Norrozila Sulaiman

A new efficient normal parameter reduction algorithm of soft sets

Kong et al. [Kong, Z., Gao, L., Wang, L., and Li, S., The normal parameter reduction of soft sets and its algorithm, Computers and Mathematics with Applications 56 (12) (2008) 3029-3037] introduced the definition of normal parameter reduction in soft sets and presented a heuristic algorithm of normal parameter reduction. However, the algorithm is hard to understand and involves a great amount of computation. In this paper, firstly, we give some new related definitions and proved theorems of normal parameter reduction. Then we propose a new efficient normal parameter reduction algorithm of soft sets based on the oriented-parameter sum, which can be carried out without parameter important degree and decision partition. The comparison result on a dataset shows that the proposed algorithm involves relatively less computation and is easier to implement and understand as compared with the algorithm of normal parameter reduction proposed by Kong et al.

A novel intrusion detection system by using intelligent data mining in weka environment

Nowadays, the using of intelligent data mining approaches to predict intrusion in local area networks has been increasing rapidly. In this paper, an improved approach for Intrusion Detection System (IDS) based on combining data mining and expert system is presented and implemented in WEKA. The taxonomy consists of a classification of the detection principle as well as certain WEKA aspects of the intrusion detection system such as open-source data mining. The combining methods may give better performance of IDS systems, and make the detection more effective. The result of the evaluation of the new design produced a better result in terms of detection efficiency and false alarm rate from the existing problems. This presents useful information in intrusion detection.

The Parameter Reduction of the Interval-Valued Fuzzy Soft Sets and Its Related Algorithms

There has been a rapid growth of interest in developing approaches that are capable of dealing with imprecision and uncertainty. To this end, an interval-valued fuzzy soft set (IVFSS) that combines soft set theory with interval-valued fuzzy set theory has been proposed to handle imprecision and uncertainty in applications such as decision-making problems. However, there has been little focus on parameter reduction of the interval-valued fuzzy soft sets, which is significant in decision-making problems. In this paper, we introduce four different definitions of parameter reduction in interval-valued fuzzy soft sets to satisfy different the needs of decision makers. We propose four heuristic algorithms of parameter reduction. Finally, the algorithms are compared and summarized from the aspects of easy degree of finding reduction, applicability, reduction result, exact level for reduction, multiusability, applied situation, and computational complexity. The results of the experiment show that the methods reduce the redundant parameters while preserving certain decision abilities.

QoS-Aware Web Services Selection with Interval-Valued Intuitionistic Fuzzy Soft Sets

With the increasing popularity of the development of service-oriented applications, it is imperative to measure the quality of services (QoS) for service consumers and providers. To find the most suitable service for different consumers, the QoS nonfunctional attribute will become an important factor in web service selection. However, non-functional QoS properties rely heavily on the subjective perceptions of service consumers that are not easy to assess due to their complexity and involvement of ill-structured information. The purpose of this paper is to introduce interval-valued intuitionistic fuzzy soft set theory for solving web service selection problems that take into account QoS requirement of consumers. Interval-valued intuitionistic fuzzy soft set theory, which is a new useful mathematical tool for dealing with uncertainties, is more effective to deal with uncertainties on non-functional QoS properties in web service selection. We present basic system architecture and the algorithm to solve fuzzy decision making problems for selecting web service based on interval-valued intuitionistic fuzzy soft sets. Finally, an illustrative example is employed to show our contribution.

A Novel Normal Parameter Reduction Algorithm of Soft Sets

In this paper, we propose a novel normal parameter reduction algorithm of soft sets based on the oriented-parameter sum, which can be carried out without parameter important degree and decision partition. We present some new related definitions and proved theorems of normal parameter reduction. The comparison result on a Boolean-valued dataset shows that, the proposed algorithm involves relatively less computation and is easier to implement and understand as compared with the soft set-based algorithm of normal parameter reduction.

Reduction the Effect of Mobility in Link Transmitting Using Efficient DSR Route Cache for MANETs

Communication between mobile users is becoming more popular than ever before due to the recent technological advances in wireless communication devices. Ad hoc networks are generally closed in that, it’s a collection of two or more devices equipped with wireless communications and networking capability; they do not connect to the Internet and are typically created between participants. This means that a formed network can be de-formed on-the-fly without the need for any system administration. Many routing protocol methods have been proposed in Mobile Ad-Hoc Network but still the challenges are to improve the routing performance. The DSR (Dynamic Source Routing) routing protocol dependent on the cache memory for every node to store the routing path from source to destination. This paper presented a new algorithm using DSR routing cache technique to improve the routing between mobile nodes to reduction the effect of mobility in link transmitting that can solve link broken problem. The simulation results show a low delay, stable link in routing and better performance in terms of Route Discovery Time. It indicates that DSR cache outperforms DSR standard and had a high quality of packet delivery.

XRecursive: AStorage Method for XML Document Based on Relational Database

Storing XML documents in a relational database is a promising solution because relational databases are mature and scale very well and they have the advantages that in a relational database XML data and structured data can coexist making it possible to build application that involve both kinds of data with little extra effort. In this paper, we propose an algorithm schema named XRecursive that translates XML documents to relational database according to the proposed storing structure. The steps and algorithm are given in details to describe how to use the storing structure to storage and query XML documents in relational database. Then we report our experimental results on a real database to show the performance of our method in some features.

Applications of Interval-Valued Intuitionistic Fuzzy Soft Sets in a Decision Making Problem

Soft set theory in combination with the interval-valued intuitionistic fuzzy set has been proposed as the concept of the interval-valued intuitionistic fuzzy soft set. However, up to the present, few documents have focused on practical applications of the interval-valued fuzzy intuitionistic soft sets. In this paper, Firstly, we present the algorithm to solve decision making problems based on interval-valued intuitionistic fuzzy soft sets, which can help decision maker obtain the optical choice. And then we propose a definition of normal parameter reduction of interval-valued intuitionistic fuzzy soft sets, which considers the problems of suboptimal choice and added parameter set, and give a heuristic algorithm to achieve the normal parameter reduction of interval-valued intuitionistic fuzzy soft sets. Finally, an illustrative example is employed to show our contribution.

A soft set model on information system and its application in clustering attribute selection

In this paper, we define a soft set model on the set of equivalence classes in an information system, which can be easily applied to obtaining approximation sets of rough set. Furthermore, we use it to select clustering attribute for categorical data clustering and a heuristic algorithm is presented. Experiment results on UCI benchmark data sets show that the proposed approach provides faster decision for selecting a clustering attribute as compared with maximum dependency attributes (MDA) approach.

Parameterization value reduction of soft sets and its algorithm

Molodtsov initiated soft set theory as a new general mathematical tool for dealing with uncertainties. There has been some progress concerning reduction of soft set. However, up to the present, all methods about reduction of soft sets only considered the parameter reduction, the parameter value reduction is not referred. In this paper, we propose the definition of parameterization value reduction of soft set theory, which holds the least parameter values and makes the optimal choice objects not change. In addition, a heuristic algorithm of parameterization value reduction of soft sets is presented. Finally, an illustrative example is employed to show our contribution.