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Epilepsy is one of the frequent brain disorder that may consequence in the brain dysfunction and cognitive disorders. Epileptic seizures can occur due to transient and unexpected electrical interruptions of brain. EEG (ElectroEncephaloGram) is one of the non-invasive methods for analyzing the human brain dynamics that affords a direct evaluation of cortical behavior. Seizures are featured by short...
An approach for document orientation detection and classification by using support vector machine (SVM) theorem is proposed in this paper. First, all the characters in a document image will be isolated and some valid ones are selected. Using the valid characters, the document image will be vectorized to a 32-dimensional vector by the feature extracting. By training lots of samples, an SVM classifier...
The purpose of this article was to build a license plates recognition system with high accuracy at night. The system, based on regular PC, catches video frames which include a visible car license plate and processes them. Once a license plate is detected, its digits are recognized, and then checked against a database. The focus is on the modified algorithms to identify the individual characters. In...
During past decades, land use and land cover change detection techniques have undergone substantial development. However, different scenarios and an integrated workflow linking remote sensing imagery and GIS are often neglected. As a result, we develop a land use and land cover change detection and extraction system and propose five scenarios considering data availability and different classification...
For large data sets and data updated situation, incremental training algorithm is an effective solution of support vector machine training. To improve speed of incremental support vector machine training algorithm, this paper combines the distance ratio method and the nearest neighbor method to extract boundary samples, and an incremental support vector machine algorithm based on distance ratio and...
The automatic description of music from traditions that do not follow the Western notation and theory needs specifically designed tools. We investigate here the makams, which are scales in the modal music of Arabic and Middle East regions. We evaluate two approaches for classifying musical pieces from the ‘makam world’, according to their scale, by using chroma features extracted from polyphonic music...
In this paper we present a new One-Versus-All or OVA-based scheme for multi-class classification problems, aiming to reduce the training time when applying support vector machines (SVMs), particularly on large datasets. The experimental results on ten benchmark datasets show that the performance of the proposed scheme, referred to as "VINE", is comparable to that of its predecessor OVA scheme,...
For estimation of single tree parameters using 1:6000 large-scale aerial photographs, tree species identification is an important starting point. This paper presents a new approach for identifying tree species, delineating individual trees and extracting single tree space coordinates in coniferous and deciduous forests of Liangshui National Nature Reserve of P. koraiensis, Northeast of China. To identify...
This paper works on the most intensively studied algorithm- k Nearest Neighbor algorithm. The purpose is to investigate the performance of different similarity measures in the kNN on Chinese texts. The two measures that we focus on are cosine value and Jensen-Shannon Divergence. We use both the corpus collected from the Sogou, whose data extracts from the website of Sohu.com, and datasets that we...
Qualitative and quantitative description of functional connectivity graphs using graph attributes is of great interest to neuroscience, and has led to remarkable insights in the field. However, the statistical techniques used have generally been limited to whole-group, post-hoc studies. In this paper, we propose instead a novel approach to perform predictive inference on single subjects. It is based...
This paper presents a palmprint identification system based on principle lines. The system is divided into two main subsystems. The first part concerns with palmprint feature extraction using a cascade of consecutive filters to obtain the principle lines. To achieve our proposed system, recognition is provided as the second part to classify principle line image. Firstly, shape histogram is constructed...
The classification of land use in karst areas is mainly through the interpretation of satellite images to get. The traditional interpretation methods are supervised classification and unsupervised classification. But the classification polygons is trivial by supervised classification, and boundary is also complex. Different categories can be distincted by unsupervised classification, however, the...
This article proposes such a question classification approach that integrates multiple semantic features. It is aimed at these two questions in Chinese question classification models: inaccurate semantic information extraction and too slow processing speed caused by too high Eigenvector dimension. With the help of HowNet and the support vector machine and syntactic and semantic information of question...
The strength of the selected feature and the effectiveness of the classifier are the two key factors determining the performance of a handwritten Character Recognition System. In this work, we implemented a feature extraction method based on Run Length Count (RLC) for the offline recognition of Handwritten Malayalam Characters. RLC is the count of contiguous group of 1's encountered in a left to right...
In text mining field, The KNN (K Nearest Neighbors) is one of the oldest and simplest methods of text classification. But it is known to be sensitive to the distance (or similarity) function used in classifying a test instance, this disadvantage can cause low classification accuracy and limit the KNN classifier's utilization in text classification in text mining. In this paper, we introduce Mahalanobis...
Printability of layout objects becomes increasingly dependent on neighboring shapes within a larger and larger context window. In this paper, we propose a two-level hotspot pattern classification methodology that examines both central and peripheral patterns. Accuracy and runtime enhancement techniques are proposed, making our detection methodology robust and efficient as a fast physical verification...
Correct and timely diagnosis of diseases is an essential matter in medical field. Limited human capability and limitations decrease the rate of correct diagnosis. Machine learning algorithms such as support vector machine (SVM) can help physicians to diagnose more correctly. In this study, Wisconsin diagnostic breast cancer (WDBC) data set is used to classify tumors as benign and malignant. Independent...
The feature-based modulation flatness measure (FMSFM) and feature-based modulation crest measure (FMSCM) are proposed as novel feature vectors for music genre and mood classification. These features are extracted using a feature-based modulation spectrum to represent time-varying characteristics of the music signal. Instead of the spectrogram of the signal, timbral features such as mel-frequency cepstral...
This study focuses on the mode classification of phones speaker modes using GMM1. In this regard, speech data in both enabled and disabled speaker modes of cell phones and telephones were collected, processed and classified into two different categories. The different mixture numbers (1 to 4) of GMM and wave files sizes of 10, 20, 40 and 80 kb were tested in order to obtain an optimal condition for...
Recently the cost-benefits of automated sensing over traditional field surveys for population management of fauna has been recognised. Remote monitoring through automatic identification based on sensor networks has followed one of two approaches; using the sensor nodes to perform data analysis within the network or alternatively using the sensor network as a means for collecting data to be centrally...
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