Search results for: Association extraction

Authors: S. M. Kamruzzaman, Md. Monirul Islam

Abstract:

Although backpropagation ANNs generally predict better than decision trees do for pattern classification problems, they are often regarded as black boxes, i.e., their predictions cannot be explained as those of decision trees. In many applications, it is desirable to extract knowledge from trained ANNs for the users to gain a better understanding of how the networks solve the problems. A new rule extraction algorithm, called rule extraction from artificial neural networks (REANN) is proposed and implemented to extract symbolic rules from ANNs. A standard three-layer feedforward ANN is the basis of the algorithm. A four-phase training algorithm is proposed for backpropagation learning. Explicitness of the extracted rules is supported by comparing them to the symbolic rules generated by other methods. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and predictive accuracy. Extensive experimental studies on several benchmarks classification problems, such as breast cancer, iris, diabetes, and season classification problems, demonstrate the effectiveness of the proposed approach with good generalization ability.

Keywords: Backpropagation, clustering algorithm, constructivealgorithm, continuous activation function, pruning algorithm, ruleextraction algorithm, symbolic rules.

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Authors: Elif Derya UBEYLI, Inan GULER

Abstract:

A new approach based on the consideration that electroencephalogram (EEG) signals are chaotic signals was presented for automated diagnosis of electroencephalographic changes. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. This paper presented the usage of statistics over the set of the Lyapunov exponents in order to reduce the dimensionality of the extracted feature vectors. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of electroencephalographic changes. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. The selected Lyapunov exponents of the EEG signals were used as inputs of the MLPNN trained with Levenberg- Marquardt algorithm. The classification results confirmed that the proposed MLPNN has potential in detecting the electroencephalographic changes.

Keywords: Chaotic signal, Electroencephalogram (EEG) signals, Feature extraction/selection, Lyapunov exponents

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Authors: Ahmad M. Sarhan, Omar I. Al Helalat

Abstract:

In this paper, an Arabic letter recognition system based on Artificial Neural Networks (ANNs) and statistical analysis for feature extraction is presented. The ANN is trained using the Least Mean Squares (LMS) algorithm. In the proposed system, each typed Arabic letter is represented by a matrix of binary numbers that are used as input to a simple feature extraction system whose output, in addition to the input matrix, are fed to an ANN. Simulation results are provided and show that the proposed system always produces a lower Mean Squared Error (MSE) and higher success rates than the current ANN solutions.

Keywords: ANN, Backpropagation, Gaussian, LMS, MSE, Neuron, standard deviation, Widrow-Hoff rule.

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Authors: Sudarat Jiamyangyuen, Tipawan Thongsook, Riantong Singanusong, Chanida Saengtubtim

Abstract:

Rice bran is normally used as a raw material for rice bran oil production or sold as feed with a low price. Conventionally, the protein in defatted rice bran was extracted using alkaline extraction and acid precipitation, which involves in chemical usage and lowering some nutritious component. This study was conducted in order to extract of rice bran protein concentrate (RBPC) from defatted rice bran using enzymes and employing polysaccharides in a precipitating step. The properties of RBPC obtained will be compared to those of a control sample extracted using a conventional method. The results showed that extraction of protein from rice bran using enzymes exhibited the higher protein recovery compared to that extraction with alkaline. The extraction conditions using alcalase 2% (v/w) at 50 C, pH 9.5 gave the highest protein (2.44%) and yield (32.09%) in extracted solution compared to other enzymes. Rice bran protein concentrate powder prepared by a precipitation step using alginate (protein in solution: alginate 1:0.016) exhibited the highest protein (27.55%) and yield (6.84%). Precipitation using alginate was better than that of acid. RBPC extracted with alkaline (ALK) or enzyme alcalase (ALC), then precipitated with alginate (AL) (samples RBP-ALK-AL and RBP-ALC-AL) yielded the precipitation rate of 75% and 91.30%, respectively. Therefore, protein precipitation using alginate was then selected. Amino acid profile of control sample, and sample precipitated with alginate, as compared to casein and soy protein isolated, showed that control sample showed the highest content among all sample. Functional property study of RBP showed that the highest nitrogen solubility occurred in pH 8-10. There was no statically significant between emulsion capacity and emulsion stability of control and sample precipitated by alginate. However, control sample showed a higher of foaming capacity and foaming stability compared to those of sample precipitated with alginate. The finding was successful in terms of minimizing chemicals used in extraction and precipitation steps in preparation of rice bran protein concentrate. This research involves in a production of value-added product in which the double amount of protein (28%) compared to original amount (14%) contained in rice bran could be beneficial in terms of adding to food products e.g. healthy drink with high protein and fiber. In addition, the basic knowledge of functional property of rice bran protein concentrate was obtained, which can be used to appropriately select the application of this value-added product from rice bran.

