Fog Computing-Integrated ML-Based Framework and Solutions for Intelligent Systems-Reference-Cited by-同舟云学术

Fog Computing-Integrated ML-Based Framework and Solutions for Intelligent Systems

Published:2024-03-18 Issue: Volume: Page:196-224
ISSN:2327-3453
Container-title:Advances in Systems Analysis, Software Engineering, and High Performance Computing
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Author:

Pitchai R.¹^ORCID,Guru K. Venkatesh²,Gandhi J. Nirmala²,Komala C. R.³,Kumar J. R. Dinesh⁴,Boopathi Sampath⁵^ORCID

Affiliation:

1. Department of Computer Science and Engineering, B.V. Raju Institute of Technology, India

2. Department of Computer Science and Engineering, KSR College of Engineering, India

3. Department of Information Science and Engineering, HKBK College of Engineering, India

4. Department of Electronics and Communication Engineering, Sri Krishna College of Engineering and Technology, India

5. Mechanical Engineering, Muthayammal Engineering College, India

Abstract

The integration of fog computing and machine learning (ML) in digital healthcare has revolutionized patient care, operations, and personalized treatment. This chapter explores the potential of fog computing in telemedicine, remote monitoring, and personalized treatment. It highlights its role in addressing data processing challenges, enabling real-time data analytics, and ensuring secure transmission of medical information. Key case studies demonstrate how these integrated solutions are driving innovation in the healthcare industry. The combination of fog computing and ML offers a promising avenue for the future of digital healthcare, focusing on data-driven decision-making and precision medicine.

Publisher

IGI Global

Reference56 articles.

1. Synergizing Artificial Intelligence, 5G, and Cloud Computing for Efficient Energy Conversion Using Agricultural Waste

2. Synergizing Artificial Intelligence, 5G, and Cloud Computing for Efficient Energy Conversion Using Agricultural Waste

3. Anitha, C., Komala, C., Vivekanand, C. V., Lalitha, S., & Boopathi, S. (2023). Artificial Intelligence driven security model for Internet of Medical Things (IoMT). IEEE Explore, 1–7.

4. Bakhshi, Z., & Balador, A. (2019). An overview on security and privacy challenges and their solutions in fog-based vehicular application. 2019 IEEE 30th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC Workshops), 1–7.

5. An experimental investigation of Quench Polish Quench (QPQ) coating on AISI 4150 steel

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