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Title	Cognitive Computing System-Based Dynamic Decision Control For Smart City Using Reinforcement Learning Model
ID_Doc	14646
Authors	Sasikumar A.; Ravi L.; Devarajan M.; Kotb H.; Subramaniyaswamy V.
Year	2024
Published	Cognitive Analytics and Reinforcement Learning: Theories, Techniques and Applications
DOI	http://dx.doi.org/10.1002/9781394214068.ch2
Abstract	Artificial intelligence (AI) is currently implemented to support many human-centered solutions, including medical, automated mobility, and other areas. As an emerging application developed with the help of AI, cognitive computing offers individualized connections and features that mimic human behavior for interpersonal interactions. On the other hand, much data are produced by applications of smart cities like healthcare, intelligent transportation, the retail sector, and disaster response. Properly managing the massive amount of created data is a constant concern. The analysis of vast amounts of data using cognitive computing has recently been the topic of many existing studies. These studies still need to address key issues, especially the portability and adaptability of data collected in a smart city context. Data gathered through thousands of sensors can be integrated into various cognitive computing systems to ensure real-time response. In this chapter, we presented the cognitive computing layer and suggested architecture for smart city applications. The proposed architecture was developed to analyze data obtained from smart city applications using a reinforcement learning model. The experimental results showed that the proposed reinforcement learning-based cognitive computing has real-time decision controls in smart industry ecosystems. © 2024 Scrivener Publishing LLC.
Author Keywords	Artificial intelligence; cognitive computing; internet of things; reinforcement learning; smart city

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