Keywords: Alginate, carrageenan, rice bran, rice bran protein.

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Authors: Htet Htike Htwe, Kay Thi Lwin

Abstract:

The principal objective of this study is to be able to extract niobium oxide from columbite-tantalite concentrate of Thayet Kon Area in Nay Phi Taw. It is recovered from columbite-tantalite concentrate which contains 19.29 % Nb2O5.The recovery of niobium oxide from columbite-tantalite concentrate can be divided into three main sections, namely, digestion of the concentrate, recovery from the leached solution and precipitation and calcinations. The concentrate was digested with hydrofluoric acid and sulfuric acid. Of the various parameters that effect acidity and time were studied. In the recovery section solvent extraction process using methyl isobutyl ketone was investigated. Ammonium hydroxide was used as a precipitating agent and the precipitate was later calcined. The percentage of niobium oxide is 74%.

Keywords: Calcination, Digestion, Precipitation, SolventExtraction.

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Authors: A. Doosti, P. Ghasemi Dehkordi, M. Zamani, S. Taheri, M. Banitalebi, M. Mahmoudzadeh

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The p53 tumor suppressor gene plays two important roles in genomic stability: blocking cell proliferation after DNA damage until it has been repaired, and starting apoptosis if the damage is too critical. Codon 72 exon4 polymorphism (Arg72Pro) of the P53 gene has been implicated in cancer risk. Various studies have been done to investigate the status of p53 at codon 72 for arginine (Arg) and proline (Pro) alleles in different populations and also the association of this codon 72 polymorphism with various tumors. Our objective was to investigate the possible association between P53 Arg72Pro polymorphism and susceptibility to colorectal cancer among Isfahan and Chaharmahal Va Bakhtiari (a part of south west of Iran) population. We investigated the status of p53 at codon 72 for Arg/Arg, Arg/Pro and Pro/Pro allele polymorphisms in blood samples from 145 colorectal cancer patients and 140 controls by Nested-PCR of p53 exon 4 and digestion with BstUI restriction enzyme and the DNA fragments were then resolved by electrophoresis in 2% agarose gel. The Pro allele was 279 bp, while the Arg allele was restricted into two fragments of 160 and 119 bp. Among the 145 colorectal cancer cases 49 cases (33.79%) were homozygous for the Arg72 allele (Arg/Arg), 18 cases (12.41%) were homozygous for the Pro72 allele (Pro/Pro) and 78 cases (53.8%) found in heterozygous (Arg/Pro). In conclusion, it can be said that p53Arg/Arg genotype may be correlated with possible increased risk of this kind of cancers in south west of Iran.

Keywords: TP53, Polymorphism, Colorectal Cancer, Iran

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Authors: R. Charoen, S. Tipkanon, W. Savedboworn, N. Phonsatta, A. Panya

