{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T09:53:56Z","timestamp":1782554036103,"version":"3.54.5"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T00:00:00Z","timestamp":1671148800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T00:00:00Z","timestamp":1671148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100009367","name":"Mansoura University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100009367","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Uncommon observations that significantly vary from the norm are referred to as outliers. Outlier detection, which aims to detect unexpected behavior, is a critical topic that has attracted significant attention in a wide range of research areas and application domains, including video surveillance, network intrusion detection, disease outbreak detection, and others. Deep learning-based techniques for outlier detection have currently outperformed machine learning and shallow approaches on streaming data, which are big and complicated datasets. Despite the fact that deep learning has been successfully applied in a variety of application domains, developing an effective and appropriate model is a difficult task due to the dynamic nature and variations of real-world applications and data. Hence, this research proposes a novel deep learning model based on a deep neural network (DNN) to handle the outlier detection problem in the context of streaming data. The proposed DNN model is developed with multiple hidden layers to improve feature abstraction and capabilities. Extensive experiments performed on four real-world outlier benchmark datasets, available at the UCI repository, and comparisons to state-of-the-art approaches are used to evaluate the proposed model's performance. Experiment results demonstrate that it outperforms both machine learning algorithms and deep learning competitors, resulting in significant performance gains. Particularly, when compared to other algorithms, the evaluation results clearly demonstrated the efficacy of the proposed approach, with much higher accuracy, recall and f1-score rates of 99.63%, 99.014% and 99.437%, respectively.<\/jats:p>","DOI":"10.1186\/s40537-022-00670-8","type":"journal-article","created":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T18:02:47Z","timestamp":1671213767000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Towards a deep learning-based outlier detection approach in the context of streaming data"],"prefix":"10.1186","volume":"9","author":[{"given":"Asmaa F.","family":"Hassan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sherif","family":"Barakat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amira","family":"Rezk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,12,16]]},"reference":[{"key":"670_CR1","doi-asserted-by":"publisher","first-page":"113252","DOI":"10.1016\/j.eswa.2020.113252","volume":"149","author":"T Kim","year":"2020","unstructured":"Kim T, Park CH. Anomaly pattern detection for streaming data. Expert Syst Appl. 2020;149:113252. https:\/\/doi.org\/10.1016\/j.eswa.2020.113252.","journal-title":"Expert Syst Appl"},{"issue":"4","key":"670_CR2","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1002\/sam.11380","volume":"11","author":"S Mansalis","year":"2018","unstructured":"Mansalis S, Ntoutsi E, Pelekis N, Theodoridis Y. An evaluation of data stream clustering algorithms. Stat Anal Data Min. 2018;11(4):167\u201387. https:\/\/doi.org\/10.1002\/sam.11380.","journal-title":"Stat Anal Data Min"},{"key":"670_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-015-3994-4","volume-title":"Identification of outliers","author":"DM Hawkins","year":"1980","unstructured":"Hawkins DM. Identification of outliers, vol. 11. Dordrecht: Springer; 1980."