XTRACTIS for Naval Security
Identification of Underwater Sounds (Virtual Golden Ear)
Benchmark vs. Logistic Regression, Random Forests, Boosted Trees & Neural Networks
Identify the specific parameters involved in the identification of underwater sounds and enhance knowledge by helping submarine staff and acoustic experts understand the causal relationships between these parameters, their combination, and the type of sound.
Help to design a virtual “Golden Ear” (expert in underwater acoustics) operating 24/7/365 with the same quality of decision, or to design by simulation undetectable objects, or to design a 24/7/365 tutor for Human Golden Ear apprentices.
Assist the military profession in making more reliable and quicker decisions, thanks to rapid, systematic, and explainable identification process with usual sensors.
Avoid many false alarms thanks to transparent and accurate diagnosis.
- The top-model is a decision system composed of 18 gradual rules without chaining.
- Each rule uses from 1 to 4 predictors among the 23 potential predictors that XTRACTIS identified as all significant.
- Only a few rules are triggered at a time to compute the decision.
It has an excellent Real Performance (on unknown data).
It computes real-time predictions up to 70,000 decisions/second, offline or online (API).
LoR = Logistic Regression RFo=Random Forests
BT=Boosted Trees
NN=Neural Networks
Detailed results and explanations in full document
Use Case 2024/11 (v1.1)
Powered by XTRACTIS® REVEAL v13.0.51395 (2024/07)
CONTENTS
- Problem Definition
- XTRACTIS-induced Decision System
- XTRACTIS Process
- Top-Model Induction
- Explained Predictions for 3 unkown cases
- Top-Models Benchmark
- Quantitative Metrics
