Search results for: Wavelet feature extraction

Authors: Gelareh Mohammadi, Parisa Shoushtari, Behnam Molaee Ardekani, Mohammad B. Shamsollahi

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A direct connection between ElectroEncephaloGram (EEG) and the genetic information of individuals has been investigated by neurophysiologists and psychiatrists since 1960-s; and it opens a new research area in the science. This paper focuses on the person identification based on feature extracted from the EEG which can show a direct connection between EEG and the genetic information of subjects. In this work the full EO EEG signal of healthy individuals are estimated by an autoregressive (AR) model and the AR parameters are extracted as features. Here for feature vector constitution, two methods have been proposed; in the first method the extracted parameters of each channel are used as a feature vector in the classification step which employs a competitive neural network and in the second method a combination of different channel parameters are used as a feature vector. Correct classification scores at the range of 80% to 100% reveal the potential of our approach for person classification/identification and are in agreement to the previous researches showing evidence that the EEG signal carries genetic information. The novelty of this work is in the combination of AR parameters and the network type (competitive network) that we have used. A comparison between the first and the second approach imply preference of the second one.

Keywords: Person Identification, Autoregressive Model, EEG, Neural Network

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Authors: Rabindra Prasad Dhakal, Tatsuya Oshima, Yoshinari Baba

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The polyfunctional and highly reactive bio-polymer, the chitosan was first regioselectively converted into dialkylated chitosan using dimsyl anionic solution(NaH in DMSO) and bromodecane after protecting amino groups by phthalic anhydride. The dibenzo-18-crown-6-ether, on the other hand, was converted into its carbonyl derivatives via Duff reaction prior to incorporate into chitosan by Schiff base formation. Thus formed diformylated dibenzo-18-crown-6-ether was condensed with lipophilic chitosan to prepare the novel solvent extraction reagent. The products were characterized mainly by IR and 1H-NMR. Hence, the multidentate crown ether-embedded polyfunctional bio-material was tested for extraction of Pd(II) and Pt(IV) in aqueous solution.

Keywords: Lipophilic chitosan, Duff reaction, crown ether and precious metal ions extraction.

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Authors: Say Wei Foo, Qi Dong

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In this paper, a novel feature-based image watermarking scheme is proposed. Zernike moments which have invariance properties are adopted in the scheme. In the proposed scheme, feature points are first extracted from host image and several circular patches centered on these points are generated. The patches are used as carriers of watermark information because they can be regenerated to locate watermark embedding positions even when watermarked images are severely distorted. Zernike transform is then applied to the patches to calculate local Zernike moments. Dither modulation is adopted to quantize the magnitudes of the Zernike moments followed by false alarm analysis. Experimental results show that quality degradation of watermarked image is visually transparent. The proposed scheme is very robust against image processing operations and geometric attacks.

Keywords: Image watermarking, Zernike moments, Featurepoint, Invariance, Robustness.

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Authors: K. Akilandeswari, G. M. Nasira

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Brain-Computer Interfaces (BCIs) measure brain signals activity, intentionally and unintentionally induced by users, and provides a communication channel without depending on the brain’s normal peripheral nerves and muscles output pathway. Feature Selection (FS) is a global optimization machine learning problem that reduces features, removes irrelevant and noisy data resulting in acceptable recognition accuracy. It is a vital step affecting pattern recognition system performance. This study presents a new Binary Particle Swarm Optimization (BPSO) based feature selection algorithm. Multi-layer Perceptron Neural Network (MLPNN) classifier with backpropagation training algorithm and Levenberg-Marquardt training algorithm classify selected features.

Keywords: Brain-Computer Interfaces (BCI), Feature Selection (FS), Walsh–Hadamard Transform (WHT), Binary Particle Swarm Optimization (BPSO), Multi-Layer Perceptron (MLP), Levenberg–Marquardt algorithm.

