Table of Contents
LMFO AI (Layered Multiple Frequency Optimization) is an artificial-intuition–driven decision support system designed for real-world environments with limited data and low signal-to-noise ratios (SNR). Through context-aware pattern recognition, LMFO distinguishes critical information from background noise and derives actionable patterns.
Weak signal → Contextual analysis → Strong pattern → Decision support
LMFO AI combines machine learning (ML) with metaheuristics, and goes further. It preserves weak signals that gain meaning in context (beyond ML’s strong-signal bias) and steers search with data-driven prioritization, adaptive search strategies (beyond random exploration in metaheuristics).
Implemented in Python as a prototype, LMFO was validated on public real-world datasets with limited data and high noise. Extensive testing confirmed its ability to deliver consistent, robust, and generalizable results.
Originally developed for predictive maintenance and risk analytics, its modular and scalable architecture enables seamless adaptation across domains — including industrial automation, automotive, energy, finance, and bioinformatics — where reliable insights under uncertainty are required.
→ Problem: High noise, limited data
→ Solution: Layered, data-driven, context-aware inference
→ Proof: Independent, statistically validated results showing generalization, robustness, and consistency
LMFO Workflow At a Glance
End-to-end workflow: from noisy data to validated patterns
LMFO Architecture At a Glance
Layered context pass (L1 → L3)
LMFO Real-World Impact At a Glance
Reliable decision support in scarce data, high noise environments
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Multi-Layer Architecture (L1 → L2 → L3)
Progressively narrows large, noisy search spaces; each layer refines candidate patterns in a coarse-to-fine flow. -
Context-Aware Inference (Strong + Weak Signals)
Retains weak as well as strong signals; uses context to turn them into meaningful patterns. -
Experience Transfer & Representation Balancing
Transfers experience across runs — using prior signal blocks as context — and rebalances under-represented signals to preserve rare, high-value patterns. -
Data-Driven Prioritization & Adaptive Search
Ranks candidates by information value and relevance, focuses on high-potential regions, and redirects away from low-yield areas as feedback accumulates. -
Relationship-Based Pattern Recognition
Regroups signals by context to build richer patterns, and restores context-relevant features lost during reduction — recovering diversity without increasing dimensionality. -
Validated, Reproducible Prototype
Independently tested on limited, high-noise real-world datasets; delivers robust, generalizable results.
Real-world business value of LMFO AI
- Problem — A line with ~100 sensors shows ~25 abnormal signals during a failure, but combinations vary each time (no single “failure signature”); the solution space is astronomical (~2×10^23).
- LMFO’s contribution — From limited failure records, extracts 10-sensor indicator patterns; issues graded alerts (5–6 = early, 7–8 = strong, 9–10 = critical), reducing false positives and anticipating unseen scenarios.
- Business value — Industry reports for predictive maintenance cite 30–50% less unplanned downtime, 20–40% longer equipment life, 5–10% lower maintenance costs, and 10–20% higher uptime (McKinsey 2017; Deloitte 2017; PwC/Mainnovation 2018; Siemens–Senseye case studies 2021–2024).
Feature-agnostic, layered architecture that generalizes across domains:
- Cybersecurity (SOC / Threat Detection) — Surfaces 8 critical signatures from ~90 log types; grades threat levels by partial matches.
- Finance / Fraud Analytics — Extracts a 7-indicator pattern from ~80 risk indicators; scores transaction flows in real time by match level.
- Energy / Smart Grid — Finds 10 critical patterns from ~75 parameters; anticipates and mitigates regional failures in advance.
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Dataset — Public On Numara (“Number Ten”, Turkey): extremely low SNR, limited observations, and a vast combinatorial search space.
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Problem framing — 80 items; 22 are active per event. The active items vary across events—there is no single "critical signature". This creates high uncertainty at scale.
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Challenges
- Scale issue: astronomical number of potential combinations — C(80,22) ≈ 2.7×10¹⁹
- Data issue: only ~1.200 events observed — >99,99% of the space remains unseen
- Noise issue: weak yet context-relevant signals are easily lost
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Real-World Relevance — Mirrors real-world cases where, under limited data and low SNR, critical event signatures must be identified through indicative patterns, even before all indicators fully emerge.
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Indicative-pattern solution — Extract 10-element indicator patterns from the 22 active items per event, yielding:
- Scalability: shrinks the search into a focused domain
- Focused visibility: turns limited observations into a tractable solution space
- Signal preservation: retains weak-but-contextual signals
Scaling At a Glance
From astronomical search space to a tractable solution space — with large relative data gain.
As pattern intensity increases, signals become exponentially rarer—hence sustaining performance at higher match levels (9/10–10/10) is especially challenging.
Pattern & Signal Intensity
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Approach — Instead of parameter optimization, LMFO directly optimizes indicative patterns — signals that may appear meaningless individually but gain significance in context. Performance is evaluated at the pattern level, not on individual signals.
