Neural Network Based Data Fusion Research Articles

A Neural Networks Pruning and Data Fusion Based Intrusion Detection Model

The abilities of summarization, learning and self-fitting and inner-parallel computing make artificial neural networks suitable for intrusion detection. On the other hand, data fusion based IDS has been used to solve the problem of distorting rate and failing-to-report rate and improve its performance. However, multi-sensor input-data makes the IDS lose its efficiency. The research of neural network based data fusion IDS tries to combine the strong process ability of neural network with the advantages of data fusion IDS. A neural network is designed to realize the data fusion and intrusion analysis and Pruning algorithm of neural networks is used for filtering information from multi-sensors. In the process of intrusion analysis pruning algorithm of neural networks is used for filtering information from multi-sensors so as to increase its performance and save the bandwidth of networks.

High Maneuvering Multiple-Underwater Robot Tracking with Optimal Two-Stage Kalman Filter and Competitive Hopfield Neural Network Based Data Fusion

Tracking high maneuvering underwater targets with radar is very complex, because the acceleration is not directly measurable. This may cause the tracking errors which will diverge the estimation when maneuver happened. In this article Optimal Two-Stage Kalman Filter (OTSKF) used for tracking purpose, which can estimate the acceleration in maneuvering targets. Under this method, Acceleration of the targets has been identified and it is estimated at the moment. Also an effective data fusion method plays critical role in order to track the objects accurately. We use Competitive Hopfield Neural Network (CHNN) to associate obtained data from radar measurements with existing target. In this paper we investigate new method, which combine these two algorithms for tracking highly maneuvering targets. As simulation results show, to solve multiple-underwater vehicles tracking problem, investigated method have good ability and suitable performance in high maneuvering targets.

Hybrid ensemble approach for classification

This paper presents a novel hybrid ensemble approach for classification in medical databases. The proposed approach is formulated to cluster extracted features from medical databases into soft clusters using unsupervised learning strategies and fuse the decisions using parallel data fusion techniques. The idea is to observe associations in the features and fuse the decisions made by learning algorithms to find the strong clusters which can make impact on overall classification accuracy. The novel techniques such as parallel neural-based strong clusters fusion and parallel neural network based data fusion are proposed that allow integration of various clustering algorithms for hybrid ensemble approach. The proposed approach has been implemented and evaluated on the benchmark databases such as Digital Database for Screening Mammograms, Wisconsin Breast Cancer, and Pima Indian Diabetics. A comparative performance analysis of the proposed approach with other existing approaches for knowledge extraction and classification is presented. The experimental results demonstrate the effectiveness of the proposed approach in terms of improved classification accuracy on benchmark medical databases.

Exploring artificial intelligence-based data fusion for conjoint analysis

Conjoint analysis is used to understand how consumers develop preferences for products or services, which encompass, as usual, multi-attributes and multi-attribute levels. Conjoint analysis has been one of the popular tools for multi-attribute decision-making problems on products and services for consumers over the last 30 years. It has also been used to market segmentation and optimal product positioning. In spite of its popularity and commercial success, a major weakness of conjoint analysis has been pointed such that respondents participating in conjoint experiment have to evaluate a number of hypothetical product profiles. To reduce the number of hypothetical products, this paper proposes a systematic method, called data fusion, and explores the usability of various data fusion techniques. The paper evaluates traditional data fusion (correlation-based), hierarchical Bayesian-based data fusion, and neural network-based data fusion.