Search results for: spectrum of linear operators.2000 mathematic subject classification: 34A05

Authors: Lilac A. E. Al-Safadi

Abstract:

This paper proposes an auto-classification algorithm of Web pages using Data mining techniques. We consider the problem of discovering association rules between terms in a set of Web pages belonging to a category in a search engine database, and present an auto-classification algorithm for solving this problem that are fundamentally based on Apriori algorithm. The proposed technique has two phases. The first phase is a training phase where human experts determines the categories of different Web pages, and the supervised Data mining algorithm will combine these categories with appropriate weighted index terms according to the highest supported rules among the most frequent words. The second phase is the categorization phase where a web crawler will crawl through the World Wide Web to build a database categorized according to the result of the data mining approach. This database contains URLs and their categories.

Keywords: Information Processing on the Web, Data Mining, Document Classification.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1616

Authors: Shuimiao Wan, Chao Yuan, Baoguang Tian

Abstract:

In statistics parameter theory, usually the parameter estimations have two kinds, one is the least-square estimation (LSE), and the other is the best linear unbiased estimation (BLUE). Due to the determining theorem of minimum variance unbiased estimator (MVUE), the parameter estimation of BLUE in linear model is most ideal. But since the calculations are complicated or the covariance is not given, people are hardly to get the solution. Therefore, people prefer to use LSE rather than BLUE. And this substitution will take some losses. To quantize the losses, many scholars have presented many kinds of different relative efficiencies in different views. For the linear weighted regression model, this paper discusses the relative efficiencies of LSE of β to BLUE of β. It also defines two new relative efficiencies and gives their lower bounds.

Keywords: Linear weighted regression, Relative efficiency, Lower bound, Parameter estimation.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2111

Authors: M. Yucel, M. Yucel, N. F. Ozturk, H. H. Goktas, C. Gemci, F. V. Celebi

Abstract:

In this study, Brillouin Gain Spectrum (BGS) is experimentally analyzed in the Brillouin Optical Time Domain Reflectometry (BOTDR) and Brillouin Optical Time Domain Analyzer (BOTDA). For this purpose, the signal level of the microwave generator is varied and the effects of BGS are investigated. In the setups, 20 km conventional single mode fiber is used to both setups and laser wavelengths are selected around 1550 nm. To achieve best results, it can be used between 5 dBm to 15 dBm signal level of microwave generator for BOTDA and BOTDR setups.

Keywords: Microwave signal level, Brillouin gain spectrum, BOTDA, BOTDR.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1892

Authors: Ekachai Phaisangittisagul, Rapeepol Chongprachawat

Abstract:

Obtaining labeled data in supervised learning is often difficult and expensive, and thus the trained learning algorithm tends to be overfitting due to small number of training data. As a result, some researchers have focused on using unlabeled data which may not necessary to follow the same generative distribution as the labeled data to construct a high-level feature for improving performance on supervised learning tasks. In this paper, we investigate the impact of the relationship between unlabeled and labeled data for classification performance. Specifically, we will apply difference unlabeled data which have different degrees of relation to the labeled data for handwritten digit classification task based on MNIST dataset. Our experimental results show that the higher the degree of relation between unlabeled and labeled data, the better the classification performance. Although the unlabeled data that is completely from different generative distribution to the labeled data provides the lowest classification performance, we still achieve high classification performance. This leads to expanding the applicability of the supervised learning algorithms using unsupervised learning.

Keywords: Autoencoder, high-level feature, MNIST dataset, selftaught learning, supervised learning.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1825

Authors: Sagaren Pillay

Abstract:

In this paper bi-annual time series data on unemployment rates (from the Labour Force Survey) are expanded to quarterly rates and linked to quarterly unemployment rates (from the Quarterly Labour Force Survey). The resultant linked series and the consumer price index (CPI) series are examined using Johansen’s cointegration approach and vector error correction modeling. The study finds that both the series are integrated of order one and are cointegrated. A statistically significant co-integrating relationship is found to exist between the time series of unemployment rates and the CPI. Given this significant relationship, the study models this relationship using Vector Error Correction Models (VECM), one with a restriction on the deterministic term and the other with no restriction.

A formal statistical confirmation of the existence of a unique linear and lagged relationship between inflation and unemployment for the period between September 2000 and June 2011 is presented. For the given period, the CPI was found to be an unbiased predictor of the unemployment rate. This relationship can be explored further for the development of appropriate forecasting models incorporating other study variables.

