In geometry, a hyperplane is a subspace whose dimension is one less than that of its ambient space. If a space is 3-dimensional then its hyperplanes are the 2-dimensional planes, while if the space is 2-dimensional, its hyperplanes are the 1-dimensional lines. A Support Vector Machine (SVM) performs classification by finding the hyperplane that maximizes the margin between the two classes.
Published in Chapter:
Machine Learning for Industrial IoT Systems
Mona Bakri Hassan (Sudan University of Science and Technology, Sudan), Elmustafa Sayed Ali Ahmed (Sudan University of Science and Technology, Sudan & Red Sea University, Sudan), and Rashid A. Saeed (Sudan University of Science and Technology, Sudan & Taif University, Saudi Arabia)
Copyright: © 2021
|Pages: 23
DOI: 10.4018/978-1-7998-6870-5.ch023
Abstract
The use of AI algorithms in the IoT enhances the ability to analyse big data and various platforms for a number of IoT applications, including industrial applications. AI provides unique solutions in support of managing each of the different types of data for the IoT in terms of identification, classification, and decision making. In industrial IoT (IIoT), sensors, and other intelligence can be added to new or existing plants in order to monitor exterior parameters like energy consumption and other industrial parameters levels. In addition, smart devices designed as factory robots, specialized decision-making systems, and other online auxiliary systems are used in the industries IoT. Industrial IoT systems need smart operations management methods. The use of machine learning achieves methods that analyse big data developed for decision-making purposes. Machine learning drives efficient and effective decision making, particularly in the field of data flow and real-time analytics associated with advanced industrial computing networks.