{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T17:30:53Z","timestamp":1770917453307,"version":"3.50.1"},"reference-count":80,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2022,3,14]],"date-time":"2022-03-14T00:00:00Z","timestamp":1647216000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,14]],"date-time":"2022-03-14T00:00:00Z","timestamp":1647216000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1007\/s00521-022-07084-w","type":"journal-article","created":{"date-parts":[[2022,3,14]],"date-time":"2022-03-14T10:03:01Z","timestamp":1647252181000},"page":"19565-19584","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["COREM2 project: a beginning to end approach for cyber intrusion detection"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3779-6754","authenticated-orcid":false,"given":"Anastasios Panagiotis","family":"Psathas","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6404-1528","authenticated-orcid":false,"given":"Lazaros","family":"Iliadis","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0545-7638","authenticated-orcid":false,"given":"Antonios","family":"Papaleonidas","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3280-6554","authenticated-orcid":false,"given":"Dimitris","family":"Bountas","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,14]]},"reference":[{"key":"7084_CR1","unstructured":"Kuypers MA, Maillart T, Pat\u00e9-Cornell E (2016) An empirical analysis of cyber security incidents at a large organization. Department of Management Science and Engineering, Stanford University, School of Information, UC Berkeley, 30"},{"key":"7084_CR2","doi-asserted-by":"crossref","unstructured":"Yadav S, Shukla S (2016) Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification. In 2016 IEEE 6th International conference on advanced computing (IACC). IEEE. pp 78\u201383","DOI":"10.1109\/IACC.2016.25"},{"issue":"9","key":"7084_CR3","doi-asserted-by":"publisher","first-page":"e3547","DOI":"10.1002\/dac.3547","volume":"31","author":"A Ahmim","year":"2018","unstructured":"Ahmim A, Derdour M, Ferrag MA (2018) An intrusion detection system based on combining probability predictions of a tree of classifiers. Int J Commun Syst 31(9):e3547","journal-title":"Int J Commun Syst"},{"key":"7084_CR4","doi-asserted-by":"crossref","unstructured":"Ahmim A, Maglaras L, Ferrag MA, Derdour M, Janicke H (2019) A novel hierarchical intrusion detection system based on decision tree and rules-based models. In 2019 15th international conference on distributed computing in sensor systems (DCOSS). IEEE. pp 228\u2013233","DOI":"10.1109\/DCOSS.2019.00059"},{"key":"7084_CR5","unstructured":"Statista, https:\/\/www.statista.com\/statistics\/273575\/average-organizational-cost-incurred-by-a-data-breach\/. Accessed 28 Nov 2021"},{"key":"7084_CR6","first-page":"42","volume":"112","author":"K Holzinger","year":"2018","unstructured":"Holzinger K, Mak K, Kieseberg P, Holzinger A (2018) Can we trust machine learning results? artificial intelligence in safety-critical decision support. Ercim News 112:42\u201343","journal-title":"Ercim News"},{"key":"7084_CR7","unstructured":"IBM, https:\/\/www.ibm.com\/topics\/cybersecurity. Accessed 30 Nov 2021"},{"key":"7084_CR8","doi-asserted-by":"crossref","unstructured":"Psathas AP, Iliadis L, Papaleonidas A, Bountas D (2021) A hybrid deep learning ensemble for cyber intrusion detection. In international conference on engineering applications of neural networks. Springer, Cham. pp 27\u201341","DOI":"10.1007\/978-3-030-80568-5_3"},{"issue":"2","key":"7084_CR9","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1111\/j.2517-6161.1974.tb00994.x","volume":"36","author":"M