Unleash your data full potential
Time series modelling and projection
We offer our clients a better understanding of their activity’s trends, seasonalities and regime shift and a wide range of efficient models and tools.
Deploy the latest AI methods
Our data scientists design, develop, and deploy machine learning tools and algorithms that make your products and operations smarter.
Optimization power to enhance Datascience
The combination of DataScience and optimization offers extended perspectives in terms of accuracy and robustness for business problems resolution.
Making visualization a business tool that your teams can use
Through maps, charts and simulations our ad-hoc graphical interfaces, helps to objectify your decisions on a quantitative basis
Simulation & Risk Management
Be ready and prepared when the real thing happens
We deliver ’What if’ and Monte Carlo simulations to our clients, and implement the latest techniques of probabilistic forecast
A redesigned relationship between Man and Machine
MMT was launched and financed by the French General Direction of Armies (DGA). It is animated by the companies Dassault Aviation and Thales. Artelys is part of the ecosystem of partners, composed of French start-ups, SMEs and research centers.
As part of the Maestro project, Artelys is exploring the application of Monte-Carlo Tree Search algorithm and Reinforcement Learning to aerial trajectory optimization through Constraint Programming.
Official information: https://man-machine-teaming.com/
Data Science and AI to support crucial usecases in the healthcare sector
For 3 years, Artelys has been supporting the ANAP to implement techniques which solve concrete use cases. Those technics involve statistical data analysis, AI prototypes and have contributed to a strategy of internal acculturation to Data Science tools and Agile methodology.
Several use cases have been identified with the experts and have given rise to different services:
- Development and integration of a predictive module of the occupation rate of beds embedded in an existing tool for hospitals.
- Segmentation of the users of the web content of ANAP, according to their user profile. Statistics and clustering methods (K-Means) have been exploited to build efficient user personae.
- Analysis of the adequacy between healthcare professional demands and content offered by ANAP on its website. A list of clusters of interest have emerged thanks to a segmentation method (Hierarchical Ascendant Classification for biclustering) and led to recommendation concerning the structure of the offer.
- State of play of the real estate owned by the medico-social establishment in France. Creation and analysis of tailored indicators.
- Development of a configurable web tool using text mining to explore the thematic of Twitter posts relative to healthcare issues. Emergence of relationship between popular themes thanks to Natural Language Processing (NLP).
Gather the diverse available data streams to understand and act for road safety
The Loiret department is a member of the European project INTERREG NWE BE-GOOD and is in charge of the challenge “SAFER ROADS” whose goal is to bring characteristics by data use for road safety improvement. It means better understanding of the accident contexts and more efficient prevention actions.
One important stake is to get the data available and actionable by concerting the diverse actors of mobility sector who own it.
Artelys organized workshops in which several actors collaborated to provide data streams and specify their needs: public administration of the department (GIS, Road Observatory, Infrastructure administration), police forces (Gendarmerie Nationale), firemen (SDIS), insurance companies (Mondial Assistance, Thelem) and navigation services (Waze, Coyote).
A web platform has been developed by Artelys and made available to the public administration, stakeholders but also to citizens. It contains:
- A data warehouse with harmonized multi-source data streams and additional information on the context. This data are directly usable through requests or simple dashboard visualization.
- More than 24 000 accidents from the previous 10 years located on the road network and complemented by infrastructure and context information.
- 8 thematic dashboards to analyze influence of multiple variables on the accident risk (infrastructure, speed, traffic, seasonality, weather, accident situation, aggravating factors).
- A prediction algorithm which evaluates, on the road network, the risk level of accidents for a given future context. This algorithm is being developed and relies on supervised machine learning using decision trees.
- A clustering algorithm which has made some reference profiles of accidents emerged.
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