International Journal of Fuzzy Systems (INT J FUZZY SYST )

Journal description

Current impact factor: 1.03

Impact Factor Rankings

2015 Impact Factor Available summer 2015
2013 / 2014 Impact Factor 1.031
2012 Impact Factor 1.506
2011 Impact Factor 1.157
2010 Impact Factor 1.362
2009 Impact Factor 1.09

Impact factor over time

Impact factor
Year

Additional details

5-year impact 1.19
Cited half-life 3.30
Immediacy index 0.25
Eigenfactor 0.00
Article influence 0.19
ISSN 1562-2479

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: To support investment decision based on technical analysis (TA), this study aims to retrieve the knowledge or rules of various indicators by a hybrid soft computing model. Though the validity of TA has been examined extensively by various statistical methods in literature, previous studies mainly explored the effectiveness of each technical indicator separately; therefore, a practical approach that may consider the inconsistency of various indicators simultaneously and control the down-side risk of an investment decision is still under-explored. Thus, a hybrid model—by constructing a variable consistency dominance-based rough set approach (VC-DRSA) information system with the fuzzy inference enhanced discretization of signals—is proposed, to retrieve the imprecise patterns from commonly adopted technical indicators. At the first stage, the trading signals (i.e., Buy, Neutral, or Sell) are pre-processed in two groups: straightforward and complicated signals. The straight-forward technical indicators (i.e., for signals that are decided by precise rules) are suggested by domain experts, and the buy-in signals are simulated by several trading strategies to examine the outcomes of each indicator. As for the complicated group (i.e., for signals that require imprecise judgments to make a decision), a fuzzy inference technique is incorporated to enhance the discretization of signals; those signals are also simulated by the aforementioned trading strategies to obtain the corresponding results. At the second stage, the trading signals generated by each technical indicator and their pertinent results from the previous stage are combined for VC-DRSA modeling to gain decision rules. To illustrate the proposed model, the weighted average index of the Taiwan stock market was examined from mid/2002 to mid/2014, and a set of decision rules with nearly 80% classification accuracy (both in the training and the testing sets) were obtained in this empirical case. The findings suggest that several technical indicators should be considered simultaneously, and the retrieved rules (knowledge) have practical implications for investors.
    International Journal of Fuzzy Systems 03/2015;
  • [Show abstract] [Hide abstract]
    ABSTRACT: To support investment decision based on technical analysis (TA), this study aims to retrieve the knowledge or rules of various indicators by a hybrid soft computing model. Though the validity of TA has been examined extensively by various statistical methods in literature, previous studies mainly explored the effectiveness of each technical indicator separately; therefore, a practical approach that may consider the inconsistency of various indicators simultaneously and control the down-side risk of an investment decision is still under-explored. Thus, a hybrid model—by constructing a variable consistency dominance-based rough set approach (VC-DRSA) information system with the fuzzy inference enhanced discretization of signals—is proposed, to retrieve the imprecise patterns from commonly adopted technical indicators. At the first stage, the trading signals (i.e., Buy, Neutral, or Sell) are pre-processed in two groups: straightforward and complicated signals. The straight-forward technical indicators (i.e., for signals that are decided by precise rules) are suggested by domain experts, and the buy-in signals are simulated by several trading strategies to examine the outcomes of each indicator. As for the complicated group (i.e., for signals that require imprecise judgments to make a decision), a fuzzy inference technique is incorporated to enhance the discretization of signals; those signals are also simulated by the aforementioned trading strategies to obtain the corresponding results. At the second stage, the trading signals generated by each technical indicator and their pertinent results from the previous stage are combined for VC-DRSA modeling to gain decision rules. To illustrate the proposed model, the weighted average index of the Taiwan stock market was examined from mid/2002 to mid/2014, and a set of decision rules with nearly 80% classification accuracy (both in the training and the testing sets) were obtained in this empirical case. The findings suggest that several technical indicators should be considered simultaneously, and the retrieved rules (knowledge) have practical implications for investors.
    International Journal of Fuzzy Systems 03/2015;
  • [Show abstract] [Hide abstract]
    ABSTRACT: International Journal of Fuzzy Systems, Vol. 16, No. 4, December 2014 © 2014 TFSA 520 Autonomous Agent for DDoS A ttack Detection and Defense in an Experimental Testbed G. Preetha, B. S. Kiruthika Devi, and S. Mercy Shalinie Abstract 1 Distributed Denial of Service (DDoS) attacks im- pinge on the availability of critical resources in the Internet domain. The objective of this paper is to de- velop an autonomous agent based DDoS defense in real time without human intervention. A mathemati- cal model based on Lanchester law has been designed to examine the strength of DDoS attack and defense group. Once attack strength is formulated efficient defense mechanism is deployed at the victim to block malicious flows. The proposed framework is vali- dated in an experimental testbed with geographically distributed testbed nodes. From the experimental re- sults, the strength of attack group is observed as 49%. The defense strength of Hop Count Filtering mechanism is obtained as 31.3% whereas the pro- posed Hybrid Model defense effectiveness is com- puted as 48.7%. Also, Adaptive Bandwidth Manage- ment (ABM) using fuzzy inference system provides sustainable bandwidth to legitimate users by provid- ing low bandwidth share for attackers. The proposed autonomous agent based model defends against DDoS attack in various aspects like prevention of IP spoofing, effective bandwidth management, im- provement of Quality of Service provisioning, avail- ability of services to legitimate clients and protecting critical infrastructure points. The defense mechanism paves way to Critical Information Infrastructure Protection.
    International Journal of Fuzzy Systems 12/2014;
  • International Journal of Fuzzy Systems 01/2014; 16(2):160-172.
  • [Show abstract] [Hide abstract]
    ABSTRACT: We generalize the power averaging operators to interval-valued Atanassov’s intuitionistic fuzzy environments, and develop a series of generalized interval-valued Atanassov’s intuitionistic fuzzy power aggregation operators. The main advantages of these operators are that they not only accommodate situations in which the input arguments are interval-valued intuitionistic fuzzy numbers (IVIFNs), but also consider information about the relationship between the IVIFNs being fused. The properties of these operators are investigated and the relationships among these operators are discussed. Moreover, approaches to multiple attributes group decision making based on the proposed operators are given and two examples are illustrated to show the feasibility and validity of the new approaches to the application of multiple attributes group decision making.
    International Journal of Fuzzy Systems 12/2013; 15(4):401-411.
  • International Journal of Fuzzy Systems 12/2013; 15(4):460-470.
  • International Journal of Fuzzy Systems 03/2013; 15(1):9-21.
  • International Journal of Fuzzy Systems 01/2013;