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Multimedia based instructional development: Bubble sort visualization

Multimedia based instructional development: Bubble sort visu...

引用

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

Primary User Identification when Secondary User is Transmitt...

引用

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

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

引用

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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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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AUTOMATIC SCREENING OF BLADDER CELLS FOR CANCER DIAGNOSIS

AUTOMATIC SCREENING OF BLADDER CELLS FOR CANCER DIAGNOSIS

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International Conference on Image Processing

作者： Grigory Begelman Ehud Rivlin Technion - Israel Institute of Technology Computer Science Department

ISBN: (纸本)9781424456536;9781424456543

This paper presents an automatic system for morphological screening of the bladder cells. This system is intended to increase efficiency of the subsequent fluorescence in situ hybridization examination by limiting the number of suspicious cells. The system works in two major phases. The first phase is slide scanning. The second stage includes cells detection and morphological analysis. Both stages refine their results using supervised classification algorithm. The developed method was tested on nine microscopical slides, containing more than 12000 manually labeled cells. The results provided by the system were compared to the ground truth labeled by a human expert.

关键词： Microscopy Biological cells cytology Microscopy neoplasm diagnosis Bladder cells Cells Urinary Bladder automation system sorting algorithm ground truth Cancer AUTOMATIC In Situ Hybridization, Fluorescent Slide Slide

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Performance Comparison of Different Classification algorithms for Household Poverty Classification

Performance Comparison of Different Classification Algorithm...

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International Conference on Information Systems Engineering

作者： Janelyn A. Talingdan Information and Communications Technology Department Abra State Institute of Sciences and Technology

ISBN: (纸本)9781728125596

There should be a way to identify poor households as the potential receiver of poverty alleviation programs if the aim of an organization is to serve the deprive one. This problem is addressed by using machine learning algorithms particularly the classification algorithms. In this study, data mining was used to analyze the poverty data that was extracted from the Community Based Monitoring System (CBMS) Database of Lagangilang, Abra, Philippines. Different methods of data mining classification were evaluated and compared like Naive Bayes, ID3, Decision Tree, Logistic Regression and KNearest Neighbor (KNN). In order to get the best predictive model, the different classification algorithms were evaluated based on the performance metrics;accuracy, precision, recall, F1 score and AUC. The rapid miner was the datamining tool used to process the different classification algorithms and to analyze the dataset. The study showed that Naive Bayes classifier is an efficient algorithm for predicting households that are poor and non-poor because it outperformed all the four algorithms in all the performance metrics used in the study. Furthermore, the error rate of the said algorithm is significant because it is 0.0014 only. Therefore, choosing the best classification algorithm for data mining task will give a better result.

关键词： Poverty Data mining Naive bayes Performance metrics Performance metrics sorting algorithm Poverty data mining Bayesian classifier household

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Evaluating Combinations of Classification algorithms and Paragraph Vectors for News Article Classification

Evaluating Combinations of Classification Algorithms and Par...

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Federated Conference on Computer Science and Information Systems

作者： Johannes Linden Stefan Forsstrom Tingting Zhang Departement of Information Systems and Technology Mid Sweden University

ISBN: (纸本)9781538623718

News companies have a need to automate and make the process of writing about popular and new events more effective. Current technologies involve robotic programs that fill in values in templates and website listeners that notify editors when changes are made so that the editor can read up on the source change on the actual website. Editors can provide news faster and better if directly provided with abstracts of the external sources and categorical meta-data that supports what the text is about. In this article, the focus is on the importance of evaluating critical parameter modifications of the four classification algorithms Decisiontree, Randomforest, Multi Layer perceptron and Long-Short-Term-Memory in a combination with the paragraph vector algorithms Distributed Memory and Distributed Bag of Words, with an aim to categorise news articles. The result shows that Decisiontree and Multi Layer perceptron are stable within a short interval, while Randomforest is more dependent on the parameters best split and number of trees. The most accurate model is Long-Short-Term-Memory model that achieves an accuracy of 71%.

关键词： news sorting algorithm Editors websites short interval multilayers Neural Networks (Computer) Writing Process Approach (Writing) Parameter modification jigs(templates)

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Mahalanobis Distance Metric Learning algorithm for Instance-based Data Stream Classification

Mahalanobis Distance Metric Learning Algorithm for Instance-...

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International Joint Conference on Neural Networks

作者： Jorge Luis Rivero Perez Bernardete Ribeiro Carlos Morell Perez Department of Informatics Engineering Faculty of Science and Technology University of Coimbra Department of Informatics Engineering CISUC-University of Coimbra Department of Computer Sciences Faculty of Computer Sciences Central University of Las Villas

ISBN: (纸本)9781509006212

With the massive data challenges nowadays and the rapid growing of technology, stream mining has recently received considerable attention. To address the large number of scenarios in which this phenomenon manifests itself suitable tools are required in various research fields. Instance-based data stream algorithms generally employ the Euclidean distance for the classification task underlying this problem. A novel way to look into this issue is to take advantage of a more flexible metric due to the increased requirements imposed by the data stream scenario. In this paper we present a new algorithm that learns a Mahalanobis metric using similarity and dissimilarity constraints in an online manner. This approach hybridizes a Mahalanobis distance metric learning algorithm and a k-NN data stream classification algorithm with concept drift detection. First, some basic aspects of Mahalanobis distance metric learning are described taking into account key properties as well as online distance metric learning algorithms. Second, we implement specific evaluation methodologies and comparative metrics such as Q statistic for data stream classification algorithms. Finally, our algorithm is evaluated on different datasets by comparing its results with one of the best instance-based data stream classification algorithm of the state of the art. The results demonstrate that our proposal is better in some scenarios and has shown to be competitive in others.

关键词： data flow sorting algorithm distance metric learning algorithms Euclidean distance Evaluation Methodology

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