Search Results (954)

The rapid growth of digital tourism platforms has intensified information overload and decision complexity for both locals and travelers, while operators struggle to differentiate their offerings and sustain profitable, data-driven e-commerce models. This paper presents Doroob, a big data and artificial intelligence (AI)-driven, context-aware recommendation system that integrates traditional recommender techniques with real-time facial emotion recognition (FER) to enable intelligent tourism commerce. Doroob combines three AI-based recommendation strategies: smart adaptive recommendation (SAR) collaborative filtering, a Vowpal Wabbit-based context-aware model, and a LightFM hybrid model. It trained on datasets built from the Google Places API and enriched with ratings adapted from MovieLens. FER, implemented with DeepFace and OpenCV, analyzes short video segments as users browse destination details, converts emotion scores into 1–5 satisfaction ratings, and stores this implicit feedback alongside explicit ratings to support adaptive, emotion-aware personalization. Experimental results show that the context-aware model achieves the strongest top-K ranking performance, the hybrid LightFM model yields the highest AUC of 0.95, and the SAR model provides the most accurate rating predictions, demonstrating that combining contextual modeling and FER-based implicit feedback can enhance personalization, mitigate cold-start, and support data-driven promotion of local tourist services in intelligent e-commerce ecosystems. Full article

With increasing demand for wellness tourism, identifying the key factors influencing emotional perceptions is essential for optimizing destination planning and management. Although Anhui Province has experienced rapid growth in wellness tourism destinations in recent years, scientific understanding of tourists’ emotional perceptions and their driving mechanisms has lagged behind this rapid expansion, a gap that can be addressed by integrating big data with spatial analysis to provide a scientific perspective for optimizing destination planning and informing regional wellness tourism policy. To address this gap, this study conducts a sentiment analysis of wellness bases in Anhui Province using user-generated content (UGC) data. Sentiment scores were quantified via SnowNLP, while kernel density, time-series, and multivariate statistical analyses were applied to examine spatial distributions, temporal dynamics of sentiments and review volumes, and emotional driving factors. The results indicate a spatial pattern of higher density in the south, lower density in the north, and dual-core agglomeration, closely linked to natural resource endowments. Temporally, sentiment scores rise in spring and summer and decline in winter, while review volumes peak in spring and autumn. Overall regression analyses reveal a significant positive effect of green coverage and a negative effect of accommodation prices. In the typological analysis, sentiment scores of Forest Wellness Bases (FWBs) relate to green coverage and negative ions, while Hydrological Wellness Bases (HWBs), Traditional Chinese Medicine Wellness Bases (TCMWBs), and Wellness Towns (WTs) are driven by the combined effects of facility services, locational price, and ecological environment. These findings provide a scientific basis for the sustainable development and differentiated management of wellness tourism destinations. Full article

Rolling contact fatigue cracks at the contact surface between a wheel and rail are evaluated based on the results of an external inspection (visual inspection). We developed a rail damage assessment technique using a fast regional convolutional neural network deep learning-based image analysis framework. We collected rail specimens from in-service tracks and performed scanning electron microscopy to correlate surface damage with subsurface crack formation, including crack depth, length, and angle. This data was input into an artificial neural network (ANN) to assess internal crack conditions using visual information obtained from rail surface damage. The resulting model achieved an average accuracy of 94.9%, outperforming other algorithms. We integrated this model into a developed rail damage diagnosis app with deep learning that links field photographs with cloud-based big data to learn, quantitatively diagnose, and present the type and scale of rail damage. We examined the field applicability of the application at a rail damage site. The standard deviation of the rail damage diagnosis results was 0.2–1.5% between different users. Appropriateness of the rail damage assessment technique using the proposed ANN image analysis technique was verified experimentally. Consistent diagnosis results could be derived regardless of the inspector, minimizing human error. Full article

