{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T14:12:40Z","timestamp":1774534360801,"version":"3.50.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,5,6]],"date-time":"2021-05-06T00:00:00Z","timestamp":1620259200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,5,6]],"date-time":"2021-05-06T00:00:00Z","timestamp":1620259200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2022,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Nowadays, fog computing as a complementary facility of cloud computing has attracted great attentions in research communities because it has extraordinary potential to provide resources and processing services requested for applications at the edge network near to users. Recent researchers focus on how efficiently engage edge networks capabilities for execution and supporting of IoT applications and associated requirement. However, inefficient deployment of applications\u2019 components on fog computing infrastructure results bandwidth and resource wastage, maximum power consumption, and unpleasant quality of service (QoS) level. This paper considers reduction of bandwidth wastage in regards to application components dependency in their distributed deployment. On the other hand, the service reliability is declined if an application\u2019s components are deployed on a single node for the sake of power consumption management viewpoint. Therefore, a mechanism for tackling single point of failure and application reliability enhancement against failure are presented. Then, the components deployment is formulated to a multi-objective optimization problem with minimization perspective of both power consumption and total latency between each pair of components associated to applications. To solve this combinatorial optimization problem, a multi-objective cuckoo search algorithm (MOCSA) is presented. To validate the work, this algorithm is assessed in different conditions against some state-of the arts. The simulation results prove the amount 42%, 29%, 46%, 13%, and 5% improvement of proposed MOCSA in terms of average overall latency respectively against MOGWO, MOGWO-I, MOPSO, MOBA, and NSGA-II algorithms. Also, in term of average total power consumption the improvement is about 43%, 28%, 41%, 30%, and 32% respectively.<\/jats:p>","DOI":"10.1007\/s40747-021-00368-z","type":"journal-article","created":{"date-parts":[[2021,5,6]],"date-time":"2021-05-06T12:02:53Z","timestamp":1620302573000},"page":"361-392","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":71,"title":["Multi-objective fault-tolerant optimization algorithm for deployment of IoT applications on fog computing infrastructure"],"prefix":"10.1007","volume":"8","author":[{"given":"Yaser","family":"Ramzanpoor","sequence":"first","affiliation":[]},{"given":"Mirsaeid","family":"Hosseini Shirvani","sequence":"additional","affiliation":[]},{"given":"Mehdi","family":"Golsorkhtabaramiri","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,6]]},"reference":[{"key":"368_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compeleceng.2020.106770","volume":"87","author":"F Foukalas","year":"2020","unstructured":"Foukalas F (2020) Cognitive IoT platform for fog computing industrial applications. Comput Electr Eng 87:1\u201313. https:\/\/doi.org\/10.1016\/j.compeleceng.2020.106770","journal-title":"Comput Electr Eng"},{"key":"368_CR2","unstructured":"OpenFog. An OpenFog Architecture Overview (2017) https:\/\/www.iiconsortium.org\/pdf\/OpenFog_Reference_Architecture_2_09_17.pdf. Accessed Feb 2017"},{"key":"368_CR3","doi-asserted-by":"publisher","first-page":"328","DOI":"10.5220\/0009391203280337","volume":"2020","author":"SH Azimi","year":"2020","unstructured":"Azimi SH, Pahl C, Hosseini-Shirvani M (2020) Particle swarm optimization for performance management in multi-cluster IoT edge architectures. Int Cloud Comput Conf CLOSER. 2020:328\u2013337. https:\/\/doi.org\/10.5220\/0009391203280337","journal-title":"Int Cloud Comput Conf CLOSER."