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The prediction result of classifier is biased towards the class with more samples, when the harmful text information is filtered. This is because that the samples that including the harmful information were difficult to gain. Construct virtual samples is an effective means to solve the problem of pattern recognition in the small sample, using the up-sampling method to construct virtual samples in...
Speech affective recognition is an important branch of speech recognition, whose main purpose is the emotional characteristics included in the analysis of speech signals. Because the use of a single model to identify which identify significant limitations. This paper presents a recognition model based on HMM and PNN, which using PNN for classification and using HMM for generating feature matching...
In this work we propose Inclusive vector to keep the key words available in natural language database. The inclusive vectors are generated by the process of extraction of words given in the source and the cited items of records published in the ISI Thompson Citation Indexes. The proposed inclusive vector exhibits related words and the degree of their relationships. In this work we present the results...
The research presented in this paper refers to classification of geometric shapes (cubes, pyramids and cylinders) using multilayer neural network. The input data of the algorithm are the images of shapes placed in different positions and distances from the camera. The classification is based on feature vectors that are obtained using methods of digital image processing. Feature vectors are inputs...
One approach for deaf signs recognition and classification is presented in the paper. It is assumed that the signs are presented in digital images. Recognition algorithm is consisted of several stages. At the beginning it is necessary to perform appropriate image processing in sense of segmentation and filtration of the input images. Aim is to detect arm position, i.e. sign of interest. For this purpose...
Support vector machine approach is an effective technique to solve poly-dimensional outlier detection, which can avoid the curse of dimensionality problem and has higher accuracy. One-class support vector machine-based outlier detection techniques take advantage of spatial and temporal correlations that exist between sensor data to cooperatively identify outliers. However, for large scale training...
Mouse dynamics is the process of identifying individual users based on their mouse operating behaviors. Many classification algorithms have been proposed for checking users' identity, thus it is natural to ask how well each classifier performs and how various classifiers compare to each other (e.g., to identify promising research directions). Unfortunately, we cannot conduct a valid comparison of...
The paper presents an innovative hierarchical classification model which should be applied for large-scale biometric identification systems, in order to improve their performance. The model is relying on a multi-level fusion approach, but completed with a feature-selection strategy. The practical application of the proposed model concerns networks security issues, especially for databases remote access,...
A model of probabilistic neural network (PNN) to classify the loess according to its collapsibility is suggested in this paper, in which five physical property indexes such as water content, dry density, void ratio, saturation degree and plastic liquid are taken as input neural cells and the output neural cell is coefficient of collapsibility. 76 groups of sample to be trained under different smoothing...
Pedestrian detection is a major difficulty in the field of object detection. In order to achieve a balance between speed and accuracy, we propose a new framework in pedestrian detection based on HOG-PCA and Gentle AdaBoost. Firstly, each block-based feature of the image is encoded using the histograms of oriented gradients (HOG), then Principal Components Analysis (PCA) is used to reduce the dimensions...
Emergence of Web 2.0, internet users can share their contents with other users using social networks. In this paper microbloggers' contents are evaluated with respect to how they reflect their categories. Migrobloggers' category information, which is one of the four categories that are economy sport, entertainment or technology, is taken from wefollow.com application. 2105 RSS news feeds, whose category...
Reject inference is a term that distinguishes attempts to correct models in view of the characteristics of rejected applicants. The main difficulty in establishing reject inference model is that the ¡®through-the-door' applicant population is unavailable. In this paper, we propose a hybrid data mining technique for reject inference. It is a three-stage approach: k-means cluster, support vector machines...
Different conditions, such as occlusions, changes of lighting, shadows and rotations, make vehicle type classification still a challenging task, especially for real-time applications. Most existing methods rely on presumptions on certain conditions, such as lighting conditions and special camera settings. However, these presumptions usually do not work for applications in real world. In this paper,...
Our research involves an original method for the intensity of speech defect monitoring in child patients with developmental dysphasia. We have drawn upon a body of knowledge consisting of phonetics, acoustics and ANN applications. The aim of the paper is to compare two methods based on the vowel detector, both of which classify the parameter of developmental dysphasia, with the results of the speech...
This paper uses genetic algorithms to train a codebook for the modeling of Discrete Hidden Markov Model (DHMM) applied to speech recognition. The GA-trained DHMM is then used to increase the recognition rate for Mandarin speeches. Vector quantization based on a codebook is a fundamental process to recognize the speech signal by DHMM. A codebook will be first trained by genetic algorithms through Mandarin...
In this paper we introduce a novel framework for image classification using local visual descriptors ¨C group fusion sparse representation (GFSR), which casts the classification problem as a linear regression model with sparse constraints of the regression coefficients. Considering the intrinsic discriminative property of prior class label information, and the requirement of local consistency within...
Steganalysis is detecting and decoding hidden data within a given media and is taken as a countermeasure to steganography. There has been quite some effort in audio steganalysis for additive embedding model. But, results are disappointing when they distinguish the cover-audio signal with multiplicative noise and the stego-audio signal. In this paper, multiplicative noise is changed to additive noise...
Web contents are going overwhelming today. The numerous online documents, webpages, e-books, etc. are much useful but obtaining them is also time-consuming. Text categorization is one of the solutions to the issue. For all text categorization method, Support Vector Machines (SVM) is one of the most acceptable one. However, to perform more efficiently on webpages, it is necessary to add improvements...
In this paper, we proposed an approach to Musical Instrument Automatic Recognition. We used seven different musical instruments to be played simultaneously from solos to quartets. Our data have 296 feature vectors that used in audio signal classification by MLP neural networks and K-NN algorithm. Finally, MLP achieved as the best neural network in musical instrument recognition.
Spam has created a significant security problem for computer users everywhere. Spammers take an advantage of defrauds to cover parts of messages that can be used for identification of spam. For instance, a spammer does not need to consume much cost and bandwidth for sending junk mails even more than one hundred emails. On the other hand, from the feature selection perspective, one of the specific...
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