Domain Applications

Domain applications show how the wiki’s modeling, evaluation, and engineering concepts appear in concrete settings. Every page carries the application topic so application pages can be found together through tags and backlinks.

Use these pages as integration examples: they show which canonical methods matter in a domain, what the real output contract is, what can go wrong, and which evaluation slices matter.

Knowledge map

Each application draws on the core methods sections; they group into text/recommendation, forecasting/operations, and vision/mobility (including autonomous driving), plus medical imaging.

flowchart TD
  Methods[Core Methods from Sections 03-12] --> Text[Text, Recommendation, Security Apps]
  Methods --> Ops[Forecasting and Operations Apps]
  Methods --> Vision[Vision, Video, and Mobility Apps]
  Vision --> Auto[Autonomous Driving and Evaluation]
  Methods --> Medical[Medical Imaging]

Reading path

The applications group by method family; read whichever cluster matches your interest.

1. Business Message Classification: text classification for message routing.
2. News Recommendation: recommendation under freshness and churn.
3. Matchmaking: reciprocal, two-sided recommendation.
4. Cultural Heritage Document Extraction and Entity Matching: OCR, extraction, and entity linking.
5. Malware Classification and Clustering: security detection and family discovery.
6. Demand Prediction in Logistics: hierarchical demand forecasting.
7. Energy Forecasting: load forecasting under weather and calendar effects.
8. Predictive Maintenance: failure and remaining-useful-life prediction.
9. Medical MRI Analysis: clinical imaging under validation constraints.
10. Gesture-Based Interaction: recognizing gestures for interfaces.
11. Real-Time Action Recognition: low-latency video understanding.
12. Road Scene Perception: detection and segmentation for driving.
13. Autonomous Driving: the full perception-prediction-planning stack.
14. Autonomous Driving Model Evaluation: safety-focused evaluation of driving models.

Connections

• Classical Machine Learning, Time-Series Forecasting, Computer Vision, and Recommendation Systems supply the methods these applications combine.
• Technical Answer Patterns helps turn these into concise explanations.