EAI Endorsed Transactions on Tourism, Technology and Intelligence

The Relationship Between Perception and Actual Behavior of Students Regarding Responsible Tourism A Case Study of Hospitality and Tourism Institute - Duy Tan University

2025-06-26T12:41:37+00:00

This study investigated the relationship between students’ perceptions of responsible tourism and their actual travel behaviors within the Hospitality and Tourism Institute - Duy Tan University. The primary objective was to determine whether students' understanding of responsible tourism influences their travel behaviors in practice. The study used quantitative methods with a survey instrument designed based on a 5-point Likert scale. A sample of 490 students participated in a structured questionnaire, followed by semi-structured interviews with a selected subset of participants. The results are expected to reveal a significant correlation between students' perceptions of responsible tourism and their actual travel behaviors. Additionally, the study aimed to identify gaps between knowledge and practice, highlighting the need for stronger educational programs to bridge this divide. Overall, the study underscores the importance of promoting deeper awareness to foster more responsible actions among future tourism professionals.

The Tourism-Migration Nexus: Accessing Care and Support as a Retired British National in Spain

2025-07-08T09:32:42+00:00

INTRODUCTION: The movement of older people from one country to another has been described in a multitude of ways, including ‘Residential Tourism’ and ‘International Retirement Migration’. Tourism is often the stepping stone to retirement migration and many older retirees live fluid lifestyles where home ownership, access to welfare and social networks are maintained across the home and host countries both physically and through technology.

OBJECTIVES: This paper focuses on older British people in Spain, and explores the strategies employed to access support in later life. It draws on Grid-Group cultural theory to explore the social network configurations that include the individual, their local and transnational community, as well as the wider socio-cultural context within which they are located.

METHODS: The paper draws on data from narrative interviews with 25 older British people in Spain.

CONCLUSION: The paper exemplifies four different ‘types’ of social network organization and how these relate to help seeking behavior in later life.

Efficient Machine Learning for Wi-Fi CSI-based Human Activity Recognition Using Fast Monte Carlo based Feature Extraction

2025-07-16T14:14:24+00:00

High-dimensional doppler data extracted from Wi-Fi channel state information (CSI) offers distinctive velocity and time patterns that are useful for human activity recognition (HAR), but its scale poses significant challenges for real-time inference and deployment on resource-constrained devices. This work proposes an efficient, fast monte carlo (MC) feature selection framework based on the frieze-kannanvempala (FKV) algorithm and coefficient estimation to address this bottleneck. The CSI is preprocessed, and doppler traces are computed to encode the velocity and direction of distinct activities. Afterwards, we perform FKV to decompose the doppler data, and the coefficient of the resulting singular vectors is estimated. Using rejection sampling, the topmost features are selected on the basis of their weights, thereby reducing the size of our features. The method identifies a compact set of velocity-time features that preserve critical motion information while significantly reducing computational overhead. The experimental evaluations demonstrated that the decision tree classifier achieved the highest precision at 99.8%, followed by convolutional neural networks (CNN) 96%, the hybrid CNN-long-short-term memory (CNN-LSTM) achieved 87%, while the LSTM model lagged at 53%. These results demonstrated that the integration of fast MC-based feature selection significantly reduced computational overhead without sacrificing classification performance, making it suitable for scalable and real-time HAR applications.