Interoperabilidade Orientada por Machine Learning para Proveniência Cross-Chain
Uma Camada de Confiança Computacionalmente Validada para o Comércio Global de Artesanato
DOI:
https://doi.org/10.63451/dti.v2i23.390Palavras-chave:
Blockchain, Design Computacional, Machine Learning, NeuromarketingResumo
O mercado global de bens artesanais enfrenta desafios significativos relacionados à autenticidade dos produtos, à rastreabilidade verificável e ao acesso eficiente ao mercado por parte dos criadores independentes. Este artigo apresenta uma plataforma de marketplace omnichannel desenvolvida para resolver essas deficiências sistêmicas por meio da integração sinérgica de Inteligência Artificial (IA) avançada e tecnologias blockchain descentralizadas. A plataforma aprimora a transparência do mercado e fomenta a confiança do comprador por meio de um mecanismo de recomendação baseado em IA que incorpora classificações de confiança e um sistema blockchain para rastreamento verificável de proveniência utilizando Tokens Não Fungíveis (NFTs). De forma crucial, a plataforma incorpora princípios neurocientíficos ao seu design, aproveitando a neuroestética para otimizar a apresentação do estilo artístico e da significância cultural, e o neuromarketing para estruturar a narrativa do artesão visando maximizar o valor percebido e a ressonância emocional. Uma inovação central é o componente de Interoperabilidade Orientada por Machine Learning (ML), que protege e otimiza mecanismos cross-chain, permitindo transferência de ativos escalável e de alta segurança entre redes distintas. Os resultados meta-analíticos iniciais indicam que as recomendações baseadas em IA proporcionam um incremento esperado de 15% na taxa de conversão (Hedges' g = 0,45) em comparação com sistemas não personalizados, e a interoperabilidade aprimorada por ML reduz a latência de transações cross-chain em média 18% em relação a pontes não otimizadas. A plataforma demonstra alto potencial para o empoderamento de artesãos, a promoção do comércio artesanal sustentável e o estabelecimento de um novo paradigma para o comércio eletrônico confiável.
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Referências
AHMAD, M. et al. Uma abordagem de aprendizado de máquina para seleção de modelos de beneficiários de ajuda social. Journal of System and Management Sciences, v. 13, n. 6, p. 230–243, 2023.
ALSHARIF, A. H. et al. A sinergia do neuromarketing e inteligência artificial na pesquisa de marketing: uma revisão sistemática da literatura. Future Business Journal, v. 11, n. 1, p. 1–30, 2025.
ALSHARKAWI, A. et al. Poverty classification using machine learning: the case of Jordan. Sustainability, v. 13, n. 3, p. 1–16, 2021.
APRILIANTY, F.; TAKAHASHI, M.; MATSUURA, Y. Bridging brains and bots: the impact of AI on neuromarketing. Procedia Computer Science, v. 246, p. 1830–1839, 2024.
BHARDWAJ, S. et al. Exploring the boundaries of neuromarketing through systematic investigation. Journal of Business Research, v. 154, p. 113371, 2023.
CELIS, J. H. Q.; PAGATPATAN, A. C. Jr. Predicting the poverty alleviation in the province of Eastern Samar using data mining techniques. International Journal of Recent Technology and Engineering, v. 8, n. 3, p. 7140–7144, 2019.
DE-ARTEAGA, M. et al. Machine learning for the developing world. ACM Transactions on Management Information Systems, v. 9, n. 2, p. 9, 2018.
DRECHSLER, K.; SCHMID, B. F. Towards a classification of meta-search engines. Electronic Markets, v. 22, n. 4, p. 237–253, 2012.
GILL, R.; SINGH, J. A study of neuromarketing techniques for proposing cost effective information driven framework for decision making. Materials Today: Proceedings, v. 49, p. 2969–2981, 2022.
GOLNAR-NIK, P.; FARASHI, S.; SAFARI, M.-S. The application of EEG power for the prediction and interpretation of consumer decision-making: a neuromarketing study. Physiology & Behavior, v. 207, p. 90–98, 2019.
JANNACH, D.; RESNICK, P.; TUZHILIN, A. Recommender systems: an introduction. In: RICCI, F. et al. (ed.). Recommender systems handbook. New York: Springer, 2016. p. 1–29.
JEAN, N. et al. Combining satellite imagery and machine learning to predict poverty. Science, v. 353, n. 6301, p. 790–794, 2016.
KAÇER, M. Can AI finish poverty? The role of artificial intelligence in poverty alleviation. Turkish Journal of Social Work, p. 1–52, 2024.
KLAIB, A. F. et al. Eye tracking algorithms, techniques, tools, and applications with an emphasis on machine learning and Internet of Things technologies. Expert Systems with Applications, v. 166, p. 114037, 2021.
KSHETRI, N. Blockchain's roles in strengthening global supply chain resilience. International Journal of Information Management, v. 39, p. 314–322, 2018.