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Rice bran protein hydrolysates (RBPH) were prepared from defatted rice bran of two different Thai rice cultivars (Plai-Ngahm-Prachinburi; PNP and Khao Dok Mali 105; KDM105) using an enzymatic method. This research aimed to optimize enzyme-assisted protein extraction. In addition, the functional properties of RBPH and their stabilities to environmental stresses including pH (3 to 8), ionic strength (0 mM to 500 mM) and the thermal treatment (30 °C to 90 °C) were investigated. Results showed that enzymatic process for protein extraction of defatted rice bran was as follows: enzyme concentration 0.075 g/ 5 g of protein, extraction temperature 50 °C and extraction time 4 h. The obtained protein hydrolysate powders had a degree of hydrolysis (%) of 21.05% in PNP and 19.92% in KDM105. The solubility of protein hydrolysates at pH 4-6 was ranged from 27.28-38.57% and 27.60-43.00% in PNP and KDM105, respectively. In general, antioxidant activities indicated by total phenolic content, FRAP, ferrous ion-chelating (FIC), and 2,2’-azino-bis-3-ethylbenzthiazoline-6-sulphonic acid (ABTS) of KDM105 had higher than PNP. In terms of functional properties, the emulsifying activity index (EAI) was was 8.78 m²/g protein in KDM105, whereas PNP was 5.05 m²/g protein. The foaming capacity at 5 minutes (%) was 47.33 and 52.98 in PNP and KDM105, respectively. Glutamine, Alanine, Valine, and Leucine are the major amino acid in protein hydrolysates where the total amino acid of KDM105 gave higher than PNP. Furthermore, we investigated environmental stresses on the stability of 5% oil in water emulsion (5% oil, 10 mM citrate buffer) stabilized by RBPH (3.5%). The droplet diameter of emulsion stabilized by KDM105 was smaller (d < 250 nm) than produced by PNP. For environmental stresses, RBPH stabilized emulsions were stable at pH around 3 and 5-6, at high salt (< 400 mM, pH 7) and at temperatures range between 30-50°C.

Keywords: Functional properties, oil in water emulsion, protein hydrolysates, rice bran protein.

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Authors: Dolapop Sribudda, Ura Pancharoen

Abstract:

The separation of Hg (II) from produced water by hollow fiber contactors (HFC) was investigation. This system included of two hollow fiber modules in the series connecting. The first module used for the extraction reaction and the second module for stripping reaction. Aliquat336 extractant was fed from the organic reservoirs into the shell side of the first hollow fiber module and continuous to the shell side of the second module. The organic liquid was continuously feed recirculate and back to the reservoirs. The feed solution was pumped into the lumen (tube side) of the first hollow fiber module. Simultaneously, the stripping solution was pumped in the same way in tube side of the second module. The feed and stripping solution was fed which had a countercurrent flow. Samples were kept in the outlet of feed and stripping solution at 1 hour and characterized concentration of Hg (II) by Inductively Couple Plasma Atomic Emission Spectroscopy (ICP-AES). Feed solution was produced water from natural gulf of Thailand. The extractant was Aliquat336 dissolved in kerosene diluent. Stripping solution used was nitric acid (HNO3) and thiourea (NH2CSNH2). The effect of carrier concentration and type of stripping solution were investigated. Results showed that the best condition were 10 % (v/v) Aliquat336 and 1.0 M NH2CSNH2. At the optimum condition, the extraction and stripping of Hg (II) were 98% and 44.2%, respectively.

Keywords: Hg (II), hollow fiber contactor, produced water, wastewater treatment.

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Authors: M. Gooroochurn, D. Kerr, K. Bouazza-Marouf, M. Vloeberghs

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This paper presents the automated methods employed for extracting craniofacial landmarks in white light images as part of a registration framework designed to support three neurosurgical procedures. The intraoperative space is characterised by white light stereo imaging while the preoperative plan is performed on CT scans. The registration aims at aligning these two modalities to provide a calibrated environment to enable image-guided solutions. The neurosurgical procedures can then be carried out by mapping the entry and target points from CT space onto the patient-s space. The registration basis adopted consists of natural landmarks (eye corner and ear tragus). A 5mm accuracy is deemed sufficient for these three procedures and the validity of the selected registration basis in achieving this accuracy has been assessed by simulation studies. The registration protocol is briefly described, followed by a presentation of the automated techniques developed for the extraction of the craniofacial features and results obtained from tests on the AR and FERET databases. Since the three targeted neurosurgical procedures are routinely used for head injury management, the effect of bruised/swollen faces on the automated algorithms is assessed. A user-interactive method is proposed to deal with such unpredictable circumstances.

