Machine Learning for Industrial IoT Systems
Autor: | Elmustafa Sayed Ali Ahmed, Mona Bakri Hassan, Rashid A. Saeed |
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Rok vydání: | 2021 |
Předmět: |
Multimedia
business.industry Computer science 0202 electrical engineering electronic engineering information engineering 020206 networking & telecommunications 020201 artificial intelligence & image processing 02 engineering and technology Internet of Things business computer.software_genre computer |
DOI: | 10.4018/978-1-7998-6870-5.ch023 |
Popis: | 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. |
Databáze: | OpenAIRE |
Externí odkaz: |
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