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Authors: N. Outili, A-H. Meniai

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Modeling transfer phenomena in several chemical engineering operations leads to the resolution of partial differential equations systems. According to the complexity of the operations mechanisms, the equations present a nonlinear form and analytical solution became difficult, we have then to use numerical methods which are based on approximations in order to transform a differential system to an algebraic one.Finite element method is one of numerical methods which can be used to obtain an accurate solution in many complex cases of chemical engineering.The packed columns find a large application like contactor for liquid-liquid systems such solvent extraction. In the literature, the modeling of this type of equipment received less attention in comparison with the plate columns.A mathematical bidimensionnal model with radial and axial dispersion, simulating packed tower extraction behavior was developed and a partial differential equation was solved using the finite element method by adopting the Galerkine model. We developed a Mathcad program, which can be used for a similar equations and concentration profiles are obtained along the column. The influence of radial dispersion was prooved and it can-t be neglected, the results were compared with experimental concentration at the top of the column in the extraction system: acetone/toluene/water.

Keywords: finite element method, Galerkine method, liquidliquid extraction modelling, packed column simulation, two dimensional model

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Authors: I. R. Farah, I. B. Ismail, M. B. Ahmed

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The huge development of new technologies and the apparition of open communication system more and more sophisticated create a new challenge to protect digital content from piracy. Digital watermarking is a recent research axis and a new technique suggested as a solution to these problems. This technique consists in inserting identification information (watermark) into digital data (audio, video, image, databases...) in an invisible and indelible manner and in such a way not to degrade original medium-s quality. Moreover, we must be able to correctly extract the watermark despite the deterioration of the watermarked medium (i.e attacks). In this paper we propose a system for watermarking satellite images. We chose to embed the watermark into frequency domain, precisely the discrete wavelet transform (DWT). We applied our algorithm on satellite images of Tunisian center. The experiments show satisfying results. In addition, our algorithm showed an important resistance facing different attacks, notably the compression (JEPG, JPEG2000), the filtering, the histogram-s manipulation and geometric distortions such as rotation, cropping, scaling.

Keywords: Digital data watermarking, Spatial Database, Satellite images, Discrete Wavelets Transform (DWT).

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Authors: F. Jiménez, R. Jódar, M. Martín, G. Sánchez, G. Sciavicco

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Abstract—Attribute or feature selection is one of the basic strategies to improve the performances of data classification tasks, and, at the same time, to reduce the complexity of classifiers, and it is a particularly fundamental one when the number of attributes is relatively high. Its application to unsupervised classification is restricted to a limited number of experiments in the literature. Evolutionary computation has already proven itself to be a very effective choice to consistently reduce the number of attributes towards a better classification rate and a simpler semantic interpretation of the inferred classifiers. We present a feature selection wrapper model composed by a multi-objective evolutionary algorithm, the clustering method Expectation-Maximization (EM), and the classifier C4.5 for the unsupervised classification of data extracted from a psychological test named BASC-II (Behavior Assessment System for Children - II ed.) with two objectives: Maximizing the likelihood of the clustering model and maximizing the accuracy of the obtained classifier. We present a methodology to integrate feature selection for unsupervised classification, model evaluation, decision making (to choose the most satisfactory model according to a a posteriori process in a multi-objective context), and testing. We compare the performance of the classifier obtained by the multi-objective evolutionary algorithms ENORA and NSGA-II, and the best solution is then validated by the psychologists that collected the data.

Keywords: Feature selection, multi-objective evolutionary computation, unsupervised classification, behavior assessment system for children.

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Authors: Hossein Nezamabadi-pour, Saeid Saryazdi

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In this paper, we present a new and effective image indexing technique that extracts features directly from DCT domain. Our proposed approach is an object-based image indexing. For each block of size 8*8 in DCT domain a feature vector is extracted. Then, feature vectors of all blocks of image using a k-means algorithm is clustered into groups. Each cluster represents a special object of the image. Then we select some clusters that have largest members after clustering. The centroids of the selected clusters are taken as image feature vectors and indexed into the database. Also, we propose an approach for using of proposed image indexing method in automatic image classification. Experimental results on a database of 800 images from 8 semantic groups in automatic image classification are reported.

Keywords: Object-based image retrieval, DCT domain, Image indexing, Image classification.

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Authors: Alexandros Leontitsis, Archana P. Sangole

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This article presents a short discussion on optimum neighborhood size selection in a spherical selforganizing feature map (SOFM). A majority of the literature on the SOFMs have addressed the issue of selecting optimal learning parameters in the case of Cartesian topology SOFMs. However, the use of a Spherical SOFM suggested that the learning aspects of Cartesian topology SOFM are not directly translated. This article presents an approach on how to estimate the neighborhood size of a spherical SOFM based on the data. It adopts the L-curve criterion, previously suggested for choosing the regularization parameter on problems of linear equations where their right-hand-side is contaminated with noise. Simulation results are presented on two artificial 4D data sets of the coupled Hénon-Ikeda map.

