Search results for: and feature extraction.

Authors: Jyh-Da Wei, Hsin-Chen Tsai

Abstract:

This work presents a novel means of extracting fixedlength parameters from voice signals, such that words can be recognized in linear time. The power and the zero crossing rate are first calculated segment by segment from a voice signal; by doing so, two feature sequences are generated. We then construct an FIR system across these two sequences. The parameters of this FIR system, used as the input of a multilayer proceptron recognizer, can be derived by recursive LSE (least-square estimation), implying that the complexity of overall process is linear to the signal size. In the second part of this work, we introduce a weighting factor λ to emphasize recent input; therefore, we can further recognize continuous speech signals. Experiments employ the voice signals of numbers, from zero to nine, spoken in Mandarin Chinese. The proposed method is verified to recognize voice signals efficiently and accurately.

Keywords: Speech Recognition, FIR system, Recursive LSE, Multilayer Perceptron

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Authors: Kaveh Shahi, Helmi Z. M. Shafri, Ebrahim Taherzadeh

Abstract:

In remote sensing, shadow causes problems in many applications such as change detection and classification. It is caused by objects which are elevated, thus can directly affect the accuracy of information. For these reasons, it is very important to detect shadows particularly in urban high spatial resolution imagery which created a significant problem. This paper focuses on automatic shadow detection based on a new spectral index for multispectral imagery known as Shadow Detection Index (SDI). The new spectral index was tested on different areas of WorldView-2 images and the results demonstrated that the new spectral index has a massive potential to extract shadows with accuracy of 94% effectively and automatically. Furthermore, the new shadow detection index improved road extraction from 82% to 93%.

Keywords: Spectral index, shadow detection, remote sensing images, WorldView-2.

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Authors: Mehdi Hosseinzadeh, Parisa Khoshvaght

Abstract:

In some applications, such as image recognition or compression, segmentation refers to the process of partitioning a digital image into multiple segments. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. Image segmentation is to classify or cluster an image into several parts (regions) according to the feature of image, for example, the pixel value or the frequency response. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics. The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image. Several image segmentation algorithms were proposed to segment an image before recognition or compression. Up to now, many image segmentation algorithms exist and be extensively applied in science and daily life. According to their segmentation method, we can approximately categorize them into region-based segmentation, data clustering, and edge-base segmentation. In this paper, we give a study of several popular image segmentation algorithms that are available.

Keywords: Image Segmentation, hierarchical segmentation, partitional segmentation, density estimation.

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Authors: Chien-Hua Lee, Cheng-Yi Chen

Abstract:

The stability test problem for homogeneous large-scale perturbed bilinear time-delay systems subjected to constrained inputs is considered in this paper. Both nonlinear uncertainties and interval systems are discussed. By utilizing the Lyapunove equation approach associated with linear algebraic techniques, several delay-independent criteria are presented to guarantee the robust stability of the overall systems. The main feature of the presented results is that although the Lyapunov stability theorem is used, they do not involve any Lyapunov equation which may be unsolvable. Furthermore, it is seen the proposed schemes can be applied to solve the stability analysis problem of large-scale time-delay systems.

Keywords: homogeneous bilinear system, constrained input, time-delay, uncertainty, transient response, decay rate.

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Authors: Michael Netzer, Michael Seger, Mahesh Visvanathan, Bernhard Pfeifer, Gerald H. Lushington, Christian Baumgartner

Abstract:

Lung cancer accounts for the most cancer related deaths for men as well as for women. The identification of cancer associated genes and the related pathways are essential to provide an important possibility in the prevention of many types of cancer. In this work two filter approaches, namely the information gain and the biomarker identifier (BMI) are used for the identification of different types of small-cell and non-small-cell lung cancer. A new method to determine the BMI thresholds is proposed to prioritize genes (i.e., primary, secondary and tertiary) using a k-means clustering approach. Sets of key genes were identified that can be found in several pathways. It turned out that the modified BMI is well suited for microarray data and therefore BMI is proposed as a powerful tool for the search for new and so far undiscovered genes related to cancer.

