Soft computing

About: Soft computing is a research topic. Over the lifetime, 6710 publications have been published within this topic receiving 118508 citations.

A software implementation of a genetic algorithm based approach to network intrusion detection

Abstract: With the rapid expansion of Internet in recent years, computer systems are facing increased number of security threats. Despite numerous technological innovations for information assurance, it is still very difficult to protect computer systems. Therefore, unwanted intrusions take place when the actual software systems are running. Different soft computing based approaches have been proposed to detect computer network attacks. This paper presents a genetic algorithm (GA) based approach to network intrusion detection, and the software implementation of the approach. The genetic algorithm is employed to derive a set of classification rules from network audit data, and the support-confidence framework is utilized as fitness function to judge the quality of each rule. The generated rules are then used to detect or classify network intrusions in a real-time environment. Unlike most existing GA-based approaches, because of the simple representation of rules and the effective fitness function, the proposed method is easier to implement while providing the flexibility to either generally detect network intrusions or precisely classify the types of attacks. Experimental results show the achievement of acceptable detection rates based on benchmark DARPA data sets on intrusions, while no other complementary techniques or relevant heuristics are applied.

Review article: A review of soft computing applications in supply chain management

Abstract: It is broadly recognised by global companies that supply chain management is one of the major core competencies for an organisation to compete in the marketplace. Organisational strategies are mainly concentrated on improvement of customer service levels as well as reduction of operational costs in order to maintain profit margins. Therefore supply chain performance has attracted researchers' attention. A variety of soft computing techniques including fuzzy logic and genetic algorithms have been employed to improve effectiveness and efficiency in various aspects of supply chain management. Meanwhile, an increasing number of papers have been published to address related issues. The aim of this paper is to summarise the findings by a systematic review of existing research papers concerning the application of soft computing techniques to supply chain management. Some areas in supply chain management that have rarely been exposed in existing papers, such as customer relationship management and reverse logistics, are therefore suggested for future research.

Fuzzy Support Vector Machine for bankruptcy prediction

Abstract: Bankruptcy prediction has been a topic of active research for business and corporate organizations since past few decades. The problem has been tackled using various models viz., Statistical, Market Based and Computational Intelligence in the past. Among Computational Intelligence models, Artificial Neural Network has become dominant modeling paradigm. In this Paper, we use a novel Soft Computing tool viz., Fuzzy Support Vector Machine (FSVM) to solve bankruptcy prediction problem. Support Vector Machine is a powerful statistical classification technique based on the idea of Structural Risk Minimization. Fuzzy Sets are capable of handling uncertainty and impreciseness in corporate data. Thus, using the advantage of Machine Learning and Fuzzy Sets prediction accuracy of whole model is enhanced. FSVM is implemented for analyzing predictors as financial ratios. A method of adapting it to default probability estimation is proposed. The test dataset comprises of 50 largest bankrupt organizations with capitalization of no less than $1 billion that filed for protection against creditors under Chapter 11 of United States Bankruptcy Code in 2001-2002 after stock marked crash of 2000. Experimental results on FSVM illustrate that it is better capable of extracting useful information from corporate data. This is followed by a comparative study of FSVM with other approaches. FSVM is effective in finding optimal feature subset and parameters. This is evident from the results thus improving prediction of bankruptcy. The choice of feature subset has positive influence on appropriate kernel parameters and vice versa which demonstrate its appreciable generalization performance than traditional bankruptcy prediction methods. Choosing appropriate value of parameter plays an important role on the performance of FSVM model. The effect of variability in prediction performance of FSVM with respect to various values of different parameters of SVM is also investigated. Finally, a comparative study of clustering power of FSVM is made with PNN on ripley and bankruptcy datasets. The results show that FSVM has superior clustering power than PNN.