Decentralized recommender system for ambient intelligence of tourism destinations serious game using known and unknown rating approach

Tourism destinations serious game (TDSG) requires the ability to respond to players through recommendations for selecting appropriate tourist destinations for them as potential tourists. This research utilizes ambient intelligence technology to regulate the response visualized through a choice of serious game scenarios. This research uses the Multi-Criteria Recommender System (MCRS) to produce recommendations for selecting tourist destinations as a reference for selecting scenario visualizations. Recommender systems require a decentralized, distributed, and secure data-sharing concept to distribute data and assignments between nodes. We propose using the Ethereum blockchain platform to handle data circulation between parts of the system and implement decentralized technology. We also use the known and unknown rating (KUR) approach to improve the system’s ability to generate recommendations for players who can provide rating values or those who cannot. This study uses the tourism theme of Batu City, Indonesia, so we use personal characteristics (PC) and rating of destinations attribute (RDA) data for tourists in that city. The test results show that the blockchain can handle decentralized data- sharing well to ensure PC and RDA data circulation between nodes. MCRS has produced rec- ommendations for players based on the KUR approach, indicating that the known rating has better accuracy than the unknown rating. Furthermore, the player can choose and run the tour visualization through game scenarios that appear based on the recommendation ranking results

Decentralized recommender system for ambient intelligence of tourism destinations serious game using known and unknown rating approach

Tourism destinations serious game (TDSG) requires the ability to respond to players through recommendations for selecting appropriate tourist destinations for them as potential tourists. This research utilizes ambient intelligence technology to regulate the response visualized through a choice of serious game scenarios. This research uses the Multi-Criteria Recommender System (MCRS) to produce recommendations for selecting tourist destinations as a reference for selecting scenario visualizations. Recommender systems require a decentralized, distributed, and secure data-sharing concept to distribute data and assignments between nodes. We propose using the Ethereum blockchain platform to handle data circulation between parts of the system and implement decentralized technology. We also use the known and unknown rating (KUR) approach to improve the system’s ability to generate recommendations for players who can provide rating values or those who cannot. This study uses the tourism theme of Batu City, Indonesia, so we use personal characteristics (PC) and rating of destinations attribute (RDA) data for tourists in that city. The test results show that the blockchain can handle decentralized data- sharing well to ensure PC and RDA data circulation between nodes. MCRS has produced rec- ommendations for players based on the KUR approach, indicating that the known rating has better accuracy than the unknown rating. Furthermore, the player can choose and run the tour visualization through game scenarios that appear based on the recommendation ranking results

Validation of design artefacts for blockchain-enabled precision healthcare as a service.

Healthcare systems around the globe are currently experiencing a rapid wave of digital disruption. Current research in applying emerging technologies such as Big Data (BD), Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Augmented Reality (AR), Virtual Reality (VR), Digital Twin (DT), Wearable Sensor (WS), Blockchain (BC) and Smart Contracts (SC) in contact tracing, tracking, drug discovery, care support and delivery, vaccine distribution, management, and delivery. These disruptive innovations have made it feasible for the healthcare industry to provide personalised digital health solutions and services to the people and ensure sustainability in healthcare. Precision Healthcare (PHC) is a new inclusion in digital healthcare that can support personalised needs. It focuses on supporting and providing precise healthcare delivery. Despite such potential, recent studies show that PHC is ineffectual due to the lower patient adoption in the system. Anecdotal evidence shows that people are refraining from adopting PHC due to distrust. This thesis presents a BC-enabled PHC ecosystem that addresses ongoing issues and challenges regarding low opt-in. The designed ecosystem also incorporates emerging information technologies that are potential to address the need for user-centricity, data privacy and security, accountability, transparency, interoperability, and scalability for a sustainable PHC ecosystem. The research adopts Soft System Methodology (SSM) to construct and validate the design artefact and sub-artefacts of the proposed PHC ecosystem that addresses the low opt-in problem. Following a comprehensive view of the scholarly literature, which resulted in a draft set of design principles and rules, eighteen design refinement interviews were conducted to develop the artefact and sub-artefacts for design specifications. The artefact and sub-artefacts were validated through a design validation workshop, where the designed ecosystem was presented to a Delphi panel of twenty-two health industry actors. The key research finding was that there is a need for data-driven, secure, transparent, scalable, individualised healthcare services to achieve sustainability in healthcare. It includes explainable AI, data standards for biosensor devices, affordable BC solutions for storage, privacy and security policy, interoperability, and usercentricity, which prompts further research and industry application. The proposed ecosystem is potentially effective in growing trust, influencing patients in active engagement with real-world implementation, and contributing to sustainability in healthcare