Keywords: lung cancer, micro arrays, data mining, feature selection.

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Authors: Ali Raad Hassan

Abstract:

Natural frequencies and dynamic response of a spur gear sector are investigated using a two dimensional finite element model that offers significant advantages for dynamic gear analyses. The gear teeth are analyzed for different operating speeds. A primary feature of this modeling is determination of mesh forces using a detailed contact analysis for each time step as the gears roll through the mesh. ANSYS software has been used on the proposed model to find the natural frequencies by Block Lanczos technique and displacements and dynamic stresses by transient mode super position method. The effect of rotational speed of the gear on the dynamic response of gear tooth has been studied and design limits have been discussed.

Keywords: Spur gear, Natural frequency, transient analysis, Mode super position technique.

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Authors: Haragobinda Srichandan, Sradhanjali Singh, Dong Jin Kim, Seoung-Won Lee

Abstract:

The recovery of metal values and safe disposal of spent catalyst is gaining interest due to both its hazardous nature and increased regulation associated with disposal methods. Prior to the recovery of the valuable metals, removal of entrained deposits limit the diffusion of lixiviate resulting in low recovery of metals must be taken into consideration. Therefore, petroleum refinery spent catalyst was subjected to acetone washing and roasting at 500oC. The treated samples were investigated for metals bioleaching using Acidithiobacillus ferrooxidans in batch reactors and the leaching efficiencies were compared. It was found out that acetone washed spent catalysts results in better metal recovery compare to roasted spent. About 83% Ni, 20% Al, 50% Mo and 73% V were leached using the acetone washed spent catalyst. In both the cases, Ni, V and Mo was high compared to Al.

Keywords: Acetone wash, At. ferrooxidans, Bioleaching, Calcined, Metal recovery.

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Authors: Isabella Bianco, Gian Andrea Blengini

Abstract:

The environmental impact related to ornamental stones (such as marbles and granites) is largely debated. Starting from the industrial revolution, continuous improvements of machineries led to a higher exploitation of this natural resource and to a more international interaction between markets. As a consequence, the environmental impact of the extraction and processing of stones has increased. Nevertheless, if compared with other building materials, ornamental stones are generally more durable, natural, and recyclable. From the scientific point of view, studies on stone life cycle sustainability have been carried out, but these are often partial or not very significant because of the high percentage of approximations and assumptions in calculations. This is due to the lack, in life cycle databases (e.g. Ecoinvent, Thinkstep, and ELCD), of datasets about the specific technologies employed in the stone production chain. For example, databases do not contain information about diamond wires, chains or explosives, materials commonly used in quarries and transformation plants. The project presented in this paper aims to populate the life cycle databases with specific data of specific stone processes. To this goal, the methodology follows the standardized approach of Life Cycle Assessment (LCA), according to the requirements of UNI 14040-14044 and to the International Reference Life Cycle Data System (ILCD) Handbook guidelines of the European Commission. The study analyses the processes of the entire production chain (from-cradle-to-gate system boundaries), including the extraction of benches, the cutting of blocks into slabs/tiles and the surface finishing. Primary data have been collected in Italian quarries and transformation plants which use technologies representative of the current state-of-the-art. Since the technologies vary according to the hardness of the stone, the case studies comprehend both soft stones (marbles) and hard stones (gneiss). In particular, data about energy, materials and emissions were collected in marble basins of Carrara and in Beola and Serizzo basins located in the province of Verbano Cusio Ossola. Data were then elaborated through an appropriate software to build a life cycle model. The model was realized setting free parameters that allow an easy adaptation to specific productions. Through this model, the study aims to boost the direct participation of stone companies and encourage the use of LCA tool to assess and improve the stone sector environmental sustainability. At the same time, the realization of accurate Life Cycle Inventory data aims at making available, to researchers and stone experts, ILCD compliant datasets of the most significant processes and technologies related to the ornamental stone sector.