Stone","year":"1974","unstructured":"Stone M (1974) Cross-validatory choice and assessment of statistical predictions. J Roy Stat Soc Ser B (Methodol) 36(2):111\u2013133","journal-title":"J Roy Stat Soc Ser B (Methodol)"},{"issue":"2","key":"7084_CR10","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1257\/jep.29.2.213","volume":"29","author":"R B\u00f6hme","year":"2015","unstructured":"B\u00f6hme R, Christin N, Edelman B, Moore T (2015) Bitcoin: economics, technology, and governance. J Econ Perspect 29(2):213\u2013238","journal-title":"J Econ Perspect"},{"issue":"1","key":"7084_CR11","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1109\/MSEC.2019.2893730","volume":"17","author":"AT Sherman","year":"2019","unstructured":"Sherman AT, Javani F, Zhang H, Golaszewski E (2019) On the origins and variations of blockchain technologies. IEEE Secur Priv 17(1):72\u201377","journal-title":"IEEE Secur Priv"},{"issue":"1","key":"7084_CR12","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1002\/sec.341","volume":"5","author":"D Damopoulos","year":"2012","unstructured":"Damopoulos D, Menesidou SA, Kambourakis G, Papadaki M, Clarke N, Gritzalis S (2012) Evaluation of anomaly-based IDS for mobile devices using machine learning classifiers. Secur Commun Netw 5(1):3\u201314","journal-title":"Secur Commun Netw"},{"issue":"1","key":"7084_CR13","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1016\/j.eswa.2011.07.032","volume":"39","author":"Y Li","year":"2012","unstructured":"Li Y, Xia J, Zhang S, Yan J, Ai X, Dai K (2012) An efficient intrusion detection system based on support vector machines and gradually feature removal method. Expert Syst Appl 39(1):424\u2013430","journal-title":"Expert Syst Appl"},{"key":"7084_CR14","doi-asserted-by":"crossref","unstructured":"Elekar KS (2015) Combination of data mining techniques for intrusion detection system. In\u00a02015 international conference on computer, communication and control (IC4). IEEE. pp 1\u20135","DOI":"10.1109\/IC4.2015.7375727"},{"issue":"3","key":"7084_CR15","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1007\/s40815-015-0080-x","volume":"18","author":"P Ganeshkumar","year":"2016","unstructured":"Ganeshkumar P, Pandeeswari N (2016) Adaptive neuro-fuzzy-based anomaly detection system in cloud. Int J Fuzzy Syst 18(3):367\u2013378","journal-title":"Int J Fuzzy Syst"},{"issue":"3","key":"7084_CR16","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1109\/MPRV.2018.03367731","volume":"17","author":"Y Meidan","year":"2018","unstructured":"Meidan Y, Bohadana M, Mathov Y, Mirsky Y, Shabtai A, Breitenbacher D, Elovici Y (2018) N-baiot\u2014network-based detection of iot botnet attacks using deep autoencoders. IEEE Pervasive Comput 17(3):12\u201322","journal-title":"IEEE Pervasive Comput"},{"issue":"16","key":"7084_CR17","doi-asserted-by":"publisher","first-page":"4372","DOI":"10.3390\/s20164372","volume":"20","author":"YN Soe","year":"2020","unstructured":"Soe YN, Feng Y, Santosa PI, Hartanto R, Sakurai K (2020) Machine learning-based IoT-botnet attack detection with sequential architecture. Sensors 20(16):4372","journal-title":"Sensors"},{"issue":"6","key":"7084_CR18","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1016\/j.patrec.2004.09.045","volume":"26","author":"C Zhang","year":"2005","unstructured":"Zhang C, Jiang J, Kamel M (2005) Intrusion detection using hierarchical neural networks. Pattern Recogn Lett 26(6):779\u2013791","journal-title":"Pattern Recogn Lett"},{"key":"7084_CR19","doi-asserted-by":"publisher","first-page":"2687","DOI":"10.1007\/s00500-015-1967-z","volume":"21","author":"T Dash","year":"2017","unstructured":"Dash