Defense Procurement Transparency — Audit Frameworks for Wartime Spending

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Economic Research
Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.20181665  13stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.20181665 13stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
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The Legal Industry AI Transformation: From Research to Courtroom

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Technical Research
Technical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20168865  46stabilfr·wdophcgmx
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[a]DOI55%○≥80% have a Digital Object Identifier
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (52 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

The legal services sector is undergoing a profound transformation driven by artificial intelligence technologies that reshape economics and workflows across core domains. This article systematically investigates AI’s impact on e-discovery, contract analysis, legal writing, and courtroom preparation, addressing three critical research questions: (RQ1) How has AI altered cost structures and effic...

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Technical Research by Oleh Ivchenko DOI: 10.5281/zenodo.20168865 46stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted73%○≥80% from verified, high-quality sources
[a]DOI55%○≥80% have a Digital Object Identifier
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[w]Words [REQ]1,401✗Minimum 2,000 words for a full research article. Current: 1,401
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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]27%✗≥60% of references from 2025–2026. Current: 27%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (52 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Trusted Open Source AI in Healthcare: Curated Stack for Clinical AI

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Open Source Research
Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20151156  70stabilfr·wdophcgmx
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[t]Trusted93%✓≥80% from verified, high-quality sources
[a]DOI79%○≥80% have a Digital Object Identifier
[b]CrossRef64%○≥80% indexed in CrossRef
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[l]Academic86%✓≥80% from journals/conferences/preprints
[f]Free Access93%✓≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]1,703✗Minimum 2,000 words for a full research article. Current: 1,703
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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]77%✓≥60% of references from 2025–2026. Current: 77%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
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[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (83 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Open-source AI technologies are rapidly transforming healthcare, especially in clinical decision support (CDS) where timely, transparent, and auditable models are essential [1]. Despite this momentum, trust in open-source solutions remains fragmented due to limited standardized evaluation frameworks and provenance tracking [2]. To address these challenges, we introduce a curated stack that aggr...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.20151156 70stabilfr·wdophcgmx
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[s]Reviewed Sources50%○≥80% from editorially reviewed sources
[t]Trusted93%✓≥80% from verified, high-quality sources
[a]DOI79%○≥80% have a Digital Object Identifier
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[l]Academic86%✓≥80% from journals/conferences/preprints
[f]Free Access93%✓≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]1,703✗Minimum 2,000 words for a full research article. Current: 1,703
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20151156
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]77%✓≥60% of references from 2025–2026. Current: 77%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (83 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Cross-Border AI Explanation Requirements: Specifying XAI for Multi-Jurisdictional Compliance

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Academic Research
Academic Research by Oleh Ivchenko  51stabilfr·wdophcgmx
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[f]Free Access92%✓≥80% are freely accessible
[r]References26 refs✓Minimum 10 references required
[w]Words [REQ]1,947✗Minimum 2,000 words for a full research article. Current: 1,947
[d]DOI [REQ]✗✗Zenodo DOI registered for persistent citation
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]64%✓≥60% of references from 2025–2026. Current: 64%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
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[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (61 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Artificial intelligence systems are increasingly deployed across jurisdictions that impose distinct obligations on the transparency and interpretability of model decisions. While the European Union’s AI Act establishes a comprehensive framework for high‑risk AI, the United States relies on sector‑specific Executive Orders and guidance from the National Institute of Standards and Technology (NIS...

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Academic Research by Oleh Ivchenko 51stabilfr·wdophcgmx
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[t]Trusted81%✓≥80% from verified, high-quality sources
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[l]Academic77%○≥80% from journals/conferences/preprints
[f]Free Access92%✓≥80% are freely accessible
[r]References26 refs✓Minimum 10 references required
[w]Words [REQ]1,947✗Minimum 2,000 words for a full research article. Current: 1,947
[d]DOI [REQ]✗✗Zenodo DOI registered for persistent citation
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]64%✓≥60% of references from 2025–2026. Current: 64%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (61 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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The Open Source XAI Ecosystem: Gaps, Opportunities, and Trusted Projects to Watch

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Open Source Research
Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20115253  64stabilfr·wdophcgmx
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[t]Trusted84%✓≥80% from verified, high-quality sources
[a]DOI78%○≥80% have a Digital Object Identifier
[b]CrossRef3%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic81%✓≥80% from journals/conferences/preprints
[f]Free Access89%✓≥80% are freely accessible
[r]References37 refs✓Minimum 10 references required
[w]Words [REQ]2,239✓Minimum 2,000 words for a full research article. Current: 2,239
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20115253
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]67%✓≥60% of references from 2025–2026. Current: 67%
[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 (63 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)

Explainable Artificial Intelligence (XAI) has moved from niche academic curiosity to a cornerstone of responsible AI deployment in enterprises worldwide. Recent industry surveys indicate that 68% of Fortune 500 companies now require interpretability mechanisms for any production model, yet only 24% of open-source AI libraries provide robust, production-grade explanation tools (see [1], [2], [3]...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.20115253 64stabilfr·wdophcgmx
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[s]Reviewed Sources3%○≥80% from editorially reviewed sources
[t]Trusted84%✓≥80% from verified, high-quality sources
[a]DOI78%○≥80% have a Digital Object Identifier
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[i]Indexed5%○≥80% have metadata indexed
[l]Academic81%✓≥80% from journals/conferences/preprints
[f]Free Access89%✓≥80% are freely accessible
[r]References37 refs✓Minimum 10 references required
[w]Words [REQ]2,239✓Minimum 2,000 words for a full research article. Current: 2,239
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20115253
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]67%✓≥60% of references from 2025–2026. Current: 67%
[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 (63 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)
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Reconstruction Economics — Preventing Shadow Economy Capture of Rebuilding Funds

