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 INTELLIGIBILITY “#1 ranking” for 14 Public Use Cases.
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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.Â
This chart shows the PERFORMANCE “#1 ranking” for 14 Public Use Cases.
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Public Use Cases on Real World Data
Our Use Cases present the results of Xtractis modeling in many application sectors and include complete Benchmarks against Neural Networks, Boosted Trees, Random Forests, 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 also show how this knowledge is used to obtain explained predictions for situations unknown from the learning data set.
Choose the sector to access the corresponding Use Cases.
Serological Diagnosis of Chronic Kidney Disease
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Discovery of strategies to detect kidney disease
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from the patient record and blood measures
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to make an explainable and accurate automated medical diagnosis
Genetic Identification of Lung Cancer
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Discovery of strategies to identify 2 types of lung cancer
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from the expression levels of 12,533 genes and for a small number of patients
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to make an explainable and accurate automated medical diagnosis
Genetic Diagnosis of Prostate Cancer
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Discovery of strategies to detect cancer
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from the expression levels of 12,600 genes and for a small number of patients
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to make an explainable and accurate automated medical diagnosis
Cardiotocographic Identification of Fetal Heart Conditions
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Discovery of strategies to identify heart conditions
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from signal characteristics of fetal heart rate and uterine contractions
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to make an explainable and accurate automated medical diagnosis
Spectrometric Diagnosis of Ovarian Cancer
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Discovery of strategies to detect cancer
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from the 15,154 mass/charge ratios originating from the protein spectrum of serum samples and for a small number of patients
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to make an explainable and accurate automated medical diagnosis
Anatomopathological Diagnosis of Breast Cancer
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Discovery of strategies to detect cancer
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from topological characteristics of mammary cells
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to make an explainable and accurate automated medical diagnosis
Prediction of the Rupture of a Flexible Underwater Pipe
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Discovery of the functional behavior of an underwater pipe
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from its composition and physical characteristics
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to predict its rupture thanks to an explainable and accurate automated diagnosis
Prediction of the Degradation of a Naval Propulsion Unit
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Discovery of the highly complex hidden behavior of compressor functional dysfunction
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from the turbine parameters
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to make an explainable and accurate automated decay prevention
Emergency Detection for an Automatic Braking Assist
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Discovery of effective emergency braking strategies
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from different driving situations
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to design an EBA that assists the driver by triggering automatically the ABS, while being able to explain each decision offline
Temporal Identification of Criminal Profiles & Action Phases from Communications Metadata
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Discovery of strategies to identify risky behaviors linked to criminal activities threatening Homeland Security
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only from analysis of communication metadata
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to make an explainable, automated and accurate diagnosis and to prevent attacks
Identification of UAV Intrusion Based on Wi-Fi Analysis
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Efficient identification of a type of invading Unmanned Aerial Vehicle (UAV) in a civilian environment
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only from Wi-Fi traffic data records
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to make an explainable, automated and accurate diagnosis and to prevent attacks
Log-based Identification of Cyber Intrusions
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Discovery of strategies of different types of cyber intrusion
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from features of connection logs
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to make an explainable, automated and accurate diagnosis and to prevent attacks
Acoustic Detection of Underwater Mines
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Discovery of strategies to detect mines
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from sonar echoes
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to equip vessels, submarines, and drones with a detector making explainable and accurate automated decisions
Log-based Detection of Cyber Intrusions
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Discovery of cyber intrusion strategies
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from features of connection logs
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to prevent attacks and understand the attacker's procedure
