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Object Detection in Fog Computing Using Machine Learning Algorithms

Object Detection in Fog Computing Using Machine Learning Algorithms
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Author(s): Peyakunta Bhargavi (Sri Padmavati Mahila Visvavidyalayam, India) and Singaraju Jyothi (Sri Padmavati Mahila Visvavidyalayam, India)
Copyright: 2020
Pages: 18
Source title: Architecture and Security Issues in Fog Computing Applications
Source Author(s)/Editor(s): Sam Goundar (The University of the South Pacific, Fiji), S. Bharath Bhushan (Sree Vidyanikethan Engineering College, India) and Praveen Kumar Rayani (National Institute of Technology, Durgapur, India)
DOI: 10.4018/978-1-7998-0194-8.ch006

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Abstract

The moment we live in today demands the convergence of the cloud computing, fog computing, machine learning, and IoT to explore new technological solutions. Fog computing is an emerging architecture intended for alleviating the network burdens at the cloud and the core network by moving resource-intensive functionalities such as computation, communication, storage, and analytics closer to the end users. Machine learning is a subfield of computer science and is a type of artificial intelligence (AI) that provides machines with the ability to learn without explicit programming. IoT has the ability to make decisions and take actions autonomously based on algorithmic sensing to acquire sensor data. These embedded capabilities will range across the entire spectrum of algorithmic approaches that is associated with machine learning. Here the authors explore how machine learning methods have been used to deploy the object detection, text detection in an image, and incorporated for better fulfillment of requirements in fog computing.

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