},{"key":"670_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-319-47578-3_1","volume-title":"Outlier Analysis","author":"CC Aggarwal","year":"2017","unstructured":"Aggarwal CC. An Introduction to Outlier Analysis. In: Aggarwal CC, editor. Outlier Analysis. Cham: Springer International Publishing; 2017. p. 1\u201334. https:\/\/doi.org\/10.1007\/978-3-319-47578-3_1."},{"issue":"1","key":"670_CR5","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/s10462-018-09679-z","volume":"52","author":"G Nguyen","year":"2019","unstructured":"Nguyen G, et al. Machine learning and deep learning frameworks and libraries for large-scale data mining: a survey. Artif Intell Rev. 2019;52(1):77\u2013124. https:\/\/doi.org\/10.1007\/s10462-018-09679-z.","journal-title":"Artif Intell Rev"},{"issue":"5","key":"670_CR6","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1016\/j.jacr.2020.02.005","volume":"17","author":"JM Czum","year":"2020","unstructured":"Czum JM. Dive into deep learning. J Am Coll Radiol. 2020;17(5):637\u20138. https:\/\/doi.org\/10.1016\/j.jacr.2020.02.005.","journal-title":"J Am Coll Radiol"},{"issue":"12","key":"670_CR7","doi-asserted-by":"publisher","first-page":"5320","DOI":"10.3390\/app11125320","volume":"11","author":"R Al-amri","year":"2021","unstructured":"Al-amri R, Murugesan RK, Man M, Abdulateef AF, Al-Sharafi MA, Alkahtani AA. A review of machine learning and deep learning techniques for anomaly detection in iot data. Appl Sci. 2021;11(12):5320. https:\/\/doi.org\/10.3390\/app11125320.","journal-title":"Appl Sci"},{"issue":"2","key":"670_CR8","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1145\/3373464.3373470","volume":"21","author":"HM Gomes","year":"2019","unstructured":"Gomes HM, Read J, Bifet A, Barddal JP, Gama J. Machine learning for streaming data: state of the art, challenges, and opportunities. SIGKDD Explor Newsl. 2019;21(2):6\u201322. https:\/\/doi.org\/10.1145\/3373464.3373470.","journal-title":"SIGKDD Explor Newsl"},{"key":"670_CR9","unstructured":"Zhang A, Lipton ZC, Li M, Smola AJ, Dive into deep learning, arXiv Prepr. arXiv2106.11342, 2021."},{"key":"670_CR10","doi-asserted-by":"publisher","unstructured":"Vargas R, Mosavi A, Ruiz R, Deep Learning: A Review, Adv Intell Syst Comput, no. October, https:\/\/doi.org\/10.20944\/preprints201810.0218.v1. 2018.","DOI":"10.20944\/preprints201810.0218.v1"},{"issue":"2","key":"670_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3439950","volume":"54","author":"G Pang","year":"2021","unstructured":"Pang G, Shen C, Cao L, Van Den Hengel A. Deep learning for anomaly detection. ACM Comput Surv. 2021;54(2):1\u201338. https:\/\/doi.org\/10.1145\/3439950.","journal-title":"ACM Comput Surv"},{"issue":"1","key":"670_CR12","doi-asserted-by":"publisher","first-page":"8","DOI":"10.36001\/phmconf.2020.v12i1.1186","volume":"12","author":"F Xue","year":"2020","unstructured":"Xue F, Yan W, Wang T, Huang H, Feng B. Deep anomaly detection for industrial systems: a case study. Annu Conf PHM Soc. 2020;12(1):8. https:\/\/doi.org\/10.36001\/phmconf.2020.v12i1.1186.","journal-title":"Annu Conf PHM Soc"},{"key":"670_CR13","doi-asserted-by":"publisher","unstructured":"Cao F, Estert M, Qian W, Zhou A, Density-based clustering over an evolving data stream with noise, in Proceedings of the 2006 SIAM International Conference on Data Mining, Apr. 2006;2006:328\u2013339. https:\/\/doi.org\/10.1137\/1.9781611972764.29.","DOI":"10.1137\/1.9781611972764.29"},{"issue":"30","key":"670_CR14","doi-asserted-by":"publisher","first-page":"845","DOI":"10.21105\/joss.00845","volume":"3","author":"V Constantinou","year":"2018","unstructured":"Constantinou V. PyNomaly: anomaly detection using local outlier probabilities (LoOP). J Open Source Softw. 