Keywords: Forecasting, lagged, linear, relationship.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2538

Authors: Nebi Gedik

Abstract:

One of the important parts of the brain-computer interface (BCI) studies is the classification of motor imagery (MI) obtained by electroencephalography (EEG). The major goal is to provide non-muscular communication and control via assistive technologies to people with severe motor disorders so that they can communicate with the outside world. In this study, an EEG signal classification approach based on multiscale and multi-resolution transform method is presented. The proposed approach is used to decompose the EEG signal containing motor image information (right- and left-hand movement imagery). The decomposition process is performed using curvelet transform which is a multiscale and multiresolution analysis method, and the transform output was evaluated as feature data. The obtained feature set is subjected to feature selection process to obtain the most effective ones using t-test methods. SVM and k-NN algorithms are assigned for classification.

Keywords: motor imagery, EEG, curvelet transform, SVM, k-NN

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 609

Authors: Kazuo Komatsu, Hitoshi Takata

Abstract:

The objective of this study is to propose an observer design for nonlinear systems by using an augmented linear system derived by application of a formal linearization method. A given nonlinear differential equation is linearized by the formal linearization method which is based on Taylor expansion considering up to the higher order terms, and a measurement equation is transformed into an augmented linear one. To this augmented dimensional linear system, a linear estimation theory is applied and a nonlinear observer is derived. As an application of this method, an estimation problem of transient state of electric power systems is studied, and its numerical experiments indicate that this observer design shows remarkable performances for nonlinear systems.

Keywords: nonlinear system, augmented linear system, nonlinear observer, formal linearization, electric power system.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1573

Authors: Ruming Yin, Jian Yuan, Qiuhua Yang, Xiuming Shan, Xiqin Wang

Abstract:

Linear cryptanalysis methods are rarely used to improve the security of chaotic stream ciphers. In this paper, we apply linear cryptanalysis to a chaotic stream cipher which was designed by strictly using the basic design criterion of cryptosystem – confusion and diffusion. We show that this well-designed chaos-based stream cipher is still insecure against distinguishing attack. This distinguishing attack promotes the further improvement of the cipher.

Keywords: Stream cipher, chaos, linear cryptanalysis, distinguishing attack.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1745

Authors: Rade Ciric, Sasa Mandic

Abstract:

Students of Higher Education Technical School of Professional Studies in Novi Sad follow the subject ‘Maintenance of Electric Power Equipment’ at the Electrotechnical Department. This paper presents educational plan and program of the subject Maintenance of Electric Power Equipment. The course deals with the problems of preventive and investing maintenance of transformer stations (TS), performing and maintenance of grounding of TS and pillars, as well as tracing and detection the location of the cables failure. There is a special elaborated subject concerning the safe work conditions for the electrician during network maintenance, as well as the basics of making and keeping technical documentation of the equipment.

Keywords: Educational plan and program, electric power equipment, maintenance, technical documentation, safe work.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1365

Authors: José M. Merigó, Anna M. Gil-Lafuente

Abstract:

We study the possibility of using geometric operators in the selection of human resources. We develop three new methods that use the ordered weighted geometric (OWG) operator in different indexes used for the selection of human resources. The objective of these models is to manipulate the neutrality of the old methods so the decision maker is able to select human resources according to his particular attitude. In order to develop these models, first a short revision of the OWG operator is developed. Second, we briefly explain the general process for the selection of human resources. Then, we develop the three new indexes. They will use the OWG operator in the Hamming distance, in the adequacy coefficient and in the index of maximum and minimum level. Finally, an illustrative example about the new approach is given.

Keywords: OWG operator, decision making, human resources, Hamming distance.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1396

Authors: Gökhan Silahtaroğlu

Abstract:

Decision tree algorithms have very important place at classification model of data mining. In literature, algorithms use entropy concept or gini index to form the tree. The shape of the classes and their closeness to each other some of the factors that affect the performance of the algorithm. In this paper we introduce a new decision tree algorithm which employs data (attribute) folding method and variation of the class variables over the branches to be created. A comparative performance analysis has been held between the proposed algorithm and C4.5.

