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  65stabilfr·wuedophcgmx
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The artificial intelligence renaissance has created a gravitational pull toward machine learning 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% lower...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18650875 65stabilfr·wuedophcgmx
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AI Economics: Transfer Learning Economics — Leveraging Pre-trained Models

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18648770  78stabilfr·wuedophcgmx
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The machine learning field has undergone a fundamental shift in how models are developed. Understanding this shift is essential for grasping transfer learning economics.

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18648770 78stabilfr·wuedophcgmx
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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  55stabilfr·wuedophcgmx
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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 55stabilfr·wuedophcgmx
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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  76stabilfr·wuedophcgmx
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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 76stabilfr·wuedophcgmx
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Anticipatory Intelligence: Gap Analysis — Cold Start Problem in Predictive Modeling

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Academic Research
Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18648784  57stabilfr·wuedophcgmx
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In March 2020, Quibi launched with $1.75 billion in funding, 175 employees, and zero understanding of its audience. The mobile streaming platform had assembled an impressive content library—short-form episodes from A-list creators—but possessed no historical viewing data, no user behavior patterns, and no recommendation engine capable of surfacing relevant content to new subscribers. Within six...

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18648784 57stabilfr·wuedophcgmx
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[s]Reviewed Sources22%○≥80% from editorially reviewed sources
[t]Trusted51%○≥80% from verified, high-quality sources
[a]DOI24%○≥80% have a Digital Object Identifier
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[l]Academic30%○≥80% from journals/conferences/preprints
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AI Economics: AutoML Economics — When Automated Machine Learning Pays Off

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18644645  77stabilfr·wuedophcgmx
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[l]Academic79%○≥80% from journals/conferences/preprints
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Automated Machine Learning (AutoML) promises to democratize AI development by automating the traditionally labor-intensive processes of feature engineering, model selection, and hyperparameter optimization. This promise has driven explosive growth in the AutoML market, projected to reach $15.5 billion by 2030. However, the economic calculus of AutoML adoption remains poorly understood, with org...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18644645 77stabilfr·wuedophcgmx
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[s]Reviewed Sources24%○≥80% from editorially reviewed sources
[t]Trusted95%✓≥80% from verified, high-quality sources
[a]DOI71%○≥80% have a Digital Object Identifier
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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  60stabilfr·wuedophcgmx
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[t]Trusted62%○≥80% from verified, high-quality sources
[a]DOI23%○≥80% have a Digital Object Identifier
[b]CrossRef8%○≥80% indexed in CrossRef
[i]Indexed54%○≥80% have metadata indexed
[l]Academic23%○≥80% from journals/conferences/preprints
[f]Free Access62%○≥80% are freely accessible
[r]References13 refs✓Minimum 10 references required
[w]Words [REQ]2,427✓Minimum 1,000 words of article body. Current: 2,427
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Score = Ref Trust (45 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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 60stabilfr·wuedophcgmx
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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
[b]CrossRef8%○≥80% indexed in CrossRef
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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]2,427✓Minimum 1,000 words of article body. Current: 2,427
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (45 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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  70stabilfr·wuedophcgmx
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[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]2,417✓Minimum 1,000 words of article body. Current: 2,417
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[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (62 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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 70stabilfr·wuedophcgmx
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[t]Trusted82%✓≥80% from verified, high-quality sources
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[w]Words [REQ]2,417✓Minimum 1,000 words of article body. Current: 2,417
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18629905
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
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[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
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Score = Ref Trust (62 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Data Mining Chapter 6: Unsupervised Learning Taxonomy — Pattern Discovery Without Labels

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Data Science
Data Science by Iryna Ivchenko & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18648774  73stabilfr·wuedophcgmx
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[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI43%○≥80% have a Digital Object Identifier
[b]CrossRef39%○≥80% indexed in CrossRef
[i]Indexed46%○≥80% have metadata indexed
[l]Academic79%○≥80% from journals/conferences/preprints
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[r]References28 refs✓Minimum 10 references required
[w]Words [REQ]4,784✓Minimum 1,000 words of article body. Current: 4,784
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (67 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

This chapter develops a systematic taxonomy of unsupervised learning methods for data mining applications. We classify approaches across four major paradigms: clustering algorithms (partitional, hierarchical, and density-based), dimensionality reduction techniques (linear and nonlinear), self-organizing maps, and modern representation learning through autoencoders and deep generative models. Fo...

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Data Science by Iryna Ivchenko & Oleh Ivchenko DOI: 10.5281/zenodo.18648774 73stabilfr·wuedophcgmx
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[s]Reviewed Sources50%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI43%○≥80% have a Digital Object Identifier
[b]CrossRef39%○≥80% indexed in CrossRef
[i]Indexed46%○≥80% have metadata indexed
[l]Academic79%○≥80% from journals/conferences/preprints
[f]Free Access43%○≥80% are freely accessible
[r]References28 refs✓Minimum 10 references required
[w]Words [REQ]4,784✓Minimum 1,000 words of article body. Current: 4,784
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]28✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 28
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[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness4%○≥60% of references from 2025–2026. Current: 4%
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Score = Ref Trust (67 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Anticipatory Intelligence: Gap Analysis — Exogenous Variable Integration in RNN Architectures

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18648776  69stabilfr·wuedophcgmx
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[t]Trusted87%✓≥80% from verified, high-quality sources
[a]DOI45%○≥80% have a Digital Object Identifier
[b]CrossRef32%○≥80% indexed in CrossRef
[i]Indexed29%○≥80% have metadata indexed
[l]Academic55%○≥80% from journals/conferences/preprints
[f]Free Access55%○≥80% are freely accessible
[r]References31 refs✓Minimum 10 references required
[w]Words [REQ]3,735✓Minimum 1,000 words of article body. Current: 3,735
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18648776
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[x]Cited by0○Referenced by 0 other hub article(s)
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Recurrent neural networks (LSTMs, GRUs) dominate time series forecasting but share a critical architectural limitation: external signals—weather forecasts, economic indicators, news sentiment—enter through the same processing pathway as historical target data, competing for representational capacity rather than receiving dedicated attention. This article examines the $176 billion annual cost of...

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18648776 69stabilfr·wuedophcgmx
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[s]Reviewed Sources35%○≥80% from editorially reviewed sources
[t]Trusted87%✓≥80% from verified, high-quality sources
[a]DOI45%○≥80% have a Digital Object Identifier
[b]CrossRef32%○≥80% indexed in CrossRef
[i]Indexed29%○≥80% have metadata indexed
[l]Academic55%○≥80% from journals/conferences/preprints
[f]Free Access55%○≥80% are freely accessible
[r]References31 refs✓Minimum 10 references required
[w]Words [REQ]3,735✓Minimum 1,000 words of article body. Current: 3,735
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18648776
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness0%○≥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
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (60 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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