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.
The article is devoted to the construction of a mathematical model and the algorithm for reducing a set of staganalytical correlated methods taking into account the computational complexity of the problem. The basic steganographic methods for the images spatial domain are described. Known steganalytical algorithms for the spatial domain are reviewed. It is shown that a significant number of steganalytical...
The Euclid distance based K-means clustering is among the hard classification algorithms. When dealing with deterministic remote sensing data, it is difficult to gain satisfactory classification results using K-means algorithm. The traditional K-means clustering algorithm is faced with several shortcomings such as locally converged optimization, being sensitive to initial clustering centers, etc....
In this paper, a corpus creation of spontaneous facial expressions focused on learning usage is presented. This has been achieved through the recognition of EEG signals, the use of OpenCV library to detect facial expressions, and the execution of an image classification process in different categories, such as Boredom, Engagement, Interesting, Excitement, Focus, and Relax. Two different versions of...
There are vigorous developments of social network which affect out life greatly. User influence is an important reason to promote the interaction in social network. When we analyze user influence, single value can’t indicate the user influence in different domains. This paper puts forward the design of User Classification PageRank (UCPR) to solve this problem. Firstly, we classify users according...
This article offers a method of structural representation of telemetry data, based on displaying the original data onto plane or torus. Such method allows potentially detect the implicit correlation dependences both in single data frame and in sequence of such frames. It offers algorithms of compression based on displaying the original data, represented as bit-form, onto the surface of plane or torus...
The biggest concern of Network is security. Intro find the tricks and tools of the Attackers. Data Mining techniques automatically learn the pattern of the tuples and Intelligent decision are made. Supervised learning methods finds the attack based on previous knowledge and unknown attacks are detected by using Unsupervised learning. Dos, Probe and Normal data are correctly detected by maximum Data...
The need of machine learning in the defence planning and strategies is increasing day by day due to the increasing amount of breaches and decimations caused by terrorist forces. A myriad of military bases, temporary campaigns, base camps etc. are being targeted and attacked by several terrorist forces. The common problem in the warfare and tumultuous international borders is the frequent and violent...
Feature selection process involves identifying a subset of features that provides same results as the original entire set of features. Feature subset selection removes irrelevant and redundant features for reducing data dimensionality. Feature selection, also known as attribute subset selection. A feature selection algorithm can be measured from both the efficiency and effectiveness points. The efficiency...
The classification recognition performance is a hot study in the field of remote sensing image. In this paper, texture feature, shape feature, radiation intensity of remote sensing image information were used to initial terrain classification. Then an improved fuzzy c-means algorithm was applied on classification, and it included optimization of determine clustering center, got the number of clustering...
Document layout segmentation and recognition is an important task in the creation of digitized documents collections, especially when dealing with historical documents. This paper presents an hybrid approach to layout segmentation as well as a strategy to classify document regions, which is applied to the process of digitization of an historical encyclopedia. Our layout analysis method merges a classic...
Solar power penetration has made the site-specific energy ratings an essential necessity for utilities, independent systems operators and regional transmission organizations. Since, it leads to the reliable and efficient energy production with the increased levels of solar power integration. This study concentrates on the partitional clustering analysis of monthly average insolation period data for...
IPTV is an increasingly important service for telecom operators. Service providers have to repair IPTV faults quickly in order to provide high quality service. It will be very helpful if faults can be predicted. Considering this situation, we combine status data from the set top box with the data of customer trouble tickets and then build a prediction system with improved AdaBoost to predict faults...
A new method for feature selection based on improved maximal relevance and minimal redundancy (mRMR) is proposed in this paper. In order to describe the influence of the added features on correlation between candidate features subset and decision, the standard mRMR was improved by introducing the calculation of parameter Sig ≥ (a, B, D). The value of Sig ≥ (a, B, D) is used to determine whether a...
According to the characteristics of power system fault data, a new attribute reduction method of rough set is proposed, which is used to diagnose the power system fault of complex equipment. First, the attribute reduction problem is transformed to the set covering problem and the correlation coefficient matrix is built based on the correlation matrix. Then based on the selection principle of high...
Models based on local operators can't preserve texture information. Nonlocal models can be used for many image processing tasks. A main advantage of nonlocal models over classical PDE-based algorithms is the ability to handle textures and repetitive structures. Some nonlocal models along with their Split Bregman algorithms are proposed for image denoising, image inpainting, and image segmentation...
Because the English and Castilian have marked acoustic and phonetic differences, this paper shows the study of the effectiveness of different algorithms VAD (Voice Activity Detection) literature, applied to the Castilian, especially riplatense. This article is intended to publicize the results achieved to date. In the first part of the document briefly explained the three implemented methods, namely...
Nowadays high dimensional data plays an important role in many scientific and research applications. A high dimensional data consists of several features or attributes. These data may contain redundant and irrelevant features. The curse of dimensionality is an important problem in data mining and machine learning. In order to reduce the dimensionality of data and to improve the classifier performance,...
Recently NSGA-III has been frequently used for performance comparison of newly proposed evolutionary many-objective optimization algorithms. That is, NSGA-III has been used as a benchmark algorithm for evolutionary many-objective optimization. However, unfortunately, its source code is not available from the authors of the NSGA-III paper. This leads to an undesirable situation where a different implementation...
Stereo matching is an extensively researched topic in computer vision. Stereo matching algorithms are essential for recovering depth information of objects. Existing state-of-the-art stereo methods require very high processing times. Consequently, we cannot employ them in commercial applications though they are very accurate and robust. With a view to reduce the computation time this paper presents...
Locality-based feature learning has drawn more and more attentions recently. However, most of locality-based feature learning methods only consider a kind of local neighbor information, and such the locality-based methods are difficult to well reveal intrinsic geometrical structure of raw high-dimensional data. In this paper, we propose a novel multi-locality correlation feature learning algorithm...
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.