Keywords: Parameter estimation, self-organizing feature maps, spherical topology.

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Authors: Randa Boukhris Trabelsi, Alima Damak Masmoudi, Dorra Sellami Masmoudi

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Nowadays, hand vein recognition has attracted more attentions in identification biometrics systems. Generally, hand vein image is acquired with low contrast and irregular illumination. Accordingly, if you have a good preprocessing of hand vein image, we can easy extracted the feature extraction even with simple binarization. In this paper, a proposed approach is processed to improve the quality of hand vein image. First, a brief survey on existing methods of enhancement is investigated. Then a Radon Like features method is applied to preprocessing hand vein image. Finally, experiments results show that the proposed method give the better effective and reliable in improving hand vein images.

Keywords: Hand Vein, Enhancement, Contrast, RLF, SDME

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Authors: Nazar Zaki, Safaai Deris, Hany Alashwal

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Detecting protein-protein interactions is a central problem in computational biology and aberrant such interactions may have implicated in a number of neurological disorders. As a result, the prediction of protein-protein interactions has recently received considerable attention from biologist around the globe. Computational tools that are capable of effectively identifying protein-protein interactions are much needed. In this paper, we propose a method to detect protein-protein interaction based on substring similarity measure. Two protein sequences may interact by the mean of the similarities of the substrings they contain. When applied on the currently available protein-protein interaction data for the yeast Saccharomyces cerevisiae, the proposed method delivered reasonable improvement over the existing ones.

Keywords: Protein-Protein Interaction, support vector machine, feature extraction, pairwise alignment, Smith-Waterman score.

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Authors: G. Renuka Devi

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This paper presents design and analysis of fault tolerate feature of n-phase induction motor drive. The n-phase induction motor (more than 3-phases) has a number of advantages over conventional 3-phase induction motor, it has low torque pulsation with increased torque density, more fault tolerant feature, low current ripple with increased efficiency. When increasing the number of phases, it has reduced current per phase without increasing per phase voltage, resulting in an increase in the total power rating of n-phase motors in the same volume machine. In this paper, the theory of operation of a multi-phase induction motor is discussed. The detailed study of d-q modeling of n-phase induction motors is elaborated. The d-q model of n-phase (5, 6, 7, 9 and 12) induction motors is developed in a MATLAB/Simulink environment. The steady state and dynamic performance of the multi-phase induction motor is studied under varying load conditions. Comparison of 5-phase induction is presented under normal and fault conditions.

Keywords: d-q model, dynamic Response, fault tolerant feature, matlab/simulink, multi-phase induction motor, transient response.

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Authors: Shafiq Alam, Mirza Hossain, Hesam Hassan Nejad

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Impurity metals such as manganese and cadmium from high-tenor cobalt electrolyte solution were selectively removed by solvent extraction method using Co-D2EHPA after converting the functional group of D2EHPA with Co2+ ions. The process parameters such as pH, organic concentration, O/A ratio, kinetics etc. were investigated and the experiments were conducted by batch tests in the laboratory bench scale. Results showed that a significant amount of manganese and cadmium can be extracted using Co-D2EHPA for the optimum processing of cobalt electrolyte solution at equilibrium pH about 3.5. The McCabe-Thiele diagram, constructed from the extraction studies showed that 100% impurities can be extracted through four stages for manganese and three stages for cadmium using O/A ratio of 0.65 and 1.0, respectively. From the stripping study, it was found that 100% manganese and cadmium can be stripped from the loaded organic using 0.4 M H2SO4 in a single contact. The loading capacity of Co-D2EHPA by manganese and cadmium were also investigated with different O/A ratio as well as with number of stages of contact of aqueous and organic phases. Valuable information was obtained for the designing of an impurities removal process for the production of pure cobalt with less trouble in the electrowinning circuit.

Keywords: Manganese, Cadmium, Cobalt, D2EHPA, Solvent extraction.

