A5037193018- Anomaly Detection Techniques and Applications
- Online Learning and Analytics
- Machine Learning and Data Classification
- Topic Modeling
- Online and Blended Learning
- Hydrological Forecasting Using AI
- Data Management and Algorithms
- Semantic Web and Ontologies
- Data Mining Algorithms and Applications
- Context-Aware Activity Recognition Systems
- Energy Load and Power Forecasting
- Oceanographic and Atmospheric Processes
- Advanced Text Analysis Techniques
- French Urban and Social Studies
- Artificial Intelligence in Healthcare
- Educational Technology and Assessment
- Career Development and Diversity
- Higher Education and Employability
- Aging, Elder Care, and Social Issues
- Human Pose and Action Recognition
- Text and Document Classification Technologies
- Advanced Clustering Algorithms Research
Université Côte d'Azur
2011-2024
Institut de Biologie Valrose
2024
Laboratoire d'Informatique, Signaux et Systèmes de Sophia Antipolis
2022
Centre National de la Recherche Scientifique
2016-2021
Observatoire de la Côte d’Azur
2021
Fondation Sophia Antipolis
2016
Today, many students are moving towards higher education courses that do not suit them and end up failing. The purpose of this study is to help provide counselors with better knowledge so they can offer future corresponding their profile. second objective allow the teaching staff propose training adapted by anticipating possible difficulties. This thanks a machine learning algorithm called Random Forest, allowing for classification depending on results. We had process data, generate models...
A lot of research has been done for human activity recognition. But most it uses a static and immutable set sensors known beforehand. This approach does not work when applied to ubiquitous or mobile system, since we cannot know which will be available in the users' surroundings. is why consider here an opportunistic approach, where each sensor individually trained are able bring its own knowledge. Inspired by Opportunity project, propose evaluate both effectiveness using Random Forest (RF)...
We propose a data-driven evolutionary approach to the modeling of marine currents in Bay Monaco. The CMA (Covariance Matrix Adaptation) evolution strategy is used optimize parameters predictive model that may be as surrogate expensive and time-consuming finite-element simulations. models obtained are reasonably accurate easy interpret.