Keywords: Face Processing, Craniofacial Feature Extraction, Preoperative to Intraoperative Registration, Registration Basis.

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Authors: Mazhar B. Tayel, Amr H. Yassin

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A proposed small-signal model parameters for a pseudomorphic high electron mobility transistor (PHEMT) is presented. Both extrinsic and intrinsic circuit elements of a smallsignal model are determined using genetic algorithm (GA) as a stochastic global search and optimization tool. The parameters extraction of the small-signal model is performed on 200-μm gate width AlGaAs/InGaAs PHEMT. The equivalent circuit elements for a proposed 18 elements model are determined directly from the measured S- parameters. The GA is used to extract the parameters of the proposed small-signal model from 0.5 up to 18 GHz.

Keywords: PHEMT, Genetic Algorithms, small signal modeling, optimization.

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Authors: Eko Budi Santoso, Kazia Laturette, Stanislaus Adnanto Mastan

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This study aims to examine the association between disclosure of social responsibility and tax aggressiveness in developing countries, namely Thailand. This is due to the increasing trend of disclosure of social responsibility in developing countries, even though this disclosure of information is still voluntary. On the other hand, developing countries have low taxation rate and investor protection infrastructures that allow the disclosure of social responsibility to be used opportunistically as a tool to fool the attainment of interests. This study also examines the role of assurance on the association between corporate social responsibility disclosure and tax aggressiveness. The assurance aims to provide confidence that the disclosure of social responsibility by the company is valid. This research builds an index to measure the disclosure of social responsibility based on the rules issued by the innovative Global Reporting. The results of the study are based on a sample of publicly traded companies in Thailand, which showed a positive association between disclosure of corporate social responsibility and tax aggressiveness, but it was further discovered that these results were mitigated by the existence of assurance against disclosure of corporate social responsibility. The results of this study indicate that the disclosure of corporate social responsibility can show that the company cares about the issue of social responsibility but does not automatically make the company as one that holds ethical values ​​in its business practices.

Keywords: Corporate Social Responsibility disclosure, tax aggressiveness, sustainability assurance, business ethics.

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Authors: F. Pedron, M. Grifoni, G. Petruzzelli, M. Barbafieri, I. Rosellini, B. Pezzarossa

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Lead contamination of agricultural land mainly vegetated with perennial ryegrass (Lolium perenne) has been investigated. The metal derived from the discharge of sludge from a ceramic industry in the past had used lead paints. The results showed very high values of lead concentration in many soil samples. In order to assess the lead soil contamination, a sequential extraction with H₂O, KNO₃, EDTA was performed, and the chemical forms of lead in the soil were evaluated. More than 70% of lead was in a potentially bioavailable form. Analysis of Lolium perenne showed elevated lead concentration. A Freundlich-like model was used to describe the transferability of the metal from the soil to the plant.

Keywords: Bioavailability, Freundlich-like equation, sequential extraction, soil lead contamination.

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Authors: Sudip Ghosh, Dipanwita Roy, Dipan Chatterjee, Paramita Bhattacharjee, Satadal Das

Abstract:

Highest yield of eugenol-rich fractions from Cinnamomum tamala (bay leaf) leaves were obtained by supercritical carbon dioxide (SC-CO₂), compared to hydro-distillation, organic solvents, liquid CO₂ and subcritical CO₂ extractions. Optimization of SC-CO₂ extraction parameters was carried out to obtain an extract with maximum eugenol content. This was achieved using a sample size of 10g at 55°C, 512 bar after 60min at a flow rate of 25.0 cm³/sof gaseous CO₂. This extract has the best combination of phytochemical properties such as phenolic content (1.77mg gallic acid/g dry bay leaf), reducing power (0.80mg BHT/g dry bay leaf), antioxidant activity (IC₅₀ of 0.20mg/ml) and anti-inflammatory potency (IC₅₀ of 1.89mg/ml). Identification of compounds in this extract was performed by GC-MS analysis and its antimicrobial potency was also evaluated. The MIC values against E. coli, P. aeruginosa and S. aureus were 0.5, 0.25 and 0.5mg/ml, respectively. 

