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A new fast neural network model
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In this paper, a new fast algorithm for information retrieval is presented. Such algorithm relies on performing cross correlation in the frequency domain between input data and the input weights of fast neural networks (FNNs). It is proved mathematically and practically that the number of computation steps required for the presented FNNs is less than that needed by conventional neural networks (CNNs). The main objective of Internet users is to find the required information with high efficiency and effectiveness. Finding information on an object's visual features is useful when specific keywords for the object are not known. Since intelligent mobile agent technology is expected to be a promising technology for information retrieval, there is a number of intelligent mobile agent based-information retrieval approaches have been proposed in recent years. Here, the work presented in [25] for image-based information retrieval using mobile agents is greatly enhanced. Multiple information agents continuously traverse the Internet and collect images that are subsequently indexed based on image information such as the URL location, size, type and the date of indexation. In the search phase, the intelligent mobile agent receives the image of object as a query and searches the set of web pages that contain information about the object. This is done by matching the query to images on web pages faster than the work presented in [25]. Furthermore, by applying cross correlation, object detection becomes position independent. Moreover, by using neural networks, the object can be detected even with rotation, scaling, noise, distortion or deformation in shape.