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1733. An accuracy-enhanced stemming algorithm for Arabic information retrieval

Creator:
Bessou , Sadik and Touahria , Mohamed
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
Arabic morphological analysis, stemming, information retrieval, and machine translation
Language:
English
Description:
This paper provides a method for indexing and retrieving Arabic texts, based on natural language processing. Our approach exploits the notion of template in word stemming and replaces the words by their stems. This technique has proven to be effective since it has returned significant relevant retrieval results by decreasing silence during the retrieval phase. Series of experiments have been conducted to test the performance of the proposed algorithm ESAIR (Enhanced Stemmer for Arabic Information Retrieval). The results obtained indicate that the algorithm extracts the exact root with an accuracy rate up to 96% and hence, improving information retrieval.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

1734. An adaptive backpropagation neural network for arrhythmia classification using R-R interval signal

Creator:
Asl, Babak Mohammadzadeh, Sharafat, Ahmad R., and Setarehdan, Kamaledin S.
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
Adaptive-learning-rate neural networks, arrhythmia classification, non-linear analysis, and R-R interval signal
Language:
English
Description:
Automatic detection and classification of cardiac arrhythmias with high accuracy and by using as little information as possible is highly useful in Holter monitoring of the high risk patients and in telemedicine applications where the amount of information which must be transmitted is an important issue. To this end, we have used an adaptive-learning-rate neural network for automatic classification of four types of cardiac arrhythmia. In doing so, we have employed a mix of linear, nonlinear, and chaotic features of the R-R interval signal to significantly reduce the required information needed for analysis, and substantially improve the accuracy, as compared to existing systems (both ECG-based and R-R interval-based). For normal sinus rhythm (NSR), premature ventricular contraction (PVC), ventricular fibrillation (VF), and atrial fibrillation (AF), the discrimination accuracies of 99.59%, 99.32%, 99.73%, and 98.69% were obtained, respectively on the MIT-BIH database, which are superior to all existing classifiers.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

1735. An adaptive long step interior point algorithm for linear optmization

Creator:
Salahi, Maziar
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
linear optimization, interior pointmethods, long step algorithms, large neighborhood, and polynomial complexity
Language:
English
Description:
It is well known that a large neighborhood interior point algorithm for linear optimization performs much better in implementation than its small neighborhood counterparts. One of the key elements of interior point algorithms is how to update the barrier parameter. The main goal of this paper is to introduce an "adaptive'' long step interior-point algorithm in a large neighborhood of central path using the classical logarithmic barrier function having O(nlog(x0)Ts0ϵ) iteration complexity analogous to the classical long step algorithms. Preliminary encouraging numerical results are reported.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

1736. An adaptive online sequential extreme learning machine for short-term wind speed prediction based on improved artificial bee colony algorithm

Creator:
Tian, Zhongda , Wang, Gang , Ren, Yi , Li, Shujiang , and Wang, Yanhong
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
extreme learning machine, improved artificial bee colony algorithm, adaptive online sequential, short-term wind speed, and prediction
Language:
English
Description:
As an improved algorithm of standard extreme learning machine, online sequential extreme learning machine achieves excellent classification and regression performance. However, online sequential extreme learning machine gives the same weight to the old and new training samples, and fails to highlight the importance of the new training samples. At the same time, the algorithm updates the network weights after obtaining the new training samples. This network weight updating mode lacks flexibility and increases unnecessary computation. This paper proposes an adaptive online sequential extreme learning machine with an effective sample updating mechanism. The new and old samples are given different weights. The effect of new training samples on the algorithm is further enhanced, which can further improve the regression prediction ability of extreme learning machine. At the same time, an improved artificial bee colony algorithm is proposed and used to optimize the parameters of the adaptive online sequential extreme learning machine. The stability and convergence property of proposed prediction method are proved. The actual collected short-term wind speed time series is used as the research object and verify the prediction performance of the proposed method. Multi step prediction simulation of short-term wind speed is performed out. Compared with other prediction methods, the simulation results show that the proposed approach has higher prediction accuracy and reliability performance, meanwhile improve the performance indicators.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