Keywords: Antimicrobial potency, Cinnamomum tamala, eugenol, supercritical carbon dioxide extraction.

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Authors: Jérôme Azé, Mathieu Roche, Yves Kodratoff, Michèle Sebag

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Term Extraction, a key data preparation step in Text Mining, extracts the terms, i.e. relevant collocation of words, attached to specific concepts (e.g. genetic-algorithms and decisiontrees are terms associated to the concept “Machine Learning" ). In this paper, the task of extracting interesting collocations is achieved through a supervised learning algorithm, exploiting a few collocations manually labelled as interesting/not interesting. From these examples, the ROGER algorithm learns a numerical function, inducing some ranking on the collocations. This ranking is optimized using genetic algorithms, maximizing the trade-off between the false positive and true positive rates (Area Under the ROC curve). This approach uses a particular representation for the word collocations, namely the vector of values corresponding to the standard statistical interestingness measures attached to this collocation. As this representation is general (over corpora and natural languages), generality tests were performed by experimenting the ranking function learned from an English corpus in Biology, onto a French corpus of Curriculum Vitae, and vice versa, showing a good robustness of the approaches compared to the state-of-the-art Support Vector Machine (SVM).

Keywords: Text-mining, Terminology Extraction, Evolutionary algorithm, ROC Curve.

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Authors: M. Omer, D. Wilson

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Creating a database scheme is essentially a manual process. From a requirement specification the information contained within has to be analyzed and reduced into a set of tables, attributes and relationships. This is a time consuming process that has to go through several stages before an acceptable database schema is achieved. The purpose of this paper is to implement a Natural Language Processing (NLP) based tool to produce a relational database from a requirement specification. The Stanford CoreNLP version 3.3.1 and the Java programming were used to implement the proposed model. The outcome of this study indicates that a first draft of a relational database schema can be extracted from a requirement specification by using NLP tools and techniques with minimum user intervention. Therefore this method is a step forward in finding a solution that requires little or no user intervention.

Keywords: Information Extraction, Natural Language Processing, Relation Extraction.

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Authors: Risto Pöykiö, Olli Dahl, Hannu Nurmesniemi

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The highest extractable concentration in the artificial sweat fluid was observed for Ba (120mg/kg; d.w.). The highest extractable concentration in the artificial gastric fluid was observed for Al (9030mg/kg; d.w.). Furthermore, the extractable concentrations of Ba (550mg/kg; d.w.) and Zn (400mg/kg: d.w.) in the bottom ash using artificial gastric fluid were elevated. The extractable concentrations of all heavy metals in the artificial gastric fluid were higher than those in the artificial sweat fluid. These results are reasonable in the light of the fact that the pH of the artificial gastric fluid was extremely acidic both before (pH 1.54) and after (pH 1.94) extraction, whereas the pH of the artificial sweat fluid was slightly alkaline before (pH 6.50) and after extraction (pH 8.51).

Keywords: Ash, artificial fluid, heavy metals, in vitro, waste.

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Authors: Mahmoud Elmezain, Ayoub Al-Hamadi, Bernd Michaelis

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Hand gesture is an active area of research in the vision community, mainly for the purpose of sign language recognition and Human Computer Interaction. In this paper, we propose a system to recognize alphabet characters (A-Z) and numbers (0-9) in real-time from stereo color image sequences using Hidden Markov Models (HMMs). Our system is based on three main stages; automatic segmentation and preprocessing of the hand regions, feature extraction and classification. In automatic segmentation and preprocessing stage, color and 3D depth map are used to detect hands where the hand trajectory will take place in further step using Mean-shift algorithm and Kalman filter. In the feature extraction stage, 3D combined features of location, orientation and velocity with respected to Cartesian systems are used. And then, k-means clustering is employed for HMMs codeword. The final stage so-called classification, Baum- Welch algorithm is used to do a full train for HMMs parameters. The gesture of alphabets and numbers is recognized using Left-Right Banded model in conjunction with Viterbi algorithm. Experimental results demonstrate that, our system can successfully recognize hand gestures with 98.33% recognition rate.

