The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this communication we explain how a Support Vector Machine (SVM) can be applied to compute the Euler number or Genus of a 2-D binary image. By taking into account the results provided by a mathematical formulation that is known producing exact results we derive two specialized SVM-based architectures, one useful for the 4-connected case and one useful for the 8-connected case. We validate the applicability...
In this paper, we develop a projection neural network to solve the convex quadratic programming problem in support vector machine (SVM) learning. Then, we obtain a unique global solution for the proposed neural network. Furthermore, we prove that this network is completely stable and finite-time convergence. To present the feasibility and efficiency of the proposed neural network for solving the SVM...
This paper presents a comparison of Electroencephalogram (EEG) signals classification for Brain Computer-Interfaces (BCI). At present, it is a challenging task to extract the meaningful EEG signal patterns from a large volume of poor quality data and simultaneously with the presence of artifacts noises. Selection of the effective classification technique of the EEG signals at classification stage...
Classification performances of the supervised machine learning techniques such as support vector machines, neural networks and logistic regression are compared for modulation recognition purposes. The simple and robust features are used to distinguish continuous-phase FSK from QAM-PSK signals. Signals having root-raised-cosine shaped pulses are simulated in extreme noisy conditions having joint impurities...
To date, paper-based examinations are still in use worldwide on all levels of education levels (e.g. secondary, tertiary levels). However, literature regarding off-line automatic assessment systems employing off-line handwriting recognition is not numerous. This paper proposes an off-line automatic assessment system employing a hybrid feature extraction technique - a newly proposed Modified Direction...
In this paper we study the problem of classification of wireless capsule endoscopy images (WCE). We aim at developing a computer system that would aid in medical diagnosis by automatically detecting images containing pathological alterations in an 8-hour-long WCE video. We focus on three classes of pathologies – ulcers, bleedings and petechia – since they are typical for several diseases of the intestines...
Nowadays there are numerous user-generated restaurant reviews available on the Internet, of which they are considered valuable resources for decision making to customers. In reality, not every reviews available online are helpful to users, so the need for filtering unqualified reviews is realized. There have been several studies on spam review detection that attempt to detect unqualified reviews using...
In the modern society, energy consumption such as gas and electricity is closely related to the weather condition because of the large share of weather-sensitive electrical appliances. Investigating how weather influences the energy consumption is of great significance for energy demand forecasting. This paper proposes an optimum regression approach for analyzing weather influence on the energy consumption...
We defined a set of quantifiable features for authorship categorization. We performed our experiments on public domain literature — all books analyzed were obtained in plain text format through Project Gutenberg's online repository of classic books. We tested three machine learning algorithms: Artificial Neural Network, Naïve Bayes Classifier, and Support Vector Machine with our features. We found...
In this article we applied Support Vector Machines to acoustic model of Speech Recognition System based on MFCC and LPC features for Azerbaijani DataSet. This DataSet has been used for speech recognition by Multilayer Artificial Neural Network and achieved some results. The main goal of this work is applying SVM techniques to the Azerbaijan Speech Recognition System. The variety of results of SVM...
Digital images have an important function in several fields like journalism, film industry and forensic investigations. Several image editing softwares can change the content of an image very easily. Attackers use contrast enhancement for avoiding the traces left by image forgery. So it is necessary to perform contrast enhancement detection for detecting an image forgery. In the proposed system, there...
Bus Transportation plays an important role in modern society and has been developed in many parts of the world. It reduces the private vehicle usage; fuel consumption and more over reduce traffic congestion, if the arrival time of the buses is accurate. In this paper, various literatures have been surveyed which is used for prediction of bus arrival time. Real time prediction of arrival time is so...
Support Vector Machine (SVM) is one of widely-used text classification method. Although SVM performs well in practice, SVM encounters two problems: the data distribution is not taken into consideration in the process of classification and its performance is greatly influenced by noises. In view of this, Fuzzy Support Vector Machine based on Manifold Discriminant Analysis (FSVM-MDA) is proposed and...
Nowadays Opinion mining is given more important, since it provides decision makers to estimate the success of a newly proposed techniques, novel ad campaign or novel product launch. In general, supervised methods such as Support Vector Machine (SVM) and Artificial Neural Network (ANN) are used to classify the opinions. In some cases SVM performs better classification and some cases ANN performs better...
The physiological and pathological information obtained by the single-point or complex multi-point pressure sensor is still less. In this paper, we adopt the pulse image sensor which can reflect the change of the pulse-taking skin surface particularly and comprehensively, we use the MM-3 pulse model (Group A) as the subjects produced by Shanghai University of Traditional Chinese Medicine to study...
Cardiovascular risk prediction is a vital aspect of personalized health care. In this study, retinal vascular function is assessed in asymptomatic participants who are classified into risk groups based on Framingham Risk Score. Feature selection, oversampling and state-of-the-art classification methods are applied to provide a sound individual risk prediction based on Retinal Vessel Analysis (RVA)...
Nowadays, classification tasks are very challenging because data is usually large and imbalanced. They can cause low prediction accuracy and high computation costs. Active Learning is a technique that employs only a small set of data to construct an initial classification model. Then, it iteratively improves the model by incrementally learning from the misclassified examples. In this paper, we aim...
The purpose of this paper is to propose a modular integrating algorithm. This algorithm can let the program detect multiple arrhythmias and is very easy to add more diseases detection algorithm. Also, it can save the repeated calculations in multiple algorithms. By a real test of the program, the result is that the computing time of the integrating algorithm is 46.86% less than the sum of the computing...
Along with the rapid expansion of the MW sized big wind turbine sector, the small wind turbine industry is also growing. Understanding the power response of these systems to the variations in wind velocity is essential for the optimal selection and efficient management of these turbines. This is defined by the power curves of wind turbines. In this paper, we propose nonparametric models for the power...
In a wind farm, where several wind turbines are arranged in rows and columns, the wind speed available for the downstream turbines are significantly reduced by the wake effect. The wake losses can reduce the total productivity of a wind farm up to 20 per cent. Understanding the wake pattern in an existing wind farm is essential for the short-term wind power forecast. In this paper, we propose the...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.