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Clinical decision support relies on the findings of epidemiological (longitudinal and cross-sectional) studies on predictive features and risk factors for diseases. Such features flow into the diagnostic procedures. Personalized medicine, which aims to optimize clinical decision making by taking individual characteristics of the patients into account, relies on the findings of epidemiology on groups...
Mild cognitive impairment (MCI) is a neurological condition that is often the early stage of Alzheimer's disease (AD). This pilot study explores event-related multiscale entropy (MSE) measures as features for effectively discriminating between normal aging, MCI, and AD participants. Thirty two-channel scalp EEG records recorded during a working memory task from 43 age-matched participants (mean age...
Incentive strategy is important in participatory sensing, especially when the budget is limited, to decide how much and where the samples should be collected. Current auction-based incentive strategies purchase sensing data with lowest price requirements to maximize the amount of samples. However, such methods may lead to inaccurate sensing result after data interpolation, particularly for participants...
The data gathering is a fundamental operation in wireless sensor networks. Among approaches of the data gathering, the compressive data gathering (CDG) is an effective solution, which exploits the spatiotemporal correlation of raw sensory data. However, in the multi-attribute scenario, the performance of CDG decreases in every attribute's capacity because more measurements are on demand. In this paper,...
In order to differentiate the affective state of a computer user as it changes from relaxation to stress, features derived from pupil dilation and periorbital temperature are processed with machine learning techniques. When absolute signal values are used together with entropy based features, the accuracy of affective classification is observed to increase. When decision tree (C4.5) is tested for...
This paper deals with the influence of global and local objective image parameters of the analyzed image on the process of finding corresponding points. We examined the impact of objective parameters on the usability of some significant point detectors. Moreover, the paper also contains the comparison of the reliability of finding corresponding points for a particular image point by using common methods...
Sources such as speakers and environments from different communication devices produce signal variations that result in interference generated by different communication devices. Despite these convolutions, signal variations produced by different mobile devices leave intrinsic fingerprints on recorded calls, thus allowing the tracking of the models and brands of engaged mobile devices. This study...
The Web is teeming with rich structured information in the form of HTML tables, which provides us with the opportunity to build a knowledge repository by integrating these tables. An essential problem of web data integration is to discover semantic correspondences between web table columns, and schema matching is a popular means to determine the semantic correspondences. However, conventional schema...
Automatic Document Image Analysis has been a prime field of research in the past few decades. Script Identification is an essential part of automatic document image analysis. Script is essentially the text of a written document and languages are written using them. A huge set of techniques have been proposed and many scripts, foreign & domestic, have been identified. But so far, trivial work has...
In this paper we propose a unified framework for learning such local image descriptors that describe pixel neighborhoods using binary codes. The descriptors are constructed using binary decision trees which are learnt from a set of training image patches. Our framework generalizes several previously proposed binary descriptors, such as BRIEF, LBP and their variants, and provides a principled way to...
The Bag-of-Visual-Words (BoVW) has been frequently used in the classification of image data. However, this modeling approach does not take into consideration the spatial relationships of these words, which is important for similarity measurement between images. We have developed a novel technique to incorporate spatial information of visual words based on the n-grams representation. The method encodes...
The requirement of Bangla character recognition has become one of the prime attentions among the current researchers due to the increase of automated systems and usage of hand held devices. This paper presents a novel approach to recognize handwritten bangla numerals and addresses a robust feature extraction scheme that spawns 23- dimensional features based on the numeral's structure and topology...
Clustering is a distribution of data into groups of similar objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups. The concept of clustering applications is particularly in the context of information retrieval and in organizing web resources. The objective of clustering is to find out information and in...
A Brain Computer Interfaces (BCI) system enables users to control devices by acquiring and processing brain activity. An important component of a BCI system is feature extraction, which is responsible for representing brain signals in terms of essential components called features. This paper presents a comparison of the following feature extraction techniques for BCI; Common Spatial Patterns (CSP),...
Background: Medical imaging is a thrust area in clinical diagnosis for internal tissue/cell abnormalities like growth of a tumor. Statistical analysis of these images has enables the use of intelligent virtual vision to eradicate the natural limitation of human vision in terms of 2-dimensional representation only. This, sometimes, leads to variability in diagnosis between classical rule based and...
Neonatal sleep stage identification is of great importance as it helps diagnosis of certain possible disabilities in newborns. The sleep stage identification is normally done manually for an entire sleep recording which requires great human resources; therefore a reliable automated sleep stage identification system offers a helpful tool for specialists. This study demonstrated a new method for automated...
Human latent of distinguish varied music natures and cluster those into classes of categories are so incredible which expert in music will achieve such categorisation using their logical judgement and hearing senses. Till now, the technical society have concerned in delve into computerize the human way of distinguish the music in view of each necessary factor of the music tune, songs from voice of...
With the rapid development of Cloud computing, more and more service providers could provide cloud services (applications) to users. Faced with mass Cloud services, trust and reputation mechanisms offer a promising way to solve the trust evaluation of Cloud services. Hence, trust and reputation play an important role in evaluating of Cloud services. In this paper, we propose a lightweight reputation...
The availability of advanced sensors on smart-phones allows feeding mobile applications with rich contextual information. Continuous sensing mechanisms in smartphones cost high energy consumption to support accurate contextual recognition. Hence, there is a trade-off between the classification accuracy and the energy consumption that needs to be identified and optimized. In this paper, we formulate...
Quaternion technique is applied to model the polarimetric statistical MIMO radar system, where electromagnetic vector sensors are employed at each receiver to exploit polarization information of reflected signal. Based on the refined quaternion model, quaternion adaptive detector and its complex counterpart are compared and analyzed. The quaternion adaptive detector outperforms its complex counterpart...
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