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Authors: Ibrahim A. Akasha, Lydia Campbell, Stephen R. Euston

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Date palm (Phoenix dactylifera L.) seeds are waste streams which are considered a major problem to the food industry. They contain potentially useful protein (10-15% of the whole date-s weight). Global production, industrialisation and utilisation of dates are increasing steadily. The worldwide production of date palm fruit has increased from 1.8 million tons in 1961 to 6.9 million tons in 2005, thus from the global production of dates are almost 800.000 tonnes of date palm seeds are not currently used [1]. The current study was carried out to convert the date palm seeds into useful protein powder. Compositional analysis showed that the seeds were rich in protein and fat 5.64 and 8.14% respectively. We used several laboratory scale methods to extract proteins from seed to produce a high protein powder. These methods included simple acid or alkali extraction, with or without ultrafiltration and phenol trichloroacetic acid with acetone precipitation (Ph/TCA method). The highest protein content powder (68%) was obtained by Ph/TCA method with yield of material (44%) whereas; the use of just alkali extraction gave the lowest protein content of 8%, and a yield of 32%.

Keywords: Date palm seed, Phoenix dactylifera L., extraction of date palm seed protein

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Authors: V. G. Rajesh, M. V. Rajesh

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In rotating machinery one of the critical components that is prone to premature failure is the rolling bearing. Consequently, early warning of an imminent bearing failure is much critical to the safety and reliability of any high speed rotating machines. This study is concerned with the application of Recurrence Quantification Analysis (RQA) in fault detection of rolling element bearings in rotating machinery. Based on the results from this study it is reported that the RQA variable, percent determinism, is sensitive to the type of fault investigated and therefore can provide useful information on bearing damage in rolling element bearings.

Keywords: Bearing fault detection, machine vibrations, nonlinear time series analysis, recurrence quantification analysis.

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Authors: A. Peter Amala Sujith, T.V. Hymavathi, P. Yasoda Devi

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Lutein is a dietary oxycarotenoid which is found to reduce the risks of Age-related Macular Degeneration (AMD). Supercritical fluid extraction of lutein esters from marigold petals was carried out and was found to be much effective than conventional solvent extraction. The saponification of pre-concentrated lutein esters to produce free lutein was studied which showed a composition of about 88% total carotenoids (UV-VIS spectrophotometry) and 90.7% lutein (HPLC). The lipase catalyzed hydrolysis of lutein esters in conventional medium was investigated. The optimal temperature, pH, enzyme concentration and water activity were found to be 50°C, 7, 15% and 0.33 respectively and the activity loss of lipase was about 25% after 8 times re-use in at 50°C for 12 days. However, the lipase catalyzed hydrolysis of lutein esters in conventional media resulted in poor conversions (16.4%).

Keywords: lutein, preconcentration, saponification, lipase

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Authors: Hassan Masoumi, Ahad Salimi, Nazanin Barhemmat, Babak Gholami

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Artificial neural networks have gained a lot of interest as empirical models for their powerful representational capacity, multi input and output mapping characteristics. In fact, most feedforward networks with nonlinear nodal functions have been proved to be universal approximates. In this paper, we propose a new supervised method for color image classification based on selforganizing feature maps (SOFM). This algorithm is based on competitive learning. The method partitions the input space using self-organizing feature maps to introduce the concept of local neighborhoods. Our image classification system entered into RGB image. Experiments with simulated data showed that separability of classes increased when increasing training time. In additional, the result shows proposed algorithms are effective for color image classification.

Keywords: Classification, SOFM, neural network, RGB images.

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Authors: Mário Silva, Filipa Gomes, Filipa Oliveira, Simone Morais, Cristina Delerue-Matos

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Brown seaweeds are abundant in Portuguese coastline and represent an almost unexploited marine economic resource. One of the most common species, easily available for harvesting in the northwest coast, is Saccorhiza polyschides grows in the lowest shore and costal rocky reefs. It is almost exclusively used by local farmers as natural fertilizer, but contains a substantial amount of valuable compounds, particularly alginates, natural biopolymers of high interest for many industrial applications. Alginates are natural polysaccharides present in cell walls of brown seaweed, highly biocompatible, with particular properties that make them of high interest for the food, biotechnology, cosmetics and pharmaceutical industries. Conventional extraction processes are based on thermal treatment. They are lengthy and consume high amounts of energy and solvents. In recent years, microwave-assisted extraction (MAE) has shown enormous potential to overcome major drawbacks that outcome from conventional plant material extraction (thermal and/or solvent based) techniques, being also successfully applied to the extraction of agar, fucoidans and alginates. In the present study, acid pretreatment of brown seaweed Saccorhiza polyschides for subsequent microwave-assisted extraction (MAE) of alginate was optimized. Seaweeds were collected in Northwest Portuguese coastal waters of the Atlantic Ocean between May and August, 2014. Experimental design was used to assess the effect of temperature and acid pretreatment time in alginate extraction. Response surface methodology allowed the determination of the optimum MAE conditions: 40 mL of HCl 0.1 M per g of dried seaweed with constant stirring at 20ºC during 14h. Optimal acid pretreatment conditions have enhanced significantly MAE of alginates from Saccorhiza polyschides, thus contributing for the development of a viable, more environmental friendly alternative to conventional processes.

