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Abstract: An outstanding problem is how to make decisions with uncertain and incomplete data from disparate sources without NP-hard algorithms. Here we introduce a new reasoning methodology, fuzzy-inferenced decisionmaking (FIND), to solve this problem in polynomial time. In this methodology, a fuzzy-belief-state base (FBSB) is created from historical data of the states of a system by clustering the set of values for each state variable into three clusters upon whose center fuzzy set membership functions LOW, MEDIUM and HIGH are defined. The FBSB is mined for fuzzy association rules using the fuzzy set memberships to infer values for the missing data via these rules. When given an incomplete and uncertain observation of the system state, the observed state is completed via fuzzy association rules. Then each case in the FBSB is matched against the inference-completed observation to retrieve the best matching fuzzy belief state record that contains a decision as an extra variable. The process is analogous to case-based reasoning, but it uses fuzzification to ameliorate uncertainty and to complete missing data. The test results on real world data prove the effectiveness of this methodology. [Copyright &y& Elsevier]

Background: Gene pathway can be defined as a group of genes that interact with each other to perform some biological processes. Along with the efforts to identify the individual genes that play vital roles in a particular disease, there is a growing interest in identifying the roles of gene pathways in such diseases. Results: This paper proposes an innovative fuzzy-set-theory-based approach, Multi-dimensional Cluster Misclassification test (MCM-test), to measure the significance of gene pathways in a particular disease. Experiments have been conducted on both synthetic data and real world data. Results on published diabetes gene expression dataset and a list of predefined pathways from KEGG identified OXPHOS pathway involved in oxidative phosphorylation in mitochondria and other mitochondrial related pathways to be deregulated in diabetes patients. Our results support the previously supported notion that mitochondrial dysfunction is an important event in insulin resistance and type-2 diabetes. Conclusion: Our experiments results suggest that MCM-test can be successfully used in pathway level differential analysis of gene expression datasets. This approach also provides a new solution to the general problem of measuring the difference between two groups of data, which is one of the most essential problems in most areas of research. [ABSTRACT FROM AUTHOR]

Our fuzzy classifier detects classes of image pixels corresponding to gray level variation in the various directions. It uses an extended Epanechnikov function as a fuzzy set membership function (FSMF) for each class where the class assigned to each pixel is the one with the greatest fuzzy truth of membership. This classification is done first, after which a competition is run as a second step to thin the edges. Like the Canny edge detector, the edge sensitivity of our competitive fuzzy edge detector (CFED) can be set from low to high by the user. The performance of our algorithm is somewhat similar to that of the Canny algorithm but ours is significantly faster. For both, the proper level of sensitivity must be chosen by the user for the best results because the tradeoff is more edges with more noise versus fewer edges and less noise. However, the settings are less sensitive and more intuitive for our algorithm. We make comparisons on good and degraded images. [Copyright &y& Elsevier]