Highly Intelligible

XTRACTIS-induced models are intelligible: humans can fully understand the internal decision-making logic of the AI systems automating the decisions.

This chart shows the “#1 ranking” counts for INTELLIGIBILITY observed from 21 Public Use Cases with benchmarks.

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Highly Predictive

XTRACTIS-induced models are robust: their performance is at least as high as mainstream AI and data-driven modeling techs, as benchmarks prove. 

This chart shows the “#1 ranking” counts for PERFORMANCE  observed from 21 Public Use Cases with benchmarks.

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XTRACTIS vs. its 4 Challengers Benchmarks Results

The benchmarks of XTRACTIS, Logistic Regression, Random Forest, Boosted Tree, and Neural Network are carried out on Test and External Test datasets (all the unknown cases of the reference dataset, i.e., not used for training or validation).

In these graphs, the Intelligibility Score IS and the Performance Score PS are calculated from the 21 public Use Cases (UC) that include benchmarks of models. As Logistic Regression is inapplicable to Regression (UC#02, UC#15 and UC#23) and unavailable for UC#01, the graph#1 displays the scores calculated for the 21 UCs, but only for 4 modeling techniques. Graph#2 displays the scores of the 5 modeling techniques, calculated only for the 17 UCs for which Logistic Regression is available.

The center of a bubble corresponds to the point of coordinates IS -on top- and  PS -on bottom. A bubble on the top-right corner is the Holy Grail for critical AI-based decision systems: an AI Technique which produces predictive models with the highest Performance and the highest Intelligibility.

Public Use Cases on Real World Data

Our Use Cases present the results of XTRACTIS modeling and include complete Benchmarks against Neural Network, Boosted Tree, Random Forest, and Logistic Regression.

These studies illustrate the ability of XTRACTIS to automatically induce knowledge in the form of predictive and intelligible mathematical relationships from real-world data (public data or authorized private data).

For each application, we show how the induced décision system uses its fuzzy rules to compute explained predictions for new situations, i.e.  unknown to the learning data set.

Choose the sector to access the corresponding Use Cases.

HEALTH / PHARMA
BUSINESS / FINANCE
INDUSTRY / R&D
DEFENSE / SECURITY

XTRACTIS discovers how to identify foetus heart conditions based on signal characteristics of fetal heart rate and the mother’s uterine contractions. As a result, you get an explainable and accurate automated medical diagnosis.

UC#03 - Release: Sep. 2022 | Update: Mar. 2024

XTRACTIS discovers how to detect breast cancer based on topological characteristics of mammary cells. As a result, you get an explainable and accurate automated medical diagnosis.

UC#04 - Release: Sep. 2022 | Update: Mar. 2024

XTRACTIS discovers how to detect ovarian cancer based on the 15,154 mass-charge ratios coming from the protein spectrum of serum samples, and for a small number of patients. As a result, you get an explainable and accurate automated medical diagnosis.

UC#05 - Release: Sep. 2022 | Uodate: Mar. 2024

XTRACTIS discovers how to detect prostate cancer based on the expression levels of 12,600 genes and for a small number of patients. As a result, you get an explainable and accurate automated medical diagnosis.

UC#08 - Release: Oct. 2022 | Update: Mar. 2024

XTRACTIS discovers how to identify 2 types of lung cancer based on the expression levels of 12,533 genes and for a small number of patients. As a result, you get an explainable and accurate automated medical diagnosis.

UC#12 - Release: Jan. 2023 | Update: Mar. 2024

XTRACTIS discovers how to detect kidney disease based on the patient record and blood measures. As a result, you get an explainable and accurate automated medical diagnosis.

UC#14 - Release: Feb. 2023 | Update: Mar. 2024

XTRACTIS discovers how to detect Parkinson’s disease based on simple voice recordings of patients. As a result, you get an explainable and accurate automated medical diagnosis.

UC#17 - Release: May 2023 | Update: Mar. 2024

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XTRACTIS discovers the strategies to evaluate customer attrition and to assign a churning score to a customer in a rational way in order to adapt anti-churning strategies.

UC#22 - Release: Feb. 2024

XTRACTIS discovers the strategy behind credit card scams based on a few characteristics of each transaction, to instantly detect the fraudulent ones and to eventually adapt protection measures.

UC#20 - Release: Nov. 2023 | Update: Feb. 2024

XTRACTIS robots explore a 27-dimension decision space of possible supply chain configurations to find the one that maximizes at best Operating Income given requests on local sales objectives, local production constraints, and ecological requirements. Objectives and constraints are either fuzzy (flexible) or binary (rigid).

UC#19 - Release: Sep. 2023

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Design an AI-based decision system that efficiently diagnoses driving situations from a few vehicle parameters and traffic light color, to select instantly and rationally the appropriate longitudinal action.

UC#21 - Release: Oct. 2023 | Update: Feb. 2024

Automatic exploration of the 86-dimension continuous decision space of possible molecules formed by the 6 models (the molecule’s efficiency and its toxicity for 5 animal species), to satisfy simultaneously the 6 objectives with the highest possible degree: a new molecular profile that is an herbicide having the lowest toxicity for all species.

UC#18 - Release: Jun. 2023 | Update: Jan. 2024

Discovery of the behavior of concrete from its age, formulation and some of manufacturing characteristics to predict its compressive strength and thus the level of its degradation, thanks to an explainable and accurate automated diagnosis.

UC#15 - Release: Mar. 2023 | Update: Mar. 2024

Design an AI-based decision system that accurately predicts the upcoming risk of underwater pipes rupture considering the apparent complexity of the phenomenon, to plan rational maintenance operations.

UC#13 - Release: Feb. 2023 | Update: Feb. 2024

Design an AI-based decision system that accurately predicts the functional degradation of a naval propulsion unit compressor, given the hyper-complexity of the phenomenon (strongly nonlinear behavior) in order to rationally plan explainable maintenance operations.

UC#02 - Release: Aug. 2022 | Update: Mar. 2024

Discovery of effective emergency braking strategies from different driving situations to design an EBA that assists the driver by triggering automatically the ABS, while being able to explain each decision offline.

UC#01 - Release: Nov. 2021 | Update: Mar. 2024

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