},{"key":"368_CR4","doi-asserted-by":"publisher","unstructured":"Hong HJ, Tsai PH, Hsu CH (2016) Dynamic module deployment in a fog computing platform. In: 18th Asia-Pacific network operations and management symposium (APNOMS), pp 1\u20136. https:\/\/doi.org\/10.1109\/APNOMS.2016.7737202","DOI":"10.1109\/APNOMS.2016.7737202"},{"key":"368_CR5","doi-asserted-by":"publisher","unstructured":"Taneja M, Davy A (2017) Resource-aware placement of IoT application modules in fog-cloud computing paradigm. In: Proc. of the IFIP\/IEEE symposium on integrated network and service management, IM \u201915, IEEE, pp 1222\u20131228. https:\/\/doi.org\/10.23919\/INM.2017.7987464","DOI":"10.23919\/INM.2017.7987464"},{"key":"368_CR6","doi-asserted-by":"publisher","first-page":"1185","DOI":"10.1109\/JIOT.2017.2701408","volume":"4","author":"A Brogi","year":"2017","unstructured":"Brogi A, Forti A (2017) QoS-aware deployment of IoT applications through the fog. IEEE Internet Things J 4:1185\u20131192. https:\/\/doi.org\/10.1109\/JIOT.2017.2701408","journal-title":"IEEE Internet Things J"},{"key":"368_CR7","doi-asserted-by":"publisher","unstructured":"Li F, Vogler M, Clae\u00dfens M, Dustdar S (2013) Towards automated IoT application deployment by a cloud-based approach. In: 6th international conference on service-oriented computing and applications, IEEE, pp 61\u201368. https:\/\/doi.org\/10.1109\/SOCA.2013.12","DOI":"10.1109\/SOCA.2013.12"},{"key":"368_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3186592","volume":"2018","author":"R Mahmud","year":"2018","unstructured":"Mahmud R, Ramamohanarao K, Buyya R (2018) Latency-aware application module management for fog computing environments. ACM Trans Internet Technol 2018:1\u201321. https:\/\/doi.org\/10.1145\/3186592","journal-title":"ACM Trans Internet Technol"},{"key":"368_CR9","doi-asserted-by":"publisher","unstructured":"V\u00f6gler M, Schleicher JM, Inzinger C, Dustdar S (2015) DIANE\u2014Dynamic IoT Application Deployment. In: IEEE international conference on mobile services, pp 298\u2013305. https:\/\/doi.org\/10.1109\/MobServ.2015.49","DOI":"10.1109\/MobServ.2015.49"},{"key":"368_CR10","doi-asserted-by":"publisher","unstructured":"Saurez E, Hong K, Lillethun D, Ramachandran U, Ottenwalder B (2016) Incremental deployment and migration of geo-distributed situation awareness applications in the fog. In: DEBS, pp 258\u2013269. https:\/\/doi.org\/10.1145\/2933267.2933317","DOI":"10.1145\/2933267.2933317"},{"key":"368_CR11","doi-asserted-by":"publisher","unstructured":"Chen BL, Huang SC, Luo YC, Chung YC, Chou J (2017) A dynamic module deployment framework for M2M platforms. In: IEEE 7th international symposium on cloud and service computing (SC2). IEEE, pp 194\u2013200. https:\/\/doi.org\/10.1109\/SC2.2017.37","DOI":"10.1109\/SC2.2017.37"},{"key":"368_CR12","doi-asserted-by":"publisher","unstructured":"Yangui S, Ravindran P, Bibani O, Glitho R. H, Hadj-Alouane NB, Morrow MJ, Polakos PA (2016) A platform as-a-service for hybrid cloud\/fog environments. In: 2016 IEEE international symposium on local and metropolitan area networks (LANMAN), pp 1\u20137. https:\/\/doi.org\/10.1109\/LANMAN.2016.7548853","DOI":"10.1109\/LANMAN.2016.7548853"},{"key":"368_CR13","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1016\/j.measurement.2018.11.001","volume":"134","author":"R Babu","year":"2019","unstructured":"Babu R, Bhattacharyya B (2019) Strategic placements of PMUs for power network observability considering redundancy measurement. Measurement 134:606\u2013623. https:\/\/doi.org\/10.1016\/j.measurement.2018.11.001","journal-title":"Measurement"},{"issue":"2","key":"368_CR14","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/s40998-018-0063-7","volume":"42","author":"R Babu","year":"2018","unstructured":"Babu R, Bhattacharyya B (2018) An approach for optimal placement of phasor measurement unit for power network observability considering various contingencies. Iran J Sci Technol Trans Electr Eng 42(2):161\u2013183. https:\/\/doi.org\/10.1007\/s40998-018-0063-7","journal-title":"Iran J Sci Technol Trans Electr Eng"},{"key":"368_CR15","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.ijepes.2016.01.011","volume":"79","author":"R Babu","year":"2016","unstructured":"Babu R, Bhattacharyya B (2016) Optimal allocation of phasor measurement unit for full observability of the connected power network. Int J Electr Power Energy Syst 79:89\u201397. https:\/\/doi.org\/10.1016\/j.ijepes.2016.01.011","journal-title":"Int J Electr Power Energy Syst"},{"key":"368_CR16","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1515\/ijeeps-2017-0073","volume":"18","author":"R Babu","year":"2017","unstructured":"Babu R, Bhattacharyya B (2017) Weak bus-oriented installation