T (2017) A study on intrusion detection using neural networks trained with evolutionary algorithms. Soft Comput 21:2687\u20132700","journal-title":"Soft Comput"},{"issue":"9","key":"7084_CR20","doi-asserted-by":"publisher","first-page":"4303","DOI":"10.1007\/s00521-019-04363-x","volume":"32","author":"K Demertzis","year":"2020","unstructured":"Demertzis K, Iliadis L, Bougoudis I (2020) Gryphon: a semi-supervised anomaly detection system based on one-class evolving spiking neural network. Neural Comput Appl 32(9):4303\u20134314","journal-title":"Neural Comput Appl"},{"issue":"18","key":"7084_CR21","doi-asserted-by":"publisher","first-page":"3799","DOI":"10.1016\/j.ins.2007.03.025","volume":"177","author":"T Shon","year":"2007","unstructured":"Shon T, Moon J (2007) A hybrid machine learning approach to network anomaly detection. Inf Sci 177(18):3799\u20133821","journal-title":"Inf Sci"},{"issue":"2","key":"7084_CR22","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/COMST.2015.2494502","volume":"18","author":"AL Buczak","year":"2015","unstructured":"Buczak AL, Guven E (2015) A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun Surv Tutor 18(2):1153\u20131176","journal-title":"IEEE Commun Surv Tutor"},{"issue":"8","key":"7084_CR23","doi-asserted-by":"publisher","first-page":"1661","DOI":"10.1109\/TPDS.2012.261","volume":"24","author":"M Xie","year":"2012","unstructured":"Xie M, Hu J, Han S, Chen HH (2012) Scalable hypergrid k-NN-based online anomaly detection in wireless sensor networks. IEEE Trans Parallel Distrib Syst 24(8):1661\u20131670","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"7084_CR24","doi-asserted-by":"crossref","unstructured":"Kolosnjaji B, Zarras A, Webster G, Eckert C (2016) Deep learning for classification of malware system call sequences. In Australasian joint conference on artificial intelligence. Springer, Cham. pp 137\u2013149","DOI":"10.1007\/978-3-319-50127-7_11"},{"key":"7084_CR25","doi-asserted-by":"crossref","unstructured":"Pascanu R, Stokes JW, Sanossian H, Marinescu M, Thomas A (2015) Malware classification with recurrent networks. In 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE. pp 1916\u20131920","DOI":"10.1109\/ICASSP.2015.7178304"},{"key":"7084_CR26","doi-asserted-by":"crossref","unstructured":"Mizuno S, Hatada M, Mori T, Goto S (2017) Botdetector: a robust and scalable approach toward detecting malware-infected devices. In 2017 IEEE international conference on communications (ICC). IEEE. pp 1\u20137","DOI":"10.1109\/ICC.2017.7997372"},{"issue":"1","key":"7084_CR27","doi-asserted-by":"publisher","first-page":"6","DOI":"10.3390\/bdcc3010006","volume":"3","author":"K Demertzis","year":"2019","unstructured":"Demertzis K, Tziritas N, Kikiras P, Sanchez SL, Iliadis L (2019) The next generation cognitive security operations center: adaptive analytic lambda architecture for efficient defense against adversarial attacks. Big Data Cognit Comput 3(1):6","journal-title":"Big Data Cognit Comput"},{"key":"7084_CR28","doi-asserted-by":"crossref","unstructured":"Cordonsky I, Rosenberg I, Sicard G, David EO (2018) DeepOrigin: end-to-end deep learning for detection of new malware families. In 2018 international joint conference on neural networks (IJCNN). IEEE. pp 1\u20137","DOI":"10.1109\/IJCNN.2018.8489667"},{"key":"7084_CR29","unstructured":"Gibert Llaurad\u00f3 D (2016). Convolutional neural networks for malware classification (Master's thesis, Universitat Polit\u00e8cnica de Catalunya)"},{"key":"7084_CR30","doi-asserted-by":"publisher","first-page":"3491","DOI":"10.1109\/ACCESS.2017.2782159","volume":"6","author":"G Loukas","year":"2017","unstructured":"Loukas