Due to the significant challenges faced by healthcare systems, medical establishments strive to set the tone by integrating new concepts to bridge this gap. Here, Lean Healthcare (LH) has been inspired by Lean Management (LM). Utilizing LM to optimize industrial processes and reduce waste presented a real opportunity to enhance the quality of medical services. For more improvement, healthcare systems pushed themselves to keep up with progress by implementing Industry 4.0 (I4.0) tools, such as IoT, Big Data analytics, and AI with LH and sustainability practices. The results promised better quality of care. Although this concept offers significant potential for more efficient workflows and optimizing medical processes, studies examining their combined implementation are still scarce. This research fills the gap via a literature review (LR) of peer-reviewed articles published between 2015 and 2025. The review investigates the impact of integrating smart technologies into LH frameworks and highlights how LH contributes to sustainability across multiple dimensions: economic, social, technological and environmental. Key findings show the impact of combining advanced tools with lean principles by reducing waiting times (25%) and length of stay while also improving satisfaction. Sustainability-centered adaptations of LH incorporate social and environmental comparative parameters such as resource consumption, for instance, reducing operational costs by up to 30–40%. Many challenges were faced with this implementation, such as cultural, technical challenges (e.g., complexity of integration with digital systems), and sustainability barriers. However, to overcome these barriers, this paper proposes a holistic implementation that aligns lean processes with organizational change and sustainability goals. Full article

In the era of big data, data is key to the accuracy of analytical models, and cloud computing services are often used to efficiently process large volumes of data. However, outsourcing sensitive data to a third-party cloud service provider results in a loss of direct control over the data, raising serious security concerns. The target of this study is to propose highly efficient and privacy-preserving protocols that compute the maximum/minimum value in large-scale data. To achieve the improvements in efficiency, the proposed protocols reuse the intermediate results generated in independent subprotocols. Existing privacy-preserving maximum/minimum protocols are based on approximation methods that sacrifice accuracy or reveal information during execution. They use costly comparison operations that are proportional to the size of the input data and are not suitable for large-scale data applications. In contrast, the proposed protocols theoretically reduce the number of communication rounds by 25%, the communication size by 50%, and the computational cost by 42% compared to the existing protocols. Nevertheless, the accuracy and privacy are fully maintained. In order to demonstrate these efficiency improvements concretely, we conducted experiments and demonstrated that the proposed protocols reduce the communication volume by half and the execution time by 22%. Because the proposed protocols support parallel execution, their performance can be substantially enhanced in cloud environments that provide large-scale parallel processing resources. Even data owners with restricted computational capabilities can use the protocols without exposing their information. Under the secure version, even cloud servers executing the protocol learn nothing about the input data or the computation results. Full article

With global urbanisation anticipated to reach 68% by 2050, there is a significant risk of exacerbating urban transport emissions. Urban transport decarbonisation is a complex adaptive system challenge, the understanding and optimisation of which could be supported by digital twins (DTs). Although prior research has explored digital and big data technology applications, creating actionable insights requires human-centred designs. We conducted a structured workshop to gather practitioner views on how urban-scale DTs can support transport decarbonisation. Specifically, we explored the outcomes they aim to achieve, the interventions they are interested in, and the value digital twinning offers compared to current methods. The data was synthesised and analysed to identify (1) impacts, (2) interventions, (3) location types, (4) data sources and (5) feedback mechanisms of importance to participants. These five aspects are proposed as a framework to support the definition of digital twinning use cases targeting urban transport decarbonisation. Application of the framework encourages creators to explicitly consider the services to be provided to users, how the derived insights influence the real world and the data connections between the physical and digital, noting that these are often overlooked in reported research. A framework application is illustrated through an example use case described for the West Midlands, UK. Full article

