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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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18672412 51stabilfr·wdophcgmx
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AI Maturity Models — Assessing Your Organization’s Readiness and Investment Path

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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 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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Chapter 8: Sequential Pattern Mining — Temporal Discoveries

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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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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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Cost-Effective AI: Deterministic AI vs Machine Learning — When Traditional Algorithms Win

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18650875  52stabilfr·wdophcgmx
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The artificial intelligence renaissance has created a gravitational pull toward machine l[REDACTED]g solutions for problems that may not require them. In my analysis of 156 enterprise AI implementations across financial services, logistics, and manufacturing sectors, I found that 34% of deployed ML systems would have achieved equal or superior outcomes using deterministic algorithms at 85-95% l...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18650875 52stabilfr·wdophcgmx
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AI Economics: Transfer Learning Economics — Leveraging Pre-trained Models

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18648770  66stabilfr·wdophcgmx
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The machine l[REDACTED]g field has undergone a fundamental shift in how models are developed. Understanding this shift is essential for grasping transfer l[REDACTED]g economics.

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18648770 66stabilfr·wdophcgmx
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]3%✗≥60% of references from 2025–2026. Current: 3%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
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Cost-Effective AI: The Hidden Costs of “Free” Open Source AI — What Nobody Tells You

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18644682  43stabilfr·wdophcgmx
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[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI28%○≥80% have a Digital Object Identifier
[b]CrossRef2%○≥80% indexed in CrossRef
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[l]Academic28%○≥80% from journals/conferences/preprints
[f]Free Access54%○≥80% are freely accessible
[r]References46 refs✓Minimum 10 references required
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Score = Ref Trust (37 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The open source AI revolution has democratized access to sophisticated language models, with Meta's Llama, Mistral AI's models, and countless fine-tuned variants available for download at zero licensing cost. Enterprise decision-makers, attracted by the promise of eliminating API fees and achieving data sovereignty, increasingly consider self-hosted open source alternatives to commercial provid...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18644682 43stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources2%○≥80% from editorially reviewed sources
[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI28%○≥80% have a Digital Object Identifier
[b]CrossRef2%○≥80% indexed in CrossRef
[i]Indexed28%○≥80% have metadata indexed
[l]Academic28%○≥80% from journals/conferences/preprints
[f]Free Access54%○≥80% are freely accessible
[r]References46 refs✓Minimum 10 references required
[w]Words [REQ]5,145✓Minimum 2,000 words for a full research article. Current: 5,145
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18644682
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]8%✗≥60% of references from 2025–2026. Current: 8%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams7✓Mermaid architecture/flow diagrams. Current: 7
[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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Data Mining Chapter 7: Association Rule Mining — Discovering Relationships

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Data Science
Data Science by Iryna Ivchenko & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18648782  64stabilfr·wdophcgmx
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[s]Reviewed Sources72%○≥80% from editorially reviewed sources
[t]Trusted88%✓≥80% from verified, high-quality sources
[a]DOI76%○≥80% have a Digital Object Identifier
[b]CrossRef76%○≥80% indexed in CrossRef
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[l]Academic76%○≥80% from journals/conferences/preprints
[f]Free Access24%○≥80% are freely accessible
[r]References25 refs✓Minimum 10 references required
[w]Words [REQ]4,601✓Minimum 2,000 words for a full research article. Current: 4,601
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18648782
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥60% of references from 2025–2026. Current: 0%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (72 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

In the early 1990s, a rumor began circulating through the corridors of data mining conferences that would become the field's most enduring urban legend. According to the story, analysts at Walmart discovered an unexpected correlation in their transaction data: purchases of beer and diapers frequently occurred together, particularly on Thursday and Saturday evenings. The explanation offered was ...

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Data Science by Iryna Ivchenko & Oleh Ivchenko DOI: 10.5281/zenodo.18648782 64stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources72%○≥80% from editorially reviewed sources
[t]Trusted88%✓≥80% from verified, high-quality sources
[a]DOI76%○≥80% have a Digital Object Identifier
[b]CrossRef76%○≥80% indexed in CrossRef
[i]Indexed16%○≥80% have metadata indexed
[l]Academic76%○≥80% from journals/conferences/preprints
[f]Free Access24%○≥80% are freely accessible
[r]References25 refs✓Minimum 10 references required
[w]Words [REQ]4,601✓Minimum 2,000 words for a full research article. Current: 4,601
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18648782
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥60% 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]Diagrams6✓Mermaid architecture/flow diagrams. Current: 6
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (72 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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