Artificial intelligence and big data analytics for the advancement of industry 4.0, 5.0, and society 5.0
Abstract
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The tidal wave of data created by connected devices and cyber-physical systems in an Industry 4.0 environment is utilized via advanced analytics to drive manufacturing efficiency, improve predictive maintenance, and empower real-time actions. The potential of big data analytics with artificial intelligence (AI) to transform the industrial landscape lies in its ability to enable the processing of enormous troves of data, revealing hidden patterns and trends that open up a range of avenues to enable industrial operations that are smarter and more responsive. Big data is equally important as moving forward to Industry 5.0. This phase focuses more on human-centric ways and human-machine collaboration matters here. This shift is supported by big data analytics which produces clever intelligence in systems that are complementary to human capabilities. With the analysis of vast pools of data, these systems can augment the way humans decide, create, and solve problems. In Society 5.0, big data analytics is an essential for super-smart society where digital transformation pervades virtually every life. Personalized services in healthcare, education, transportation, urban planning, public safety, sustainable development. It is essential to produce accessible machine learning (ML) tools that can meet the needs of both people and communities, unlocking broader economic benefits as well as enhancing overall quality of life. In a future where the volumes of data collected through the Internet of Things (IoT), introduction of file types that allows more data to be stored for adopting the big data analytics stack to the enterprises applications to provide resiliency and agility in a data driven world.
Record information
- Authors
- Nitin Liladhar Rane; Ömer Kaya; Jayesh Rane
- Publisher
- Deep Science Publishing
- Publication
- Artificial Intelligence, Machine Learning, and Deep Learning for Sustainable Industry 5.0
- DOI
- 10.70593/978-81-981271-8-1_8
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