The transition to electric mobility requires the coordinated evolution of vehicles, charging infrastructure, power systems, and intelligent digital platforms. This study examines the role of Industry 4.0 technologies in enabling large-scale electric vehicle (EV) adoption and effective EV grid integration and synthesizes the existing evidence into a coherent analytical framework to support planning and policy decision-making. A systematic review of 27 peer-reviewed studies published between 2018 and 2025 was conducted in accordance with PRISMA 2020 guidelines, capturing the acceleration of electromobility following the consolidation of Industry 4.0 technologies and the emergence of large-scale policy commitments worldwide. The analysis covers six technology families, including the Internet of Things, big data and analytics, artificial intelligence and machine learning, blockchain, digital twins, and extended reality, and examines their applications in smart charging, grid vehicle coordination, fleet optimization, and vehicle-to-grid services. The findings show that analytics and artificial intelligence consistently enhance operational reliability and efficiency, while digital twins are increasingly applied to infrastructure siting, grid impact assessment, and scenario analysis. Building on these results, the study proposes a three-layer analytical framework composed of physical, digital, and decision layers, together with a functional EV grid generation integration model that links technology readiness to system-level deployment. In addition, a transition timeline for the 2025–2040 period and a concise set of key performance indicators are introduced to support evaluation and comparison. Policy implications for Ecuador and Latin America emphasize interoperability, data governance, realistic cost assessment, and a phased approach to vehicle-to-grid deployment. Full article

Collaborative projects under the Horizon Europe Framework Program of the European Union typically involve a large number of partners from multiple countries. Data-centric projects, among them, often require integration of disparate data source formats and collection methods, leading to complex data management architectures and policies. This article is an extended version of an article presented at the 1st International Conference on Big Data Analytics and Applications (BDAA’2025). It explores design decisions, organisational principles, and technological solutions to address these challenges by focusing on data integration of data sources and the hybridisation of data services. This experience was gathered while working on DiverSea, a project dedicated to the analysis of biodiversity dynamics along European coastlines—ranging from the Black Sea to the Mediterranean and the North Sea. While grounded in established technologies, the project’s takeaways offer valuable insights for environmental data projects across aquatic, terrestrial, and atmospheric domains. Full article

Analysis of game management and trophy game populations in Osijek-Baranja County shows that this region is one of the most valuable hunting areas in Croatia, with rich populations of red deer, roe deer, and wild boar, as well as stable annual population growth. The methodological framework included products and services in hunting based on data analysis from the Croatian Hunting Association, big-game trophy records (ETD forms), the Central Hunting Register, and the official price list of game culling and hunting services. Data on harvests and trophy values indicate long-term population stability and high economic potential of hunting, with red deer generating the highest total revenue (EUR 7.29 million), while roe deer and wild boar contribute to overall stability and harvest volume. The total trophy value over 12 hunting seasons reaches EUR 11.99 million, underscoring the economic importance of hunting tourism for local communities. Differences among hunting ground users suggest that private companies and the state company Croatian Forests Ltd. often achieve higher trophy values, while county hunting associations report more modest results. However, regression analysis shows there is not a strong statistical correlation between management structure and trophy outcomes, highlighting the significant influence of ecological and spatial factors on game quality. International hunters, primarily from Germany and Austria, represent a key segment of demand, confirming the market potential for further development of hunting tourism. Despite the rich natural base, results indicate the need for better marketing approach, digital visibility, and integration of hunting products with other forms of tourism, such as gastronomic, wine, and nature tourism. Effective positioning of Croatia as a competitive hunting destination requires adaptation to contemporary market trends and adherence to international sustainable management guidelines (FAO, ELC, CBD). In conclusion, hunting in Croatia represents an important non-wood forest product and a vital resource for rural and economic development. Sustainable population management, quality promotion, and integration of traditional and innovative practices are essential for reinforcing biodiversity conservation, supporting community livelihoods, and strengthening Croatia’s role in the European and global hunting-tourism market. Full article

In the big data era, understanding and influencing consumer behavior in digital marketing increasingly relies on large-scale data and AI-driven analytics. This narrative, concept-driven review examines how big data technologies and machine learning reshape consumer behavior analysis across key decision-making areas. After outlining the theoretical foundations of consumer behavior in digital settings and the main data and AI capabilities available to marketers, this paper discusses five application domains: personalized marketing and recommender systems, dynamic pricing, customer relationship management, data-driven product development and fraud detection. For each domain, it highlights how algorithmic models affect targeting, prediction, consumer experience and perceived fairness. This review then turns to synthetic data as a privacy-oriented way to support model development, experimentation and scenario analysis, and to dark data as a largely underused source of behavioral insight in the form of logs, service interactions and other unstructured records. A discussion section integrates these strands, outlines implications for digital marketing practice and identifies research needs related to validation, governance and consumer trust. Finally, this paper sketches future directions, including deeper integration of AI in real-time decision systems, increased use of edge computing, stronger consumer participation in data use, clearer ethical frameworks and exploratory work on quantum methods. Full article