Keywords: Classification, decision tree, split, pruning, entropy, gini.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1365

Authors: Suresh S. Salankar, Balasaheb M. Patre

Abstract:

Research into the problem of classification of sonar signals has been taken up as a challenging task for the neural networks. This paper investigates the design of an optimal classifier using a Multi layer Perceptron Neural Network (MLP NN) and Support Vector Machines (SVM). Results obtained using sonar data sets suggest that SVM classifier perform well in comparison with well-known MLP NN classifier. An average classification accuracy of 91.974% is achieved with SVM classifier and 90.3609% with MLP NN classifier, on the test instances. The area under the Receiver Operating Characteristics (ROC) curve for the proposed SVM classifier on test data set is found as 0.981183, which is very close to unity and this clearly confirms the excellent quality of the proposed classifier. The SVM classifier employed in this paper is implemented using kernel Adatron algorithm is seen to be robust and relatively insensitive to the parameter initialization in comparison to MLP NN.

Keywords: Classification, MLP NN, backpropagation algorithm, SVM, Receiver Operating Characteristics.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1814

Authors: Yury Nikulin

Abstract:

A multicriteria linear programming problem with integer variables and parameterized optimality principle "from lexicographic to Slater" is considered. A situation in which initial coefficients of penalty cost functions are not fixed but may be potentially a subject to variations is studied. For any efficient solution, appropriate measures of the quality are introduced which incorporate information about variations of penalty cost function coefficients. These measures correspond to the so-called stability and accuracy functions defined earlier for efficient solutions of a generic multicriteria combinatorial optimization problem with Pareto and lexicographic optimality principles. Various properties of such functions are studied and maximum norms of perturbations for which an efficient solution preserves the property of being efficient are calculated.

Keywords: Stability and accuracy, multicriteria optimization, lexicographic optimality.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1036

Authors: Amin Sahraeian

Abstract:

One of the criteria in production scheduling is Make Span, minimizing this criteria causes more efficiently use of the resources specially machinery and manpower. By assigning some budget to some of the operations the operation time of these activities reduces and affects the total completion time of all the operations (Make Span). In this paper this issue is practiced in parallel flow shops. At first we convert parallel flow shop to a network model and by using a linear programming approach it is identified in order to minimize make span (the completion time of the network) which activities (operations) are better to absorb the predetermined and limited budget. Minimizing the total completion time of all the activities in the network is equivalent to minimizing make span in production scheduling.

Keywords: parallel flow shop, make span, linear programming, budget

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1774

Authors: Chi Jung Kim, Mincheol Whang, Eui Chul Lee

Abstract:

In this paper, we propose a new method to distinguish between arousal and relaxation states by using multiple features acquired from a photoplethysmogram (PPG) and support vector machine (SVM). To induce arousal and relaxation states in subjects, 2 kinds of sound stimuli are used, and their corresponding biosignals are obtained using the PPG sensor. Two features–pulse to pulse interval (PPI) and pulse amplitude (PA)–are extracted from acquired PPG data, and a nonlinear classification between arousal and relaxation is performed using SVM. This methodology has several advantages when compared with previous similar studies. Firstly, we extracted 2 separate features from PPG, i.e., PPI and PA. Secondly, in order to improve the classification accuracy, SVM-based nonlinear classification was performed. Thirdly, to solve classification problems caused by generalized features of whole subjects, we defined each threshold according to individual features. Experimental results showed that the average classification accuracy was 74.67%. Also, the proposed method showed the better identification performance than the single feature based methods. From this result, we confirmed that arousal and relaxation can be classified using SVM and PPG features.

Keywords: Support Vector Machine, PPG, Emotion Recognition, Arousal, Relaxation

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2475

Authors: M. Zamani, O. Kahar

Abstract:

Solution to unsteady Navier-Stokes equation by Splitting method in physical orthogonal algebraic curvilinear coordinate system, also termed 'Non-linear grid system' is presented. The linear terms in Navier-Stokes equation are solved by Crank- Nicholson method while the non-linear term is solved by the second order Adams-Bashforth method. This work is meant to bring together the advantage of Splitting method as pressure-velocity solver of higher efficiency with the advantage of consuming Non-linear grid system which produce more accurate results in relatively equal number of grid points as compared to Cartesian grid. The validation of Splitting method as a solution of Navier-Stokes equation in Nonlinear grid system is done by comparison with the benchmark results for lid driven cavity flow by Ghia and some case studies including Backward Facing Step Flow Problem.