Keywords: Gesture Recognition, Computer Vision & Image Processing, Pattern Recognition.

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Authors: Marcin Perzyk, Artur Soroczynski

Abstract:

General requirements for knowledge representation in the form of logic rules, applicable to design and control of industrial processes, are formulated. Characteristic behavior of decision trees (DTs) and rough sets theory (RST) in rules extraction from recorded data is discussed and illustrated with simple examples. The significance of the models- drawbacks was evaluated, using simulated and industrial data sets. It is concluded that performance of DTs may be considerably poorer in several important aspects, compared to RST, particularly when not only a characterization of a problem is required, but also detailed and precise rules are needed, according to actual, specific problems to be solved.

Keywords: Knowledge extraction, decision trees, rough setstheory, industrial processes.

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Authors: Nicolás H. Beltrán, Manuel A. Duarte-Mermoud, Víctor A. Soto, Sebastián A. Salah, and Matías A. Bustos

Abstract:

Results of Chilean wine classification based on the information provided by an electronic nose are reported in this paper. The classification scheme consists of two parts; in the first stage, Principal Component Analysis is used as feature extraction method to reduce the dimensionality of the original information. Then, Radial Basis Functions Neural Networks is used as pattern recognition technique to perform the classification. The objective of this study is to classify different Cabernet Sauvignon, Merlot and Carménère wine samples from different years, valleys and vineyards of Chile.

Keywords: Feature extraction techniques, Pattern recognitiontechniques, Principal component analysis, Radial basis functionsneural networks, Wine classification.

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Authors: Amir Ngah, Masita Abdul Jalil, Zailani Abdullah

Abstract:

The aim of software maintenance is to maintain the software system in accordance with advancement in software and hardware technology. One of the early works on software maintenance is to extract information at higher level of abstraction. In this paper, we present the process of how to design an information extraction tool for software maintenance. The tool can extract the basic information from old programs such as about variables, based classes, derived classes, objects of classes, and functions. The tool have two main parts; the lexical analyzer module that can read the input file character by character, and the searching module which users can get the basic information from the existing programs. We implemented this tool for a patterned sub-C++ language as an input file.

Keywords: Extraction tool, software maintenance, reverse engineering, C++.

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Authors: Payel Ghosh, Rama Chandra Pradhan, Sabyasachi Mishra

Abstract:

Jamun (Syzygium cuminii L.) is one of the important indigenous minor fruit with high medicinal value. The jamun cultivation is unorganized and there is huge loss of this fruit every year. The perishable nature of the fruit makes its postharvest management further difficult. Due to the strong cell wall structure of pectin-protein bonds and hard seeds, extraction of juice becomes difficult. Enzymatic treatment has been commercially used for improvement of juice quality with high yield. The objective of the study was to optimize the best treatment method for juice extraction. Enzymes (Pectinase and Tannase) from different stains had been used and for each enzyme, best result obtained by using response surface methodology. Optimization had been done on the basis of physicochemical property, nutritional property, sensory quality and cost estimation. According to quality aspect, cost analysis and sensory evaluation, the optimizing enzymatic treatment was obtained by Pectinase from Aspergillus aculeatus strain. The optimum condition for the treatment was 44 ^oC with 80 minute with a concentration of 0.05% (w/w). At these conditions, 75% of yield with turbidity of 32.21NTU, clarity of 74.39%T, polyphenol content of 115.31 mg GAE/g, protein content of 102.43 mg/g have been obtained with a significant difference in overall acceptability.

Keywords: Jamun, enzymatic treatment, physicochemical property, sensory analysis, optimization.

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Authors: Nafaâ Nacereddine, Latifa Hamami, Djemel Ziou

Abstract:

In non destructive testing by radiography, a perfect knowledge of the weld defect shape is an essential step to appreciate the quality of the weld and make decision on its acceptability or rejection. Because of the complex nature of the considered images, and in order that the detected defect region represents the most accurately possible the real defect, the choice of thresholding methods must be done judiciously. In this paper, performance criteria are used to conduct a comparative study of thresholding methods based on gray level histogram, 2-D histogram and locally adaptive approach for weld defect extraction in radiographic images.

