Main Banner

Conference Theme

Data-driven and knowledge driven approaches and techniques have been widely used in decision making, and they are increasingly attracting great attention due to their importance and effectiveness in addressing uncertainty and incompleteness. The information explosion via the spread of digital technologies impacts the ways we can study and understand the dynamics of socio-economic-political systems by increasing the variety, availability, and complexity of the data available to both qualitative and quantitative research scientists. These new information sources can importantly also support integrated approaches that can be more effective than either pure approach. Accordingly, there are many challenges and open research problems to be explored as well as many issues to be addressed.

Within the key theme of FLINS on computational intelligence and its applications, the 2018 International FLINS Conference (FLINS 2018) provides an international forum that brings together those actively involved in areas of interest to Data Science and Knowledge Engineering with their applications in decision making problems under uncertainty and incompleteness to support sensible decision making in different areas. The conference will feature plenary talks given by world renowned scholars, regular sessions with broad coverage, and special sessions focusing on popular topics.

FLINS2018 invites submissions related to all aspects of computational intelligence and its application, specially within the following four research fields, which can be theoretical formalisms, methodologies; algorithms investigation, survey or practical applications:

1) Contributions on development of models, algorithms and systems to handle multiple and heterogeneous information for sensing decision making purpose. The approaches could be either numerical or symbolic, or both; could be based on fuzzy/possibility theory, neural network, genetic algorithm, rough set theory, different varieties of machine learning/data mining methods, probability theory and belief function theory, and different varieties of logic based approaches (classical logics or non-classical logics);

2) Contributions on investigating, reviewing and assessing the principles, explanation, and strategies on how humans represent and use incomplete and uncertain data and knowledge from a cognitive science perspective;

3) Contributions on investigating, reviewing and assessing the principles, algorithms, methodologies and tools on how artificial intelligence can help in data science, data mining, big data, machine learning, and predictive analytics;

4) Especially welcome written contributions representing advanced theories and innovative applications in computational intelligence as well as the integration of both quantitative and qualitative formalisms and modelling approaches in science, engineering, business and education.