2018;3(30):845. https:\/\/doi.org\/10.21105\/joss.00845.","journal-title":"J Open Source Softw"},{"key":"670_CR15","doi-asserted-by":"publisher","unstructured":"Yang X, Zhou W, Shu N, Zhang H, A Fast and Efficient Local Outlier Detection in Data Streams, in Proceedings of the 2019 International Conference on Image, Video and Signal Processing, 2019;111\u2013116. doi: https:\/\/doi.org\/10.1145\/3317640.3317653.","DOI":"10.1145\/3317640.3317653"},{"issue":"20","key":"670_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s20205829","volume":"20","author":"JW Huang","year":"2020","unstructured":"Huang JW, Zhong MX, Jaysawal BP. Tadilof: time aware density-based incremental local outlier detection in data streams. Sensors. 2020;20(20):1\u201325. https:\/\/doi.org\/10.3390\/s20205829.","journal-title":"Sensors"},{"issue":"15","key":"670_CR17","doi-asserted-by":"publisher","first-page":"9607","DOI":"10.1007\/s00521-021-05725-0","volume":"33","author":"M Singh","year":"2021","unstructured":"Singh M, Pamula R. ADINOF: adaptive density summarizing incremental natural outlier detection in data stream. Neural Comput Appl. 2021;33(15):9607\u201323. https:\/\/doi.org\/10.1007\/s00521-021-05725-0.","journal-title":"Neural Comput Appl"},{"issue":"10","key":"670_CR18","doi-asserted-by":"publisher","first-page":"2275","DOI":"10.1007\/s00607-021-00939-5","volume":"103","author":"A Abid","year":"2021","unstructured":"Abid A, El Khediri S, Kachouri A. Improved approaches for density-based outlier detection in wireless sensor networks. Computing. 2021;103(10):2275\u201392. https:\/\/doi.org\/10.1007\/s00607-021-00939-5.","journal-title":"Computing"},{"issue":"4","key":"670_CR19","first-page":"66","volume":"1","author":"A Hassan","year":"2011","unstructured":"Hassan A, Mokhtar H, Hegazy O. A heuristic approach for sensor network outlier detection. Int J Res Rev Wirel Sens Netw. 2011;1(4):66\u201372.","journal-title":"Int J Res Rev Wirel Sens Netw"},{"issue":"2","key":"670_CR20","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.eij.2013.06.001","volume":"14","author":"A Fawzy","year":"2013","unstructured":"Fawzy A, Mokhtar HMO, Hegazy O. Outliers detection and classification in wireless sensor networks. Egypt Informatics J. 2013;14(2):157\u201364. https:\/\/doi.org\/10.1016\/j.eij.2013.06.001.","journal-title":"Egypt Informatics J"},{"key":"670_CR21","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1016\/j.jnca.2014.11.007","volume":"59","author":"A Amini","year":"2016","unstructured":"Amini A, Saboohi H, Herawan T, Wah TY. MuDi-Stream: a multi density clustering algorithm for evolving data stream. J Netw Comput Appl. 2016;59:370\u201385. https:\/\/doi.org\/10.1016\/j.jnca.2014.11.007.","journal-title":"J Netw Comput Appl"},{"key":"670_CR22","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.ins.2016.12.004","volume":"382\u2013383","author":"R Hyde","year":"2017","unstructured":"Hyde R, Angelov P, MacKenzie AR. Fully online clustering of evolving data streams into arbitrarily shaped clusters. Inf Sci. 2017;382\u2013383:96\u2013114. https:\/\/doi.org\/10.1016\/j.ins.2016.12.004.","journal-title":"Inf Sci"},{"key":"670_CR23","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.ins.2019.12.022","volume":"518","author":"CG Bezerra","year":"2020","unstructured":"Bezerra