Keywords: 1D and 2D histogram, locally adaptive approach, performance criteria, radiographic image, thresholding, weld defect.

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Authors: M. Jamali, F. Taghiyareh

Abstract:

The problem of frequent itemset mining is considered in this paper. One new technique proposed to generate frequent patterns in large databases without time-consuming candidate generation. This technique is based on focusing on transaction instead of concentrating on itemset. This algorithm based on take intersection between one transaction and others transaction and the maximum shared items between transactions computed instead of creating itemset and computing their frequency. With applying real life transactions and some consumption is taken from real life data, the significant efficiency acquire from databases in generation association rules mining.

Keywords: Association rules, data mining, frequent patterns, shared itemset.

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Authors: M. Mydlárová Blaščáková, J. Poráčová, Z. Tomková, Ľ. Blaščáková, M. Nagy, M. Konečná, E. Petrejčíková, Z. Gogaľová, V. Sedlák, J. Mydlár, M. Zahatňanská, K. Hricová

Abstract:

Osteoporosis is a multifactorial disease that results in reduced quality of life, causes decreased bone strength, and changes in their microarchitecture. Mostly postmenopausal women are at risk. In our study, we measured anthropometric parameters of postmenopausal women (104 women of control group – CG and 105 women of osteoporotic group - OG) and determined TSH hormone levels and PTH as well as mineral elements - Ca, P, Mg and enzyme alkaline phosphatase. Through the correlation analysis in CG, we have found association based on age and BMI, P and Ca, as well as Mg and Ca; in OG we determined interdependence based on an association of age and BMI, age and Ca. Using the Student's t test, we found significantly important differences in biochemical parameters of Mg (p ˂ 0,001) and TSH (p ˂ 0,05) between CG and OG.

Keywords: Factors, bone mass density, Central Europe, biomarkers.

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Authors: Elsayed A. Habib Elamir

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In social network analysis the mean nodal degree and density of the graph can be considered as a measure of the activity of all actors in the network and this is an important property of a graph and for making comparisons among networks. Since subjects in a family or organization are subject to common environment factors, it is prime interest to study the association between responses. Therefore, we study the distribution of the mean nodal degree and density of the graph under correlated binary units. The cross product ratio is used to capture the intra-units association among subjects. Computer program and an application are given to show the benefits of the method.

Keywords: Correlated Binary data, cross product ratio, densityof the graph, multiplicative binomial distribution.

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Authors: Y. L. Yung, Kong Mun Lo

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In this study, solid phase micro-extraction (SPME) was optimized to improve the sensitivity and accuracy in formaldehyde determination for plywood panels. Further work has been carried out to compare the newly developed technique with existing method which reacts formaldehyde collected in desiccators with acetyl acetone reagent (DC-AA). In SPME, formaldehyde was first derivatized with O-(2,3,4,5,6 pentafluorobenzyl)-hydroxylamine hydrochloride (PFBHA) and analysis was then performed by gas chromatography in combination with mass spectrometry (GC-MS). SPME data subjected to various wood species gave satisfactory results, with relative standard deviations (RSDs) obtained in the range of 3.1-10.3%. It was also well correlated with DC values, giving a correlation coefficient, RSQ, of 0.959. The quantitative analysis of formaldehyde by SPME was an alternative in wood industry with great potential

Keywords: Formaldehyde, GCMS, Plywood and SPME

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Authors: Amani Ashari, Julia Omar, Arif Hashim, Shahrul Hamid

