Machine Learning-Driven Interoperability for Cross-Chain Provenance
A Computationally Validated Trust Layer for Global Artisanal Sourcing
DOI:
https://doi.org/10.63451/dti.v2i23.390Palabras clave:
Blockchain, Computational Design, Machine Learning, NeuromarketingResumen
The global artisanal goods market faces significant challenges related to product authenticity, verifiable provenance, and efficient market access for independent creators. This paper presents a novel omnichannel marketplace platform designed to resolve these systemic deficiencies through the synergistic integration of advanced Artificial Intelligence (AI) and decentralized blockchain technologies. The platform enhances market transparency and fosters buyer trust via an AI-powered recommendation engine and art intelligence that incorporates trust ratings and a blockchain system for verifiable provenance tracking using Non-Fungible Tokens (NFTs). Crucially, the platform incorporates neuroscientific principles into its design, leveraging neuroaesthetics to optimize the presentation of artistic style and cultural significance, and neuromarketing to structure the artisan's narrative for maximal perceived value and emotional resonance. A key innovation is the Machine Learning (ML)-Driven Interoperability component, which secures and optimizes cross-chain mechanisms, enabling scalable, high-security asset transfer across disparate networks. Initial meta-analytic results suggest that AI recommendations provide an expected conversion rate lift of 15% (Hedges' g = 0.45) compared to non-personalized systems, and ML-enhanced interoperability reduces cross-chain transaction latency by an average of 18% compared to non-optimized bridges. The platform demonstrates high potential for empowering artisans, promoting sustainable artisanal commerce, and establishing a new paradigm for trustworthy e-commerce.
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Derechos de autor 2026 Ruth Rodriguez
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