Keywords: Navier-Stokes, 'Non-linear grid system', Splitting method.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1523

Authors: Ehsan Amid, Sina Rezaei Aghdam, Hamidreza Amindavar

Abstract:

Steel surface defect detection is essentially one of pattern recognition problems. Support Vector Machines (SVMs) are known as one of the most proper classifiers in this application. In this paper, we introduce a more accurate classification method by using SVMs as our final classifier of the inspection system. In this scheme, multiclass classification task is performed based on the "one-againstone" method and different kernels are utilized for each pair of the classes in multiclass classification of the different defects. In the proposed system, a decision tree is employed in the first stage for two-class classification of the steel surfaces to "defect" and "non-defect", in order to decrease the time complexity. Based on the experimental results, generated from over one thousand images, the proposed multiclass classification scheme is more accurate than the conventional methods and the overall system yields a sufficient performance which can meet the requirements in steel manufacturing.

Keywords: Steel Surface Defect Detection, Support Vector Machines, Kernel Methods.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1910

Authors: Lazim Abdullah, Nurhakimah Ab. Rahman

Abstract:

Linear systems are widely used in many fields of science and engineering. In many applications, at least some of the parameters of the system are represented by fuzzy rather than crisp numbers. Therefore it is important to perform numerical algorithms or procedures that would treat general fuzzy linear systems and solve them using iterative methods. This paper aims are to solve fuzzy linear systems using four types of Jacobi based iterative methods. Four iterative methods based on Jacobi are used for solving a general n × n fuzzy system of linear equations of the form Ax = b , where A is a crisp matrix and b an arbitrary fuzzy vector. The Jacobi, Jacobi Over-Relaxation, Refinement of Jacobi and Refinement of Jacobi Over-Relaxation methods was tested to a five by five fuzzy linear system. It is found that all the tested methods were iterated differently. Due to the effect of extrapolation parameters and the refinement, the Refinement of Jacobi Over-Relaxation method was outperformed the other three methods.

Keywords: Fuzzy linear systems, Jacobi, Jacobi Over- Relaxation, Refinement of Jacobi, Refinement of Jacobi Over- Relaxation.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2406

Authors: Ali Mohammadi Torkashvand, Hamid Reza Alipour

Abstract:

This study investigates the possibility providing gully erosion map by the supervised classification of satellite images (ETM+) in two mountainous and plain land types. These land types were the part of Varamin plain, Tehran province, and Roodbar subbasin, Guilan province, as plain and mountain land types, respectively. The position of 652 and 124 ground control points were recorded by GPS respectively in mountain and plain land types. Soil gully erosion, land uses or plant covers were investigated in these points. Regarding ground control points and auxiliary points, training points of gully erosion and other surface features were introduced to software (Ilwis 3.3 Academic). The supervised classified map of gully erosion was prepared by maximum likelihood method and then, overall accuracy of this map was computed. Results showed that the possibility supervised classification of gully erosion isn-t possible, although it need more studies for results generalization to other mountainous regions. Also, with increasing land uses and other surface features in plain physiography, it decreases the classification of accuracy.

Keywords: Supervised classification, Gully erosion, Map.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1817

Authors: Shu-Xin Miao

Abstract:

In this paper, we present an efficient numerical algorithm, namely block homotopy perturbation method, for solving fuzzy linear systems based on homotopy perturbation method. Some numerical examples are given to show the efficiency of the algorithm.

Keywords: Homotopy perturbation method, fuzzy linear systems, block linear system, fuzzy solution, embedding parameter.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1367

Authors: Nagesha, G. Hemantha Kumar

Abstract:

Although the level crossing concept has been the subject of intensive investigation over the last few years, certain problems of great interest remain unsolved. One of these concern is distribution of threshold levels. This paper presents a new threshold level allocation schemes for level crossing based on nonuniform sampling. Intuitively, it is more reasonable if the information rich regions of the signal are sampled finer and those with sparse information are sampled coarser. To achieve this objective, we propose non-linear quantization functions which dynamically assign the number of quantization levels depending on the importance of the given amplitude range. Two new approaches to determine the importance of the given amplitude segment are presented. The proposed methods are based on exponential and logarithmic functions. Various aspects of proposed techniques are discussed and experimentally validated. Its efficacy is investigated by comparison with uniform sampling.

