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How Our War Prediction Model Anticipated the Iran Conflict

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DOI: 10.5281/zenodo.18816597  27stabilfr·wdophcgmx
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On February 28, 2026, the United States and Israel launched coordinated military strikes on Iran, marking the most significant Middle Eastern conflict escalation since the Iraq War. Our Stabilarity War Prediction Model had been tracking Iran's conflict probability for weeks, showing a 49.7% conflict probability with an increasing trend — a warning that materialized into reality within hours of ...

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DOI: 10.5281/zenodo.18816597 27stabilfr·wdophcgmx
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When AI Finally Beats the Experts: DeepRare and the End of the Diagnostic Odyssey

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Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18730582  49stabilfr·wdophcgmx
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A new AI system published in Nature has achieved what many thought impossible: diagnosing rare diseases more accurately than experienced physicians. DeepRare, developed by researchers led by Zhao et al., demonstrates 64.4% top-1 diagnostic accuracy compared to 54.6% for human experts with over a decade of clinical experience. Tested across 6,401 cases spanning 2,919 diseases, the system provide...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.18730582 49stabilfr·wdophcgmx
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The Cognitive Shift: A Creative Vision of How AI Will Change the Way We Think and Perceive

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Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18685239  57stabilfr·wdophcgmx
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Artificial intelligence is not primarily a threat to human labour — it is a repricing of human cognition. Drawing on Jürgen Schmidhuber's formal theory of intelligence as compression, Robert Sheckley's satirical science fiction, and Isaac Asimov's prescient design specifications for autonomous systems, this essay argues that AI is catalysing the most significant cognitive economy shift since th...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.18685239 57stabilfr·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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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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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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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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[a]DOI51%○≥80% have a Digital Object Identifier
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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Score = Ref Trust (53 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

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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[w]Words [REQ]4,818✓Minimum 2,000 words for a full research article. Current: 4,818
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]13%✗≥60% of references from 2025–2026. Current: 13%
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Score = Ref Trust (53 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Cost-Effective AI: Total Cost of Ownership for LLM Deployments — A Practitioner’s Calculator

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18630010  48stabilfr·wdophcgmx
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[t]Trusted62%○≥80% from verified, high-quality sources
[a]DOI23%○≥80% have a Digital Object Identifier
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[l]Academic23%○≥80% from journals/conferences/preprints
[f]Free Access62%○≥80% are freely accessible
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]11%✗≥60% of references from 2025–2026. Current: 11%
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Score = Ref Trust (45 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Large Language Model deployments present enterprises with a deceptively complex cost structure that extends far beyond simple API pricing. After analyzing 47 enterprise LLM implementations across my consulting work, I have identified that organizations consistently underestimate their true Total Cost of Ownership by 340-580%, primarily due to overlooked indirect costs including prompt engineeri...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18630010 48stabilfr·wdophcgmx
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[s]Reviewed Sources15%○≥80% from editorially reviewed sources
[t]Trusted62%○≥80% from verified, high-quality sources
[a]DOI23%○≥80% have a Digital Object Identifier
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[l]Academic23%○≥80% from journals/conferences/preprints
[f]Free Access62%○≥80% are freely accessible
[r]References13 refs✓Minimum 10 references required
[w]Words [REQ]2,415✓Minimum 2,000 words for a full research article. Current: 2,415
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18630010
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]11%✗≥60% 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 (45 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Economics: Model Selection Economics — The Hidden Cost-Performance Tradeoffs That Make or Break AI ROI

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18629905  58stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources18%○≥80% from editorially reviewed sources
[t]Trusted82%✓≥80% from verified, high-quality sources
[a]DOI36%○≥80% have a Digital Object Identifier
[b]CrossRef18%○≥80% indexed in CrossRef
[i]Indexed82%✓≥80% have metadata indexed
[l]Academic36%○≥80% from journals/conferences/preprints
[f]Free Access82%✓≥80% are freely accessible
[r]References11 refs✓Minimum 10 references required
[w]Words [REQ]2,384✓Minimum 2,000 words for a full research article. Current: 2,384
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18629905
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]17%✗≥60% of references from 2025–2026. Current: 17%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (62 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Model selection represents one of the most consequential economic decisions in enterprise AI deployment, yet organizations consistently underestimate its financial implications. This paper examines the economics of choosing between model architectures—from simple linear regression to complex transformer networks—through the lens of total cost of ownership, inference economics, and organizationa...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18629905 58stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources18%○≥80% from editorially reviewed sources
[t]Trusted82%✓≥80% from verified, high-quality sources
[a]DOI36%○≥80% have a Digital Object Identifier
[b]CrossRef18%○≥80% indexed in CrossRef
[i]Indexed82%✓≥80% have metadata indexed
[l]Academic36%○≥80% from journals/conferences/preprints
[f]Free Access82%✓≥80% are freely accessible
[r]References11 refs✓Minimum 10 references required
[w]Words [REQ]2,384✓Minimum 2,000 words for a full research article. Current: 2,384
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18629905
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]17%✗≥60% of references from 2025–2026. Current: 17%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (62 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Economics: Bias Costs — Regulatory Fines, Legal Liability, and the Economics of Reputational Damage

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18627664  58stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources14%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI43%○≥80% have a Digital Object Identifier
[b]CrossRef21%○≥80% indexed in CrossRef
[i]Indexed64%○≥80% have metadata indexed
[l]Academic43%○≥80% from journals/conferences/preprints
[f]Free Access86%✓≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]5,280✓Minimum 2,000 words for a full research article. Current: 5,280
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18627664
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]11%✗≥60% 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]Diagrams8✓Mermaid architecture/flow diagrams. Current: 8
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (63 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Algorithmic bias represents one of the most economically significant risks in enterprise AI deployment, yet its true costs remain chronically underestimated in project planning. This article presents a comprehensive economic analysis of bias-related costs spanning regulatory penalties, legal liability, remediation expenses, and the often-catastrophic impact of reputational damage. Drawing from ...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18627664 58stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources14%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI43%○≥80% have a Digital Object Identifier
[b]CrossRef21%○≥80% indexed in CrossRef
[i]Indexed64%○≥80% have metadata indexed
[l]Academic43%○≥80% from journals/conferences/preprints
[f]Free Access86%✓≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]5,280✓Minimum 2,000 words for a full research article. Current: 5,280
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18627664
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]11%✗≥60% 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]Diagrams8✓Mermaid architecture/flow diagrams. Current: 8
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (63 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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