KUSÁ, A.; BELIČKOVÁ, P. Using neuromarketing to create more effective messages in sustainable marketing. In: European Conference on Innovation and Entrepreneurship, 18., 2023. Proceedings [...]. [S. l.]: ECIE, 2023. p. 493–496.
LAMICHHANE, B. R.; ISNAN, M.; HORANONT, T. Exploring machine learning trends in poverty mapping: a review and meta-analysis. Science of Remote Sensing, v. 11, p. 100200, 2025.
LASFETO, D. B. et al. A simple classification and clustering of poverty in rural areas using machine learning. Journal of Infrastructure, Policy and Development, v. 8, n. 8, p. 5938, 2024.
LI, Y. et al. Machine learning for blockchain: a survey. IEEE Access, v. 7, p. 138670–138687, 2019.
LINHARES, F. C.; ARRUDA, F. A. C.; LARRIERA, C. A new era in poverty diagnostics: experiential knowledge from machine learning to improve the efficiency of social programs in combating poverty. Journal of Economy and Technology, v. 4, p. 104–131, 2026.
MASHRUR, F. R. et al. An intelligent neuromarketing system for predicting consumers' future choice from electroencephalography signals. Physiology & Behavior, v. 253, p. 113847, 2022.
ONSAY, E. A.; BALTAR, K. C.; RABAJANTE, J. F. Predicting dual poverty in informal transportation: a machine learning and econometrics approach. Human Settlements and Sustainability, v. 1, p. 158–179, 2025.
ONSAY, E. A.; RABAJANTE, J. F. When machine learning meets econometrics: can it build a better measure to predict multidimensional poverty and examine unmeasurable economic conditions? Science Talks, v. 11, p. 100387, 2024.
ORGANISATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENT (OECD). Counterfeiting and the Indian Economy. Paris: OECD Publishing, 2018.
PANTELI, A.; KALAITZI, E.; FIDAS, C. A. A review on the use of EEG for the investigation of the factors that affect consumer's behavior. Physiology & Behavior, v. 278, p. 114509, 2024.
PATI, A. et al. Forty years of brand logo literature: a review and future research directions. Journal of Business Research, v. 200, p. 115655, 2025.
PUTRA, G. A. The journey of marketing research in driving successful marketing strategies. Advances in Business & Industrial Marketing Research, v. 2, n. 1, p. 26–35, 2024.
QUILES PÉREZ, M. et al. Data fusion in neuromarketing: multimodal analysis of biosignals, lifecycle stages, current advances, datasets, trends, and challenges. Information Fusion, v. 105, p. 102231, 2024.
QUILES PÉREZ, M. et al. EEG channel reconstruction using convolutional neural networks in limited BCIs: a proposed method for neuromarketing applications. Applied Soft Computing Journal, v. 181, p. 113455, 2025.
RICCI, F.; ROKACH, L.; SHAPIRA, B. Introduction to recommender systems. In: RICCI, F. et al. (ed.). Recommender systems handbook. New York: Springer, 2011. p. 1–35.
SABERI, S. et al. Blockchain technology for supply chain sustainability. International Journal of Production Economics, v. 207, p. 201–217, 2019.
SALVADOR, E. L. V. Use of boosting algorithms in household level poverty measurement: a machine learning approach. Preprint submitted to Elsevier, p. 1–9, 2024.
TANG, Z. The role of AI and ML in transforming marketing strategies: insights from recent studies. In: International Conference an Financial Technology And Business Analysis, 3., 2024. Proceedings [...]. [S. l.: s. n.], 2024. p. 132–139.
TINGZON, I. et al. Mapping poverty in the Philippines using machine learning, satellite imagery, and crowd-sourced geospatial information. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. XLII-4/W19, p. 425–431, 2019.
TOHARI, A.; PARSONS, C.; RAMMOHAN, A. Does information empower the poor? Evidence from Indonesia's social security card. IZA Discussion Paper, n. 11137, p. 1–30, 2017.
TOMAŠEV, N. et al. AI for social good: unlocking the opportunity for positive impact. Nature Communications, v. 11, p. 2468, 2020.
UNITED NATIONS CONFERENCE ON TRADE AND DEVELOPMENT (UNCTAD). Digital Economy Report 2019: value creation and capture: implications for developing countries. New York: United Nations, 2019.
XIE, S. M. et al. Transfer learning from deep features for remote sensing and poverty mapping. CoRR, abs/1510.00098, p. 1–42, 2015.
YADAV, T. AI in neuromarketing: understanding consumer emotions and behavior through machine learning. International Journal for Multidisciplinary Research, v. 6, n. 5, p. 1–24, 2024.
ZHAO, T. et al. AutoML-based EEG signal analysis in neuro-marketing classification using biclustering method. Applied Soft Computing Journal, v. 181, p. 113412, 2025.
ZHENG, Z. et al. Blockchain challenges and opportunities: a survey. International Journal of Web and Grid Services, v. 14, n. 4, p. 352–375, 2018.
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Copyright (c) 2026 Ruth Rodriguez
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
