AI System Invariants: Identifying and Encoding the Properties That Must Never Change

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Academic Research
Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21899296  69stabilfr·wdophcgmx
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Artificial intelligence systems are increasingly deployed in safety-critical, high-stakes domains where failure can have severe societal consequences. Despite growing attention to AI safety, there remains no systematic methodology for identifying and enforcing invariant properties — characteristics that must remain unchanged across model versions, configuration updates, and deployment contexts....

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.21899296 69stabilfr·wdophcgmx
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[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI92%✓≥80% have a Digital Object Identifier
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[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]2,069✓Minimum 2,000 words for a full research article. Current: 2,069
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21899296
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]55%✗≥60% of references from 2025–2026. Current: 55%
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[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)
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AI in Conflict Zone Logistics: Autonomous Supply Chain Optimization Under Adversarial Conditions

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Geopolitical Research
Geopolitical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21893428  63stabilfr·wdophcgmx
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[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI82%✓≥80% have a Digital Object Identifier
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[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References11 refs✓Minimum 10 references required
[w]Words [REQ]1,828✗Minimum 2,000 words for a full research article. Current: 1,828
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21893428
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]80%✓≥60% of references from 2025–2026. Current: 80%
[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 (71 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Autonomous supply chain optimization in conflict-adjacent environments presents unique challenges characterized by adversarial interference, dynamic demand fluctuations, and humanitarian constraints. This article investigates the performance of three algorithmic frameworks—reinforcement l[REDACTED]g-based routing, graph neural network forecasting, and mixed‑integer programming optimization—unde...

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Geopolitical Research by Oleh Ivchenko DOI: 10.5281/zenodo.21893428 63stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI82%✓≥80% have a Digital Object Identifier
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[l]Academic100%✓≥80% from journals/conferences/preprints
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[r]References11 refs✓Minimum 10 references required
[w]Words [REQ]1,828✗Minimum 2,000 words for a full research article. Current: 1,828
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21893428
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]80%✓≥60% of references from 2025–2026. Current: 80%
[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 (71 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Iterative Quality Improvement in AI Writing Pipelines: Measuring Redactor Cycle Effectiveness

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Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21884458  66stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted97%✓≥80% from verified, high-quality sources
[a]DOI93%✓≥80% have a Digital Object Identifier
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[l]Academic97%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References29 refs✓Minimum 10 references required
[w]Words [REQ]1,792✗Minimum 2,000 words for a full research article. Current: 1,792
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21884458
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]61%✓≥60% of references from 2025–2026. Current: 61%
[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 (75 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

This article investigates how iterative refinement cycles within AI‑driven writing pipelines affect overall content quality across multiple measurable dimensions. We designed a closed‑loop workflow where an initial draft generated by a large language model undergoes automated editorial evaluation, targeted revisions, and re‑generation, repeating until convergence criteria are met or a maximum i...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.21884458 66stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted97%✓≥80% from verified, high-quality sources
[a]DOI93%✓≥80% have a Digital Object Identifier
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[l]Academic97%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References29 refs✓Minimum 10 references required
[w]Words [REQ]1,792✗Minimum 2,000 words for a full research article. Current: 1,792
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21884458
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]61%✓≥60% of references from 2025–2026. Current: 61%
[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 (75 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Causal Graph-Based Observability for Multi-Modal AI Pipelines

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Technical Research
Technical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21880464  64stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted98%✓≥80% from verified, high-quality sources
[a]DOI96%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic98%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References54 refs✓Minimum 10 references required
[w]Words [REQ]1,069✗Minimum 2,000 words for a full research article. Current: 1,069
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21880464
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]98%✓≥60% of references from 2025–2026. Current: 98%
[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 (73 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The rapid deployment of multi-modal AI systems—integrating vision, language, and sensor streams—has exposed critical gaps in interpretable fault diagnosis and root-cause attribution. This article proposes a causal graph–based observability framework that embeds probabilistic causality into the data flow of AI pipelines, enabling precise identification of failure origins and adaptive remediation...

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Technical Research by Oleh Ivchenko DOI: 10.5281/zenodo.21880464 64stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted98%✓≥80% from verified, high-quality sources
[a]DOI96%✓≥80% have a Digital Object Identifier
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[l]Academic98%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References54 refs✓Minimum 10 references required
[w]Words [REQ]1,069✗Minimum 2,000 words for a full research article. Current: 1,069
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21880464
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]98%✓≥60% of references from 2025–2026. Current: 98%
[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 (73 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Infrastructure Cost Attribution: Chargeback Models for Internal AI Platform Teams

