical networks using hybrid unsupervised\/supervised learning. In: 2018 European Conference on Optical Communication (ECOC). IEEE. pp 1\u20133","DOI":"10.1109\/ECOC.2018.8535185"},{"key":"2134_CR72","unstructured":"Bastien F, Bengio Y, Bergeron A, Boulanger-Lewandowski N, Breuel T, Chherawala Y, Cisse M, C\u00f4t\u00e9 M, Erhan D, Eustache J et al (2010) Deep self-taught learning for handwritten character recognition. arXiv preprint arXiv:1009.3589"},{"key":"2134_CR73","unstructured":"Lee H, Raina R, Teichman A, Ng A (2009) Exponential family sparse coding with applications to self-taught learning. In: Twenty-First International Joint Conference on Artificial Intelligence"},{"key":"2134_CR74","unstructured":"Chu K-F, Zhang L (2021) Privacy-preserving self-taught federated learning for heterogeneous data. arXiv preprint arXiv:2102.05883"},{"key":"2134_CR75","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2019.105343","volume":"192","author":"S Feng","year":"2020","unstructured":"Feng S, Yu H, Duarte MF (2020) Autoencoder based sample selection for self-taught learning. Knowl-Based Syst 192:105343","journal-title":"Knowl-Based Syst"},{"issue":"3","key":"2134_CR76","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1007\/s10115-018-1218-6","volume":"59","author":"P Razzaghi","year":"2019","unstructured":"Razzaghi P (2019) Self-taught support vector machines. Knowl Inf Syst 59(3):685\u2013709","journal-title":"Knowl Inf Syst"},{"key":"2134_CR77","doi-asserted-by":"crossref","unstructured":"Liu F, Dai S (2019) Tri-self-taught learning of artificial neural networks. In: Proceedings of 2018 Chinese Intelligent Systems Conference. Springer. pp 807\u2013814","DOI":"10.1007\/978-981-13-2291-4_78"},{"key":"2134_CR78","unstructured":"Le N (2019) Evolving self-taught neural networks: the Baldwin effect and the emergence of intelligence. arXiv preprint arXiv:1906.08854"},{"key":"2134_CR79","doi-asserted-by":"crossref","unstructured":"Le N, Brabazon A, O\u2019Neill M (2019) The evolution of self-taught neural networks in a multi-agent environment. In: International Conference on the Applications of Evolutionary Computation (Part of EvoStar). Springer. pp 457\u2013472","DOI":"10.1007\/978-3-030-16692-2_31"},{"key":"2134_CR80","doi-asserted-by":"crossref","unstructured":"Kamath U, Liu J, Whitaker J (2019) Transfer learning: Scenarios, self-taught learning, and multitask learning. In: Deep Learning for NLP and Speech Recognition. Springer. pp 463\u2013493","DOI":"10.1007\/978-3-030-14596-5_10"},{"key":"2134_CR81","doi-asserted-by":"crossref","unstructured":"Han X-H, Sun J, Lin L, Chen Y-W (2018) Self-taught learning with residual sparse autoencoders for hep-2 cell staining pattern recognition. In: International Workshop on Machine Learning in Medical Imaging. Springer. pp 134\u2013142","DOI":"10.1007\/978-3-030-00919-9_16"},{"issue":"3","key":"2134_CR82","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TNNLS.2016.2633275","volume":"29","author":"S Li","year":"2017","unstructured":"Li S, Li K, Fu Y (2017) Self-taught low-rank coding for visual learning. IEEE Trans Neural Netw Learn Syst 29(3):645\u2013656","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2134_CR83","doi-asserted-by":"crossref","unstructured":"Zhao F, Cao Z, Xiao Y, Zhang X, Xian K, Li R (2017) Depth image super-resolution via semi self-taught learning framework. In: Videometrics, Range Imaging, and Applications XIV, vol. 10332. International Society for Optics and Photonics. p 103320","DOI":"10.1117\/12.2270079"},{"issue":"1","key":"2134_CR84","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1687-4722-2013-6","volume":"2013","author":"K Markov","year":"2013","unstructured":"Markov K, Matsui T (2013) High level feature extraction STL. EURASIP J Audio, Speech, Music Process 2013(1):1\u201311","journal-title":"EURASIP J Audio, Speech, Music Process"},{"issue":"1","key":"2134_CR85","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.patcog.2012.07.018","volume":"46","author":"X Zhu","year":"2013","unstructured":"Zhu X, Huang Z, Yang Y, Shen