Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/124221
DC FieldValueLanguage
dc.contributor.authorGoyal, Dineshen_US
dc.contributor.authorKumar, Anilen_US
dc.contributor.authorDadheech, Pankajen_US
dc.contributor.authorTravieso-González, Carlos M.en_US
dc.date.accessioned2023-08-31T12:27:33Z-
dc.date.available2023-08-31T12:27:33Z-
dc.date.issued2023en_US
dc.identifier.issn0972-0529en_US
dc.identifier.otherScopus-
dc.identifier.urihttp://hdl.handle.net/10553/124221-
dc.description.abstractSecurity has been the most perpetual domain, with advancements in other domains and applications like Computer vision, IoT and Machine learning, is today’s most rapidly growing technical domains to facilitate better human life across the globe and is highly dominated with statistics and data analytics using data science, artificial intelligence, and Machine Learning. All these application areas have developed new security requirements. Recent progress in these fields has been driven both by the development of new learning algorithms and theory by the ongoing explosion in the availability of online data and low-cost computation. With more advancements in technology compelled with need of scaling has provided a challenge to security of system and its architecture. Thus there is a diehard need for secure models or architectures in domains of computer vision, IoT models and Data analytics using Machine Learning.en_US
dc.languageengen_US
dc.relation.ispartofJournal of Discrete Mathematical Sciences and Cryptographyen_US
dc.sourceJournal of Discrete Mathematical Sciences and Cryptography[ISSN 0972-0529],v. 26 (3), p. i-vii, (Enero 2023)en_US
dc.subject3307 Tecnología electrónicaen_US
dc.titleCurrent Research Trends in Secure Computer Vision, IoT and Machine Learningen_US
dc.typeinfo:eu-repo/semantics/annotationen_US
dc.typeArticleen_US
dc.identifier.doi10.47974/JDMSC-26-3-Foreworden_US
dc.identifier.scopus85166329880-
dc.contributor.orcidNO DATA-
dc.contributor.orcidNO DATA-
dc.contributor.orcidNO DATA-
dc.contributor.orcidNO DATA-
dc.contributor.authorscopusid57211719867-
dc.contributor.authorscopusid57214420522-
dc.contributor.authorscopusid57189097825-
dc.contributor.authorscopusid57219115631-
dc.description.lastpageviien_US
dc.identifier.issue3-
dc.description.firstpageien_US
dc.relation.volume26en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Comentarioen_US
dc.utils.revisionen_US
dc.date.coverdateEnero 2023en_US
dc.identifier.ulpgcen_US
dc.identifier.ulpgcen_US
dc.identifier.ulpgcen_US
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-TELen_US
dc.description.sjr0,422
dc.description.sjrqQ3
dc.description.esciESCI
dc.description.miaricds9,9
item.grantfulltextnone-
item.fulltextSin texto completo-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.orcid0000-0002-4621-2768-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.fullNameTravieso González, Carlos Manuel-
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