Thesis (Ph.D.) - Cairo University - Faculty of Computers and Information - Department of Computer Science

Making a decision is one of the most fundamental activities of human being. The huge amount of information and complex relations under uncertainty conditions represent real challenging problems in most decision making models. Human problem solving involves perception, abstraction, representation and understanding of real world problems, as well as their solutions, at different levels of granularity. Granular computing, therefore, focuses on problem solving based on the commonsense concepts of granule, granulated view, granularity, and hierarchy. Granular computing unifies a variety of fundamentals, specially the existing formalisms of set theory, starting from interval analysis, fuzzy sets, rough sets and others. The objective of this thesis is developing a new improved type of shadowed sets that preserve different types of uncertainty of information granules sets and enhancing the ability of dealing with multi-models of information granules sets. The proposed type of shadowed sets can be applied in many complex decision making problems. The main characteristic of such kind of problems is the variety of uncertain sets that represent imprecision and vagueness in information. The new approach of shadowed fuzzy numbers approximation is induced from type-1 fuzzy numbers and the intuitionistic fuzzy numbers. The objective of the new approach is introducing shadowed sets that are more accurate in terms of preserving more aspects of uncertainty types. A new model of information granule is applied on hybrid data decision making problem

Issued also as CD