HT, Xu C, Luo J (2013) Self-taught dimensionality reduction on the high-dimensional small-sized data. Pattern Recognit 46(1):215\u2013229","journal-title":"Pattern Recognit"},{"key":"2134_CR86","doi-asserted-by":"crossref","unstructured":"Dai W, Yang Q, Xue G-R, Yu Y (2008) Self-taught clustering. In: Proceedings of the 25th International Conference on Machine Learning, pp 200\u2013207","DOI":"10.1145\/1390156.1390182"},{"issue":"7","key":"2134_CR87","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.3390\/diagnostics11071155","volume":"11","author":"N El-Rashidy","year":"2021","unstructured":"El-Rashidy N, Abdelrazik S, Abuhmed T, Amer E, Ali F, Hu J-W, El-Sappagh S (2021) Comprehensive survey of using machine learning in the COVID-19 pandemic. Diagnostics 11(7):1155","journal-title":"Diagnostics"},{"key":"2134_CR88","doi-asserted-by":"crossref","unstructured":"Panday A, Kabir MA, Chowdhury NK (2021) A survey of machine learning techniques for detecting and diagnosing COVID-19 from imaging. arXiv preprint arXiv:2108.04344","DOI":"10.15302\/J-QB-021-0274"},{"issue":"2","key":"2134_CR89","doi-asserted-by":"crossref","first-page":"506","DOI":"10.3390\/iot1020028","volume":"1","author":"S Abir","year":"2020","unstructured":"Abir S, Islam SN, Anwar A, Mahmood AN, Oo AMT (2020) Building resilience against COVID-19 pandemic using artificial intelligence, machine learning, and iot: a survey of recent progress. IoT 1(2):506\u2013528","journal-title":"IoT"},{"key":"2134_CR90","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2021.103751","volume":"117","author":"O Shahid","year":"2021","unstructured":"Shahid O, Nasajpour M, Pouriyeh S, Parizi RM, Han M, Valero M, Li F, Aledhari M, Sheng QZ (2021) Machine learning research towards combating COVID-19: virus detection, spread prevention, and medical assistance. J Biomed Inform 117:103751","journal-title":"J Biomed Inform"},{"issue":"1","key":"2134_CR91","first-page":"1","volume":"4","author":"SA Kumar","year":"2020","unstructured":"Kumar SA, Kumar H, Dutt V, Dixit P (2020) The role of machine learning in COVID-19 in medical domain: a survey. J Recent Innov Cloud Comput, Virtualiz Web Appl 4(1):1\u201312 (ISSN: 2581-544X (online))","journal-title":"J Recent Innov Cloud Comput, Virtualiz Web Appl"},{"key":"2134_CR92","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1109\/RBME.2021.3069213","volume":"15","author":"CC John","year":"2021","unstructured":"John CC, Ponnusamy V, Chandrasekaran SK, Nandakumar R (2021) A survey on mathematical, machine learning and deep learning models for COVID-19 transmission and diagnosis. IEEE Rev Biomed Eng 15:325\u201340","journal-title":"IEEE Rev Biomed Eng"},{"key":"2134_CR93","doi-asserted-by":"crossref","unstructured":"Islam MN, Inan TT, Rafi S, Akter SS, Sarker IH, Islam A (2020) A survey on the use of ai and ml for fighting the COVID-19 pandemic. arXiv preprint arXiv:2008.07449","DOI":"10.1109\/TAI.2021.3062771"},{"key":"2134_CR94","doi-asserted-by":"crossref","unstructured":"Sinha IK, Singh KP, Verma S (2020) Dp-ann: A new differential private artificial neural network with application on health data (workshop paper). In: 2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM). IEEE. pp 351\u2013355","DOI":"10.1109\/BigMM50055.2020.00061"},{"key":"2134_CR95","doi-asserted-by":"crossref","unstructured":"M\u00fcft\u00fco\u011flu Z, Kizrak MA, Yildlnm T (2020) Differential privacy practice on diagnosis of COVID-19 radiology imaging using efficientnet. In: 2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA). IEEE. pp 1\u20136","DOI":"10.1109\/INISTA49547.2020.9194651"},{"key":"2134_CR96","unstructured":"Bavadekar S, Dai A, Davis J, Desfontaines D, Eckstein I, Everett K, Fabrikant A, Flores G, Gabrilovich E, Gadepalli K et al (2020) Google COVID-19 search trends symptoms dataset: anonymization process description (version 1.0). arXiv preprint arXiv:2009.01265"},{"key":"2134_CR97","unstructured":"Ulhaq A, Burmeister O (2020) COVID-19 imaging data privacy by federated learning design: a theoretical framework. arXiv preprint