Defining Anticipatory Intelligence: Taxonomy and Scope

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18749471  68stabilfr·wdophcgmx
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In 2019, the U.S. Intelligence Community formally adopted "Anticipatory Intelligence" as a strategic priority, defining it as the ability to "sense, anticipate, and warn of emerging conditions, trends, threats, and opportunities that may require a rapid shift in national security posture, priorities, or emphasis" [1]. Yet when the same term appears in machine learning literature, healthcare inf...

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18749471 68stabilfr·wdophcgmx
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The Black Swan Problem: Why Traditional AI Fails at Prediction

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18749477  27stabilfr·wdophcgmx
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Traditional recurrent neural network architectures—including LSTMs and GRUs—exhibit systematic failure modes when confronted with Black Swan events: rare, high-impact occurrences that fall outside the training distribution. This technical analysis quantifies the economic impact of prediction failures, examines the mathematical foundations of why these architectures fail, and introduces the conc...

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18749477 27stabilfr·wdophcgmx
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Data Mining Chapter 2: Evolution of Data Mining Techniques (1960s-2000s)

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Data Science by Iryna Ivchenko & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18749485  67stabilfr·wdophcgmx
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This chapter chronicles the remarkable metamorphosis of data mining techniques across four transformative decades, from the pioneering expert systems of the 1960s to the sophisticated ensemble methods and standardized methodologies of the early 2000s. We trace the intellectual lineage from DENDRAL's rule-based reasoning through Quinlan's revolutionary decision tree algorithms, the renaissance o...

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Data Science by Iryna Ivchenko & Oleh Ivchenko DOI: 10.5281/zenodo.18749485 67stabilfr·wdophcgmx
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Data Mining Chapter 3: The Modern Era — Big Data and Intelligent Mining

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Data Science by Iryna Ivchenko & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18749487  69stabilfr·wdophcgmx
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This chapter chronicles the revolutionary transformation of data mining during the big data era, spanning from Google's MapReduce paradigm in 2004 to the present age of intelligent, automated mining systems. We examine how the confluence of distributed computing, deep learning, and cloud infrastructure fundamentally redefined both the scale and sophistication of knowledge discovery from data. T...

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Data Science by Iryna Ivchenko & Oleh Ivchenko DOI: 10.5281/zenodo.18749487 69stabilfr·wdophcgmx
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Data Mining Chapter 1: The Genesis of Data Mining — From Statistics to Discovery

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Data Science by Iryna Ivchenko & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18749494  68stabilfr·wdophcgmx
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This chapter traces the fascinating journey of data mining from its embryonic roots in 19th-century statistics to its crystallization as a formal discipline in the 1990s. We explore how Francis Galton's pioneering work on regression analysis and Karl Pearson's correlation coefficients laid the mathematical groundwork for pattern discovery. The narrative advances through the computational revolu...

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Data Science by Iryna Ivchenko & Oleh Ivchenko DOI: 10.5281/zenodo.18749494 68stabilfr·wdophcgmx
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]8%✗≥80% of references from 2025–2026. Current: 8%
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Medical ML: Cost-Benefit Analysis of AI Implementation for Ukrainian Hospitals

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752830  69stabilfr·wdophcgmx
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The adoption of artificial intelligence in medical imaging presents Ukrainian healthcare institutions with a complex economic decision. This article provides a comprehensive cost-benefit analysis framework specifically designed for the Ukrainian healthcare context, accounting for the country's unique economic conditions, wartime constraints, and institutional structures. We examine the total co...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752830 69stabilfr·wdophcgmx
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]67%✗≥80% of references from 2025–2026. Current: 67%
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Medical ML: Legal Framework for AI in Ukrainian Healthcare — Regulations, Liability, and EU Harmonization

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Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752832  53stabilfr·wdophcgmx
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[l]Academic29%○≥80% from journals/conferences/preprints
[f]Free Access71%○≥80% are freely accessible
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[w]Words [REQ]4,555✓Minimum 2,000 words for a full research article. Current: 4,555
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752832
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]6%✗≥80% of references from 2025–2026. Current: 6%
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Odesa National Polytechnic University (ONPU) Stabilarity Hub Research Initiative Medical ML Diagnostic Systems Research Program

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752832 53stabilfr·wdophcgmx
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[l]Academic29%○≥80% from journals/conferences/preprints
[f]Free Access71%○≥80% are freely accessible
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[w]Words [REQ]4,555✓Minimum 2,000 words for a full research article. Current: 4,555
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752832
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]6%✗≥80% of references from 2025–2026. Current: 6%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams6✓Mermaid architecture/flow diagrams. Current: 6
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (54 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Medical ML: Language Localization for Ukrainian Medical AI User Interfaces

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18704562  43stabilfr·wdophcgmx
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[t]Trusted58%○≥80% from verified, high-quality sources
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[i]Indexed5%○≥80% have metadata indexed
[l]Academic26%○≥80% from journals/conferences/preprints
[f]Free Access26%○≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]4,974✓Minimum 2,000 words for a full research article. Current: 4,974
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18704562
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]11%✗≥80% of references from 2025–2026. Current: 11%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (37 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The successful deployment of machine learning-based diagnostic systems in Ukrainian healthcare facilities requires comprehensive language localization that extends far beyond simple text translation. This article presents a systematic framework for adapting medical AI user interfaces to the Ukrainian linguistic and cultural context, addressing the unique challenges posed by Cyrillic script inte...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18704562 43stabilfr·wdophcgmx
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[t]Trusted58%○≥80% from verified, high-quality sources
[a]DOI32%○≥80% have a Digital Object Identifier
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[l]Academic26%○≥80% from journals/conferences/preprints
[f]Free Access26%○≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]4,974✓Minimum 2,000 words for a full research article. Current: 4,974
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18704562
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]11%✗≥80% of references from 2025–2026. Current: 11%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (37 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Medical ML: Ukrainian Medical Imaging Infrastructure — Current State and AI Readiness Assessment

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752836  54stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
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Ukraine's medical imaging infrastructure stands at a critical inflection point, shaped by decades of post-Soviet underinvestment, ambitious healthcare reform since 2017, and the devastating impact of the ongoing Russian invasion since February 2022. This comprehensive analysis examines the current state of diagnostic imaging capabilities across Ukrainian healthcare facilities, assessing equipme...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752836 54stabilfr·wdophcgmx
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[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI100%✓≥80% have a Digital Object Identifier
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752836
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (56 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Medical ML: Training Programs for Physicians — Building AI Competency in Medical Imaging

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752838  34stabilfr·wdophcgmx
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The successful integration of artificial intelligence into clinical radiology practice hinges upon physicians' comprehensive understanding of AI principles, capabilities, and limitations. This research article examines the current landscape of physician training programs for AI in medical imaging, analyzing curriculum frameworks, competency standards, and pedagogical approaches across internati...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752838 34stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752838
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥80% of references from 2025–2026. Current: 0%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (22 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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