Organized by the Big Data and Forecasting of Economic Developments project (bigNOMICS) of the Centre for Advanced Studies of the European Commission, Joint Research Centre.
- Novel data sources for economic analysis
- Natural Language Processing, semantics and sentiment analysis to build economic indicators
- Big data and advanced Machine Learning methods covering economics, finance, businesses
- Economic time series analysis and forecasting
- Knowledge-base, Information Retrieval, Cognitive Computing for prediction and understanding of the economy
- Insights mining in business economics and financial services
Chairs: Vincenzo Sciacca – Almawave, Giovanni Giuffrida – Neodata.
Explainability is essential for users to effectively understand, trust, and manage powerful artificial intelligence applications.
The Special Session on Multi-Objective Optimization (MOO) & Multi Criteria Decision Aiding (MCDA)
We welcome all contributions on theory, methodology and applications of multi-objective optimization and multi-criteria decision aiding.
Relevant topics include, but are not limited to, the following:
- Comparative studies of various many-objective optimisation techniques;
- Designing and constructing many-objective benchmark test problems;
- Designing quality/performance metrics for many-objective solutions/algorithms;
- Development of meta-heuristic algorithms for many-objective optimisation problems;
- Evolutionary many-objective optimisation methods in search-based software engineering;
- Evolutionary many-objective optimisation methods applied to real-world problems;
- Exact methods from mathematical programming for many-objective optimisation problems;
- Many-objective optimisation in bi-level optimisation problems;
- Many-objective optimisation in combinatorial/discrete optimisation problems;
- Many-objective optimisation in computational expensive optimisation problems;
- Many-objective optimisation in constrained optimisation problems;
- Many-objective optimisation in dynamic environments;
- Many-objective optimisation in large-scale optimisation problems;
- Objective reduction techniques;
- Preference articulation in many-objective optimisation;
- Preference-based search in many-objective optimisation;
- Study of parameter sensitivity in many-objective optimisation;
- Theoretical analysis and developments in many-objective optimisation;
- Visualisation for decision-making in many-objective optimisation;
- Visualisation for many-objective solution sets;
- Visualisation for search process of meta-heuristic algorithms.
- Multi-objective Optimization: new algorithms and concrete applications
- Industrial problems, transportation and logistics problems
- contributions to theoretical aspects of Multi-Objective Optimization (MOO) and Multi-Criteria Decision Aiding (MCDA)
- descriptions of actual application cases.
- software contributions to MOO or MCDA.
- inter-disciplinary research, presenting the contributions of MOO and/or MCDA to other scientific disciplines, or integrating other disciplines into MOO or/and MCDA
- decision aiding and multi-objective optimization for sustainability.
- Multi-Task Learning
- Reinforcement Learning
- Deep Learning
- Generative Adversarial Networks
- Deep Neuroevolution
- Networks with Memory
- Learning from Less Data and Building Smaller Models
- Simulation Environments to understand how AI Systems Learn
- Chatbots and Conversational Agents
- Data Science at Scale & Data in the Cloud
- Urban Informatics & Data-Driven Modelling of Complex Systems
- Data-centric Engineering
- Data Security, Traceability of Information & GDPR
- Economic Data Science