Keywords: speech signals, sampling, signal reconstruction, asynchronousdelta modulation, non-linear quantization.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1646

Authors: Lily Ingsrisawang, Tasanee Nacharoen

Abstract:

The problems arising from unbalanced data sets generally appear in real world applications. Due to unequal class distribution, many researchers have found that the performance of existing classifiers tends to be biased towards the majority class. The k-nearest neighbors’ nonparametric discriminant analysis is a method that was proposed for classifying unbalanced classes with good performance. In this study, the methods of discriminant analysis are of interest in investigating misclassification error rates for classimbalanced data of three diabetes risk groups. The purpose of this study was to compare the classification performance between parametric discriminant analysis and nonparametric discriminant analysis in a three-class classification of class-imbalanced data of diabetes risk groups. Data from a project maintaining healthy conditions for 599 employees of a government hospital in Bangkok were obtained for the classification problem. The employees were divided into three diabetes risk groups: non-risk (90%), risk (5%), and diabetic (5%). The original data including the variables of diabetes risk group, age, gender, blood glucose, and BMI were analyzed and bootstrapped for 50 and 100 samples, 599 observations per sample, for additional estimation of the misclassification error rate. Each data set was explored for the departure of multivariate normality and the equality of covariance matrices of the three risk groups. Both the original data and the bootstrap samples showed nonnormality and unequal covariance matrices. The parametric linear discriminant function, quadratic discriminant function, and the nonparametric k-nearest neighbors’ discriminant function were performed over 50 and 100 bootstrap samples and applied to the original data. Searching the optimal classification rule, the choices of prior probabilities were set up for both equal proportions (0.33: 0.33: 0.33) and unequal proportions of (0.90:0.05:0.05), (0.80: 0.10: 0.10) and (0.70, 0.15, 0.15). The results from 50 and 100 bootstrap samples indicated that the k-nearest neighbors approach when k=3 or k=4 and the defined prior probabilities of non-risk: risk: diabetic as 0.90: 0.05:0.05 or 0.80:0.10:0.10 gave the smallest error rate of misclassification. The k-nearest neighbors approach would be suggested for classifying a three-class-imbalanced data of diabetes risk groups.

Keywords: Bootstrap, diabetes risk groups, error rate, k-nearest neighbors.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2003

Authors: Vijaya K. Srivastava, Davide Spinello

Abstract:

This paper presents performance of two robust gradient-based heuristic optimization procedures based on 3ⁿ enumeration and tunneling approach to seek global optimum of constrained integer problems. Both these procedures consist of two distinct phases for locating the global optimum of integer problems with a linear or non-linear objective function subject to linear or non-linear constraints. In both procedures, in the first phase, a local minimum of the function is found using the gradient approach coupled with hemstitching moves when a constraint is violated in order to return the search to the feasible region. In the second phase, in one optimization procedure, the second sub-procedure examines 3ⁿ integer combinations on the boundary and within hypercube volume encompassing the result neighboring the result from the first phase and in the second optimization procedure a tunneling function is constructed at the local minimum of the first phase so as to find another point on the other side of the barrier where the function value is approximately the same. In the next cycle, the search for the global optimum commences in both optimization procedures again using this new-found point as the starting vector. The search continues and repeated for various step sizes along the function gradient as well as that along the vector normal to the violated constraints until no improvement in optimum value is found. The results from both these proposed optimization methods are presented and compared with one provided by popular MS Excel solver that is provided within MS Office suite and other published results.

Keywords: Constrained integer problems, enumerative search algorithm, Heuristic algorithm, tunneling algorithm.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 795

Authors: Christopher Paterson, Richard Curry, Alan Purvis, Simon Johnson

Abstract:

Emerging Bio-engineering fields such as Brain Computer Interfaces, neuroprothesis devices and modeling and simulation of neural networks have led to increased research activity in algorithms for the detection, isolation and classification of Action Potentials (AP) from noisy data trains. Current techniques in the field of 'unsupervised no-prior knowledge' biosignal processing include energy operators, wavelet detection and adaptive thresholding. These tend to bias towards larger AP waveforms, AP may be missed due to deviations in spike shape and frequency and correlated noise spectrums can cause false detection. Also, such algorithms tend to suffer from large computational expense. A new signal detection technique based upon the ideas of phasespace diagrams and trajectories is proposed based upon the use of a delayed copy of the AP to highlight discontinuities relative to background noise. This idea has been used to create algorithms that are computationally inexpensive and address the above problems. Distinct AP have been picked out and manually classified from real physiological data recorded from a cockroach. To facilitate testing of the new technique, an Auto Regressive Moving Average (ARMA) noise model has been constructed bases upon background noise of the recordings. Along with the AP classification means this model enables generation of realistic neuronal data sets at arbitrary signal to noise ratio (SNR).

