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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2204.02521 |
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| _version_ | 1866909086557143040 |
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| author | Fang, Ji Lee, Vincent CS Wang, Haiyan |
| author_facet | Fang, Ji Lee, Vincent CS Wang, Haiyan |
| contents | This paper explores optimal service resource management strategy, a continuous challenge for health information service to enhance service performance, optimise service resource utilisation and deliver interactive health information service. An adaptive optimal service resource management strategy was developed considering a value co-creation model in health information service with a focus on collaborative and interactive with users. The deep reinforcement learning algorithm was embedded in the Internet of Things (IoT)-based health information service system (I-HISS) to allocate service resources by controlling service provision and service adaptation based on user engagement behaviour. The simulation experiments were conducted to evaluate the significance of the proposed algorithm under different user reactions to the health information service. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_02521 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Optimal service resource management strategy for IoT-based health information system considering value co-creation of users Fang, Ji Lee, Vincent CS Wang, Haiyan Machine Learning Optimization and Control This paper explores optimal service resource management strategy, a continuous challenge for health information service to enhance service performance, optimise service resource utilisation and deliver interactive health information service. An adaptive optimal service resource management strategy was developed considering a value co-creation model in health information service with a focus on collaborative and interactive with users. The deep reinforcement learning algorithm was embedded in the Internet of Things (IoT)-based health information service system (I-HISS) to allocate service resources by controlling service provision and service adaptation based on user engagement behaviour. The simulation experiments were conducted to evaluate the significance of the proposed algorithm under different user reactions to the health information service. |
| title | Optimal service resource management strategy for IoT-based health information system considering value co-creation of users |
| topic | Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2204.02521 |