Research
Advestis has been developing and operating ML algorithms in commercial production since 2013.
Since 2014, Advestis has grown an expertise in ESG features engineering
and solving complex problems involving them.
Explore our Research below
Integrated sources model: A new space-learning model for heterogeneous multi-view data reduction, visualization, and clustering
Applying non-negative tensor factorization to centered data
Impact of surgical approach on 90-day mortality after lung resection for non-small cell lung cancer in high-risk operable patients
An adaptative approach for estimating the remaining useful life of a heavy-duty fuel cell vehicle
Using Federated Learning for Collaborative Intrusion Detection Systems
On Rank Selection in Non-Negative Matrix Factorization using Concordance
Preventing Acute Limb Ischemia during VA-ECMO—In Silico Analysis of Physical Parameters
Using Federated Learning for collaborative intrusion detection systems
ChatGPT: Usages, impacts & recommandations
Time to market reduction for hydrogen fuel cell stacks using Generative Adversarial Networks
Generative Adversarial Networks applied to synthetic financial scenarios generation
CHAT GPT : USAGES, IMPACTS ET RECOMMANDATIONS
Analysis of the Relevance of Sentiment Data for the Prediction of ERs in a Multi-Asset Framework
Usage of GAMS-Based Digital Twins and Clustering to Improve Energetic Systems Control
Partenariat SIMSEO Advestis x CRIANN
A Pilot Study on the Use of Generative Adversarial Networks for Data Augmentation of Time Series
An entry point to clustering methods
Stress testing electrical grids: Generative Adversarial Networks for load scenario generation
Relevance of Optimized Low-Scale Green H2 Systems in a French Context: Two Case Studies
Quadratic Optimization with Constraints in Python using CVXOPT