Data Science and Engineering and Artificial Intelligence
The objective of this area is to contribute to exploit the interactions of data science and engineering techniques with artificial intelligence methods (metaheuristics and machine and deep learning) towards explicable decisions, and to propose new techniques and methodologies in fields like optimization.
Research Topics
- Data Management and Processing Scalability.
- IoT Data Ingestion.
- Real Time Data Processing and Unstructured Data Management.
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Research Topics
- Data Profiling.
- Data Representation.
- Data Acquisition.
- Data Curation.
- Data Quality.
- Data Veracity.
- Data Integration.
- Semantic Annotation.
- Semantic Reconciliation.
- Semantic Integration.
- Semantic Reasoning.
- Metadata, Context and Domain Semantics.
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Research Topics
- AutoML.
- Artificial Intelligence for Sustainable Computing and Communications.
- Distributed and Ubiquitous Intelligent Systems.
- AI in Quantum Computers.
- Predictive Modeling, Prescriptive Modeling, Explainable, Trustworthy, and Ethical Machine Learning.
- Machine Learning as a Service, Scalable Machine Learning.
- Ensemble Learning, Feature Engineering.
- EDAs Analysis.
- Natural Processing Language.
- Model Metric and Validation.
- Hyper Parameter Tuning.
- Visualization.
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Research Topics
- 5G and Intelligent Systems.
- Memetic Computing.
- Multidisciplinary Optimization.
- Optimization and Machine Learning.
- Multi-objective Optimization.
- Large-Scale Optimization.
- Memetic Computing, HPC and Big Data Optimization.
- Automatic Metaheuristic Configuration and Design
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