G, Vuong T, Heartfield R, Sakellari G, Yoon Y, Gan D (2017) Cloud-based cyber-physical intrusion detection for vehicles using deep learning. IEEE Access 6:3491\u20133508","journal-title":"IEEE Access"},{"issue":"9","key":"7084_CR31","doi-asserted-by":"publisher","first-page":"1977","DOI":"10.3390\/s19091977","volume":"19","author":"G Thamilarasu","year":"2019","unstructured":"Thamilarasu G, Chawla S (2019) Towards deep-learning-driven intrusion detection for the internet of things. Sensors 19(9):1977","journal-title":"Sensors"},{"issue":"1","key":"7084_CR32","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 (2018) A deep learning approach to network intrusion detection. IEEE Trans Emerg Topics Comput Intell 2(1):41\u201350","journal-title":"IEEE Trans Emerg Topics Comput Intell"},{"key":"7084_CR33","unstructured":"Kdd Cup 1999. http:\/\/kdd.ics.uci.edu\/databases\/kddcup99\/kddcup99.html. Accessed 8 Mar 2021"},{"key":"7084_CR34","unstructured":"Nsl kdd. https:\/\/www.unb.ca\/cic\/datasets\/nsl.html. Accessed 8 Mar 2021"},{"issue":"14","key":"7084_CR35","doi-asserted-by":"publisher","first-page":"4966","DOI":"10.3390\/app10144966","volume":"10","author":"M Nisa","year":"2020","unstructured":"Nisa M, Shah JH, Kanwal S, Raza M, Khan MA, Dama\u0161evi\u010dius R, Bla\u017eauskas T (2020) Hybrid malware classification method using segmentation-based fractal texture analysis and deep convolution neural network features. Appl Sci 10(14):4966","journal-title":"Appl Sci"},{"issue":"5","key":"7084_CR36","doi-asserted-by":"publisher","first-page":"2505","DOI":"10.1109\/TSG.2017.2703842","volume":"8","author":"Y He","year":"2017","unstructured":"He Y, Mendis GJ, Wei J (2017) Real-time detection of false data injection attacks in smart grid: a deep learning-based intelligent mechanism. IEEE Trans Smart Grid 8(5):2505\u20132516","journal-title":"IEEE Trans Smart Grid"},{"key":"7084_CR37","doi-asserted-by":"crossref","unstructured":"Miller ST, Busby-Earle C (2017) Multi-perspective machine learning a classifier ensemble method for intrusion detection. In\u00a0proceedings of the 2017 international conference on machine learning and soft computing, pp 7\u201312","DOI":"10.1145\/3036290.3036303"},{"key":"7084_CR38","unstructured":"Nakamoto S (2008) Bitcoin: a peer-to-peer electronic cash system.\u00a0Decentralized Business Review, 21260"},{"key":"7084_CR39","doi-asserted-by":"crossref","unstructured":"Serrano W (2019) The blockchain random neural network in cybersecurity and the Internet of Things. In IFIP international conference on artificial intelligence applications and innovations. Springer, Cham. pp 50\u201363","DOI":"10.1007\/978-3-030-19823-7_4"},{"key":"7084_CR40","doi-asserted-by":"crossref","unstructured":"Giannoutakis KM, Spathoulas G, Filelis-Papadopoulos CK, Collen A, Anagnostopoulos M, Votis K, Nijdam NA (2020) A blockchain solution for enhancing cybersecurity defence of IoT. In\u00a02020 IEEE international conference on blockchain (Blockchain). IEEE. pp 490\u2013495","DOI":"10.1109\/Blockchain50366.2020.00071"},{"issue":"23","key":"7084_CR41","doi-asserted-by":"publisher","first-page":"17361","DOI":"10.1007\/s00521-020-05189-8","volume":"32","author":"K Demertzis","year":"2020","unstructured":"Demertzis K, Iliadis L, Tziritas N, Kikiras P (2020) Anomaly detection via blockchained deep learning smart contracts in industry 4.0. Neural Comput Appl 32(23):17361\u201317378","journal-title":"Neural Comput Appl"},{"key":"7084_CR42","doi-asserted-by":"crossref","unstructured":"Mora OB, Rivera R, Larios VM, Beltr\u00e1n-Ram\u00edrez JR, Maciel R, Ochoa A (2018) A Use Case in Cybersecurity