The theory of compressed sensing (CS) has shown tremendous potential in many fields, especially the signal processing area, due to its utility recovering unknown signals with far lower sampling rates than Nyquist frequency. In this paper, we present a novel, optimized recovery algorithm named supp-BPDN. proposed executes step selecting and recording support set original before using traditional...

Decision support systems (DSSs) are prevalent information systems for decision making in many competitive business environments. In a DSS, decision making process is intimately related to some factors which determine the quality of information systems and their related products. Traditional approaches to data analysis usually cannot be implemented in sophisticated Companies, where managers ne...

Concept lattices and rough sets abstractions are applied for combining firms’ characteristics obtained from different sources in a decision support system prototype, based on P -products. Applying methods from rough sets abstractions allows obtaining information on which elements belong to the kernel system and which ones belong to the closure system. Key–Words: Rough sets, Concept lattices, De...

Fuzziness must be considered in systems where available information is uncertain. A model of such a vague phenomenon might be represented as a fuzzy system equation which can be described by the fuzzy functions defined by Zadeh’s extension principle. In this paper, we incorporate the concept of fuzzy set theory into the support vector machine (SVM). This integration preserves the benefits of SV...