1737. An adaptive-distance artificial immune recognition system

Creator:
Zare, A., Zolghadri Jaromi, M., and Boostani , R.
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
Adaptive distance, artificial immune recognition system, nearest neighbor, and weighted distance
Language:
English
Description:
The artificial Immune Recognition System (AIRS) algorithm inspired by a natural immune system makes use of the training data to generate memory cells (or prototypes). These memory cells are used in the test phase to classify unseen data using the K-nearest neighbor (K-NN) algorithm. The performance of the AIRS algorithm, similar to other distance-based classifiers, is highly dependent on the distance function used to classify a test instance. In this paper, we present a new version of the AIRS algorithm named Adaptive Distance AIRS (AD-AIRS) that uses an adaptive distance metric to improve the generalization accuracy of the basic AIRS algorithm. The adaptive distance metric is based on assigning weights to the evolved memory cells. The weights of memory cells are used in the test phase to classify test instances. Apart from this, the AD-AIRS algorithm uses the concept of clustering to modify the way that memory cells are generated. Each memory cell represents a group of similar instances (or antigens). A subset of the UCI datasets is used to evaluate the effectiveness of the proposed AD-AIRS algorithm in comparison with the basic AIRS. Experimental results show that the AD-AIRS achieves higher accuracy with a fewer number of memory cells when compared with the basic AIRS algorithm.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

1738. An admissible estimator of a lower-bounded scale parameter under squared-log error loss function

Creator:
Mahmoudi, Eisa and Zakerzadeh, Hojatollah
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
admissibility, Bayes estimator, truncated parameter spaces, and squared-log error los
Language:
English
Description:
Estimation in truncated parameter space is one of the most important features in statistical inference, because the frequently used criterion of unbiasedness is useless, since no unbiased estimator exists in general. So, other optimally criteria such as admissibility and minimaxity have to be looked for among others. In this paper we consider a subclass of the exponential families of distributions. Bayes estimator of a lower-bounded scale parameter, under the squared-log error loss function with a sequence of boundary supported priors is obtained. An admissible estimator of a lower-bounded scale parameter, which is the limiting Bayes estimator, is given. Also another class of estimators of a lower-bounded scale parameter, which is called the truncated linear estimators, is considered and several interesting properties of the estimators in this class are studied. Some comparisons of the estimators in this class with an admissible estimator of a lower-bounded scale parameter are presented.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

1739. An advanced assessment of mechanical fracture parameters of sandstones depending on the internal rock texture features

Creator:
Vavro , Leona, Malíková , Lucie, Frantík , Petr, Kubeš , Pavel, Keršner , Zbyněk, and Vavro , Martin
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
sandstone, mode I fracture toughness, bending Young's modulus, fracture energy, load–displacement diagram, fracture test, and chevron type notch
Language:
English
Description:
In this paper, sandstones from three Czech localities were subjected to mechanical fracture tests in order to obtain their properties. Carboniferous sandstone from the Staříč site was primarily different from the two other Cretaceous sandstones from Podhorní Újezd and Javorka localities in the type of grain contact, as well as in their mineralogical composition of the rock matrix and cement. These differences were primarily reflected in different rock porosities. An advanced assessment of the fracture response of the chevron notch specimens made of sandstones subjected to three-point bending test was carried out by means of the GTDiPS program suggested for processing the loading diagrams. Bending Young's modulus, mode I fracture toughness, and fracture energy were subsequently calculated for all tested sandstone samples. Obtained outcomes show that the sandstone from the Staříč mine exhibits several times higher values of investigated properties than the Podhorní Újezd and Javorka sandstones. This was a result of a higher degree of rock compaction, siliciferous rock cement and, therefore, relatively low total porosity. Internal rock texture and mineralogical composition of matrix or cement are thus one of the most important factors influencing the values of mechanical fracture parameters of sandstones.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

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