Five Years in the Deep End: How Two Researchers Are Mapping the Uncharted Territory of AI

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DOI: 10.5281/zenodo.18730550  21stabilfr·wdophcgmx
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In a hospital radiology department in Kyiv, a doctor named Iryna stares at a scan on her monitor. An AI system blinks its verdict: no malignancy detected. She trusts it. She is right to trust it. But here's the thing about Iryna's story — she was also lucky. And the difference between those two things is precisely what Oleh Ivchenko and Dmytro Grybeniuk have spent five years trying to understand.

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DOI: 10.5281/zenodo.18730550 21stabilfr·wdophcgmx
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The ROI Timeline — Realistic Expectations for Enterprise AI Projects

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18672405  30stabilfr·wdophcgmx
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The single most damaging piece of misinformation in enterprise AI is the promise of rapid return. Vendor decks routinely project ROI within 6-12 months; the empirical reality is 18-36 months for most use cases, with a mandatory investment trough in between. Drawing on 52 enterprise AI deployments analyzed or directly managed between 2021 and 2025, alongside published data from McKinsey, Gartner...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18672405 30stabilfr·wdophcgmx
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Chapter 9: Clustering and Segmentation — Grouping Strategies in Data Mining

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Data Science
Data Science by Iryna Ivchenko & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18672455  67stabilfr·wdophcgmx
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Clustering stands at the heart of unsupervised data mining — it is the art of asking a machine to look at raw, unlabeled data and answer a deceptively simple question: what belongs together? This chapter offers a comprehensive taxonomy of clustering and segmentation strategies, tracing the intellectual lineage of each major family of algorithms and e[REDACTED]sing the often-overlooked gaps in h...

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Data Science by Iryna Ivchenko & Oleh Ivchenko DOI: 10.5281/zenodo.18672455 67stabilfr·wdophcgmx
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AI Economics: MLOps Infrastructure Costs — The Hidden Price of Production AI

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18672439  43stabilfr·wdophcgmx
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Machine l[REDACTED]g operations (MLOps) infrastructure has become the defining cost center for enterprise AI programs, yet it remains systematically underestimated in project planning and ROI calculations. This research presents a comprehensive economic analysis of MLOps infrastructure costs across the full production AI lifecycle — from continuous integration pipelines and feature stores throu...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18672439 43stabilfr·wdophcgmx
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Gap Analysis: Real-Time Adaptation to Distribution Shift

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18672412  51stabilfr·wdophcgmx
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Distribution shift — the statistical divergence between the data a model trained on and the data it encounters in production — is the quiet destroyer of AI reliability. Unlike model bugs or data quality failures that manifest acutely, distribution shift degrades performance gradually, silently, until the system is making decisions optimized for a world that no longer exists. For anticipatory AI...

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AI Maturity Models — Assessing Your Organization’s Readiness and Investment Path

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18662988  13stabilfr·wdophcgmx
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(!)️ Citation Freshness Notice: This article contains citations primarily from 2019–2023. While the foundational research remains valid, readers are encouraged to verify current developments, as the field may have evolved significantly since publication.

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18662988 13stabilfr·wdophcgmx
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The Spec-First Revolution: Why Enterprise AI Needs Formal Specifications

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18666032  67stabilfr·wdophcgmx
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timeline title Evolution of Software Specification Practices 1950s-1960s : Ad-hoc specifications : Natural language : Manual testing 1970s-1980s : Formal methods : Hoare logic, VDM, Z notation : Mathematical proofs 1990s : Design-by-contract : Preconditions, postconditions : Eiffel, JML 2000s...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.18666032 67stabilfr·wdophcgmx
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Gap Analysis: Explainability-Accuracy Tradeoff in High-Stakes Domains

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Academic Research
Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18662985  66stabilfr·wdophcgmx
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Academic Citation: Dmytro Grybeniuk & Oleh Ivchenko. (2026). Gap Analysis: Explainability-Accuracy Tradeoff in High-Stakes Domains. Anticipatory Intelligence Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18662985 Abstract The explainability-accuracy tradeoff represents one of the most economically consequential yet technically intractable gaps in anticipatory AI syste...

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18662985 66stabilfr·wdophcgmx
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Chapter 8: Sequential Pattern Mining — Temporal Discoveries

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Data Science
Data Science by Iryna Ivchenko & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18666030  40stabilfr·wdophcgmx
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Academic Citation: Iryna Ivchenko & Oleh Ivchenko. (2026). Chapter 8: Sequential Pattern Mining — Temporal Discoveries. Intellectual Data Analysis Series, Chapter 8. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18666030 Abstract Sequential pattern mining represents one of the most intellectually compelling challenges in data mining: discovering meaningful patterns hidden with...

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Data Science by Iryna Ivchenko & Oleh Ivchenko DOI: 10.5281/zenodo.18666030 40stabilfr·wdophcgmx
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[r]References3 refs○Minimum 10 references required
[w]Words [REQ]5,125✓Minimum 2,000 words for a full research article. Current: 5,125
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Federated Learning Economics: Privacy vs Efficiency

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18662973  67stabilfr·wdophcgmx
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After seven years of implementing AI systems across healthcare, finance, and enterprise domains, I've observed a fundamental tension in modern machine l[REDACTED]g: organizations need data to build effective models, but privacy regulations, competitive concerns, and ethical considerations prevent centralized data collection. Federated l[REDACTED]g promises to resolve this paradox by training mo...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18662973 67stabilfr·wdophcgmx
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Score = Ref Trust (77 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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