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Evaluation of and improvement planning for smart homes using rough knowledge-based rules on a hybrid multiple attribute decision-making model

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Abstract

This paper proposes fuzzy integral-based decision methods to identify the core factors and their relationships for smart homes product improvement. The dominance-based rough set approach was used to retrieve core attributes and obtain rough knowledge-based rules. The decision-making trial and evaluation laboratory (DEMATEL) technique was used to build an influential network relationship map, and influential weights were determined through the DEMATEL-based analytic network process. Subsequently, the inter-relationships among criteria were calculated. Finally, the fuzzy integral method was used to measure the plausible synergy effects among the criteria, evaluate/rank alternatives for smart homes, and then provide suggestions for product improvement. The main innovation is the use of rough knowledge-based rule retrieval procedures and fuzzy measures for exploring the synergy effects on smart home improvement. Three smart home products/systems were examined to illustrate their performance on each criterion for improvement planning. This study contributes knowledge to research on consumer adoption of smart homes and presents improvement strategies.

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Acknowledgements

Special thanks are extended to all the tutors for checking this paper and giving direct or indirect help. This paper was sponsored by The National Natural Science Foundation of China, Grant 71402040. This study was also supported by Chinese Postdoctoral Science Foundation, Grant 2015M571310.

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Liu, Y., Li, M., Chen, Y. et al. Evaluation of and improvement planning for smart homes using rough knowledge-based rules on a hybrid multiple attribute decision-making model. Soft Comput 24, 7781–7800 (2020). https://doi.org/10.1007/s00500-019-04396-3

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