of phasor measurement unit for power network observability. Int J Emerg Electr Power Syst 18:5. https:\/\/doi.org\/10.1515\/ijeeps-2017-0073","journal-title":"Int J Emerg Electr Power Syst"},{"issue":"2","key":"368_CR17","doi-asserted-by":"publisher","first-page":"135","DOI":"10.11591\/ijape.v9.i2.pp135-146","volume":"9","author":"R Babu","year":"2020","unstructured":"Babu R, Bhattacharyya B (2020) Optimal placement of PMU for complete observability of the interconnected power network considering zero-injection bus. Int J Appl Power Eng 9(2):135\u2013146. https:\/\/doi.org\/10.11591\/ijape.v9.i2.pp135-146","journal-title":"Int J Appl Power Eng"},{"key":"368_CR18","doi-asserted-by":"publisher","unstructured":"Hosseini Shirvani M (2018) Web service composition in multi-cloud environment: a bi-objective genetic optimization algorithm. In: 2018 IEEE (SMC) international conference on innovations in intelligent systems and applications. https:\/\/doi.org\/10.1109\/INISTA.2018.8466267","DOI":"10.1109\/INISTA.2018.8466267"},{"issue":"4","key":"368_CR19","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1504\/IJCC.2020.112313","volume":"9","author":"M Hosseini Shirvani","year":"2020","unstructured":"Hosseini Shirvani M, Gorji AB (2020) Optimization of automatic web services composition using genetic algorithm. Int J Cloud Comput 9(4):397\u2013411. https:\/\/doi.org\/10.1504\/IJCC.2020.112313","journal-title":"Int J Cloud Comput"},{"key":"368_CR20","first-page":"19","volume":"2018","author":"M Hosseini-Shirvani","year":"2018","unstructured":"Hosseini-Shirvani M (2018) A new shuffled genetic-based task scheduling algorithm in heterogeneous distributed systems. J Adv Comput Res 2018:19\u201336","journal-title":"J Adv Comput Res"},{"issue":"6","key":"368_CR21","first-page":"1431","volume":"2","author":"S Hosseinzadeh","year":"2015","unstructured":"Hosseinzadeh S, Hosseini SM (2015) Optimizing energy consumption in clouds by using genetic algorithm. J Multidiscipl Eng Sci Technol 2(6):1431\u20131434","journal-title":"J Multidiscipl Eng Sci Technol"},{"issue":"3","key":"368_CR22","first-page":"1","volume":"7","author":"F Razavi","year":"2016","unstructured":"Razavi F, Zabihi F, Hosseini SM (2016) Multi-layer perceptron neural network training based on improved of stud GA. J Adv Comput Res 7(3):1\u201314","journal-title":"J Adv Comput Res"},{"issue":"2","key":"368_CR23","doi-asserted-by":"publisher","first-page":"180","DOI":"10.22094\/joie.2020.1877455.1685","volume":"14","author":"A Javadian Kootanaee","year":"2021","unstructured":"Javadian Kootanaee A, Poor Aghajan A, Hosseini SM (2021) A hybrid model based on machine learning and genetic algorithm for detecting fraud in financial statements. J Optim Ind Eng 14(2):180\u2013201. https:\/\/doi.org\/10.22094\/joie.2020.1877455.1685","journal-title":"J Optim Ind Eng"},{"key":"368_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/0952813X.2020.1725652","volume":"2020","author":"M Hosseini-Shirvani","year":"2020","unstructured":"Hosseini-Shirvani M (2020) Bi-objective web service composition problem in multi-cloud environment: a bi-objective time-varying particle swarm optimisation algorithm. J Exp Theor Artif Intell 2020:1\u201324. https:\/\/doi.org\/10.1080\/0952813X.2020.1725652","journal-title":"J Exp Theor Artif Intell"},{"key":"368_CR25","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.engappai.2020.103501","volume":"2019","author":"M Hosseini-Shirvani","year":"2019","unstructured":"Hosseini-Shirvani M (2019) A hybrid meta-heuristic algorithm for scientific workflow scheduling in heterogeneous distributed computing systems. Eng Appl Artif Intell 2019:90. https:\/\/doi.org\/10.1016\/j.engappai.2020.103501","journal-title":"Eng Appl Artif Intell"},{"key":"368_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-020-05523-1","author":"P Saeedi","year":"2021","unstructured":"Saeedi P, Hosseini SM (2021) An improved thermodynamic simulated annealing-based approach for resource-skewness-aware and power-efficient virtual machine consolidation in cloud datacenters. Soft Comput. https:\/\/doi.org\/10.1007\/s00500-020-05523-1","journal-title":"Soft Comput"},{"key":"368_CR27","doi-asserted-by":"crossref","unstructured":"Noorian Talooki R, Hosseini Shirvani M, Motameni H (2021) A Hybrid Meta-heuristic scheduler algorithm for optimization of workflow scheduling in