Keywords: Action potential detection, Low SNR, Phase spacediagrams/trajectories, Unsupervised/no-prior knowledge.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1637

Authors: Dewi Y. Liliana, Agung Hardianto, M. Ridok

Abstract:

Digital news with a variety topics is abundant on the internet. The problem is to classify news based on its appropriate category to facilitate user to find relevant news rapidly. Classifier engine is used to split any news automatically into the respective category. This research employs Support Vector Machine (SVM) to classify Indonesian news. SVM is a robust method to classify binary classes. The core processing of SVM is in the formation of an optimum separating plane to separate the different classes. For multiclass problem, a mechanism called one against one is used to combine the binary classification result. Documents were taken from the Indonesian digital news site, www.kompas.com. The experiment showed a promising result with the accuracy rate of 85%. This system is feasible to be implemented on Indonesian news classification.

Keywords: classification, Indonesian news, text processing, support vector machine

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3483

Authors: Reza Hayati, Maryam Sadi, Saeid Shokri, Mehdi Ahmadi Marvast, Saeid Hassan Boroojerdi, Amin Hamzavi Abedi

Abstract:

Efficient and safe plant operation can only be achieved if the operators are able to monitor all key process parameters. Instrumentation is used to measure many process variables, like temperatures, pressures, flow rates, compositions or other product properties. Therefore Performance monitoring is a suitable tool for operators. In this paper, we integrate rigorous simulation model, data reconciliation and parameter estimation to monitor process equipments and determine key performance indicator (KPI) of them. The applied method here has been implemented in two case studies.

Keywords: Data Reconciliation, Measurement, Optimization, Parameter Estimation, Performance Monitoring.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2079

Authors: Hassan El Maia, Ahmed Hammouch, Driss Aboutajdine

Abstract:

This work presents an approach for the construction of a hybrid color-texture space by using mutual information. Feature extraction is done by the Laws filter with SVM (Support Vectors Machine) as a classifier. The classification is applied on the VisTex database and a SPOT HRV (XS) image representing two forest areas in the region of Rabat in Morocco. The result of classification obtained in the hybrid space is compared with the one obtained in the RGB color space.

Keywords: Color, texture, laws filter, mutual information, SVM, hybrid space.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1818

Authors: Asha Rani, Christian Micheloni, Gian Luca Foresti

Abstract:

In this paper, a neural tree (NT) classifier having a simple perceptron at each node is considered. A new concept for making a balanced tree is applied in the learning algorithm of the tree. At each node, if the perceptron classification is not accurate and unbalanced, then it is replaced by a new perceptron. This separates the training set in such a way that almost the equal number of patterns fall into each of the classes. Moreover, each perceptron is trained only for the classes which are present at respective node and ignore other classes. Splitting nodes are employed into the neural tree architecture to divide the training set when the current perceptron node repeats the same classification of the parent node. A new error function based on the depth of the tree is introduced to reduce the computational time for the training of a perceptron. Experiments are performed to check the efficiency and encouraging results are obtained in terms of accuracy and computational costs.

Keywords: Neural Tree, Pattern Classification, Perceptron, Splitting Nodes.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1219

Authors: Anamika Gupta, Naveen Kumar, Vasudha Bhatnagar

Abstract:

This paper focuses on analyzing medical diagnostic data using classification rules in data mining and context reduction in formal concept analysis. It helps in finding redundancies among the various medical examination tests used in diagnosis of a disease. Classification rules have been derived from positive and negative association rules using the Concept lattice structure of the Formal Concept Analysis. Context reduction technique given in Formal Concept Analysis along with classification rules has been used to find redundancies among the various medical examination tests. Also it finds out whether expensive medical tests can be replaced by some cheaper tests.

Keywords: Data Mining, Formal Concept Analysis, Medical Data, Negative Classification Rules.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1727

Authors: Bin Yu, Minghui Wang, Juntao Zhang

Abstract:

By the real representation of the quaternionic matrix, an iterative method for quaternionic linear equations Ax = b is proposed. Then the convergence conditions are obtained. At last, a numerical example is given to illustrate the efficiency of this method.

Keywords: Quaternionic linear equations, Real representation, Iterative algorithm.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1759