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检索条件"主题词=Sorting algorithm"

共 482 条记录，以下是381-390 订阅

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CLASSIFICATION OF EX-VIVO BREAST CANCER POSITIVE MARGINS MEASURED BY HYPERSPECTRAL IMAGING

CLASSIFICATION OF EX-VIVO BREAST CANCER POSITIVE MARGINS MEA...

引用

IEEE International Conference on Image Processing

作者： R. Pourreza-Shahri F. Saki N. Kehtarnavaz P. Leboulluec H. Liu University of Texas at Dallas University of Texas at Arlington

ISBN: (纸本)9781479923427

This paper presents our recent development of a classification algorithm for identification of breast cancer margins measured by hyperspectral imaging for the purpose of lowering the number of missed positive margins in breast cancer lumpectomy. After extracting Fourier coefficient selection features and reducing the dimensionality of hyperspectral image data via the Minimum Redundancy Maximum Relevance method, an SVM classifier involving a radial basis kernel function is deployed to separate cancerous tissues from normal tissues. By examining exvivo breast cancer hyperspectral images tagged by a pathologist, the developed classification approach is shown to achieve a sensitivity of about 98% and a specificity of about 99%.

关键词： Hyperspectral imaging of breast cancer breast cancer positive margin classification Fourier coefficient selection features support vector machine Support Vector Network Breast Cancer margins sorting algorithm hyperspectral imagery EX-VIVO image data Segmental Mastectomy Imaging Radial basis function Taxonomy

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Design of Adaptive Feature Extraction algorithm Based on Fuzzy Classifier in Hyperspectral Imagery Classification for Big Data Analysis

Design of Adaptive Feature Extraction Algorithm Based on Fuz...

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World Congress on Intelligent Control and Automation

作者： Juan F. Ramirez Rochac Nian Zhang Pradeep Behera Dept. of Electr. & Comput. Eng. Univ. of the District of Columbia Columbia WA USA Department of Electrical and Computer Engineering University of the District of Columbia 4200 Connecticut Avenue NW Washington D.C. 20008 USA Department of Civil Engineering University of the District of Columbia 4200 Connecticut Avenue NW Washington D.C. 20008 USA

ISBN: (纸本)9781467384155

We proposed a new adaptive feature extraction (FEA) approach that integrates concepts of per-pixel/field classification and spectral ummixing. It combines their advantages in adaptive feature selection while minimizing the disadvantages associated with the high-complexity of each technique. The approach consists of local gradients calculation, reference clusters determination, prototype classification using fuzzy classifier, and feature vectors selection. Multiple experiments were performed using a simulated hyperspectral cube composed by 123 samples and 1254 features and classification was done only for verification purposes. Cross-validation demonstrated that FEA generated an average improvement of 7% on the misclassification error when compared to full feature analysis.

关键词： Prototypes Hyperspectral imaging Classification algorithms Substrates Cameras Feature extraction Feature extraction hyperspectral imagery sorting algorithm Cameras PROTOTYPE Substrates Finite element method big data Classifiers Self tuning

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Load shedding techniques based on windows in data stream systems

Load shedding techniques based on windows in data stream sys...

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International Conference on Emerging Trends in Science, Engineering and Technology (INCOSET)

作者： S. Senthamilarasu M. Hemalatha Department of Computer Science Karpagam University Coimbatore India

ISBN: (纸本)9781467351416

Many applications need to process streams, for analyzing and monitoring their data for inferring useful information. Database analysts are structuring Data Stream Management systems (DSMS) so that applications can subject queries to get timely information from streams. Unlike the traditional database system, managing and processing of stream database raise several challenges. In this paper, we portrayed a new load shedding system for queries consisting of one or more aggregate operators with sliding windows. We utilized different types of window aggregate function to drop the tuple in DataStream. This method is conscious of the window properties of its window aggregate operators in the query plan. Accordingly, it plausibly divides the input stream into windows and probabilistically decides which tuple to drop based on the window function. This decision is further encoded into tuple by marking the ones that are disallowed from starting new windows. Unlike previous methods, our method conserve consistency of windows all over a query plan, and always distributes subsets of original query responds with negligible deprivation in the quality of the result.

