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In data mining, link prediction for the networks is one of the areas of greatest interest today. Research achievements of link prediction problem can be applied in many fields such as study genetically transferred diseases, online marketing, e-commerce services, discover the structure of criminal networks, friend request in social networks … However, most of researchers focused on predicting the existence...
Lack of safety and efficacy are the two major reasons for the failures of drug candidates in drug discovery and development. Reliable prediction of blood-brain barrier permeation even before chemical synthesis still remains as one of the major challenges in drug discovery. New approaches and models that are reliable and can reduce experimental evaluations of pre-clinical candidates are in urgent need...
The standard dementia screening tool Mini Mental State Examination (MMSE) and the standard dementia staging tool Clinical Dementia Rating Scale (CDR) are prominent methods for answering questions whether a person might have dementia and about the dementia severity respectively. These methods are time consuming and require well-educated personnel to administer. Conversely, cognitive tests, such as...
A novel composite approach through integration of variational optical flow and surface splines is presented to obtain sub-pixel accurate dense disparity map for remotely sensed stereo image pair. It is well known that, surface splines handle geometric distortion very well. The performance of surface splines for dense correspondence can be significantly improved by the reliable control points, but...
Telecommunication networks are designed for high reliability but, given their complexity, when problems do occur they are difficult to detect and diagnose. Anomaly detection approaches typically provide cryptic results, resulting in extensive human effort for diagnosis. Using data from a 4G network, we focus on non-parametric change detection algorithms for anomaly detection and evaluate the performance...
This paper presents a rapidly and lower neural networks to treat those waste water index that is difficult to be measured. Model called soft sensor is composited two parts: one is used to estimate the principal linear output, the other one is used to adjust estimated error to obtain better accuracy. Selection of features that effects greatly computation scale and predict accuracy is discussed also...
The large number of malicious files that are produced daily outpaces the current capacity of malware analysis and detection. For example, Intel Security Labs reported that during the second quarter of 2016, their system found more than 40M of new malware [1]. The damage of malware attacks is also increasingly devastating, as witnessed by the recent Cryptowall malware that has reportedly generated...
We introduce a methodology for efficient monitoring of processes running on hosts in a corporate network. The methodology is based on collecting streams of system calls produced by all or selected processes on the hosts, and sending them over the network to a monitoring server, where machine learning algorithms are used to identify changes in process behavior due to malicious activity, hardware failures,...
Image enhancement is a common pre-processing step before the extraction of biometric features from a fingerprint sample. This can be essential especially for images of low image quality. An ideal fingerprint image enhancement should intend to improve the end-to-end biometric performance, i.e. the performance achieved on biometric features extracted from enhanced fingerprint samples. We use a model...
Confidence measures aim at detecting unreliable depth measurements and play an important role for many purposes and in particular, as recently shown, to improve stereo accuracy. This topic has been thoroughly investigated by Hu and Mordohai in 2010 (and 2012) considering 17 confidence measures and two local algorithms on the two datasets available at that time. However, since then major breakthroughs...
Motion analysis and tracking often relies on multimodal signals, e.g., video, depth map, motion capture (MoCap), due to the completeness of information they jointly provide. The joint analysis of multimodal signals requires to know the correct timing, i.e., the signals to be aligned. In this paper we propose an approach to automatically estimate the correct matching and alignment between a video and...
Interest on palmprint biometrics has experimented a strong growth in the last decades due to its useful characteristics as uniqueness, permanence, reliability, user-friendliness, acceptability, non-intrusiveness, and low cost of the acquisition devices, which make it attractive for civil and commercial applications. Accordingly, a wide research has been developed in this field. Nevertheless, there...
Feature ranking from video-wide temporal evolution brings reliable information for complex action recognition. However, a video may contain similar features in the sequence of frames which deliver unnecessary information to the ranking function. This paper proposes a method to improve the rank-pooling strategy which captures the optimized latent structure of the video sequence data. The optimization...
In modern electronic warfare, it is a severe challenge for the Electronic Support Measure (ESM) system to realize the recognition of the threat radiation source, the modulation of the pulse repetition interval (PRI) is closely related to the function and application of the radar radiation source. Therefore, the recognition of PRI modulation is greatly significance to the identification of the threat...
A lot of products are sold on electronic commerce websites. There are many customer reviews available on the Internet. Users read them to select a product to purchase. However, going through the reviews is time-consuming because the number of reviews is large for each product. It is important to reflect reviews in a ranking to make a decision easily. The aim of this research is ranking products based...
Convolutional neural network has made major progress in classification problems of general object recognition. Classification of facial images is one of them. However, expression of the networks for classification depends on datasets and the network model, which is vulnerable to changes in the adaptation range. We propose the network that has the two convolutional parts of pre-trained CNN by transfer...
In this paper, we propose the new iris feature extraction method that uses local thresholding with block size fitting to achieve a reliable iris authentication technique. The dispersion index is used to analyze the degree of variation in the pixel intensities. By calculating the pixel intensity variance for the block size, it is possible to quantify the degree of contrast and brightness in the block...
This paper presents a novel methodology to obtain a measure of the difficulty of a scenario to obtain accurate localization results when testing an indoor positioning method. The variables used to measure indoor localization methods' accuracy are strongly dependent on the radio map used to test them. This makes it hard to compare different methods' performance. The proposed RMID indicator can be used...
This article describes a completely new, fully automatic line detector algorithm that takes advantage of look-up tables to recognize and fit straight line patterns. The algorithm first recognizes any possible 4×4 pixel line patterns among the binary edge pixels and then uses several small look up tables to decide whether the connected patterns form a line or not. It is designed for real time processing...
This article presents a two-stage fault detection and classification scheme, for induction motor drives in wind turbine pitch systems. The presented approach is suitable for application in offshore wind farms. The adopted strategy utilizes three phase motor current sensing at the pitch drives for fault detection and only when a fault is detected at this stage, features extracted from the current signals...
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