Narrative-driven optimization for sustainable museum networks: integrating freytag’s pyramid and hybrid pso-machine learning framework. AI optimizes sustainable museum networks using Freytag’s Pyramid & hybrid PSO-ML. Enhances urban heritage tourism, cuts CO₂ emissions, and boosts operational efficiency.
This study addresses sustainable urban heritage management needs through an AI-optimized methodology for Government-Museum networks. Integrating dramaturgical storytelling with computational intelligence, we develop a framework combining Freytag's Pyramid narrative framework with a hybrid Particle Swarm Optimization (PSO)-Machine Learning (ML) model. This sustainability-driven design aligns spatial routing with low-carbon objectives and thematic continuity, enhancing tourist itineraries while reducing environmental impact. Our model integrates GIS analysis of museum connectivity, accessibility criteria, and emissions indicators. Validated via Orange ML, the PSO-ML model achieves route optimization by minimizing distance, time, and CO₂ emissions. Results demonstrate significantly reduced travel distances/emissions and improved narrative coherence. The paradigm advances geographical justice, operational efficiency, and AI-mobility systems in promoting urban sustainability.
This study introduces a highly innovative and interdisciplinary approach to sustainable urban heritage management, particularly for government-museum networks. The core strength lies in its novel integration of dramaturgical storytelling, specifically Freytag's Pyramid, with advanced computational intelligence through a hybrid Particle Swarm Optimization (PSO) and Machine Learning (ML) framework. This unique fusion addresses a critical need for sustainable tourism and heritage preservation, promising to enhance tourist itineraries through narrative coherence while simultaneously achieving significant environmental benefits via low-carbon routing. The paper’s ambitious scope in aligning spatial routing with both thematic continuity and environmental objectives is commendable. Methodologically, the proposed framework is compelling. It adeptly combines GIS analysis to understand museum connectivity and accessibility with robust emissions indicators, feeding into a sophisticated hybrid PSO-ML model. The explicit goals of minimizing distance, time, and CO₂ emissions directly tackle pressing challenges in sustainable urban planning and mobility. The validation via Orange ML lends credibility to the computational approach, suggesting a practical and implementable solution for complex optimization problems. This dual emphasis on quantitative environmental metrics and qualitative narrative engagement represents a significant methodological advancement in the fields of AI-mobility and smart heritage systems. The potential implications of this paradigm are substantial, promising advancements in geographical justice, operational efficiency, and broader urban sustainability. The reported outcomes—significantly reduced travel distances/emissions and improved narrative coherence—suggest a potent tool for redefining how urban heritage networks are managed and experienced. While the abstract powerfully articulates the theoretical framework and potential benefits, the full paper will undoubtedly need to elaborate on the specific machine learning algorithms employed, the characteristics of the datasets used for GIS analysis, and the detailed empirical validation process. Given its innovative approach and profound potential, this paper represents a highly promising and timely contribution to the literature, and I recommend it for publication.
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By Sciaria
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By Sciaria
By Sciaria
By Sciaria
By Sciaria