A KNOWLEDGE-BASED DSS FOR MANAGERIAL PROBLEM DIAGNOSIS

Effective Selection of Entities From Heterogeneous and Large Resources Using a Cooperative Neuro-Fuzzy System

This paper focuses on using the cooperative neuro-fuzzy system for the effective and customised selection of entities from large and heterogeneous resources by presenting a general architecture. An experiment is carried out with the fast-moving consumer goods to prove the utility of the architecture. It is observed that most consumers go for the frequent purchase of fast-moving consumer items. Further, various brands, costs, discounts, schemes, quantities, and reviews might make it challenging. Hence, such decisions need to be intelligent and practically feasible in terms of time and effort. The paper discusses neural networks to categorise the entities, type-1 & 2 fuzzy membership functions with rules, training sets, and graphical views of the fuzzy rules and the experiment details. Besides the generic approach and experiment, the paper also discusses the work done so far with their limitations and applications in other domains. At the end, the paper presents the limitations and possible future enhancements.

Automated financial diagnosis: a comparison with other diagnostic domains

Diagnosis is a well known task in the medical and the physical domain. However, in the financial domain it is not so clear what is encompassed by financial diagnosis. In practice, financial analysts and managers often encounter diagnostic tasks that incorporate much financial data. The objective of this article is to characterise financial diagnosis in order to indicate guidelines for automation of financial diag nosis. We will start with a general, domain independent, description of the problem of diagnosis. Further, we will compare the problem of financial diagnosis with diagnostic problems from the medical and physical domains. We will not give a complete survey of diagnostic research performed in these domains, but look instead at domain specific features that constitute points of comparison with financial diagnosis.