Keywords: Acid pretreatment, Alginate, Brown seaweed, Microwave-assisted extraction, Response surface methodology.

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Authors: Mohd Fahmi Mohamad Amran, Riza Sulaiman, Saliyah Kahar, Suziyanti Marjudi, Muhammad FairuzAbd Rauf

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The extensive number of engineering drawing will be referred for planning process and the changes will produce a good engineering design to meet the demand in producing a new model. The advantage in reuse of engineering designs is to allow continuous product development to further improve the quality of product development, thus reduce the development costs. However, to retrieve the existing engineering drawing, it is time consuming, a complex process and are expose to errors. Engineering drawing file searching system will be proposed to solve this problem. It is essential for engineer and designer to have some sort of medium to enable them to search for drawing in the most effective way. This paper lays out the proposed research project under the area of information extraction in engineering drawing.

Keywords: Computer aided design, information extraction, engineering drawing, reuse design.

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Authors: Adel Sabry Eesa, Adnan Mohsin Abdulazeez Brifcani, Zeynep Orman

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Feature selection has been used in many fields such as classification, data mining and object recognition and proven to be effective for removing irrelevant and redundant features from the original dataset. In this paper, a new design of distributed intrusion detection system using a combination feature selection model based on bees and decision tree. Bees algorithm is used as the search strategy to find the optimal subset of features, whereas decision tree is used as a judgment for the selected features. Both the produced features and the generated rules are used by Decision Making Mobile Agent to decide whether there is an attack or not in the networks. Decision Making Mobile Agent will migrate through the networks, moving from node to another, if it found that there is an attack on one of the nodes, it then alerts the user through User Interface Agent or takes some action through Action Mobile Agent. The KDD Cup 99 dataset is used to test the effectiveness of the proposed system. The results show that even if only four features are used, the proposed system gives a better performance when it is compared with the obtained results using all 41 features.

Keywords: Distributed intrusion detection system, mobile agent, feature selection, Bees Algorithm, decision tree.

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Authors: María S. Avila-García, John N. Carter, Robert I. Damper

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An important problem in speech research is the automatic extraction of information about the shape and dimensions of the vocal tract during real-time speech production. We have previously developed Southampton dynamic magnetic resonance imaging (SDMRI) as an approach to the solution of this problem.However, the SDMRI images are very noisy so that shape extraction is a major challenge. In this paper, we address the problem of tongue shape extraction, which poses difficulties because this is a highly deforming non-parametric shape. We show that combining active shape models with the dynamic Hough transform allows the tongue shape to be reliably tracked in the image sequence.

Keywords: Vocal tract imaging, speech production, active shapemodels, dynamic Hough transform, object tracking.

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Authors: M. Sedighizadeh, A. Rezazadeh

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Movable power sources of proton exchange membrane fuel cells (PEMFC) are the important research done in the current fuel cells (FC) field. The PEMFC system control influences the cell performance greatly and it is a control system for industrial complex problems, due to the imprecision, uncertainty and partial truth and intrinsic nonlinear characteristics of PEMFCs. In this paper an adaptive PI control strategy using neural network adaptive Morlet wavelet for control is proposed. It is based on a single layer feed forward neural networks with hidden nodes of adaptive morlet wavelet functions controller and an infinite impulse response (IIR) recurrent structure. The IIR is combined by cascading to the network to provide double local structure resulting in improving speed of learning. The proposed method is applied to a typical 1 KW PEMFC system and the results show the proposed method has more accuracy against to MLP (Multi Layer Perceptron) method.