关键词： Data Stream Load Shedding Window functions Classification algorithms. load shedding window function windows sorting algorithm FENSTER tuple data flow Input Stream window streams data stream management system

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An Ensemble Learning Approach to Detect Epileptic Seizures from Long Intracranial EEG Recordings

An Ensemble Learning Approach to Detect Epileptic Seizures f...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： J.-B. Schiratti Jean-Eudes Le Douget Michel Le Van Quyen Slim Essid Alexandre Gramfort LTCI Telecom ParisTech Universite Paris-Saclay Paris 75013 France Bioelectrics lab Brain and Spine Institute (ICM) Paris France

ISBN: (纸本)9781538646595

This paper proposes a patient-specific supervised classification algorithm to detect seizures in long offline intracranial electroencephalographic (iEEG) recordings. The main idea of the proposed algorithm is to combine a set of probabilistic classifiers, trained on a dataset of 1 s epochs, into a weighted ensemble classifier which can be used to analyze longer 5 s data segments. The method is trained and evaluated on 24 patients, all suffering from focal medically intractable epilepsy, from the Epilepsiae database. The evaluation of the method, conducted using an average of 113 hours (min: 32 h, max: 229 h) of iEEG data per patient, shows that the proposed algorithm improves upon existing methods for seizure detection with iEEG.

关键词： Seizure detection intracranial EEG supervised learning Semi-supervised Learning epileptic seizure Electrocorticography Recordings Seizures sorting algorithm Classifiers evaluation method Dataset

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Primary User Identification when Secondary User is Transmitting without using Quiet Period

Primary User Identification when Secondary User is Transmitt...

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International Symposium on Communications and Informaiton Technologies

作者： Dorin Panaitopol Abdoulaye Bagayoko Christian Mouton Philippe Delahaye Guillaume Abril NEC Technologies (UK) Ltd. FMDC Lab. ComTech Department

ISBN: (纸本)9781467311564

The goal of this paper is to identify primary (licensed) users when the secondary (non-licensed) system is transmitting without Quiet Period in the same frequency band. The paper further presents the LTE operating in TV channels, where DVB-T and PMSE transmitters are considered as primary users by default. In this context, the LTE system is acting as a secondary system which during a first step i) verifies if a TV channel is available for opportunistic access, and which during a second step ii) will start using the TV channel if the band has been previously identified as free. However, since a primary user may start transmitting anytime, the identification becomes very difficult if the LTE system continues to transmit, because a secondary receiver could detect its own secondary system rather than the primary user. This paper therefore supposes that LTE system embeds classification capabilities, allowing primary user discrimination from LTE transmissions. Finally, simulation results are showing the efficiency of the proposed classification algorithm with respect to regulatory requirements.

关键词： Television channels user id Local thermodynamic equilibrium Long Term Evolution quadratic system TRANSMITTING sorting algorithm

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Multimedia based instructional development: Bubble sort visualization

Multimedia based instructional development: Bubble sort visu...

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IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

作者： Hadi Sutopo Faculty of Computer Science and Communication Science Kalbis Institute Indonesia

ISBN: (纸本)9781479983544

This paper is intended to develop a bubble sort algorithm visualization for an algorithm and Programming course in computer education. This learning model should encourage student's ability to learn the variety of sorting, such as selection sort, merge sort. etc. This research consists of 6 steps in visualization development such as need assessment, front-end analysis, design, development, implementation, and evaluation. During the testing step, the application is run and checked to confirm that it performs exactly what the author has intended and the students can learn bubble sorting algorithm by studying the visualization. Research findings indicated that the students increased their motivation and ability to create sorting visualitation application. Based on the data of process and product competencies taken from the evaluation, 85 per cent of the students were competent in sorting visualization programming and there were 15 per cent of the students were not competent.

关键词： algorithm Bubble sort Visualization Learning Visualization Students sorting programs MULTI-MEDIA SYSTEM algorithms Learning sorting algorithm bubbles grade computer-assisted instruction

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Hyperspectral Data Analysis for Arid Vegetation Species : Smart & Sustainable Growth

Hyperspectral Data Analysis for Arid Vegetation Species : Sm...

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International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

作者： S. L. Borana S.K. Yadav S.K. Parihar DL Jodhpur India Dept. of Mining JNV University Jodhpur India

ISBN: (纸本)9781728148274

Hyperspectral Images are continuous narrow spectral bands provides a wealth of information which can be used in different applications. Advance developments in hyperspectral remote sensing technology since two decade have opened new opportunity to explore innovative ways to study vegetation species. In this research work, ground-based AISA Vis-NIR hyper spectral image system of 240 bands, wavelength range from 390 to 960 nm with 2.5 nm spectral resolution and 1 cm spatial resolution at a distance of 10 m was used for classification of prominent vegetation species (Cactus, Neem and Babool). Machine learning supervised classification algorithms are used to classifying the Hyperspectral data. In supervised classification, four methods have been used viz. Spectral Angle Mapper (SAM), Minimum Distance (MD), Support Vector Machine (SVM) and Spectral Information Divergence (SID) Classifier. Environment of Visualize Images (ENVI) software is used for processing and analysis of hyperspectral images for classification of vegetation species in Jodhpur study area. Accuracy assessments were also carried out for classified output images and estimate the performance of a classifier. The overall accuracy for SVM classification algorithm is best (81.2%) when 237 hyperspectral bands were used and SAM classification algorithm has provided a better overall accuracy (76.6%) when maximum noise function (MNF) 11 bands were used. This research demonstrated the efficient use of contiguous fine bands of Hyperspectral data in discrimination and classification of vegetation species.