cloud heterogeneous computing environment. J Eng Design Technol Emerald Publ (In Press)","DOI":"10.1108\/JEDT-11-2020-0474"},{"key":"368_CR28","doi-asserted-by":"crossref","unstructured":"Tanha M, Hosseini Shirvani M, Rahmani AM (2020) GATSA: a hybrid meta-heuristic task scheduling algorithm based on genetic and thermodynamic simulated annealing algorithms in cloud computing environment. Neural Comput Appl Springer Publ (In Press)","DOI":"10.1007\/s00521-021-06289-9"},{"issue":"2","key":"368_CR29","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multi objective genetic algorithm: Nsga-II. IEEE Trans Evol Comput 6(2):182\u2013197. https:\/\/doi.org\/10.1109\/4235.996017","journal-title":"IEEE Trans Evol Comput"},{"key":"368_CR30","doi-asserted-by":"publisher","unstructured":"Coello CAC, Lechuga MS (2002) MOPSO: a proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 congress on evolutionary computation (CEC'02). USA: IEEE Publications. https:\/\/doi.org\/10.1109\/CEC.2002.1004388","DOI":"10.1109\/CEC.2002.1004388"},{"issue":"3","key":"368_CR31","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1002\/spe.2528","volume":"48","author":"M Hosseini-Shirvani","year":"2018","unstructured":"Hosseini-Shirvani M, Rahmani AM, Sahafi A (2018) An iterative mathematical decision model for cloud migration: a cost and security risk approach. Softw Pract Exp Homepage 48(3):449\u2013485. https:\/\/doi.org\/10.1002\/spe.2528","journal-title":"Softw Pract Exp Homepage"},{"key":"368_CR32","doi-asserted-by":"crossref","unstructured":"Yang XS (2011) Bat algorithm for multiobjective optimization. Int J Bio-Inspired Comput 3(5):267\u2013274. https:\/\/arxiv.org\/abs\/1203.6571v1","DOI":"10.1504\/IJBIC.2011.042259"},{"key":"368_CR33","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.eswa.2015.10.039","volume":"47","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S, Saremi S, Mirjalili SM, Coelho LDS (2016) Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. J Expert Syst Appl Elsevier 47:106\u2013119. https:\/\/doi.org\/10.1016\/j.eswa.2015.10.039","journal-title":"J Expert Syst Appl Elsevier"},{"issue":"2","key":"368_CR34","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1109\/TEVC.2018.2844286","volume":"23","author":"Z Wang","year":"2019","unstructured":"Wang Z, Ong Y, Ishibuchi H (2019) On scalable multiobjective test problems with hardly dominated boundaries. IEEE Trans Evol Comput 23(2):217\u2013231. https:\/\/doi.org\/10.1109\/TEVC.2018.2844286","journal-title":"IEEE Trans Evol Comput"},{"issue":"4","key":"368_CR35","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1109\/TEVC.2018.2872453","volume":"23","author":"Z Wang","year":"2019","unstructured":"Wang Z, Ong Y, Sun J, Gupta A, Zhang Q (2019) A generator for multiobjective test problems with difficult-to-approximate pareto front boundaries. IEEE Trans Evol Comput 23(4):556\u2013571. https:\/\/doi.org\/10.1109\/TEVC.2018.2872453","journal-title":"IEEE Trans Evol Comput"},{"issue":"2","key":"368_CR36","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1109\/TCYB.2015.2403849","volume":"46","author":"Z Wang","year":"2016","unstructured":"Wang Z, Zhang Q, Zhou A, Gong M, Jiao L (2016) Adaptive replacement strategies for MOEA\/D. IEEE Trans Cybern 46(2):474\u2013486. https:\/\/doi.org\/10.1109\/TCYB.2015.2403849","journal-title":"IEEE Trans Cybern"},{"key":"368_CR37","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.swevo.2017.01.002","volume":"34","author":"Z Wang","year":"2017","unstructured":"Wang Z, Zhang Q, Li H, Shibuchi H, Jiao L (2017) On the use of two reference points in decomposition based multiobjective evolutionary algorithms. Swarm Evol Comput 34:89\u2013102. https:\/\/doi.org\/10.1016\/j.swevo.2017.01.002","journal-title":"Swarm Evol Comput"},{"key":"368_CR38","first-page":"294","volume":"2019","author":"LB Ali","year":"2019","unstructured":"Ali LB, Helaoui M, Naanaa W (2019) Pareto-based soft arc consistency for multi-objective valued CSPs. ICAART. 2019:294\u2013305","journal-title":"ICAART."