Decision support systems using hybrid neurocomputing

Tran, Cong; Abraham, Ajith; Jain, Lakhmi C.

doi:10.1016/j.neucom.2004.03.006

Cited by 13 publications

(9 citation statements)

References 15 publications

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“…To assess the effectiveness of the proposed model and the bond (based on trust), five benchmark data sets are tested (Quteishat et al 2009). Tran et al (2004) suggests a decision support system for tactical air combat environment where not much prior information is available about the decision regions ). Tran et al (2004) proposed a combination of unsupervised learning for clustering the data (to develop decision regions) and a feed forward neural network to classify the decision regions accurately.…”

Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 99%

“…Tran et al (2004) suggests a decision support system for tactical air combat environment where not much prior information is available about the decision regions ). Tran et al (2004) proposed a combination of unsupervised learning for clustering the data (to develop decision regions) and a feed forward neural network to classify the decision regions accurately. The clustered data is used as the inputs to the multi-layered feed forward neural network, which is trained using several higher order learning paradigms.…”

Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 99%

“…CWA is suitable to analyze complex systems that has high-level of cognitive input from human operators, which contributes to the strong success during unpredictable situations and assist hard decision-making. The decision support system should have high level of automation and information integration with a role of operation shift to high task level that involves problem solving, hard decision making, conceptual understanding, planning and workload management (Tran et al 2004).…”

Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 99%

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Intelligent Decision Support Systems

Kaklauskas

2014

Intelligent Systems Reference Library

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This chapter presents the latest IDSSs, such as text analytics and mining based DSSs; ambient intelligence and the internet of things-based DSSs; biometrics-based DSSs; recommender, advisory and expert systems; data mining, data analytics, neural networks, remote sensing and their integration with decision support systems and other IDSSs. These other IDSSs include GA-based DSS; fuzzy sets DSS; rough sets-based DSS; intelligent agent-assisted DSS; process mining integration to decision support, adaptive DSS; computer vision based DSS; sensory DSS and robotic DSS. In addition to acquainting these IDSSs, author introduce practical examples where they have been effectively applied. 2

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Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 99%

Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 99%

Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 99%

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Intelligent Decision Support Systems

Kaklauskas

2014

Intelligent Systems Reference Library

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“…The problem of training time is also a current problem for other network-based and numerical methods. Nevertheless, Møller's algorithm has been applied widely in solving large-scale problems (Abraham 2004;Kashiyama et al 2000;Mukkamala et al 2005;Sözen et al 2005;Tran et al 2004). …”

Section: Introductionmentioning

confidence: 99%

Speeding up the scaled conjugate gradient algorithm and its application in neuro-fuzzy classifier training

Çetişli

Barkana

2009

Soft Comput

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The aim of this study is to speed up the scaled conjugate gradient (SCG) algorithm by shortening the training time per iteration. The SCG algorithm, which is a supervised learning algorithm for network-based methods, is generally used to solve large-scale problems. It is well known that SCG computes the second-order information from the two first-order gradients of the parameters by using all the training datasets. In this case, the computation cost of the SCG algorithm per iteration is more expensive for large-scale problems. In this study, one of the first-order gradients is estimated from the previously calculated gradients without using the training dataset. To estimate this gradient, a least square error estimator is applied. The estimation complexity of the gradient is much smaller than the computation complexity of the gradient for large-scale problems, because the gradient estimation is independent of the size of dataset. The proposed algorithm is applied to the neuro-fuzzy classifier and the neural network training. The theoretical basis for the algorithm is provided, and its performance is illustrated by its application to several examples in which it is compared with several training algorithms and well-known datasets. The empirical results indicate that the proposed algorithm is quicker per iteration time than the SCG. The algorithm decreases the training time by 20-50% compared to SCG; moreover, the convergence rate of the proposed algorithm is similar to SCG.

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“…Hybrid Artificial Intelligence Systems (HAISs) [44], [49], [59], [70] combine both symbolic and subsymbolic paradigms in order to build more robust and trustworthy problem-solving models. These systems are becoming more popular due to their ability to solve a wide range of complex real-world problems related to such aspects as imprecision, uncertainty or high dimensionality among others.…”

Section: Introductionmentioning

confidence: 99%

MOVIH-IDS: A mobile-visualization hybrid intrusion detection system

et al. 2009

Self Cite

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Abstract.A novel hybrid artificial intelligent system for Intrusion Detection, called MOVIH-IDS, is presented in this study. A hybrid model built by means of a multiagent system that incorporates an unsupervised connectionist Intrusion Detection System (IDS) has been defined to guaranty an efficient computer network security architecture. This hybrid IDS facilitates the intrusion detection in dynamic networks, in a more flexible and adaptable manner. The proposed improvement of the system in this paper includes deliberative agents characterized by the use of an unsupervised connectionist model to identify intrusions in computer networks. This hybrid IDS has been probed through several real anomalous situations related to the Simple Network Management Protocol as it is potentially dangerous. Experimental results probed the successful detection of such attacks through MOVIH-IDS.

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Decision support systems using hybrid neurocomputing

Cited by 13 publications

References 15 publications

Intelligent Decision Support Systems

Intelligent Decision Support Systems

Speeding up the scaled conjugate gradient algorithm and its application in neuro-fuzzy classifier training

MOVIH-IDS: A mobile-visualization hybrid intrusion detection system

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