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Economic Research
Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.20113107  45stabilfr·wdophcgmx
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[a]DOI60%○≥80% have a Digital Object Identifier
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[i]Indexed0%○≥80% have metadata indexed
[l]Academic70%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References10 refs✓Minimum 10 references required
[w]Words [REQ]1,584✗Minimum 2,000 words for a full research article. Current: 1,584
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20113107
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams0○Mermaid architecture/flow diagrams. Current: 0
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (55 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)

1 Ivchenko, Oleh, Ivchenko, Iryna 3 Reconstruction Economics — Preventing Shadow Economy Capture of Rebuilding Funds. Research article: Reconstruction Economics — Preventing Shadow Economy Capture of Rebuilding Funds. Odessa National Polytechnic University, Department of Economic Cybernetics. DOI: 10.5281/zenodo.20113107  ·  View on Zenodo (CERN) 1 Ivchenko, O. & Ivchenko, I. 3 R...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.20113107 45stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted80%✓≥80% from verified, high-quality sources
[a]DOI60%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
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[l]Academic70%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References10 refs✓Minimum 10 references required
[w]Words [REQ]1,584✗Minimum 2,000 words for a full research article. Current: 1,584
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20113107
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams0○Mermaid architecture/flow diagrams. Current: 0
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (55 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)
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The Financial Industry AI Transformation: From Trading to Compliance

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Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20110009  55stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI78%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed11%○≥80% have metadata indexed
[l]Academic89%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References9 refs○Minimum 10 references required
[w]Words [REQ]1,417✗Minimum 2,000 words for a full research article. Current: 1,417
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20110009
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]56%✗≥60% of references from 2025–2026. Current: 56%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (67 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

The financial services sector is undergoing a profound transformation driven by artificial intelligence, with algorithmic trading, fraud detection, credit underwriting, and regulatory compliance representing key application domains. This article examines the current state of AI adoption across these domains, analyzing both the technological innovations and the associated risks. Through a synthe...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.20110009 55stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI78%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed11%○≥80% have metadata indexed
[l]Academic89%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References9 refs○Minimum 10 references required
[w]Words [REQ]1,417✗Minimum 2,000 words for a full research article. Current: 1,417
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20110009
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]56%✗≥60% of references from 2025–2026. Current: 56%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (67 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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The Healthcare AI Transformation Map: From Diagnosis to Treatment Planning

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Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20103434  77stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources71%○≥80% from editorially reviewed sources
[t]Trusted88%✓≥80% from verified, high-quality sources
[a]DOI76%○≥80% have a Digital Object Identifier
[b]CrossRef71%○≥80% indexed in CrossRef
[i]Indexed71%○≥80% have metadata indexed
[l]Academic82%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]2,490✓Minimum 2,000 words for a full research article. Current: 2,490
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20103434
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]81%✓≥60% of references from 2025–2026. Current: 81%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams1✓Mermaid architecture/flow diagrams. Current: 1
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (84 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)

The transformation of healthcare through artificial intelligence is no longer a speculative vision but an unfolding reality that reshapes diagnostic workflows, treatment personalization, drug discovery, and operational efficiency across clinical ecosystems. Despite rapid advances, the sector grapples with fragmented adoption pathways, regulatory uncertainty, and the challenges of integrating AI...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.20103434 77stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources71%○≥80% from editorially reviewed sources
[t]Trusted88%✓≥80% from verified, high-quality sources
[a]DOI76%○≥80% have a Digital Object Identifier
[b]CrossRef71%○≥80% indexed in CrossRef
[i]Indexed71%○≥80% have metadata indexed
[l]Academic82%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]2,490✓Minimum 2,000 words for a full research article. Current: 2,490
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20103434
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]81%✓≥60% of references from 2025–2026. Current: 81%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams1✓Mermaid architecture/flow diagrams. Current: 1
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (84 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)
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The ISO/IEC 24027 Bias in AI Explanations: Specification Implications

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Academic Research
Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20095806  49stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
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Explainability frameworks increasingly intertwine technical desiderata with normative commitments, yet the standards community struggles to reconcile algorithmic transparency with equitable outcomes. ISO/IEC 24027—Artificial intelligence—Explainability requirements for AI systems—offers the first international attempt to codify explanatory integrity, but its pragmatic implementation e[REDACTED]...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.20095806 49stabilfr·wdophcgmx
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Trusted Open Source AI in Finance: Compliance-Ready Stack for Financial AI

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Technical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20084678  70stabilfr·wdophcgmx
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Financial regulators worldwide are accelerating the integration of explainable AI into supervised lending, risk assessment, and algorithmic trading workflows. Despite rapid adoption of open source models, few solutions provide built-in compliance metadata, audit trails, and verifiable explanation frameworks that satisfy emerging jurisdictional standards. This article addresses this gap by prese...

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Technical Research by Oleh Ivchenko DOI: 10.5281/zenodo.20084678 70stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
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Score = Ref Trust (69 × 60%) + Required (4/5 × 30%) + Optional (2/4 × 10%)
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