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Apolipoprotein E (APOE) gene polymorphism has influence on serum lipids which relates to cardiovascular risk. The purpose of this study was to determine the frequency distribution of APOE alleles among Malaysian Type 2 Diabetes Mellitus (DM) patients with and without coronary artery disease (CAD) and their association with serum lipid profiles. A total of 115 patients were recruited in which 78 patients had Type 2 DM without CAD and 37 patients had Type 2 DM with CAD. The APOE polymorphism was detected by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). The APOE ɛ3 allele was the most common one in both groups. There was no significant association between the APOE genotypes and the CAD status in Type 2 DM using Pearson χ² test. Further analysis indicated there were no significant differences in all lipid parameters between E2, E3 and E4 subgroups in both groups. The study showed that the E4 allele carriers of Type 2 DM with CAD patients had higher LDL-C level and lower HDL-C level compared to the other allele carriers. However, analyses showed these levels were not statistically different. The study also showed that the Type 2 DM with CAD group with E2 allele had higher triglyceride (TG). In conclusion, further study with larger sample size is needed to confirm role of E4 as a marker of CAD among Type 2 DM patients in Malaysian population.

Keywords: Apolipoprotein E, diabetes mellitus, cardiovascular disease, lipids.

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Authors: Rami N. Khushaba, Adel Al-Jumaily

Abstract:

The myoelectric signal (MES) is one of the Biosignals utilized in helping humans to control equipments. Recent approaches in MES classification to control prosthetic devices employing pattern recognition techniques revealed two problems, first, the classification performance of the system starts degrading when the number of motion classes to be classified increases, second, in order to solve the first problem, additional complicated methods were utilized which increase the computational cost of a multifunction myoelectric control system. In an effort to solve these problems and to achieve a feasible design for real time implementation with high overall accuracy, this paper presents a new method for feature extraction in MES recognition systems. The method works by extracting features using Wavelet Packet Transform (WPT) applied on the MES from multiple channels, and then employs Fuzzy c-means (FCM) algorithm to generate a measure that judges on features suitability for classification. Finally, Principle Component Analysis (PCA) is utilized to reduce the size of the data before computing the classification accuracy with a multilayer perceptron neural network. The proposed system produces powerful classification results (99% accuracy) by using only a small portion of the original feature set.

Keywords: Biomedical Signal Processing, Data mining andInformation Extraction, Machine Learning, Rehabilitation.

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Authors: Fetah Eren, Ismail Sahin, Ismail Celik, Ahmet Oguz Akturk

Abstract:

The aim of this study is to discover secondary school students’ perceptions related to information technologies and the connections between concepts in their cognitive structures. A word association test consisting of six concepts related to information technologies is used to collect data from 244 secondary school students. Concept maps that present students’ cognitive structures are drawn with the help of frequency data. Data are analyzed and interpreted according to the connections obtained as a result of the concept maps. It is determined students associate most with these concepts—computer, Internet, and communication of the given concepts, and associate least with these concepts—computer-assisted education and information technologies. These results show the concepts, Internet, communication, and computer, are an important part of students’ cognitive structures. In addition, students mostly answer computer, phone, game, Internet and Facebook as the key concepts. These answers show students regard information technologies as a means for entertainment and free time activity, not as a means for education.

Keywords: Word association test, cognitive structure, information technology, secondary school.

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Authors: Mrs.K.Kavitha, S.Arivazhagan

Abstract:

A spatial classification technique incorporating a State of Art Feature Extraction algorithm is proposed in this paper for classifying a heterogeneous classes present in hyper spectral images. The classification accuracy can be improved if and only if both the feature extraction and classifier selection are proper. As the classes in the hyper spectral images are assumed to have different textures, textural classification is entertained. Run Length feature extraction is entailed along with the Principal Components and Independent Components. A Hyperspectral Image of Indiana Site taken by AVIRIS is inducted for the experiment. Among the original 220 bands, a subset of 120 bands is selected. Gray Level Run Length Matrix (GLRLM) is calculated for the selected forty bands. From GLRLMs the Run Length features for individual pixels are calculated. The Principle Components are calculated for other forty bands. Independent Components are calculated for next forty bands. As Principal & Independent Components have the ability to represent the textural content of pixels, they are treated as features. The summation of Run Length features, Principal Components, and Independent Components forms the Combined Features which are used for classification. SVM with Binary Hierarchical Tree is used to classify the hyper spectral image. Results are validated with ground truth and accuracies are calculated.

Keywords: Multi-class, Run Length features, PCA, ICA, classification and Support Vector Machines.

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