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An artificial neural network is an information or signal processing system composed of a large number of simple processing elements, called artificial neurons or simply nodes, which are interconnected by direct links called connections and which cooperate to perform parallel distributed processing in order to solve a desired computational task. The potential benefits of neural networks extend beyond the high computation rates provided by massive parallelism. The neural network models are specified by the net topology, node characteristics, and training or learning rules. These rules specify an initial set of weights and indicate how weights should be adapted during use to improve performance. Roughly speaking, these computations fall into two categories: natural problems and optimization problems. Natural problems, such as pattern recognition, are typically implemented on a feed-forward neural network.

Neural Nets were employed for detection