Most of the real-world problems ranging from engineering to medical or social fields involve uncertainty in data. Soft computing models, including m-polar fuzzy sets, intuitionistic fuzzy sets, soft sets and rough sets are used to deal with uncertain and incomplete information. The objective of this thesis is to present certain novel hybrid models, namely, m-polar fuzzy N-soft rough sets, intuitionistic fuzzy N-soft sets and intuitionistic fuzzy N-soft rough sets, for modeling incomplete information in information systems. These models are obtained by the hybridization of N-soft sets with m-polar fuzzy sets, intuitionistic fuzzy sets and rough sets, which are more precise and flexible for modeling and processing of vague information. These models provide us information about the occurrence of ratings or grades and enable us to tackle multi-polar information. Certain novel concepts concerning these newly hybrid models are discussed. Four types of parameter reductions of bipolar fuzzy soft sets are presented. The significance of bipolar fuzzy sets is discussed, by analyzing relation systems and relation decision systems, specifically attribute reduction of bipolar fuzzy relation decision systems is presented. The proposed methods are applied to some real life decision making problems for representation of multi-attribute data, including multi-criteria selection of suitable place for tour. Efficient algorithms are developed to solve decision-making problems based on the proposed hybrid models.