},{"issue":"4","key":"368_CR39","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1016\/j.comnet.2004.12.001","volume":"47","author":"IF Akyildiz","year":"2005","unstructured":"Akyildiz IF, Wang X, Wang W (2005) Wireless mesh networks: asurvey. Comput Netw 47(4):445\u2013487. https:\/\/doi.org\/10.1016\/j.comnet.2004.12.001","journal-title":"Comput Netw"},{"key":"368_CR40","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.jss.2015.01.040","volume":"3","author":"JP Arcangeli","year":"2015","unstructured":"Arcangeli JP, Boujbel R, Leriche S (2015) Automatic deployment of distributed software systems: definitions and state of the art. J Syst Softw 3:198\u2013218. https:\/\/doi.org\/10.1016\/j.jss.2015.01.040","journal-title":"J Syst Softw"},{"key":"368_CR41","doi-asserted-by":"publisher","unstructured":"Bonomi F, Milito R, Natarajan P, Zhu J (2014) Fog computing: a platform for internet of things and analytics. In: Big data and internet of things: a roadmap for smart environments, Springer, pp 169\u2013186. https:\/\/doi.org\/10.1007\/978-3-319-05029-4_7","DOI":"10.1007\/978-3-319-05029-4_7"},{"key":"368_CR42","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.suscom.2020.100374","volume":"2020","author":"S Farzai","year":"2020","unstructured":"Farzai S, Hosseini-Shirvani M, Rabbani M (2020) Multi-objective communication-aware optimization for virtual machine placement in cloud datacenters. Sustain Comput Inf Syst 2020:28. https:\/\/doi.org\/10.1016\/j.suscom.2020.100374","journal-title":"Sustain Comput Inf Syst"},{"key":"368_CR43","doi-asserted-by":"publisher","unstructured":"Yang XS, Deb S (2009) Cuckoo search via Levy flights. In: Proceedings of world congress on nature & biologically inspired computing, pp 210\u2013214. https:\/\/doi.org\/10.1109\/NABIC.2009.5393690","DOI":"10.1109\/NABIC.2009.5393690"},{"key":"368_CR44","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1007\/s10489-015-0710-x","volume":"44","author":"SM Sait","year":"2016","unstructured":"Sait SM, Bala A, El-Maleh AH (2016) Cuckoo search based resource optimization of datacenters. Appl Intell 44:489\u2013506. https:\/\/doi.org\/10.1007\/s10489-015-0710-x","journal-title":"Appl Intell"},{"key":"368_CR45","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1016\/j.cie.2017.12.001","volume":"115","author":"M Tavana","year":"2017","unstructured":"Tavana M, Shahdi-Pashaki S, Teymourian E, Santos-Arteaga FJ, Komaki M (2017) A discrete cuckoo optimization algorithm for consolidation in cloud computing. Comput Ind Eng 115:495\u2013511. https:\/\/doi.org\/10.1016\/j.cie.2017.12.001","journal-title":"Comput Ind Eng"},{"key":"368_CR46","unstructured":"Hosseini Shirvani M, Farzai S (2020) Optimal deployment of IoT application components on hybrid fog2cloud infrastructure for reduction of power consumption toward green computing by cuckoo search algorithm. In: The first national conference of New Development in Green Studies, Computations, Applications, and Challenges, NGIS01"},{"issue":"9","key":"368_CR47","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1016\/j.chaos.2011.06.004","volume":"44","author":"S Walton","year":"2011","unstructured":"Walton S, Hassan O, Morgan K, Brown MR (2011) Modified cuckoo search: a new gradient free optimisation algorithm. Chaos Solitons Fractals 44(9):710\u2013718. https:\/\/doi.org\/10.1016\/j.chaos.2011.06.004","journal-title":"Chaos Solitons Fractals"},{"key":"368_CR48","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/978-3-642-12538-6_6","volume":"284","author":"XS Yang","year":"2010","unstructured":"Yang XS (2010) A new metaheuristic bat-inspired algorithm, in nature inspired cooperative strategies for optimization. Stud Comput Intell 284:65\u201374. https:\/\/doi.org\/10.1007\/978-3-642-12538-6_6","journal-title":"Stud Comput Intell"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-021-00368-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-021-00368-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-021-00368-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,3]],"date-time":"2022-03-03T12:35:18Z","timestamp":1646310918000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-021-00368-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,6]]},"references-count":48,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,2]]}},"alternative-id":["368"],"URL":"https:\/\/doi.org\/10.1007\/s40747-021-00368-z","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,6]]},"assertion":[{"value":"24 November 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 April 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There is not any conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflic of interest"}}]}}