International Journal of Man-Machine Studies
AIP Conference Proceedings 151 on Neural Networks for Computing
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Augmented fuzzy cognitive maps for modelling LMS critical success factors
Knowledge-Based Systems
Fuzzy Cognitive Map Based Approach for Assessing Pulmonary Infections
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Genetic learning of fuzzy cognitive maps
Fuzzy Sets and Systems
Structural damage detection using fuzzy cognitive maps and Hebbian learning
Applied Soft Computing
Modeling complex systems using fuzzy cognitive maps
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Use of genetic algorithms for neural networks to predict community-acquired pneumonia
Artificial Intelligence in Medicine
Contextual fuzzy cognitive map for decision support in geographic information systems
IEEE Transactions on Fuzzy Systems
Numerical and Linguistic Prediction of Time Series With the Use of Fuzzy Cognitive Maps
IEEE Transactions on Fuzzy Systems
Bifurcating fuzzy sets: Theory and application
Neurocomputing
Review article: Computational intelligence techniques in bioinformatics
Computational Biology and Chemistry
Modeling maintenance projects risk effects on ERP performance
Computer Standards & Interfaces
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The task of prediction in the medical domain is a very complex one, considering the level of vagueness and uncertainty management. The main objective of the presented research is the multi-step prediction of state of pulmonary infection with the use of a predictive model learnt on the basis of changing with time data. The contribution of this paper is twofold. In the application domain, in order to predict the state of pneumonia, the approach of fuzzy cognitive maps (FCMs) is proposed as an easy of use, interpretable, and flexible predictive model. In the theoretical part, addressing the requirements of the medical problem, a multi-step enhancement of the evolutionary algorithm applied to learn the FCM was introduced. The advantage of using our method was justified theoretically and then verified experimentally. The results of our investigation seem to be encouraging, presenting the advantage of using the proposed multi-step prediction approach.