Keywords: LCA datasets, life cycle assessment, ornamental stone, stone environmental impact.

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Authors: Abhijit Mitra, Pranab Kumar Banerjee, C. Ardil

Abstract:

We introduce an effective approach for automatic offline au- thentication of handwritten samples where the forgeries are skillfully done, i.e., the true and forgery sample appearances are almost alike. Subtle details of temporal information used in online verification are not available offline and are also hard to recover robustly. Thus the spatial dynamic information like the pen-tip pressure characteristics are considered, emphasizing on the extraction of low density pixels. The points result from the ballistic rhythm of a genuine signature which a forgery, however skillful that may be, always lacks. Ten effective features, including these low density points and den- sity ratio, are proposed to make the distinction between a true and a forgery sample. An adaptive decision criteria is also derived for better verification judgements.

Keywords: Handwritten document verification, Skilled forgeries, Low density pixels, Adaptive decision boundary.

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Authors: S. Sendhil Kumar, S. N. Vijayan

Abstract:

Utilization of solar energy can be found in various domestic and industrial applications. The performance of any solar collector is largely affected by various parameters such as glazing, absorber plate, top covers, and heating pipes. Technology improvements have brought us another method for conversion of solar energy to direct electricity using solar photovoltaic system. Utilization and extraction of solar energy is the biggest problem in these conversion methods. This paper aims to overcome these problems and take the advantages of available energy from solar by maximizing the utilization through solar tracking system using a refrigerant as a working medium. The use of this tracking system can help increase the efficiency of conversion devices by maximum utilization of solar energy. The dual axis tracking system gives maximum energy output compared to single axis tracking system.

Keywords: Refrigerant, solar collector, solar energy, solar panel, solar tracking.

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Authors: M. V. Sudhamani, C. R. Venugopal

Abstract:

This paper deals with the application for contentbased image retrieval to extract color feature from natural images stored in the image database by segmenting the image through clustering. We employ a class of nonparametric techniques in which the data points are regarded as samples from an unknown probability density. Explicit computation of the density is avoided by using the mean shift procedure, a robust clustering technique, which does not require prior knowledge of the number of clusters, and does not constrain the shape of the clusters. A non-parametric technique for the recovery of significant image features is presented and segmentation module is developed using the mean shift algorithm to segment each image. In these algorithms, the only user set parameter is the resolution of the analysis and either gray level or color images are accepted as inputs. Extensive experimental results illustrate excellent performance.

Keywords: Segmentation, Clustering, Image Retrieval, Features.

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Authors: Sakol Udomsiri, Masahiro Iwahashi

Abstract:

This paper proposes a new design of spatial FIR filter to automatically detect water level from a video signal of various river surroundings. A new approach in this report applies "addition" of frames and a "horizontal" edge detector to distinguish water region and land region. Variance of each line of a filtered video frame is used as a feature value. The water level is recognized as a boundary line between the land region and the water region. Edge detection filter essentially demarcates between two distinctly different regions. However, the conventional filters are not automatically adaptive to detect water level in various lighting conditions of river scenery. An optimized filter is purposed so that the system becomes robust to changes of lighting condition. More reliability of the proposed system with the optimized filter is confirmed by accuracy of water level detection.

Keywords: water level, video, filter, detection.