This paper analyzes the effects of service recovery and the double-deviation phenomenon on customer satisfaction using an analysis of online reviews. With the application of Word2Vec and AFINN sentiment analysis to 4793 Skytrax review comments, this paper bridges the gap between conceptual theories about customer behavior and big data analysis. Findings from this research indicate that although overall types of service recovery increase customer satisfaction, explanation is the most preferable yet effective type of recovery. Most importantly, this analysis shows that service recovery disconfirmation has been found to be a highly important moderator. When service efforts involve intangible actions, such as an apology or explanation, that fail to satisfy customers, they tend to perform worse than no recovery at all, thus supporting the double-deviation phenomenon. Compensation, however, has been found to be an efficient type of recovery since its impacts are not reduced much due to service recovery disconfirmation. These insights provide service providers with critical guidance on prioritizing transparent communication and avoiding poorly executed intangible recoveries. Full article

Investigating the characteristics and influencing mechanisms of urban vitality in mountainous cities can contribute to enhanced urban resilience, optimised resource allocation, and sustainable development. However, most existing studies have focused on static analyses at single spatial scales, making it difficult to fully reveal the evolutionary trends of urban vitality under complex topographic constraints or the spatiotemporal heterogeneity of its influencing factors. This study examines Guiyang, one of China’s fastest-growing cities, focusing on both its economic development and population growth. Based on social media data and geospatial big data from 2019 to 2024, the spatiotemporal permutation scan statistics (STPSS) model was employed to identify spatiotemporal areas of interest (ST-AOIs) and to analyse the spatial distribution and day-night dynamics of urban vitality across different phases. Furthermore, by incorporating transportation and topographic factors characteristic of mountainous cities, the multiscale geographically and temporally weighted regression (MGTWR) model was applied to reveal the driving mechanisms of urban vitality. The main findings are as follows: (1) Urban vitality exhibits a multi-center, clustered structure, gradually expanding from gentle to steeper slopes over time, with activity patterns shifting from an afternoon peak to an all-day distribution. (2) Significant differences in regional vitality resilience were observed: the core vitality areas exhibited stable ST-AOI spatial patterns, flexible temporal rhythms, and strong adaptability; the emerging vitality areas recovered quickly with low losses, while low-vitality areas showed slow recovery and insufficient resilience. (3) The density of commercial service facilities and the level of housing prices were continuously enhancing factors for vitality improvement, whereas the density of subway stations and the degree of functional mix played key roles in supporting resilience during the COVID-19 pandemic. (4) The synergistic effect between transportation systems and commercial facilities is crucial for forming high-vitality zones in mountainous cities. In contrast, reliance on a single factor tends to lead to vitality spillover. This study provides a crucial foundation for promoting sustainable urban development in Guiyang and other mountainous regions. Full article