based in Blockchain to deal with the security and privacy of citizens and Smart Cities Cyberinfrastructures. In\u00a02018 IEEE international smart cities conference (ISC2). IEEE. pp 1\u20134","DOI":"10.1109\/ISC2.2018.8656694"},{"key":"7084_CR43","doi-asserted-by":"crossref","unstructured":"Mylrea M, Gourisetti SNG (2018) Blockchain for supply chain cybersecurity, optimization and compliance. In\u00a02018 Resilience Week (RWS). IEEE. pp 70\u201376","DOI":"10.1109\/RWEEK.2018.8473517"},{"issue":"6","key":"7084_CR44","doi-asserted-by":"publisher","first-page":"7300","DOI":"10.1109\/TIA.2019.2919820","volume":"55","author":"B Wang","year":"2019","unstructured":"Wang B, Dabbaghjamanesh M, Kavousi-Fard A, Mehraeen S (2019) Cybersecurity enhancement of power trading within the networked microgrids based on blockchain and directed acyclic graph approach. IEEE Trans Ind Appl 55(6):7300\u20137309","journal-title":"IEEE Trans Ind Appl"},{"issue":"4","key":"7084_CR45","doi-asserted-by":"publisher","first-page":"122","DOI":"10.3390\/info10040122","volume":"10","author":"DS Berman","year":"2019","unstructured":"Berman DS, Buczak AL, Chavis JS, Corbett CL (2019) A survey of deep learning methods for cyber security. Information 10(4):122","journal-title":"Information"},{"key":"7084_CR46","unstructured":"Ctu-13 Dataset. https:\/\/mcfp.weebly.com\/the-ctu-13-dataset-a-labeleddataset-with-botnet-normal-and-background-traffic.html. Accessed 8 Mar 2021"},{"key":"7084_CR47","unstructured":"Unsw-nb15 Dataset. https:\/\/www.unsw.adfa.edu.au\/unsw-canberra-cyber\/cybersecurity\/ADFA-NB15-Datasets\/. Accessed 30 Nov 2021"},{"key":"7084_CR48","unstructured":"Bot-IoT Dataset. https:\/\/www.unsw.adfa.edu.au\/unsw-canberra-cyber\/cybersecurity\/ADFA-NB15-Datasets\/bot_iot.php. Accessed 8 Mar 2021"},{"key":"7084_CR49","unstructured":"CSE-CIC-IDS2018 Dataset. https:\/\/www.unb.ca\/cic\/datasets\/ids-2018.html. Accessed 30 May 2019"},{"key":"7084_CR50","unstructured":"Tor-Nontor Dataset. https:\/\/www.unb.ca\/cic\/datasets\/tor.html. Accessed 8 Mar 2021"},{"key":"7084_CR51","unstructured":"Android Malware Dataset. https:\/\/www.unb.ca\/cic\/datasets\/andmal2017.html. Accessed 30 Nov 2021"},{"key":"7084_CR52","doi-asserted-by":"crossref","unstructured":"Mirsky Y, Doitshman T, Elovici Y, Shabtai A (2018) Kitsune: an ensemble of autoencoders for online network intrusion detection. arXiv preprint arXiv:1802.09089","DOI":"10.14722\/ndss.2018.23204"},{"key":"7084_CR53","unstructured":"Kitsune Network Attack Dataset. https:\/\/archive.ics.uci.edu\/ml\/datasets\/Kitsune+Network+Attack+Dataset. Accessed 8 Mar 2021"},{"key":"7084_CR54","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1016\/j.matcom.2020.04.031","volume":"177","author":"MV Valueva","year":"2020","unstructured":"Valueva MV, Nagornov NN, Lyakhov PA, Valuev GV, Chervyakov NI (2020) Application of the residue number system to reduce hardware costs of the convolutional neural network implementation. Math Comput Simul 177:232\u2013243","journal-title":"Math Comput Simul"},{"key":"7084_CR55","unstructured":"O'Shea K, Ryan N (2015) \"An introduction to convolutional neural networks.