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Authors: Agâh Tuğrul Korucu, Ismail Fatih Yavuzaslan, Lale Toraman

Abstract:

Nowadays, thanks to development of technology, integration of technology into teaching and learning activities is spreading. Increasing technological literacy which is one of the expected competencies for individuals of 21st century is associated with the effective use of technology in education. The most important factor in effective use of technology in education institutions is ICT teachers. The concept of computer assisted instruction (CAI) refers to the utilization of information and communication technology as a tool aided teachers in order to make education more efficient and improve its quality in the process of educational. Teachers can use computers in different places and times according to owned hardware and software facilities and characteristics of the subject and student in CAI. Analyzing teachers’ use of computers in education is significant because teachers are the ones who manage the course and they are the most important element in comprehending the topic by students. To accomplish computer-assisted instruction efficiently is possible through having positive attitude of teachers. Determination the level of knowledge, attitude and behavior of teachers who get the professional knowledge from educational faculties and elimination of deficiencies if any are crucial when teachers are at the faculty. Therefore, the aim of this paper is to identify ICT teachers' attitudes toward computer-assisted instruction in terms of different variables. Research group consists of 200 prospective ICT teachers studying at Necmettin Erbakan University Ahmet Keleşoğlu Faculty of Education CEIT department. As data collection tool of the study; “personal information form” developed by the researchers and used to collect demographic data and "the attitude scale related to computer-assisted instruction" are used. The scale consists of 20 items. 10 of these items show positive feature, while 10 of them show negative feature. The Kaiser-Meyer-Olkin (KMO) coefficient of the scale is found 0.88 and Barlett test significance value is found 0.000. The Cronbach’s alpha reliability coefficient of the scale is found 0.93. In order to analyze the data collected by data collection tools computer-based statistical software package used; statistical techniques such as descriptive statistics, t-test, and analysis of variance are utilized. It is determined that the attitudes of prospective instructors towards computers do not differ according to their educational branches. On the other hand, the attitudes of prospective instructors who own computers towards computer-supported education are determined higher than those of the prospective instructors who do not own computers. It is established that the departments of students who previously received computer lessons do not affect this situation so much. The result is that; the computer experience affects the attitude point regarding the computer-supported education positively.

Keywords: Attitude, computer based instruction, information and communication technologies, technology based instruction, teacher candidate.

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Authors: Syed Islam, Yiyun Huang, Sebastian Magierowski, Ebrahim Ghafar-Zadeh

Abstract:

This paper presents system level CMOS solid-state nanopore techniques enhancement for speedup next generation molecular recording and high throughput channels. This discussion also considers optimum number of base-pair (bp) measurements through channel as an important role to enhance potential read accuracy. Effective power consumption estimation offered suitable range of multi-channel configuration. Nanopore bp extraction model in statistical method could contribute higher read accuracy with longer read-length (200 < read-length). Nanopore ionic current switching with Time Multiplexing (TM) based multichannel readout system contributed hardware savings.

Keywords: DNA, Nanopore, Amplifier, ADC, Multichannel.

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Authors: Yinggan Tang, Xinping Guan

Abstract:

Color image segmentation can be considered as a cluster procedure in feature space. k-means and its adaptive version, i.e. competitive learning approach are powerful tools for data clustering. But k-means and competitive learning suffer from several drawbacks such as dead-unit problem and need to pre-specify number of cluster. In this paper, we will explore to use competitive and cooperative learning approach to perform color image segmentation. In competitive and cooperative learning approach, seed points not only compete each other, but also the winner will dynamically select several nearest competitors to form a cooperative team to adapt to the input together, finally it can automatically select the correct number of cluster and avoid the dead-units problem. Experimental results show that CCL can obtain better segmentation result.

Keywords: Color image segmentation, competitive learning, cluster, k-means algorithm, competitive and cooperative learning.

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Authors: N. Ghatwary, A. Ahmed, H. Jalab

Abstract:

In this paper, an approach for the liver tumor detection in computed tomography (CT) images is represented. The detection process is based on classifying the features of target liver cell to either tumor or non-tumor. Fractional differential (FD) is applied for enhancement of Liver CT images, with the aim of enhancing texture and edge features. Later on, a fusion method is applied to merge between the various enhanced images and produce a variety of feature improvement, which will increase the accuracy of classification. Each image is divided into NxN non-overlapping blocks, to extract the desired features. Support vector machines (SVM) classifier is trained later on a supplied dataset different from the tested one. Finally, the block cells are identified whether they are classified as tumor or not. Our approach is validated on a group of patients’ CT liver tumor datasets. The experiment results demonstrated the efficiency of detection in the proposed technique.

