Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Rehman, Zubaida | - |
dc.contributor.author | Tariq, Noshina | - |
dc.contributor.author | Moqurrab, Syed Atif | - |
dc.contributor.author | Yoo, Joon | - |
dc.contributor.author | Srivastava, Gautam | - |
dc.date.accessioned | 2024-02-08T02:30:21Z | - |
dc.date.available | 2024-02-08T02:30:21Z | - |
dc.date.issued | 2024-01 | - |
dc.identifier.issn | 0266-4720 | - |
dc.identifier.issn | 1468-0394 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/90328 | - |
dc.description.abstract | The rapid growth of the Internet of Things (IoT) has led to its widespread adoption in various industries, enabling enhanced productivity and efficient services. Integrating IoT systems with existing enterprise application systems has become common practice. However, this integration necessitates reevaluating and reworking current Enterprise Architecture (EA) models and Expert Systems (ES) to accommodate IoT and cloud technologies. Enterprises must adopt a multifaceted view and automate various aspects, including operations, data management, and technology infrastructure. Machine Learning (ML) is a powerful IoT and smart automation tool within EA. Despite its potential, a need for dedicated work focuses on ML applications for IoT services and systems. With IoT being a significant field, analyzing IoT-generated data and IoT-based networks is crucial. Many studies have explored how ML can solve specific IoT-related challenges. These mutually reinforcing technologies allow IoT applications to leverage sensor data for ML model improvement, leading to enhanced IoT operations and practices. Furthermore, ML techniques empower IoT systems with knowledge and enable suspicious activity detection in smart systems and objects. This survey paper conducts a comprehensive study on the role of ML in IoT applications, particularly in the domains of automation and security. It provides an in-depth analysis of the state-of-the-art ML approaches within the context of IoT, highlighting their contributions, challenges, and potential applications. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | WILEY | - |
dc.title | Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues | - |
dc.type | Article | - |
dc.identifier.wosid | 001085775500001 | - |
dc.identifier.doi | 10.1111/exsy.13467 | - |
dc.identifier.bibliographicCitation | EXPERT SYSTEMS, v.41, no.1 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85174232167 | - |
dc.citation.title | EXPERT SYSTEMS | - |
dc.citation.volume | 41 | - |
dc.citation.number | 1 | - |
dc.type.docType | Article | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | enterprise architectures | - |
dc.subject.keywordAuthor | expert systems | - |
dc.subject.keywordAuthor | intelligent infrastructures | - |
dc.subject.keywordAuthor | internet of things | - |
dc.subject.keywordAuthor | machine learning applications | - |
dc.subject.keywordPlus | BIG DATA | - |
dc.subject.keywordPlus | INTRUSION DETECTION | - |
dc.subject.keywordPlus | SMART HOME | - |
dc.subject.keywordPlus | ARTIFICIAL-INTELLIGENCE | - |
dc.subject.keywordPlus | ACTIVITY RECOGNITION | - |
dc.subject.keywordPlus | ENERGY MANAGEMENT | - |
dc.subject.keywordPlus | IOT | - |
dc.subject.keywordPlus | SYSTEM | - |
dc.subject.keywordPlus | ALGORITHMS | - |
dc.subject.keywordPlus | ANALYTICS | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.