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Conventional multi-atlas-based segmentation demands pairwise full-fledged registration between each atlas image and the target image, which leads to high computational cost and poses great challenge in the new era of big data. On the other hand, only the most relevant atlases should contribute to final label fusion. In this work, we introduce a two-stage fusion set selection method by first trimming...
Lung cancer is the second most common cancer in the United States, regardless of gender. Lung cancer staging is a critical process for diagnosis and prognosis that is commonly done through the analysis of computed tomography of the chest. Analysis can be done by extracting quantitative metrics from clinician defined contours; however, defining contours manually can be a time consuming process which,...
The increasing number of elderly persons, in addition to the lack of infrastructures designed to manage them brings an awareness of the importance of maintaining them at home by developing assistive technologies. Recent research on the latter focused on Human Activity Recognition (HAR). HAR aims to recognize the sequence of actions by a specific resident at home using sensor readings. In eldercare...
There has been an increasing attention on Electroencephalograph (EEG) based personal identification over the last decade. Most existing methods address this problem by Euclidean metric based Nearest Neighbor (NN) search. However, under various recording conditions, simple Euclidean distance cannot model the similarity relations between EEG signals precisely. To overcome this drawback, a local metric...
Traditional network monitoring involving packet capturing or flow sampling has many challenges such as scalability, accuracy and availability of processing resource when networks become large-scale, high-speed and heterogeneous. SDN is a promising approach to address these challenges, but each SDN switch has it's own capacity limitation, such as it's cache memory called TCAM, and thus it needs coordination...
Developers choose identifiers to name entities during software coding. While these names are lexically restricted by the language, they reflect the understanding of the developer on the requirements that the entity is devoted for. In this paper, we analyze the use of such vocabularies to identify experts on code entities. For a real software development, e-Pol (Management Information System for Federal...
With the increasing popularity of Location-based Social Networks (LBSNs), users have shared information about places they have visited, creating a link between the real world (their movements on the globe) and the virtual world (what they express about these movements on the LBSNs). In this article, we propose the SiST model, which contains information captured from different dimensions (Social, Spatial...
Graph robustness metrics have been used largely to study the behavior of communication networks in the presence of targeted attacks and random failures. Several researchers have proposed new graph metrics to better predict network resilience and survivability against such attacks. Most of these metrics have been compared to a few established graph metrics for evaluating the effectiveness of measuring...
In this paper, an automated model selection approach guided by Cuckoo search is proposed for k-nearest neighbor (KNN) learning algorithm. The performance of KNN mostly depends on the value of k and the distance metric used. The values of these parameters are computed by optimizing an objective function designed for measuring the classification accuracy of KNN. Cuckoo search being an efficient optimization...
This work proposes a new segmentation algorithm for three-dimensional dense point clouds and has been specially designed for natural environments where the ground is unstructured and may include big slopes, non-flat areas and isolated areas. This technique is based on a Geometric-Featured Voxel map (GFV) where the scene is discretized in constant size cubes or voxels which are classified in flat surface,...
Orientation fields (OFs) are a key element of fingerprint recognition systems. They are a requirement for important processing steps such as image enhancement by contextual filtering, and typically, they are estimated from fingerprint images. If information about a fingerprint is available only in form of a stored minutiae template, an OF can be reconstructed from this template up to a certain degree...
The method of MultiScale Entropy (MSE) is an invaluable tool to quantify and compare the complexity of physiological time series at different time scales. Although MSE traditionally employs sample entropy to measure the unpredictability of each coarse-grained series, the same framework can be applied to other metrics.
Cloud networks underpin most of todays' socio-economical Information Communication Technology (ICT) environments due to their intrinsic capabilities such as elasticity and service transparency. Undoubtedly, this increased dependence of numerous always-on services with the cloud is also subject to a number of security threats. An emerging critical aspect is related with the adequate identification...
A recommendation system learns a user's interests from her historical purchasing or watching behavior, which is disclosed to the recommendation system inevitably. Such a disclosure raises a serious concern in the public for the leak of users' privacy. For instance, a person who watches a lot of videos which are more preferred by women than men can be inferred as female. Recently, as a response to...
The massive data exchange on the web has deeply increased the risk of malicious activities thereby propelling the research in the area of Intrusion Detection System (IDS). This paper aims to first select ten classification algorithms based on their efficiency in terms of speed, capability to handle large dataset and dependency on parameter tuning and then simulates the ten selected existing classifiers...
Accurate robot mapping, localisation and navigation remains an unsolved problem for challenging real-life indoor environments. Many approaches to Simultaneous Localisation And Mapping (SLAM) have been proposed but few attempts have been made to improve performance by using appropriate prior maps. Information such as floor plans or architectural drawings is available and there is a rich literature...
The aim of clustering is to discover the clusters based on the similarity features of objects. The present algorithm of visual access tendency (VAT) can access an exact number of clusters by its VAT image. The VAT image displays the squared shaped dark blocks along the diagonal; number of cluster information is accessed by counting the number of obtaining square blocks. Other extended versions are...
In this paper, we introduce a new scalable platform for knowledge sharing based group learning in an adaptive boosting(AdaBoost) environment for supervised learning Though knowledge sharing has been an active area of research in semi supervised learning, the concept has not been explored thoroughly in supervised learning framework. In our proposed algorithm, several learner members are trained simultaneously...
Classifying large-scale image data into object categories is an important problem that has received increasing research attention. Given the huge amount of data, non-parametric approaches such as nearest neighbor classifiers have shown promising results, especially when they are underpinned by a learned distance or similarity measurement. Although metric learning has been well studied in the past...
Achieving sub-pixel accuracy with face alignment algorithms is a difficult task given the diversity of appearance in real world facial profiles. To capture variations in perspective, occlusion, and illumination with adequate precision, current face alignment approaches rely on detecting facial landmarks and iteratively adjusting deformable models that encode prior knowledge of facial structure. However,...
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