On Spam Detection Based on Cognitive Pattern Recognition
CISW '07 Proceedings of the 2007 International Conference on Computational Intelligence and Security Workshops
Single-Cycle Image Recognition Using an Adaptive Granularity Associative Memory Network
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
EUC'05 Proceedings of the 2005 international conference on Embedded and Ubiquitous Computing
A survey and comparison of peer-to-peer overlay network schemes
IEEE Communications Surveys & Tutorials
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Morphological associative memories
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A Hierarchical Graph Neuron Scheme for Real-Time Pattern Recognition
IEEE Transactions on Neural Networks
Recurrent Correlation Associative Memories: A Feature Space Perspective
IEEE Transactions on Neural Networks
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The significant growing amount of shared files in distributed and serverless P2P networks has impacted heavily on content management and retrieval field. In this paper, we propose an associative memory network with intention to provide efficient pattern recognition within P2P networks. An efficient pattern recognition algorithm which works within distributed and dynamic nature of P2P network would be beneficial for better content handling and distribution. Our approach, which is called multi-wheel Graph Neuron (mWGN), is a variant of Graph Neuron (GN)-based algorithm built on top of Chord overlay network. GN algorithm is an associative memory designed for wireless sensor environment. mWGN is a restructured design from Hierarchical Graph Neuron (HGN), preserving its single cycle learning, lightweight and accuracy feature while requiring fewer number of nodes. Result from the experiment shows the proposed approach is highly accurate and the fault tolerance of Chord protocol provides stability for the proposed approach.