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Spam mails are one of the greatest challenges faced by internet service providers, organizations and internet users in unison. Spam mails may be targeted, with a malicious intent or just as a commercial marketing activity - on the whole unwanted by everyone except the dispatcher. Spam filters continuously evolve as spammers go techno-savvy and creative. Machine learning algorithms have been popularly...
Markov Model/ Artificial Neural Network (HMM/ANN) keyword spotting framework. The feature extraction method used was Mel-Frequency Cepstral Coefficients (MFCC). The ANN is a 3-layer feedforward neural network using Multi-Layer Perceptron (MLP). In recognizing the words, an HMM decoder was used which implemented the Viterbi
Addressing the problem of spam emails in the Internet, this paper presents a comparative study on Nai??ve Bayes and Artificial Neural Networks (ANN) based modeling of spammer behavior. Keyword-based spam email filtering techniques fall short to model spammer behavior as the spammer constantly changes tactics to
Due to the exponential growth of available text documents in digital form, it is of great importance to develop techniques for automatic document classification based on the textual contents. Earlier document classification techniques have used keyword-based features and related statistics to achieve good results when
The aim of the spoken term detection task is to find the occurrence of user-entered keywords in an archive of audio recordings. The kind of techniques that are used usually are vocabulary-independent, using only the acoustic information available. In this scenario, however, we rely exclusively on the acoustic model
when the sentence is analyzed. The goal is to put each noun and verb of the sentence on the right place on the tree. Taking this information into account, it is possible to solve the ambiguity problem for the query keywords and create the indicative summaries taking into account query words, and semantically related
Genre classification for musical documents is conventionally based on keywords, statistical features or low-level acoustic features. Such features are either lack of in-depth information of music content or incomprehensible for music professionals. This paper proposed a classification scheme based on the correlation
The aim of the spoken term detection task is to find the occurrence of user-entered keywords in an archive of audio recordings. In this area, besides the accuracy of hits returned, the speed of search is also very important, for which an intermediate representation of recordings is normally used. In this paper we
recognition application, the technique show a very promising result with average 95% accuracy. Keywords: feature extraction, structural feature, statistical feature, pattern recognition, character recognition.
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