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Hybrid learning approach toward situation recognition and handling

Rajaby Faghihi, H ; Sharif University of Technology | 2022

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  1. Type of Document: Article
  2. DOI: 10.1093/comjnl/bxaa179
  3. Publisher: Oxford University Press , 2022
  4. Abstract:
  5. We propose a novel hybrid learning approach to gain situation awareness in smart environments by introducing a new situation identifier that combines an expert system and a machine learning approach. Traditionally, expert systems and machine learning approaches have been widely used independently to detect ongoing situations as the main functionality in smart environments in various domains. Expert systems lack the functionality to adapt the system to each user and are expensive to design based on each setting. On the other hand, machine learning approaches fail in the challenge of cold start and making explainable decisions. Using both of these approaches enables the system to use user's feedback and capture environmental changes while exploiting the initial expert knowledge to solve the mentioned challenges. We use decision trees and situation templates as the core structure to interpret sensor data. To evaluate the proposed method, we generate a new human-annotated dataset simulating a smart environment. Our experiments show superior results compared with the initial expert system and the machine learning approach while preserving the initial expert system's interpretability. © 2021 The Author(s)
  6. Keywords:
  7. Hybridlearning;situation awareness ; Internet of things ; Situation recognition ; Decision trees ; Internet of things ; Machine learning ; Cold-start ; Environmental change ; Hybrid learning approach ; Hybridlearning ; Machine learning approaches ; Situation awareness ; Situation recognition ; Smart environment ; System learning ; User feedback ; Expert systems
  8. Source: Computer Journal ; Volume 65, Issue 5 , 2022 , Pages 1293-1305 ; 00104620 (ISSN)
  9. URL: https://academic.oup.com/comjnl/article/65/5/1293/6103953