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<p>Tourism 5.0 demands scalable, secure, and transparent systems to enhance service quality, destination image, and visitor loyalty. Traditional approaches face challenges in integrating Artificial Intelligence (AI) with blockchain due to scalability and computational costs. This paper introduces SQDeChain (SQcoin), a hybrid framework combining lightweight AI models with a sharded, Byzantine Fault-Tolerant (BFT) blockchain. Using DistilDeBERTa for text analysis, MobileViT for image processing, and GRU for behavioural predictions, the framework generates key performance indicators (KPIs), Service Quality Trust Index (SQTI), Destination Image Reliability Score (DIRS), and Tokenised Loyalty Propensity (TLP). These KPIs are verified using a novel ClusterPioneer BFT consensus mechanism and energy-efficient operation. The system was evaluated using real-world tourism datasets from Karachi, showing a 41% improvement in scalability, a 0.11 increase in predictive accuracy, and low consensus latency (400 ms). The results demonstrate that SQDeChain offers a sustainable, transparent solution for the future of smart tourism.</p>

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SQDeChain: Enhancing tourist trust and destination image through a hybrid AI-blockchain framework for tourism 5.0

  • 期刊论文

  • 发表日期：

    2026年6月17日

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  • Ghulam Murtaza Lahbar,
  • Zhang Guanghai,
  • Donny Juliandri Prihadi,
  • Raja Vavekanand

摘要

Tourism 5.0 demands scalable, secure, and transparent systems to enhance service quality, destination image, and visitor loyalty. Traditional approaches face challenges in integrating Artificial Intelligence (AI) with blockchain due to scalability and computational costs. This paper introduces SQDeChain (SQcoin), a hybrid framework combining lightweight AI models with a sharded, Byzantine Fault-Tolerant (BFT) blockchain. Using DistilDeBERTa for text analysis, MobileViT for image processing, and GRU for behavioural predictions, the framework generates key performance indicators (KPIs), Service Quality Trust Index (SQTI), Destination Image Reliability Score (DIRS), and Tokenised Loyalty Propensity (TLP). These KPIs are verified using a novel ClusterPioneer BFT consensus mechanism and energy-efficient operation. The system was evaluated using real-world tourism datasets from Karachi, showing a 41% improvement in scalability, a 0.11 increase in predictive accuracy, and low consensus latency (400 ms). The results demonstrate that SQDeChain offers a sustainable, transparent solution for the future of smart tourism.