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21877823  39stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic67%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]988✗Minimum 2,000 words for a full research article. Current: 988
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21877823
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]50%✗≥60% of references from 2025–2026. Current: 50%
[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 (40 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Internal AI platform teams face significant challenges in transparently charging back infrastructure costs to business units. Current metering approaches often lack fairness considerations and fail to provide clear adoption incentives. This article resolves critical gaps in cost attribution frameworks by analyzing state-of-the-art models and proposing a novel integrated approach. We address thr...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.21877823 39stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic67%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]988✗Minimum 2,000 words for a full research article. Current: 988
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21877823
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]50%✗≥60% of references from 2025–2026. Current: 50%
[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 (40 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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AI Value Attribution in Multi-System Workflows: Untangling ROI When AI is One of Many Tools

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21867269  39stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted60%○≥80% from verified, high-quality sources
[a]DOI40%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic60%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References5 refs○Minimum 10 references required
[w]Words [REQ]1,497✗Minimum 2,000 words for a full research article. Current: 1,497
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21867269
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[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 (40 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Enterprises increasingly deploy heterogeneous AI solutions across finance, operations, and customer service. While each AI component promises efficiency gains, the fragmented nature of these deployments makes it difficult to isolate the contribution of AI to overall return on investment (ROI). This article addresses the core challenge: how to attribute business value to AI when it operates as p...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21867269 39stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted60%○≥80% from verified, high-quality sources
[a]DOI40%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic60%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References5 refs○Minimum 10 references required
[w]Words [REQ]1,497✗Minimum 2,000 words for a full research article. Current: 1,497
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21867269
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[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 (40 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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The Governance Gap: How AI Policy Voids Block Adoption in Regulated Industries

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Gap Research
Gap Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21863068  61stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted95%✓≥80% from verified, high-quality sources
[a]DOI84%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic89%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]1,372✗Minimum 2,000 words for a full research article. Current: 1,372
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21863068
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]88%✓≥60% of references from 2025–2026. Current: 88%
[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 (68 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Regulated industries such as finance, healthcare, and energy are encountering a critical adoption barrier: ambiguous internal AI governance frameworks that fail to translate high‑level policy commitments into actionable technical and operational procedures. This article investigates how this governance gap manifests across three sectors, quantifies its impact on deployment timelines, and propos...

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Gap Research by Oleh Ivchenko DOI: 10.5281/zenodo.21863068 61stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted95%✓≥80% from verified, high-quality sources
[a]DOI84%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic89%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]1,372✗Minimum 2,000 words for a full research article. Current: 1,372
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21863068
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]88%✓≥60% of references from 2025–2026. Current: 88%
[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 (68 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Reproducibility Infrastructure for Open-Source AI: MLflow, DVC, and Weights & Biases at Scale

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Open Source Research
Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21857228  57stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted96%✓≥80% from verified, high-quality sources
[a]DOI93%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic96%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References28 refs✓Minimum 10 references required
[w]Words [REQ]100✗Minimum 2,000 words for a full research article. Current: 100
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21857228
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]4%✗≥60% of references from 2025–2026. Current: 4%
[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 (71 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Open-source AI projects increasingly rely on experiment tracking and reproducibility infrastructures to ensure that results can be independently replicated. Despite the growing importance of reproducibility, many projects struggle to preserve the full context of experiments, leading to gaps in verification and trust. This article evaluates the capability of three prominent tools—MLflow, DVC, an...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.21857228 57stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted96%✓≥80% from verified, high-quality sources
[a]DOI93%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic96%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References28 refs✓Minimum 10 references required
[w]Words [REQ]100✗Minimum 2,000 words for a full research article. Current: 100
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21857228
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]4%✗≥60% of references from 2025–2026. Current: 4%
[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 (71 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Mixture of Experts Scaling Laws: What MoE Architectures Mean for 2025-2026 Model Development

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Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21851091  63stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted95%✓≥80% from verified, high-quality sources
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Mixture of Experts (MoE) architectures have emerged as a pivotal scaling strategy for large language models, promising superior capacity efficiency and specialized functional modularity. This article interrogates the scaling behaviors of MoE systems—specifically Mixtral, DeepSeek‑MoE, and Grok‑1—relative to conventional dense models, aiming to elucidate predictive patterns for frontier model de...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.21851091 63stabilfr·wdophcgmx
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Score = Ref Trust (70 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Real-Time Fraud Detection in Mobile Payments: Behavioral Biometrics and Transaction Anomaly Fusion

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Economic Research
Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.21844580  43stabilfr·wdophcgmx
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Mobile payment ecosystems generate rich behavioral signals during user interaction, including typing patterns, device motion, and transaction graph topology. This article investigates the efficacy of fusing these behavioral biometrics with graph-based anomaly detection to achieve real-time fraud detection in mobile payment platforms. We address three core research questions: (RQ1) How do behavi...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.21844580 43stabilfr·wdophcgmx
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