Keywords: Fractional differential (FD), Computed Tomography (CT), fusion.

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Authors: F. Garousi, S. Veres, É. Bódi, S. Várallyay, B. Kovács

Abstract:

Extracting and determining chlorophyll pigments (chlorophyll a and b) in green leaves are the procedures based on the solvent extraction of pigments in samples using N,Ndimethylformamide as the extractant. In this study, two species of soluble inorganic selenium forms, selenite (SeIV) and selenate (SeVI) at different concentrations were investigated on maize plants that were growing in nutrient solutions during 2 weeks and at the end of the experiment, amounts of chlorophyll a and b for first and second leaves of maize were measured. In accordance with the results we observed that our regarded Se concentrations in both forms of SeIV and SeVI were not effective on maize plants’ chlorophyll a and b significantly although high level of 3 mg.kg-1 SeIV had negative affect on growth of the samples that had been treated by it but about SeVI samples we did not observe this state and our different considered SeVI concentrations were not toxic for maize plants.

Keywords: Maize, sodium selenate, sodium selenite, chlorophyll a and b.

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Authors: Ali Cheraghian, Farshid Hajati, Soheila Gheisari, Yongsheng Gao

Abstract:

In this paper, we present a novel 2.5D face recognition method based on Gabor Discrete Cosine Transform (GDCT). In the proposed method, the Gabor filter is applied to extract feature vectors from the texture and the depth information. Then, Discrete Cosine Transform (DCT) is used for dimensionality and redundancy reduction to improve computational efficiency. The system is combined texture and depth information in the decision level, which presents higher performance compared to methods, which use texture and depth information, separately. The proposed algorithm is examined on publically available Bosphorus database including models with pose variation. The experimental results show that the proposed method has a higher performance compared to the benchmark.

Keywords: Gabor filter, discrete cosine transform, 2.5D face recognition, pose.

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Authors: Jaroslav Polec, Petra Heribanová, Tomáš Hirner

Abstract:

In this paper we proposed a method for finding video frames representing one sign in the finger alphabet. The method is based on determining hands location, segmentation and the use of standard video quality evaluation metrics. Metric calculation is performed only in regions of interest. Sliding mechanism for finding local extrema and adaptive threshold based on local averaging is used for key frames selection. The success rate is evaluated by recall, precision and F1 measure. The method effectiveness is compared with metrics applied to all frames. Proposed method is fast, effective and relatively easy to realize by simple input video preprocessing and subsequent use of tools designed for video quality measuring.

Keywords: Key frame, video, quality, metric, MSE, MSAD, SSIM, VQM, sign language, finger alphabet.

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Authors: Terrence Chen, Thomas S. Huang

Abstract:

In this paper, we present the region based hidden Markov random field model (RBHMRF), which encodes the characteristics of different brain regions into a probabilistic framework for brain MR image segmentation. The recently proposed TV+L1 model is used for region extraction. By utilizing different spatial characteristics in different brain regions, the RMHMRF model performs beyond the current state-of-the-art method, the hidden Markov random field model (HMRF), which uses identical spatial information throughout the whole brain. Experiments on both real and synthetic 3D MR images show that the segmentation result of the proposed method has higher accuracy compared to existing algorithms.

Keywords: Finite Gaussian mixture model, Hidden Markov random field model, image segmentation, MRI.

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Authors: Sushma Satpute, Vaishali Katkar, Nilesh Sahare

Abstract:

XML files contain data which is in well formatted manner. By studying the format or semantics of the grammar it will be helpful for fast retrieval of the data. There are many algorithms which describes about searching the data from XML files. There are no. of approaches which uses data structure or are related to the contents of the document. In these cases user must know about the structure of the document and information retrieval techniques using NLPs is related to content of the document. Hence the result may be irrelevant or not so successful and may take more time to search.. This paper presents fast XML retrieval techniques by using new indexing technique and the concept of RXML. When indexing an XML document, the system takes into account both the document content and the document structure and assigns the value to each tag from file. To query the system, a user is not constrained about fixed format of query.