arXiv:2010.06177"},{"key":"2134_CR98","unstructured":"Triastcyn A, Faltings B (2020) Bayesian differential privacy for machine learning. In: International Conference on Machine Learning. PMLR. pp 9583\u20139592"},{"issue":"3\u20134","key":"2134_CR99","first-page":"211","volume":"9","author":"C Dwork","year":"2014","unstructured":"Dwork C, Roth A et al (2014) The algorithmic foundations of differential privacy. Found Trends Theor Comput Sci 9(3\u20134):211\u2013407","journal-title":"Found Trends Theor Comput Sci"},{"key":"2134_CR100","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.procs.2012.09.050","volume":"12","author":"K Mivule","year":"2012","unstructured":"Mivule K, Turner C, Ji S-Y (2012) Towards a differential privacy and utility preserving machine learning classifier. Procedia Comput Sci 12:176\u2013181","journal-title":"Procedia Comput Sci"},{"key":"2134_CR101","first-page":"15479","volume":"32","author":"E Bagdasaryan","year":"2019","unstructured":"Bagdasaryan E, Poursaeed O, Shmatikov V (2019) Differential privacy has disparate impact on model accuracy. Adv Neural Inf Process Syst 32:15479\u201315488","journal-title":"Adv Neural Inf Process Syst"},{"key":"2134_CR102","unstructured":"McMahan HB, Andrew G, Erlingsson U, Chien S, Mironov I, Papernot N, Kairouz P (2018) A general approach to adding differential privacy to iterative training procedures. arXiv preprint arXiv:1812.06210"},{"issue":"7","key":"2134_CR103","doi-asserted-by":"crossref","first-page":"5827","DOI":"10.1109\/JIOT.2019.2952146","volume":"7","author":"PCM Arachchige","year":"2019","unstructured":"Arachchige PCM, Bertok P, Khalil I, Liu D, Camtepe S, Atiquzzaman M (2019) Local differential privacy for deep learning. IEEE Internet Things J 7(7):5827\u20135842","journal-title":"IEEE Internet Things J"},{"key":"2134_CR104","doi-asserted-by":"crossref","first-page":"152103","DOI":"10.1109\/ACCESS.2019.2947295","volume":"7","author":"Z Sun","year":"2019","unstructured":"Sun Z, Wang Y, Shu M, Liu R, Zhao H (2019) Differential privacy for data and model publishing of medical data. IEEE Access 7:152103\u2013152114","journal-title":"IEEE Access"},{"issue":"4","key":"2134_CR105","first-page":"1","volume":"8","author":"N Li","year":"2016","unstructured":"Li N, Lyu M, Su D, Yang W (2016) Differential privacy: from theory to practice. Synth Lect Inf Secur, Priv, Trust 8(4):1\u2013138","journal-title":"Synth Lect Inf Secur, Priv, Trust"},{"key":"2134_CR106","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-62004-6","volume-title":"Differential privacy and applications","author":"T Zhu","year":"2017","unstructured":"Zhu T, Li G, Zhou W, Philip SY (2017) Differential privacy and applications. Springer"},{"issue":"10","key":"2134_CR107","doi-asserted-by":"crossref","first-page":"2269","DOI":"10.1093\/jamia\/ocab135","volume":"28","author":"J Ficek","year":"2021","unstructured":"Ficek J, Wang W, Chen H, Dagne G, Daley E (2021) Differential privacy in health research: a scoping review. J Am Med Inform Assoc 28(10):2269\u20132276","journal-title":"J Am Med Inform Assoc"},{"issue":"10","key":"2134_CR108","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345\u20131359","journal-title":"IEEE Trans Knowl Data Eng"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02134-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02134-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02134-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,17]],"date-time":"2024-08-17T08:32:02Z","timestamp":1723883522000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02134-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,9]]},"references-count":108,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["2134"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02134-2","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,9]]},"assertion":[{"value":"25 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors declare that there is no Conflict of interest in the subject matter discussed in this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}