Keywords: Adaptive Control, Morlet Wavelets, PEMFC.

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Authors: A. Hossein Sahoolizadeh, B. Zargham Heidari, C. Hamid Dehghani

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In this paper, a new face recognition method based on PCA (principal Component Analysis), LDA (Linear Discriminant Analysis) and neural networks is proposed. This method consists of four steps: i) Preprocessing, ii) Dimension reduction using PCA, iii) feature extraction using LDA and iv) classification using neural network. Combination of PCA and LDA is used for improving the capability of LDA when a few samples of images are available and neural classifier is used to reduce number misclassification caused by not-linearly separable classes. The proposed method was tested on Yale face database. Experimental results on this database demonstrated the effectiveness of the proposed method for face recognition with less misclassification in comparison with previous methods.

Keywords: Face recognition Principal component analysis, Linear discriminant analysis, Neural networks.

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Authors: Teerapon Pirom, Ura Pancharoen

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Amoxicillin is an antibiotic which is widely used to treat various infections in both human beings and animals. However, when amoxicillin is released into the environment, it is a major problem. Amoxicillin causes bacterial resistance to these drugs and failure of treatment with antibiotics. Liquid membrane is of great interest as a promising method for the separation and recovery of the target ions from aqueous solutions due to the use of carriers for the transport mechanism, resulting in highly selectivity and rapid transportation of the desired metal ions. The simultaneous processes of extraction and stripping in a single unit operation of liquid membrane system are very interesting. Therefore, it is practical to apply liquid membrane, particularly the HFSLM for industrial applications as HFSLM is proved to be a separation process with lower capital and operating costs, low energy and extractant with long life time, high selectivity and high fluxes compared with solid membranes. It is a simple design amenable to scaling up for industrial applications. The extraction and recovery for (Amoxicillin) through the hollow fiber supported liquid membrane (HFSLM) using aliquat336 as a carrier were explored with the experimental data. The important variables affecting on transport of amoxicillin viz. extractant concentration and operating time were investigated. The highest AMOX- extraction percentages of 85.35 and Amoxicillin stripping of 80.04 were achieved with the best condition at 6 mmol/L [aliquat336] and operating time 100 min. The extraction reaction order (n) and the extraction reaction rate constant (kf) were found to be 1.00 and 0.0344 min-1, respectively.

Keywords: Aliquat336, amoxicillin, HFSLM, kinetic.

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Authors: H. El Fadili, K. Zenkouar, H. Qjidaa

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This paper proposes a neural network weights and topology optimization using genetic evolution and the backpropagation training algorithm. The proposed crossover and mutation operators aims to adapt the networks architectures and weights during the evolution process. Through a specific inheritance procedure, the weights are transmitted from the parents to their offsprings, which allows re-exploitation of the already trained networks and hence the acceleration of the global convergence of the algorithm. In the preprocessing phase, a new feature extraction method is proposed based on Legendre moments with the Maximum entropy principle MEP as a selection criterion. This allows a global search space reduction in the design of the networks. The proposed method has been applied and tested on the well known MNIST database of handwritten digits.

Keywords: Genetic algorithm, Legendre Moments, MEP, Neural Network.

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Authors: Kasra Majbouri Yazdi, Adel Majbouri Yazdi, Saeid Khodayi, Jingyu Hou, Wanlei Zhou, Saeed Saedy

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Fake news and false information are big challenges of all types of media, especially social media. There is a lot of false information, fake likes, views and duplicated accounts as big social networks such as Facebook and Twitter admitted. Most information appearing on social media is doubtful and in some cases misleading. They need to be detected as soon as possible to avoid a negative impact on society. The dimensions of the fake news datasets are growing rapidly, so to obtain a better result of detecting false information with less computation time and complexity, the dimensions need to be reduced. One of the best techniques of reducing data size is using feature selection method. The aim of this technique is to choose a feature subset from the original set to improve the classification performance. In this paper, a feature selection method is proposed with the integration of K-means clustering and Support Vector Machine (SVM) approaches which work in four steps. First, the similarities between all features are calculated. Then, features are divided into several clusters. Next, the final feature set is selected from all clusters, and finally, fake news is classified based on the final feature subset using the SVM method. The proposed method was evaluated by comparing its performance with other state-of-the-art methods on several specific benchmark datasets and the outcome showed a better classification of false information for our work. The detection performance was improved in two aspects. On the one hand, the detection runtime process decreased, and on the other hand, the classification accuracy increased because of the elimination of redundant features and the reduction of datasets dimensions.

