This paper describes methodology and performance of an experimental research on filtering of web search results. Filtering was performed on the basis of predicted relevance of search results derived from usersâ€™ implicit feedback. The feedback was obtained from usersâ€™ web browsers and consisted of a set of browsing behavioral metrics, including reading time, clicks on links, mouse pointer and wheel movement patterns, bookmarking, sharing, copying, and whether the search was continued after the page was closed. A multi-layer neural network used to infer from the behaviors how much the user was interested in each filtered document. Neural network, therefore, performed deep learning without human supervision. Predicted relevance measure was compared to the explicit feedback. Obtained results of 89% correct relevance rating prediction suggest that selected set of metrics was successful in terms of correctly predict how relevant the web page was for the user involved in the study. More research is recommended for further advances of information filtering methods.Â

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User Behavior for Neural Network-based Web Search Results Filtering

Biometrics refer to the system of authenticating identities of humans, using features like retina scans, thumb and fingerprint scanning, face recognition and also signature recognition. Signatures are a simple and natural method of verifying a person’s identity. It can be saved as an image and verified by matching, using neural networks. Signature verification can be offline or online. In this work, we present a system for offline signature verification. The user has to submit a number of signatures that are used to extract two types of features, statistical features and structural features. A vector obtained from each of them is used to train propagation neural network in the verification stage. A test signature is then taken from the user, to compare it with those the network had been trained with. A test experiment was carried out with two sets of data. One set is used as a training set for the propagation neural network in its verification stage. This set with four signatures form each user is used for the training purpose. The second set consists of one sample of signature for each of the 20 persons is used as a test set for the system. A negative identification test was carried out using a signature of one person to test others’ signatures. The experimental results for the accuracy showed excellent false reject rate and false acceptance rate.

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H. Said-Ahmed

Papers

Efficient Signatures Verification System Based on Artificial Neural Networks