\" arXiv preprint arXiv:1511.08458"},{"key":"7084_CR56","unstructured":"Ciresan DC, Meier U, Masci J, Gambardella LM, Schmidhuber J (2011) Flexible, high performance convolutional neural networks for image classification. In Twenty-second international joint conference on artificial intelligence"},{"key":"7084_CR57","doi-asserted-by":"crossref","unstructured":"Ciregan D, Meier U, Schmidhuber J (2012) Multi-column deep neural networks for image classification. In\u00a02012 IEEE conference on computer vision and pattern recognition. IEEE. pp 3642\u20133649","DOI":"10.1109\/CVPR.2012.6248110"},{"key":"7084_CR58","unstructured":"Martin E, Cundy C (2017) Parallelizing linear recurrent neural nets over sequence length. arXiv preprint arXiv:1709.04057"},{"issue":"6","key":"7084_CR59","doi-asserted-by":"publisher","first-page":"1695","DOI":"10.1109\/JBHI.2019.2959843","volume":"24","author":"S Ayd\u0131n","year":"2019","unstructured":"Ayd\u0131n S (2019) Deep learning classification of neuro-emotional phase domain complexity levels induced by affective video film clips. IEEE J Biomed Health Inform 24(6):1695\u20131702","journal-title":"IEEE J Biomed Health Inform"},{"key":"7084_CR60","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.neucom.2019.02.056","volume":"347","author":"S Mahdavifar","year":"2019","unstructured":"Mahdavifar S, Ghorbani AA (2019) Application of deep learning to cybersecurity: a survey. Neurocomputing 347:149\u2013176","journal-title":"Neurocomputing"},{"issue":"7","key":"7084_CR61","doi-asserted-by":"publisher","first-page":"1387","DOI":"10.3390\/w11071387","volume":"11","author":"XH Le","year":"2019","unstructured":"Le XH, Ho HV, Lee G, Jung S (2019) Application of long short-term memory (LSTM) neural network for flood forecasting. Water 11(7):1387","journal-title":"Water"},{"issue":"3","key":"7084_CR62","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1007\/s10462-017-9567-1","volume":"51","author":"AI Saleh","year":"2019","unstructured":"Saleh AI, Talaat FM, Labib LM (2019) A hybrid intrusion detection system (HIDS) based on prioritized k-nearest neighbors and optimized SVM classifiers. Artif Intell Rev 51(3):403\u2013443","journal-title":"Artif Intell Rev"},{"issue":"5","key":"7084_CR63","doi-asserted-by":"publisher","first-page":"978","DOI":"10.1109\/TNNLS.2015.2431251","volume":"27","author":"DS Yeung","year":"2015","unstructured":"Yeung DS, Li JC, Ng WW, Chan PP (2015) MLPNN training via a multiobjective optimization of training error and stochastic sensitivity. IEEE Trans Neural Netw Learn Syst 27(5):978\u2013992","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"9","key":"7084_CR64","doi-asserted-by":"publisher","first-page":"28","DOI":"10.3390\/cli9020028","volume":"2021","author":"A Malik","year":"2021","unstructured":"Malik A, Kumar A, Rai P, Kuriqi A (2021) Prediction of multi-scalar standardized precipitation index by using artificial intelligence and regression models. Climate 2021(9):28","journal-title":"Climate"},{"issue":"1","key":"7084_CR65","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1177\/030913330102500104","volume":"25","author":"CW Dawson","year":"2001","unstructured":"Dawson CW, Wilby RL (2001) Hydrological modelling using artificial neural networks. Prog Phys Geogr 25(1):80\u2013108","journal-title":"Prog Phys Geogr"},{"issue":"4","key":"7084_CR66","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1109\/TNSRE.2018.2813138","volume":"26","author":"S Chambon","year":"2018","unstructured":"Chambon S, Galtier MN, Arnal PJ, Wainrib G, Gramfort A (2018) A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series. IEEE Trans Neural Syst Rehabil Eng 26(4):758\u2013769","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"1","key":"7084_CR67","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"7084_CR68","doi-asserted-by":"crossref","unstructured":"Ketkar N (2017) Introduction to keras. In Deep learning with Python. Apress, Berkeley, CA. pp 97\u2013111","DOI":"10.1007\/978-1-4842-2766-4_7"},{"key":"7084_CR69","unstructured":"Dillon JV, Langmore I, Tran D, Brevdo E, Vasudevan S, Moore D, Saurous, RA (2017) Tensorflow distributions. arXiv