Keywords: XML Retrieval, Indexed Search, Information Retrieval.

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Authors: Shigeaki Sakurai, Ryohei Orihara

Abstract:

This paper proposes an improvement method of classification efficiency in a classification model. The model is used in a risk search system and extracts specific labels from articles posted at bulletin board sites. The system can analyze the important discussions composed of the articles. The improvement method introduces ensemble learning methods that use multiple classification models. Also, it introduces expressions related to the specific labels into generation of word vectors. The paper applies the improvement method to articles collected from three bulletin board sites selected by users and verifies the effectiveness of the improvement method.

Keywords: Text mining, Risk search system, Corporate reputation, Bulletin board site, Ensemble learning

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Authors: Hossein Morshedlou, Ahmad Abdollahzade Barforoush

Abstract:

Recent developments in storage technology and networking architectures have made it possible for broad areas of applications to rely on data streams for quick response and accurate decision making. Data streams are generated from events of real world so existence of associations, which are among the occurrence of these events in real world, among concepts of data streams is logical. Extraction of these hidden associations can be useful for prediction of subsequent concepts in concept shifting data streams. In this paper we present a new method for learning association among concepts of data stream and prediction of what the next concept will be. Knowing the next concept, an informed update of data model will be possible. The results of conducted experiments show that the proposed method is proper for classification of concept shifting data streams.

Keywords: Data Stream, Classification, Concept Shift, History.

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Authors: D. Laiadi, A. Hasseine, A. Merzougui

Abstract:

In this work, Experimental tie-line results and solubility (binodal) curves were obtained for the ternary systems (water + acetic acid + methyl isobutyl ketone (MIBK)), (water + lactic acid+ methyl isobutyl ketone) at T = 294.15K and atmospheric pressure. The consistency of the values of the experimental tie-lines was determined through the Othmer-Tobias and Hands correlations. For the extraction effectiveness of solvents, the distribution and selectivity curves were plotted. In addition, these experimental tieline data were also correlated with NRTL model. The interaction parameters for the NRTL model were retrieved from the obtained experimental results by means of a combination of the homotopy method and the genetic algorithms.

Keywords: Liquid-liquid equilibria, homotopy methods, carboxylic acid, NRTL.

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Authors: Takaaki Ishibashi

Abstract:

Independent component analysis can estimate unknown source signals from their mixtures under the assumption that the source signals are statistically independent. However, in a real environment, the separation performance is often deteriorated because the number of the source signals is different from that of the sensors. In this paper, we propose an estimation method for the number of the sources based on the joint distribution of the observed signals under two-sensor configuration. From several simulation results, it is found that the number of the sources is coincident to that of peaks in the histogram of the distribution. The proposed method can estimate the number of the sources even if it is larger than that of the observed signals. The proposed methods have been verified by several experiments.

Keywords: blind source separation, independent component analysys, estimation for the number of the blind sources, voice activity detection, target extraction.

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Authors: Sawsan Dacrory, Hussein Abou-Yousef, Samir Kamel, Ragab E. Abou-Zeid, Mohamed S. Abdel-Aziz, Mohamed Elbadry

Abstract:

A considerable amount of lignocellulosic by-product could be obtained from olive pulp during olive oil extraction industry. The major constituents of the olive pulp are husks and seeds. The separation of each portion of olive pulp (seeds and husks) was carried out by water flotation where seeds were sediment in the bottom. Both seeds and husks were dignified by 15% NaOH followed by complete lignin removal by using sodium chlorite in acidic medium. The isolated holocellulose, α-cellulose, hydrogel and CMC of both seeds and husk fractions were characterized by FTIR and SEM. The present study focused on the investigation of the chemical components of the lignocellulosic fraction of olive pulp and using them in medical application. Carboxymethyl cellulose (CMC) is produced and applied in the preparation of antimicrobial hydrogel.