Keywords: Fake news detection, feature selection, support vector machine, K-means clustering, machine learning, social media.

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Authors: Nadia Hmad, Tony Allen

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This paper presents an ESN-based Arabic phoneme recognition system trained with supervised, forced and combined supervised/forced supervised learning algorithms. Mel-Frequency Cepstrum Coefficients (MFCCs) and Linear Predictive Code (LPC) techniques are used and compared as the input feature extraction technique. The system is evaluated using 6 speakers from the King Abdulaziz Arabic Phonetics Database (KAPD) for Saudi Arabia dialectic and 34 speakers from the Center for Spoken Language Understanding (CSLU2002) database of speakers with different dialectics from 12 Arabic countries. Results for the KAPD and CSLU2002 Arabic databases show phoneme recognition performances of 72.31% and 38.20% respectively.

Keywords: Arabic phonemes recognition, echo state networks (ESNs), neural networks (NNs), supervised learning.

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Authors: Tomyslav Sledevič, Artūras Serackis, Gintautas Tamulevičius, Dalius Navakauskas

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Paper presents an comparative evaluation of features extraction algorithm for a real-time isolated word recognition system based on FPGA. The Mel-frequency cepstral, linear frequency cepstral, linear predictive and their cepstral coefficients were implemented in hardware/software design. The proposed system was investigated in speaker dependent mode for 100 different Lithuanian words. The robustness of features extraction algorithms was tested recognizing the speech records at different signal to noise rates. The experiments on clean records show highest accuracy for Mel-frequency cepstral and linear frequency cepstral coefficients. For records with 15 dB signal to noise rate the linear predictive cepstral coefficients gives best result. The hard and soft part of the system is clocked on 50 MHz and 100 MHz accordingly. For the classification purpose the pipelined dynamic time warping core was implemented. The proposed word recognition system satisfy the real-time requirements and is suitable for applications in embedded systems.

Keywords: Isolated word recognition, features extraction, MFCC, LFCC, LPCC, LPC, FPGA, DTW.

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Authors: Abdulfattah M. Alkherraz, Zaineb I. Lusta, Ahmed E. Zubi

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The environmental pollution by heavy metals became  more problematic nowadays. To solve the problem of Cadmium  accumulation in human organs which lead to dangerous effects on  human health, and to determine its concentration, the organic legand  1-phenyl-3-benzoyl-2-thiourea was used to extract the cadmium ions  from its solution. This legand as one of thiourea derivatives was  successfully synthesized. The legand was characterized by NMR and  CHN elemental analysis, and used to extract the cadmium from its  solutions by formation of a stable complex at neutral pH. The  complex was characterized by elemental analysis and melting point.  The concentrations of cadmium ions before and after the extraction  were determined by Atomic Absorption Spectrophotometer (AAS).  The data show the percentage of the extract was more than 98.7% of  the concentration of cadmium used in the study

Keywords: Thiourea derivatives, cadmium extraction.

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Authors: Stefan Wagenpfeil, Felix Engel, Paul McKevitt, Matthias Hemmje

Abstract:

Multimedia Indexing and Retrieval is generally de-signed and implemented by employing feature graphs. These graphs typically contain a significant number of nodes and edges to reflect the level of detail in feature detection. A higher level of detail increases the effectiveness of the results but also leads to more complex graph structures. However, graph-traversal-based algorithms for similarity are quite inefficient and computation intensive, espe-cially for large data structures. To deliver fast and effective retrieval, an efficient similarity algorithm, particularly for large graphs, is mandatory. Hence, in this paper, we define a graph-projection into a 2D space (Graph Code) as well as the corresponding algorithms for indexing and retrieval. We show that calculations in this space can be performed more efficiently than graph-traversals due to a simpler processing model and a high level of parallelisation. In consequence, we prove that the effectiveness of retrieval also increases substantially, as Graph Codes facilitate more levels of detail in feature fusion. Thus, Graph Codes provide a significant increase in efficiency and effectiveness (especially for Multimedia indexing and retrieval) and can be applied to images, videos, audio, and text information.

Keywords: indexing, retrieval, multimedia, graph code, graph algorithm

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