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Software Defined Networking (SDN) is a new promising networking concept which has a centralized control over the network and separates the data and control planes. This new approach provides abstraction of lower-level functionality and allows the network administrators to initialize, control, change, and manage network behavior programmatically. The centralized control, being the major advantage of...
Huge amount of data in today's world are stored in the form of electronic documents. Text mining is the process of extracting the information out of those textual documents. Text classification is the process of classifying text documents into fixed number of predefined classes. The application of text classification includes spam filtering, email routing, sentiment analysis, language identification...
This paper proposes a method for construction of classifiers for discharge summaries. First, morphological analysis is applied to a set of summaries and a term matrix is generated. Second, correspond analysis is applied to the classification labels and the term matrixand generates two dimensional coordinates. By measuring thedistance between categories and the assigned points, ranking of key wordswill...
The paper presents the results of research on the use of Deep Neural Networks (DNN) for automatic classification of the skin lesions. The authors have focused on the most effective kind of DNNs for image processing, namely Convolutional Neural Networks (CNN). In particular, three kinds of CNN were analyzed: VGG19, Residual Networks (ResNet) and the hybrid of VGG19 CNN with the Support Vector Machine...
Based on the spectral data from SDSS, Kernel Support Vector Machines (K-SVM) is applied to classify quasars from other celestial body. Firstly, the basic theory of the SVM(Support Vector Machine) with relaxation factor and kernel function is introduced. Then, the main parameters are designed and selected. Finally, the method is applied to the classification and identification of the quasars. The classification...
As network intrusion data's scale gets larger and larger, designing parallel schemes for intrusion detection have been becoming research focus in the field of information security. In order to solve the problem that the intrusion detection algorithm is high time-consuming, the classification of large amounts of data occupies lots of memory and the efficiency of single detection is low, a parallel...
In this paper, we focus on rat's behavior classification for biorobot-rat interaction. The automatic behavior analysis and classification of laboratory rats can effectively improve the adaptivity of interaction between rat-like robot and biological rats. Basic image processing algorithm as Labeling and Contour Finding were employed to extract feature parameters (body length, body area, body radius,...
At present, it is a great challenge that solving high-dimension and text sparsity problems in short text classification. To resolve these problems, this paper proposes a method which takes the correlation between lexical items and tags before completing Latent Dirichlet Allocation(LDA) topic model. Meanwhile, this paper adjusts parameters of Support Vector Machine(SVM) to find the optimal values by...
Stereotypic behaviours are present in both human and nonhuman primates. Usually, these behaviours are a welfare indicator. However, the stereotypic behaviours may be also a symptom of some mental disorder in the humans. A specific case is Autism Spectrum Disorder (ASD). The individuals with ASD may exhibit stereotypic behaviours through some gestures. The classic stereotyped gestures of autism are:...
Spare parts are indispensable resources to ensure equipment the normal operation and continuous production, especially for urban raü vehicles. When the spare parts storage is insufficient, the equipment can't be replaced or repair ed in time, which can cause serious loss. Therefore, it is important to forecast the demand of the urban rail vehicle spare parts. A combination forecasting method based...
High dimensionality of feature space is a problem in supervised machine learning. Redundant or superfluous features either slow down the training process or dilute the quality of classification. Many methods are available in literature for dimensionality reduction. Earlier studies explored a discernibility matrix (DM) based reduct calculation for dimensionality reduction. Discernibility matrix works...
The basic idea behind the classifier ensembles is to use more than one classifier by expecting to improve the overall accuracy. It is known that the classifier ensembles boost the overall classification performance by depending on two factors namely, individual success of the base learners and diversity. One way of providing diversity is to use the same or different type of base learners. When the...
Twitter has eased real-time information flow for decision makers, it is also one of the key enablers for Open-source Intelligence (OSINT). Tweets mining has recently been used in the context of incident response to estimate the location and damage caused by hurricanes and earthquakes. We aim to research the detection of a specific type of high-risk natural disasters frequently occurring and causing...
In recent years, the problem of classification for high dimensional and class-imbalanced data is found in many fields like bioinformatics and so on. High dimensional problem result in bad classification results because of some combinations of features have adverse effect on classification. Class-imbalanced problem means the number of samples of one class is more than another class, which would make...
Speaker identification systems are becoming more important in today's world. This is especially true as devices rely on the user to speak commands. In this article, an analysis of how a text-independent voice identification system can be built is presented. Extracting the Mel-Frequency Cepstral Coefficients is evaluated and a support vector machine is trained and tested on two different data sets,...
During the pharmaceutical process, it is inevitable that various defects emerge in the medicine vials which may greatly affect the product quality and reduce the productive efficiency. To address these problems, a method based on feature extraction and machine learning is developed for vial defect inspection. On image preprocessing, we used threshold algorithm to acquire the region of interest (ROI)...
This paper presents a grid search approach to optimize the kernel's parameters for the support vector machines classifier. The most encountered three kernels are considered: linear, radial basis, and sigmoid. We show that the optimization of parameters improves the recognition performance for audio signals classification, especially in the case of sigmoid kernel. The behavior of the model is very...
Based on time series data of SPOT / VGT and DMSP / OLS, the built-up and non-built-up areas of countries in South Asia were extracted via the local support vector machine method. Accuracy assessment showed that Kappa coefficients were above 0.85 were achieved in 1998 and 2013. Based on built-up areas detected, the urban expansion of the eight South Asian countries and their major cities were analyzed...
Offline signature identification and verification systems encounter several challenges such as the diversity of signatories and the limited number of references. To address these problems we propose a new writer-independent system for signature identification and verification. Besides, a new feature generation scheme is proposed by using the Histogram Of Templates (HOT). The identification and verification...
This paper focuses on the problem of osteoporosis disease diagnosis from bone X-ray images. The proposed approach takes advantage of the deep learning robustness to extract high-level features from low-level image (pixel intensities). However, the diagnosis of osteoporosis confronts two major challenges, the difficulty of distinguishing between osteoporosis and healthy subjects just from the visual...
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