关键词： Hyperspectral image MNF Classification Remote sensing Accuracy Vegetation Species Taxonomy Data analysis hyperspectral imagery sorting algorithm remote sensing Support Vector Network

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Smart Meter Systems Measurements for the Verification of the Detection & Classification algorithms

Smart Meter Systems Measurements for the Verification of the...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Thomas Bier Dirk Benyoucef Djaffar Ould Abdeslam Jean Merckle Philipp Klein University Furtwangen University of Haute Alsace

ISBN: (纸本)9781479902255

In this paper the Non-Intrusive Appliance Load Monitoring (NALM) will be described as a technique to improve the current smart meter systems. The development and verification of a measurement system is described. With that system it is possible to simulate and measure the energy consumption of appliances in residential buildings. These measurements are used to develop and verify the disaggregation algorithms for the event detection and the classification. A first event detection algorithm, based on the calculation of the difference values of the power signals is presented. A comparison with another algorithm is shown. The usefulness of that algorithm for the event detection is discussed at the end of the paper.

关键词： Event detection Smart meters Verification measurement system signal power sorting algorithm energy consumption algorithms

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Learning Label Specific Features for Multi-Label Classification

Learning Label Specific Features for Multi-Label Classificat...

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IEEE International Conference on Data Mining

作者： Jun Huang Guorong Li Qingming Huang Xindong Wu School of Computer and Control Engineering University of Chinese Academy of Sciences Department of Computer Science University of Vermont

ISBN: (纸本)9781467395052

Binary relevance (BR) is a well-known framework for multi-label classification. It decomposes multi-label classification into binary (one-vs-rest) classification subproblems, one for each label. The BR approach is a simple and straightforward way for multi-label classification, but it still has several drawbacks. First, it does not consider label correlations. Second, each binary classifier may suffer from the issue of class-imbalance. Third, it can become computationally unaffordable for data sets with many labels. Several remedies have been proposed to solve these problems by exploiting label correlations between labels and performing label space dimension reduction. Meanwhile, inconsistency, another potential drawback of BR, is often ignored by researchers when they construct multi-label classification models. Inconsistency refers to the phenomenon that if an example belongs to more than one class label, then during the binary training stage, it can be considered as both positive and negative example simultaneously. This will mislead binary classifiers to learn suboptimal decision boundaries. In this paper, we seek to solve this problem by learning label specific features for each label. We assume that each label is only associated with a subset of features from the original feature set, and any two strongly correlated class labels can share more features with each other than two uncorrelated or weakly correlated ones. The proposed method can be applied as a feature selection method for multi-label learning and a general strategy to improve multi-label classification algorithms comprising a number of binary classifiers. Comparison with the state-of-the-art approaches manifests competitive performance of our proposed method.

关键词： binary sorter bromine Binary sorting algorithm Inconsistency

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STARIMA-based Traffic Prediction with Time-varying Lags

STARIMA-based Traffic Prediction with Time-varying Lags

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IEEE International Conference on Intelligent Transportation Systems

作者： Peibo Duan Guoqiang Mao Changsheng Zhang Shangbo Wang College of Computer Science and Engineering Northeastern University School of Computing and Communication The University of Technology Sydney

ISBN: (纸本)9781509018901

Based on the observation that the correlation between observed traffic at two measurement points or traffic stations may be time-varying, attributable to the time-varying speed which subsequently causes variations in the time required to travel between the two points, in this paper, we develop a modified Space-Time Autoregressive Integrated Moving Average (STARIMA) model with time-varying lags for short-term traffic flow prediction. Particularly, the temporal lags in the modified STARIMA change with the time-varying speed at different time of the day or equivalently change with the (time-varying) time required to travel between two measurement points. Firstly, a technique is developed to evaluate the temporal lag in the STARIMA model, where the temporal lag is formulated as a function of the spatial lag (spatial distance) and the average speed. Secondly, an unsupervised classification algorithm based on ISODATA algorithm is designed to classify different time periods of the day according to the variation of the speed. The classification helps to determine the appropriate time lag to use in the STARIMA model. Finally, a STARIMA-based model with time-varying lags is developed for short-term traffic prediction. Experimental results using real traffic data show that the developed STARIMA-based model with time-varying lags has superior accuracy compared with its counterpart developed using the traditional cross-correlation function and without employing time-varying lags.

关键词： time varying Traffic forecasting Traffic data time periods temporal variations sorting algorithm STMN1 gene

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