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Accurate monitoring of urban areas using remote sensing data requires reliable change detection techniques. Nevertheless, while most of the changes are optically visible and easily detectable by an expert user, automatic processes are quite difficult to develop. That is why, the interpretation of changes has remained up-to-now visual in most operational applications in remote sensing. This paper provides...
E-mail is a major revolution taking place over traditional communication systems due to its convenient, economical, fast, and easy to use nature. A major bottleneck in electronic communications is the enormous dissemination of unwanted, harmful emails known as spam emails. In this paper, a novel spam filtering framework (NSFF) is proposed, which is based on particle swarm optimization, fuzzy logic...
Features extracted from cell networks have become popular tools in histological image analysis. However, existing features do not take sufficient advantage of the cycle structure present within the cell networks. We introduce a new class of network cycle features that take advantage of such structures. We demonstrate the utility of these features for automated prostate cancer scoring using histological...
In this paper, the recognition method based on non-negative matrix factorization with sparseness constraint (NMFs) combined with the support vector machine (SVM) was proposed to identify the type of the common pulse condition of Chinese Traditional Medicine (TCM). First, pulse data were factorized by NMFs to obtain projection coefficients as training sample set to build recognition mode with SVM....
One of the most important parts of search engines is the ranking unit. Many different classical ranking algorithms based on content (such as TF-IDF and BM25) and connectivity (such as HITS and PageRank) have been used in web search engines to find pages in response to a user query. Although these algorithms have been developed to improve retrieval results, none of them can take advantage of power...
Knowledge of structural classes is useful in understanding of folding patterns in proteins. Although numerous methods were proposed and achieved promising results in structural class prediction, some problems in using protein-sequence information have impeded the development. In this paper, a combined representation of protein-sequence information is proposed for prediction of protein structural class,...
Next generation sequencing (NGS) technology has increasingly become the backbone of transcriptomics analysis, but sequencer error causes biases in the read counts. In this paper we establish a framework for predicting true sequences from NGS data. We formulate this task as a classification problem. We define several features, such as log likelihood ratio of estimated true counts, error probability...
In this paper, we propose an approach for fast pedestrian detection in images. Inspired by the histogram of oriented gradient (HOG) features, a set of multi-scale orientation (MSO) features are proposed as the feature representation. The features are extracted on square image blocks of various sizes (called units), containing coarse and fine features in which coarse ones are the unit orientations...
Feature selection is an important issue for object detection. In this paper, we propose an effective wrapper-based feature selection scheme using Binary Particle Swarm Optimization (BPSO) and Support Vector Machine (SVM) for object detection. In our algorithm, Scale-Invariant Feature Transform (SIFT) descriptors in a patch around the keypoints are extracted as the initial feature representations....
Support Vector Machines (SVMs) are popular for pattern classification. However, training a SVM requires large memory and high processing time, especially for large datasets, which limits their applications. To speed up their training, we present a new efficient support vector selection method based on ensemble margin, a key concept in ensemble classifiers. This algorithm exploits a new version of...
This paper discusses the synergistic use of multi-temporal ALOS/PALSAR and SPOT multi-spectral images for land cover classification in the Ho Chi Minh city area in Vietnam. Five PALSAR images and SPOT 2 multispectral image were used for classification. Integration of additional information such as interferometric coherence, textural data was also studied. Different combinations of multi-temporal SAR...
The definition of the Mahalanobis kernel for the classification of hyperspectral remote sensing images is addressed. Class specific covariance matrices are regularized by a probabilistic model which is based on the data living in a subspace spanned by the p first principal components. The inverse of the covariance matrix is computed in a closed form and is used in the kernel to compute the distance...
Combined Support Vector Machine (SVM) and Principal Component Analysis (PCA) was used to recognize the infant cries with asphyxia. SVM classifier based on features selected by the PCA was trained to differentiate between pathological and healthy cries. The PCA was applied to reduce dimensionality of the vectors that serve as inputs to the SVM. The performance of the SVM utilizing linear and RBF kernel...
This research constructs the CSO+SVM model for data classification through integrating cat swam optimization into SVM classifier. There are two factors (i.e. feature selection and parameter determination) of classification problems will mainly discuss in this study. The objectives of feature selection are to reduce number of features and remove irrelevant, noisy and redundant data. Besides, the parameter...
Feature selection and feature weight calculating are key preprocesses in text classification. A new feature selection approach based on average interaction gain (AIG) is presented and a new feature weight adjustment technique (WA) taking inter-class distribution and intra-class distribution into consideration is presented too. Then a new approach combining AIG with WA called AIG-WA is presented. In...
Too many unimportant attributes are ended up specifying in medical disease sample data sets if we are not sure which attribute to include for disease prediction, which could spoil the classification and increase many unwanted calculations of the medical disease prediction. Thus how to preprocess these medical data and enhance the prediction performance is worth a problem to research. In the paper,...
Font Recognition is one of the Challenging tasks in Optical Character Recognition. Most of the existing methods for font recognition make use of local typographical features and connected component analysis. In this paper, English font recognition is done based on global texture analysis. The main objective of this proposal is to employ support vector machines (SVM) in identifying various fonts. The...
Using frequency weighted mining algorithm with real-time data processing capability to calculate each system call's frequency value for existed audit records, and we got a vector set of progress. The vector set was linearly scanned and its progresses were labeled as ??normal?? or ??attack?? according to their distance relations. Then we got a SVM training set without man-made supervision. Finally,...
The Gender of a face is almost its most salient feature, and realizing automatic gender classification according to the face image will boost the performance of face retrieval and face recognition in large face database. This paper proposed a new gender classification method combining independent component features selected based on genetic algorithm and support vector machine (SVM). First, the FastICA...
Chemokine receptors represent a prime target for the development of novel therapeutic strategies in a variety of disease processes. The prediction of interesting proteins types by computational methods can provide new clues in functional studies of uncharacterized proteins without performing extensive experiments. Support vector machine (SVM) is a new kind of approach to supervised pattern classification...
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