CG, Costa BSJ, Guedes LA, Angelov PP. An evolving approach to data streams clustering based on typicality and eccentricity data analytics. Inf Sci. 2020;518:13\u201328.","journal-title":"Inf Sci"},{"key":"670_CR24","doi-asserted-by":"publisher","first-page":"672","DOI":"10.1016\/j.future.2020.01.017","volume":"106","author":"J Maia","year":"2020","unstructured":"Maia J, et al. Evolving clustering algorithm based on mixture of typicalities for stream data mining. Futur Gener Comput Syst. 2020;106:672\u201384.","journal-title":"Futur Gener Comput Syst"},{"key":"670_CR25","doi-asserted-by":"publisher","unstructured":"Kontaki M, Gounaris A, Papadopoulos AN, Tsichlas K, Manolopoulos Y, Continuous monitoring of distance-based outliers over data streams, in Proceedings - International Conference on Data Engineering, 2011;135\u2013146. https:\/\/doi.org\/10.1109\/ICDE.2011.5767923.","DOI":"10.1109\/ICDE.2011.5767923"},{"issue":"12","key":"670_CR26","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.14778\/2994509.2994526","volume":"9","author":"L Tran","year":"2016","unstructured":"Tran L, Fan L, Shahabi C. Distance-based outlier detection in data streams. Proc ofthe VLDB Endow. 2016;9(12):1089\u2013100.","journal-title":"Proc ofthe VLDB Endow"},{"key":"670_CR27","doi-asserted-by":"publisher","unstructured":"Tran L, Fan L, Shahabi C, Fast distance-based outlier detection in data streams based on micro-clusters, ACM Int. Conf. Proceeding Ser, 2019; 162\u2013169, https:\/\/doi.org\/10.1145\/3368926.3369667.","DOI":"10.1145\/3368926.3369667"},{"issue":"2","key":"670_CR28","doi-asserted-by":"publisher","first-page":"141","DOI":"10.14778\/3425879.3425885","volume":"14","author":"L Tran","year":"2020","unstructured":"Tran L, Mun MY, Shahabi C. Real-time distance-based outlier detection in data streams. Proc VLDB Endow. 2020;14(2):141\u201353. https:\/\/doi.org\/10.14778\/3425879.3425885.","journal-title":"Proc VLDB Endow"},{"key":"670_CR29","doi-asserted-by":"publisher","unstructured":"Bose B, Dutta J, Ghosh S, Pramanick P, Roy S, \u201cDetection of Driving Patterns and Road Anomalies,\u201d in 2018 3rd International Conference On Internet of Things: Smart Innovation and Usages (IoT-SIU), 2018;1\u20137. https:\/\/doi.org\/10.1109\/IoT-SIU.2018.8519861.","DOI":"10.1109\/IoT-SIU.2018.8519861"},{"issue":"3","key":"670_CR30","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.1007\/s10845-017-1315-5","volume":"30","author":"M Wu","year":"2019","unstructured":"Wu M, Song Z, Moon YB. Detecting cyber-physical attacks in cybermanufacturing systems with machine learning methods. J Intell Manuf. 2019;30(3):1111\u201323. https:\/\/doi.org\/10.1007\/s10845-017-1315-5.","journal-title":"J Intell Manuf"},{"key":"670_CR31","doi-asserted-by":"publisher","first-page":"100059","DOI":"10.1016\/j.iot.2019.100059","volume":"7","author":"M Hasan","year":"2019","unstructured":"Hasan M, Islam MM, Zarif MII, Hashem MMA. Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet Things. 2019;7:100059. https:\/\/doi.org\/10.1016\/j.iot.2019.100059.","journal-title":"Internet Things"},{"key":"670_CR32","doi-asserted-by":"publisher","unstructured":"Haque MA, Mineno H, Proposal of Online Outlier Detection in Sensor Data Using Kernel Density Estimation, Proc.\u20142017 6th IIAI Int Congr Adv Appl Informatics, IIAI-AAI 2017, 2017; July 2017: 1051\u20131052. https:\/\/doi.org\/10.1109\/IIAI-AAI.2017.41.","DOI":"10.1109\/IIAI-AAI.2017.41"},{"key":"670_CR33","doi-asserted-by":"publisher","first-page":"102756","DOI":"10.1016\/j.jnca.2020.102756","volume":"168","author":"S Daneshgadeh \u00c7akmak\u00e7\u0131","year":"2020","unstructured":"Daneshgadeh \u00c7akmak\u00e7\u0131 S, Kemmerich T, Ahmed T, Baykal N. Online DDoS attack detection using mahalanobis distance and Kernel-based learning algorithm. J Netw Comput Appl. 2020;168:102756. https:\/\/doi.org\/10.1016\/j.jnca.2020.102756.","journal-title":"J. Netw. Comput. Appl."