A Hybrid Intelligent Multiagent System for E‐Business

Abstract

The paper describes a new multiagent system with enhanced capabilities obtained through a hybrid of intelligent techniques. The processing in the model is handled by two types of agents: distributed agents and a central administrator agent. Localized processing at the individual agents is carried out using mathematical techniques and genetic algorithms. The central administrator agent dynamically obtains information about the problem domain from the Internet and maintains a knowledge pool using a clustering technique called the growing self-organizing map (GSOM). Distributed agents communicate with the central administrator agent if they need further knowledge about the problem domain to provide solutions to user-defined tasks. The approach integrates traditional mathematical, data mining, and evolutionary techniques with a multiagent system. The proposed system is implemented as a travel optimizer application for the e-tourism domain. Finally, the possibilities of integrating the proposed technique with currently available e-tourism applications to provide the customer with enhanced solutions are identified.

Feature Extraction of Fraudulent Financial Reporting Through Unsupervised Neural Networks

“…Specifically, this study proposes using an unsupervised neural networks-based method, the hierarchical self-organizing map (GHSOM) [12], which is an extension of the self-organizing map (SOM) [29], to classify financial statements. The SOM has been studied in terms of methodology and statistical features [10,21,27,48], and GHSOMs are gradually being used more and are being integrated with other methods because of their flexible and hierarchical features [34,36,42,49]. Based on the clustering results generated by the GHSOM, this study adopts principal component analysis (PCA) to discover common embedded features and fraud patterns from each group of financial data.…”