Engineering and Technology International Journal

Towards Integrated Smart Tourism Systems in Urban Destinations: A Systematic Literature Review on End-to-End Journey and SME Digital Integration

2026-03-29T14:55:31+00:00

The rapid development of digital technologies has significantly transformed the tourism sector, particularly in urban destinations characterized by complex ecosystems and diverse stakeholders. Smart tourism systems have emerged as a key approach to enhancing service efficiency, improving tourist experiences, and enabling data-driven decision-making. However, existing studies are still fragmented and largely focus on partial implementations, lacking comprehensive end-to-end integration.

This study aims to conduct a systematic literature review on smart tourism systems in urban destinations, with a focus on system integration, end-to-end tourist journey, and Small and Medium Enterprises (SMEs) digital integration. The review was conducted using selected articles from reputable international journals published between 2023 and 2025. The analysis categorizes the studies into three groups: same system and same scope, same system and similar scope, and same system and different scope.

The results indicate that only a limited number of studies have developed fully integrated smart tourism systems, while most studies focus on specific components or are applied in different domains. In addition, stakeholder integration and SME digital inclusion remain key challenges in developing comprehensive smart tourism ecosystems.

This study contributes by identifying research gaps and proposing future research directions focused on developing integrated, scalable, and inclusive smart tourism systems. The findings are expected to support the advancement of smart tourism system design in urban destinations.

Data Driven Smart Tourism Management: A Literature Review on System Integration, Digital Tourist Journey, and UMKM Connectivity in Smart Cities

2026-03-31T11:14:03+00:00

The rapid development of smart city initiatives has significantly transformed the tourism sector through the adoption of digital technologies and data-driven systems. This study aims to analyze the development of data-driven smart tourism management by focusing on system integration, digital tourist journey, and UMKM connectivity within smart city environments. A Systematic Literature Review (SLR) method was employed to examine 30 relevant articles published between 2020 and 2025. The findings indicate that most studies utilize similar methodological approaches but are applied to different research objects, resulting in fragmented research outcomes. Furthermore, the lack of integration among systems and limited involvement of UMKM in digital platforms remain major challenges in developing effective smart tourism ecosystems. This study highlights the need for integrated, interoperable, and scalable smart tourism systems supported by advanced technologies such as artificial intelligence, big data analytics, and Internet of Things (IoT). The results of this study provide a conceptual foundation and research directions for developing more comprehensive and sustainable smart tourism systems in smart city contexts.

An Adaptive Hybrid Clustering Framework Integrating K-Means and Differential Evolution for High-Dimensional Data Analysis

2026-04-19T06:29:21+00:00

Clustering high-dimensional data remains a foundational yet persistently challenging problem in unsupervised machine learning, primarily because the performance of centroid-based methods such as K-Means degrades sharply in high-dimensional spaces due to local optima sensitivity and the curse of dimensionality. This paper proposes an Adaptive Hybrid Clustering Framework (AHCF) that integrates K-Means with Differential Evolution (DE) optimisation to systematically overcome K-Means's dependence on initial centroid placement in high-dimensional settings. The proposed framework introduces three novel components: (1) an adaptive mutation factor (F) governed by a monotonically decreasing annealing schedule that transitions from broad global exploration (F=0.90) to fine local exploitation (F=0.40) across generations; (2) an adaptive crossover probability (CR) that increases linearly from 0.50 to 0.90, progressively favouring population diversity as the search converges; and (3) a centroid refinement step that projects each DE trial solution back to the cluster mean, ensuring geometrically valid centroid positions throughout the evolutionary search. Experiments on a synthetically generated high-dimensional dataset (n=1,500, d=32, k=5) demonstrate that AHCF achieves a Silhouette Score of 0.6127, Davies-Bouldin Index of 0.5023, and Calinski-Harabasz Index of 2834.6 — improvements of 2.7%, 7.2%, and 6.9% respectively over the strong K-Means baseline (n_init=20). The proposed adaptive mechanism delivers a 75.2% reduction in Within-Cluster Sum of Squares (from 22,516 to 5,592) and achieves faster convergence compared to a static parameter equivalent. These results establish AHCF as a robust, theoretically grounded, and practically deployable framework for high-dimensional clustering tasks in data mining and machine learning applications.