},{"key":"670_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10044-021-00998-6","volume":"24","author":"P Bhattacharjee","year":"2021","unstructured":"Bhattacharjee P, Garg A, Mitra P. KAGO: an approximate adaptive grid-based outlier detection approach using kernel density estimate. Pattern Anal Appl. 2021;24:1\u201322.","journal-title":"Pattern Anal Appl"},{"key":"670_CR35","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1016\/j.procs.2020.09.112","volume":"176","author":"N Iftikhar","year":"2020","unstructured":"Iftikhar N, Baattrup-Andersen T, Nordbjerg FE, Jeppesen K. Outlier detection in sensor data using ensemble learning. Procedia Comput Sci. 2020;176:1160\u20139. https:\/\/doi.org\/10.1016\/j.procs.2020.09.112.","journal-title":"Procedia Comput Sci"},{"key":"670_CR36","unstructured":"Kashef RF, Ensemble-based anomaly detection using cooperative learning, Proc Mach Learn. Res, 2017;71: 43\u201355, http:\/\/proceedings.mlr.press\/v71\/kashef18a\/kashef18a.pdf"},{"key":"670_CR37","doi-asserted-by":"publisher","unstructured":"Ghomeshi H, Gaber MM, Kovalchuk Y, Ensemble Dynamics in Non-stationary Data Stream Classification, 2019;123\u2013153https:\/\/doi.org\/10.1007\/978-3-319-89803-2_6.","DOI":"10.1007\/978-3-319-89803-2_6"},{"key":"670_CR38","doi-asserted-by":"publisher","DOI":"10.1007\/s41870-021-00717-8","author":"P Biswas","year":"2021","unstructured":"Biswas P, Samanta T. Anomaly detection using ensemble random forest in wireless sensor network. Int J Inf Technol. 2021. https:\/\/doi.org\/10.1007\/s41870-021-00717-8.","journal-title":"Int J Inf Technol"},{"issue":"4","key":"670_CR39","doi-asserted-by":"publisher","first-page":"1162","DOI":"10.1016\/j.matpr.2020.11.491","volume":"7","author":"N Jayanthi","year":"2021","unstructured":"Jayanthi N, Vijaya Babu B, Rao NS. An ensemble framework based outlier detection system in high dimensional data. Mater Today Proc. 2021;7(4):1162\u201375. https:\/\/doi.org\/10.1016\/j.matpr.2020.11.491.","journal-title":"Mater Today Proc"},{"issue":"6","key":"670_CR40","doi-asserted-by":"publisher","first-page":"886","DOI":"10.4218\/etrij.2019-0205","volume":"42","author":"JK Bii","year":"2020","unstructured":"Bii JK, Rimiru R, Mwangi RW. Adaptive boosting in ensembles for outlier detection: base learner selection and fusion via local domain competence. ETRI J. 2020;42(6):886\u201398. https:\/\/doi.org\/10.4218\/etrij.2019-0205.","journal-title":"ETRI J"},{"key":"670_CR41","unstructured":"Chambers L, Gaber MM, Abdallah ZS. DeepStreamCE: a streaming approach to concept evolution detection in deep neural networks, 2020;http:\/\/arxiv.org\/abs\/2004.04116"},{"key":"670_CR42","doi-asserted-by":"publisher","unstructured":"Amarasinghe K, Kenney K, Manic M, Toward explainable deep neural network based anomaly detection, Proc\u20142018 11th Int Conf Hum. Syst Interact HSI 2018, 2018;2:311\u2013317. https:\/\/doi.org\/10.1109\/HSI.2018.8430788.","DOI":"10.1109\/HSI.2018.8430788"},{"issue":"2019 January","key":"670_CR43","doi-asserted-by":"publisher","first-page":"1991","DOI":"10.1109\/ACCESS.2018.2886457","volume":"7","author":"M Munir","year":"2019","unstructured":"Munir M, Siddiqui SA, Dengel A, Ahmed S. DeepAnT: a deep learning approach for unsupervised anomaly detection in time series. IEEE Access. 