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Protocol
- Validation set — held out from training to get the first performance signal
- Test set — independent data to verify generalization
- Stepwise evaluation — assess performance across pattern-intensity buckets (e.g., 5/10 … 10/10) for consistency and robustness
- Joint assessment — compare validation vs test to confirm stability
Validation & test workflow
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Metrics
- PRS (Pattern Recognition Score) — accuracy normalized by signal density (>1 = above expected; <1 = below)
- EPS (Expected Pattern Score) — standardized scale with baseline = 1
- Significance — one-tailed z-test, p < 0,05
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Interpretation example
- 8/10 bucket: PRS = 1,72 → 72% above expected
- 9/10 bucket: PRS = 0,95 → 5% below expected
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Initialization — Runs start either from scratch or with small seeds (3–4 items) to trigger context analysis from Layer 1.
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Seeding regimes — Two core regimes are evaluated: optimized seeds (from prior runs) and random seeds; both are supplied as initial solutions.
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Layered expansion — Seeds propagate across layers (L1→L2→L3) and consolidate into 10-item indicative patterns via a coarse-to-fine flow.
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Objectives:
- assess pattern recognition under high noise
- test whether weak signals become strong in context
- verify extraction from random seeds
- check consistency across initial conditions
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Evaluation setup — Multiple predefined configs; train on train, validate on val, test on test; results averaged across configs (metrics: PRS, EPS).
Across all signal densities (6/10–10/10), LMFO exceeds the expected baseline (EPS=1) with statistical significance (p < 0,05).
- 6/10–7/10 (high): 31–54% higher score
- 8/10 (medium): 81% higher score
- 9/10–10/10 (low): ×2,64 and ×8,12 higher score
Takeaway: Patterns extracted under low signal density achieved scores up to 8× higher than the baseline (EPS = 1). This finding highlights LMFO’s mechanism of “reconstructing weak signals within context”.
Note: In the 10/10, random references deviated from baseline, but not significant (p = 0,2556).
Across 6/10–9/10, results still remain above baseline (p < 0,05); but 10/10 not significant (p = 0,4325).
- 6/10–7/10 (high): 5–8% higher score
- 8/10 (medium): 19% higher score
- 9/10 (low): 86% higher score
Takeaway: Even with no prior information, the strong performance at low signal density (86%) demonstrates the algorithm’s ability to "quickly identify relevant signals" and "adaptively reorient the search". These results confirm LMFO’s robustness under high-noise and low-signal conditions.
Validation trends are preserved on the independent data (6/10–9/10 significant at p < 0,05).
- 6/10–7/10 (high): 30–34% higher score
- 8/10 (medium): 78% higher score
- 9/10 (low): ×2,68 higher score
Takeaway: Context-aware inference generalizes beyond validation; stability holds across densities 6/10–9/10.
Note: At the weakest signal density (10/10), no statistically significant difference was observed (p = 0,5956), likely due to the limited size of the test set.
Generalization maintained across all signal densities in the 6/10–9/10 range even with random seeds (p < 0,05).
- 6/10–7/10 (high): 5–18% higher score
- 8/10 (medium): 39% higher score
- 9/10 (low): ×2,80 higher score
- 10/10 (weakest): ×7,36 higher score (significant at 90%, p = 0,0691)
Takeaway: Robustness persists with random seeds; strongest lifts appear in low-signal regimes.
The table below summarizes the pattern scores and significance levels across the validation and test sets by density and seed type.
LMFO extracts stable, generalizable patterns under both strong and weak contextual conditions.
Pattern recognition performance of LMFO across different signal densities in the validation and test sets:
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High & medium (6/10–8/10)
- Optimized seeds: Validation ≈ Test (1,30–1,81).
- Random seeds: Lower (1,05–1,39) yet above baseline; generalization preserved.
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Low (9/10–10/10)
- Optimized seeds: 9/10 strong & stable (2,64 → 2,68); 10/10 strong on validation (8,12) but not significant on test.
- Random seeds: 9/10 consistent & significant (1,86 → 2,80); 10/10 not significant on validation (1,46) but 7,36× on test (90% level).
Takeaway: LMFO remains reliable under high noise and the largest gains emerge as signal density decreases. The consistency between validation and test sets demonstrates the algorithm’s ability to deliver reliable and generalizable results in challenging scenarios. Moreover, maintaining consistency across both different initialization conditions and varying signal densities strongly confirms LMFO’s robustness.
To ensure full reproducibility and transparency, all system files, control tools, and supplementary guides are included in:
This package contains:
- Python Core (L1–L3 + Runner) — main LMFO algorithm
- Control File — companion tool for data preparation, decoding, and validation/test workflows
- Supplementary READMEs — detailed guides covering Control File usage, LMFO run procedures, and step-by-step validation/test reproduction
- Benchmark Reports — Excel tables, charts, and screenshots of validation/test results
The package is also pre-configured with a quick run profile (3 optimized seeds, short runtime).
This allows users to verify the workflow end-to-end (run → output → validation) in just a few minutes, before moving on to full-scale runs with all seeds and profiles.
- Python 3.9+
- Packages:
numpy,pandas,imblearn
- Clone the repository
- Run
RUN_LMFO.py - Open
control_file-validation.xlsm→ LMFO Tab → click Decode Set and then Test Set
For readers interested in the architectural foundations, intuition-driven mechanisms, and real-world applications of LMFO—in particular, its conceptual “artificial intuition” framework—check out this detailed blog article.
It delves into the layered design, data-driven prioritization, validation pipeline, and business value in far greater depth than the README.