preprint arXiv:1711.10604"},{"key":"7084_CR70","unstructured":"Tensorflow. https:\/\/www.tensorflow.org\/. Accessed 8 Mar 2021"},{"key":"7084_CR71","unstructured":"Mirchev A, Ahmadi SA (2018) Classification of sparsely labeled spatio-temporal data through semi-supervised adversarial learning. arXiv preprint arXiv:1801.08712"},{"key":"7084_CR72","doi-asserted-by":"crossref","unstructured":"Zhang Z (2018) Improved adam optimizer for deep neural networks. In 2018 IEEE\/ACM 26th international symposium on quality of service (IWQoS). IEEE. pp 1\u20132","DOI":"10.1109\/IWQoS.2018.8624183"},{"key":"7084_CR73","unstructured":"Agarap AF (2018) Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375"},{"key":"7084_CR74","doi-asserted-by":"crossref","unstructured":"Psathas AP, Papaleonidas A, Iliadis L (2021) A Machine Learning Approach for Recognition of Elders\u2019 Activities Using Passive Sensors. In\u00a0IFIP International Conference on Artificial Intelligence Applications and Innovations. Springer, Cham. pp 157\u2013170","DOI":"10.1007\/978-3-030-79157-5_14"},{"key":"7084_CR75","doi-asserted-by":"crossref","unstructured":"Psathas AP, Papaleonidas A, Papathanassiou G, Iliadis L, Valkaniotis S (2021) Hybrid computational intelligence modeling of coseismic landslides\u2019 severity. in\u00a0international conference on computational collective intelligence. Springer, Cham. pp 427\u2013442","DOI":"10.1007\/978-3-030-88081-1_32"},{"key":"7084_CR76","doi-asserted-by":"crossref","unstructured":"Psathas AP, Papaleonidas A, Iliadis L (2020) Machine learning modeling of human activity using PPG signals. In: international conference on computational collective intelligence. Springer, Cham. pp 543\u2013557","DOI":"10.1007\/978-3-030-63007-2_42"},{"key":"7084_CR77","doi-asserted-by":"crossref","unstructured":"Psathas AP, Papaleonidas A, Papathanassiou G, Valkaniotis S, Iliadis L (2020) Classification of coseismic landslides using fuzzy and machine learning techniques. In: international conference on engineering applications of neural networks. Springer, Cham. pp 15\u201331","DOI":"10.1007\/978-3-030-48791-1_2"},{"key":"7084_CR78","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357","journal-title":"J Artif Intell Res"},{"key":"7084_CR79","unstructured":"Pyshark. https:\/\/pypi.org\/project\/pyshark\/. Accessed 30 Nov 2021"},{"key":"7084_CR80","unstructured":"Wireshark. https:\/\/www.wireshark.org\/. Accessed 30 Nov 2021"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07084-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07084-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07084-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T06:22:12Z","timestamp":1726813332000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07084-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,14]]},"references-count":80,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["7084"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07084-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,14]]},"assertion":[{"value":"2 December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 February 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 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":"The authors declare no conflict of interest. There is no financial and personal relationships with other people or organizations that can inappropriately influence their work. There is no professional or other personal interest of any nature or kind in any product, service or company that could be constructed as influencing the position presented in, or the review of, the manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}