Zero-Human AI: Autonomous Domain-Adaptive Framework for Self-Learning Predictive Systems Across Healthcare, Finance, and Manufacturing Domains

Zero-Human AI: Autonomous Domain-Adaptive Framework for Self-Learning Predictive Systems Across Healthcare, Finance, and Manufacturing Domains | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Zero-Human AI: Autonomous Domain-Adaptive Framework for Self-Learning Predictive Systems Across Healthcare, Finance, and Manufacturing Domains Nnaemeka Kingsley Ugwumba, Peter Sunday Jaja This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8065458/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The rapid expansion of artificial intelligence across critical sectors such as healthcare, finance, and manufacturing has led to a demand for systems capable of adapting autonomously to new data environments without human oversight. This study introduces Zero-Human AI, an autonomous domain-adaptive framework designed to enable self-learning predictive systems that continually improve their performance across multiple domains. The framework integrates automated data preprocessing, feature standardization, model selection, and self-tuning mechanisms, allowing it to operate independently from manual supervision. A total of 23 diverse datasets, comprising both real and synthetic sources, were used to train and evaluate several machine learning models including Support Vector Machines, Linear Regression, Random Forest, Decision Trees, and ensemble variants. The results demonstrate that the proposed framework consistently achieves stable and high predictive accuracy across heterogeneous domains, showing a remarkable capacity for transfer learning and adaptation to unseen data distributions. Furthermore, the implementation is deployed through a scalable FastAPI web application, using Python, HTML, CSS, and JavaScript, enabling practical integration into real-world decision environments. This research contributes to the ongoing pursuit of trustworthy, autonomous, and adaptive AI systems capable of learning without continuous human intervention. Artificial Intelligence and Machine Learning Autonomous learning Domain adaptation Self-learning systems Cross-domain AI Zero-human intelligence Transfer learning Predictive modeling AutoML FastAPI deployment Healthcare analytics Financial forecasting Manufacturing optimization Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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