Keywords: Carboxymethyl cellulose, cellulose, hydrogel olive pulp.

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Authors: Hiroyuki Ohsaki, Makoto Imase

Abstract:

In Grid computing, a data transfer protocol called GridFTP has been widely used for efficiently transferring a large volume of data. Currently, two versions of GridFTP protocols, GridFTP version 1 (GridFTP v1) and GridFTP version 2 (GridFTP v2), have been proposed in the GGF. GridFTP v2 supports several advanced features such as data streaming, dynamic resource allocation, and checksum transfer, by defining a transfer mode called X-block mode. However, in the literature, effectiveness of GridFTP v2 has not been fully investigated. In this paper, we therefore quantitatively evaluate performance of GridFTP v1 and GridFTP v2 using mathematical analysis and simulation experiments. We reveal the performance limitation of GridFTP v1, and quantitatively show effectiveness of GridFTP v2. Through several numerical examples, we show that by utilizing the data streaming feature, the average file transfer time of GridFTP v2 is significantly smaller than that of GridFTP v1.

Keywords: Grid Computing, GridFTP, Performance Evaluation, Queuing Theory.

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Authors: M. Castellano, G. Mastronardi, A. Aprile, G. Tarricone

Abstract:

Text Mining is an important step of Knowledge Discovery process. It is used to extract hidden information from notstructured o semi-structured data. This aspect is fundamental because much of the Web information is semi-structured due to the nested structure of HTML code, much of the Web information is linked, much of the Web information is redundant. Web Text Mining helps whole knowledge mining process to mining, extraction and integration of useful data, information and knowledge from Web page contents. In this paper, we present a Web Text Mining process able to discover knowledge in a distributed and heterogeneous multiorganization environment. The Web Text Mining process is based on flexible architecture and is implemented by four steps able to examine web content and to extract useful hidden information through mining techniques. Our Web Text Mining prototype starts from the recovery of Web job offers in which, through a Text Mining process, useful information for fast classification of the same are drawn out, these information are, essentially, job offer place and skills.

Keywords: Web text mining, flexible architecture, knowledgediscovery.

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Authors: Laura Lukmanto, Harya Widiputra, Lukas

Abstract:

Studies in economics domain tried to reveal the correlation between stock markets. Since the globalization era, interdependence between stock markets becomes more obvious. The Dynamic Interaction Network (DIN) algorithm, which was inspired by a Gene Regulatory Network (GRN) extraction method in the bioinformatics field, is applied to reveal important and complex dynamic relationship between stock markets. We use the data of the stock market indices from eight countries around the world in this study. Our results conclude that DIN is able to reveal and model patterns of dynamic interaction from the observed variables (i.e. stock market indices). Furthermore, it is also found that the extracted network models can be utilized to predict movement of the stock market indices with a considerably good accuracy.

Keywords: complex dynamic relationship, dynamic interaction network, interactive stock markets, stock market interdependence.

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Authors: César Garza

Abstract:

In supervised binary classification and regression problems, it is well-known that learnability is equivalent to uniform convergence of the hypothesis class, and if a problem is learnable, it is learnable by empirical risk minimization. For the general learning setting of unsupervised learning tasks, there are non-trivial learning problems where uniform convergence does not hold. We present here the task of learning centers of mass with an extra feature that “activates” some of the coordinates over the unit ball in a Hilbert space. We show that the learning problem is learnable under a stable RLM rule. We introduce a family of distributions over the domain space with some mild restrictions for which the sample complexity of uniform convergence for these problems must grow logarithmically with the dimension of the Hilbert space. If we take this dimension to infinity, we obtain a learnable problem for which the uniform convergence property fails for a vast family of distributions.

Keywords: Statistical learning theory, learnability, uniform convergence, stability, regularized loss minimization.

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