Urban forests and green spaces are increasingly promoted as Nature-Based Solutions (NbS) to mitigate climate risks, enhance human well-being, and support resilient and sustainable cities. Focusing on the theme of optimizing urban green space ecosystem services to foster resilient and sustainable cities, this study systematically analyzes 861 relevant publications indexed in the Web of Science Core Collection from 2005 to 2025. Using bibliometric analysis and scientific knowledge mapping methods, the research examines publication characteristics, spatial distribution patterns, collaboration networks, knowledge bases, research hotspots, and thematic evolution trajectories. The results reveal a rapid upward trend in this field over the past two decades, with the gradual formation of a multidisciplinary knowledge system centered on environmental science and urban research. China, the United States, and several European countries have emerged as key nodes in global knowledge production and collaboration networks. Keyword co-occurrence and cluster analyses indicate that research themes are mainly concentrated in four clusters: (1) ecological foundations and green process orientation, (2) nature-based solutions and blue–green infrastructure configuration, (3) social needs and environmental justice, and (4) macro-level policies and the sustainable development agenda. Overall, the field has evolved from a focus on ecological processes and individual service functions toward a comprehensive transition emphasizing climate resilience, human well-being, and multi-actor governance. Based on these findings, this study constructs a knowledge ecosystem framework encompassing knowledge base, knowledge structure, research hotspots, frontier trends, and future pathways. It further identifies prospective research directions, including climate change adaptation, integrated planning of blue–green infrastructure, refined monitoring driven by remote sensing and spatial big data, and the embedding of urban green space ecosystem services into the Sustainable Development Goals and multi-level governance systems. These insights provide data support and decision-making references for deepening theoretical understanding of Urban Green Space Ecosystem Services (UGSES), improving urban green infrastructure planning, and enhancing urban resilience governance capacity. Full article

The convergence of artificial intelligence (AI), machine learning (ML), blockchain, and big data analytics is transforming the governance, sustainability, and resilience of modern banking ecosystems. This study provides a multivariate bibliometric analysis using Principal Component Analysis (PCA) of research indexed in Scopus and Web of Science to explore how decentralized digital infrastructures and AI-driven analytical capabilities contribute to sustainable financial development, transparent governance, and climate-resilient digital societies. Findings indicate a rapid increase in interdisciplinary work integrating Distributed Ledger Technology (DLT) with large-scale data processing, federated learning, privacy-preserving computation, and intelligent automation—tools that can enhance financial inclusion, regulatory integrity, and environmental risk management. Keyword network analyses reveal blockchain’s growing role in improving data provenance, security, and trust—key governance dimensions for sustainable and resilient financial systems—while AI/ML and big data analytics dominate research on predictive intelligence, ESG-related risk modeling, customer well-being analytics, and real-time decision support for sustainable finance. Comparative analyses show distinct emphases: Web of Science highlights decentralized architectures, consensus mechanisms, and smart contracts relevant to transparent financial governance, whereas Scopus emphasizes customer-centered analytics, natural language processing, and high-throughput data environments supporting inclusive and equitable financial services. Patterns of global collaboration demonstrate strong internationalization, with Europe, China, and the United States emerging as key hubs in shaping sustainable and digitally resilient banking infrastructures. By mapping intellectual, technological, and collaborative structures, this study clarifies how decentralized intelligence—enabled by the fusion of AI/ML, blockchain, and big data—supports secure, scalable, and sustainability-driven financial ecosystems. The results identify critical research pathways for strengthening financial governance, enhancing climate and social resilience, and advancing digital transformation, which contributes to more inclusive, equitable, and sustainable societies. Full article

A robust audit services market is essential for ensuring financial transparency, regulatory compliance, and investor confidence. As a dimension of organizational sustainability, the capacity of audit firms to remain competitive and resilient under market pressures is increasingly relevant. However, existing research has paid insufficient attention to the stability of audit firms and the survival dynamics of mid-sized players. The present study addresses this gap by examining the volatility of the U.S. audit services market and the sustained dominance of the Big Four firms over the 2019–2023 period. Based on data from Accounting Today’s annual rankings, the study employs Kaplan–Meier survival analysis to assess the probability of audit firms remaining in the Top 100 over time. Furthermore, K-means clustering is used to identify structural factors contributing to firm exit, including revenue, number of employees, branches, and partners. The results indicate that, while the Big Four retained stable leadership, 19 firms exited the rankings, with revenue and number of specialists being the most influential exit factors. These findings provide insights for enhancing risk assessment, strategic planning, and regulatory design. Moreover, the study contributes to broader discussions on organizational sustainability and long-term competitiveness within the context of the U.S. audit sector, while offering insights that may be informative for understanding similar dynamics in other markets rather than aiming for direct global generalization. Full article