2019;7(2019 January):1991\u20132005. https:\/\/doi.org\/10.1109\/ACCESS.2018.2886457.","journal-title":"IEEE Access"},{"key":"670_CR44","unstructured":"Gao J, Song X, Wen Q, Wang P, Sun L, Xu H, \u201cRobustTAD: Robust time series anomaly detection via decomposition and convolutional neural networks, Feb. 2020, http:\/\/arxiv.org\/abs\/2002.09545."},{"issue":"1","key":"670_CR45","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/TETCI.2017.2772792","volume":"2","author":"N Shone","year":"2018","unstructured":"Shone N, Ngoc TN, Phai VD, Shi Q. A deep learning approach to network intrusion detection. IEEE Trans Emerg Top Comput Intell. 2018;2(1):41\u201350. https:\/\/doi.org\/10.1109\/TETCI.2017.2772792.","journal-title":"IEEE Trans Emerg Top Comput Intell"},{"key":"670_CR46","doi-asserted-by":"publisher","first-page":"59657","DOI":"10.1109\/ACCESS.2018.2875045","volume":"6","author":"N Marir","year":"2018","unstructured":"Marir N, Wang H, Feng G, Li B, Jia M. Distributed abnormal behavior detection approach based on deep belief network and ensemble SVM using spark. IEEE Access. 2018;6:59657\u201371. https:\/\/doi.org\/10.1109\/ACCESS.2018.2875045.","journal-title":"IEEE Access"},{"issue":"6","key":"670_CR47","doi-asserted-by":"publisher","first-page":"5352","DOI":"10.1166\/asl.2017.7374","volume":"23","author":"N Khan","year":"2017","unstructured":"Khan N, Abdullah J, Khan AS. A dynamic method of detecting malicious scripts using classifiers. Adv Sci Lett. 2017;23(6):5352.","journal-title":"Adv Sci Lett"},{"key":"670_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s19112451","volume":"19","author":"M Munir","year":"2019","unstructured":"Munir M, Siddiqui SA, Chattha MA, Dengel A, Ahmed S. FuseAD\u202f: unsupervised anomaly detection in deep learning models. Sensors. 2019;19:1\u201315. https:\/\/doi.org\/10.3390\/s19112451.","journal-title":"Sensors"},{"key":"670_CR49","unstructured":"Silva PR, Vinagre J, Gama J, Federated anomaly detection over distributed data streams, 2022, http:\/\/arxiv.org\/abs\/2205.07829"},{"key":"670_CR50","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1007\/978-981-15-3383-9_54","volume-title":"Advanced machine learning technologies and applications. AMLTA 2020. Advances in intelligent systems and computing","author":"A Mathew","year":"2021","unstructured":"Mathew A, Amudha P, Sivakumari S. Deep learning techniques: an overview. In: Hassanien AE, Bhatnagar R, Darwish A, editors. Advanced machine learning technologies and applications. AMLTA 2020. Advances in intelligent systems and computing. Singapore: Springer Singapore; 2021. p. 599\u2013608. https:\/\/doi.org\/10.1007\/978-981-15-3383-9_54."},{"key":"670_CR51","unstructured":"Dua D, Gra C, UCI machine learning repository, 2017;http:\/\/archive.ics.uci.edu\/ml."},{"key":"670_CR52","unstructured":"Google Research Colaboratory, 2021; https:\/\/colab.research.google.com."}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-022-00670-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-022-00670-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-022-00670-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T18:05:38Z","timestamp":1671213938000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-022-00670-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,16]]},"references-count":52,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["670"],"URL":"https:\/\/doi.org\/10.1186\/s40537-022-00670-8","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,16]]},"assertion":[{"value":"10 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"120"}}