Search results for: characteristics extraction andclassification

Authors: Dim En Nyaung, Thin Lai Lai Thein

Abstract:

Due to the rapid increase of Internet, web opinion sources dynamically emerge which is useful for both potential customers and product manufacturers for prediction and decision purposes. These are the user generated contents written in natural languages and are unstructured-free-texts scheme. Therefore, opinion mining techniques become popular to automatically process customer reviews for extracting product features and user opinions expressed over them. Since customer reviews may contain both opinionated and factual sentences, a supervised machine learning technique applies for subjectivity classification to improve the mining performance. In this paper, we dedicate our work is the task of opinion summarization. Therefore, product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the identification of semantic relationships. The polarity and numeric score of all the features are determined by Senti-WordNet Lexicon. The problem of opinion summarization refers how to relate the opinion words with respect to a certain feature. Probabilistic based model of supervised learning will improve the result that is more flexible and effective.

Keywords: Opinion Mining, Opinion Summarization, Sentiment Analysis, Text Mining.

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Authors: Javadi Alimohammad, Zanjanizadeh Homa, Javadi Maryam

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Regarding the multi-media property of internet and the facilities that can be provided for the users, the purpose of this paper is to investigate the users- behavioral patterns and the impact of internet on taboos of marriage. For this purpose a survey technique on the sample size amounted 403 students of governmental guidance schools of city of Mashhad in country of Iran were considered. The results showed, the process of using various internet environments depends on the degree of the users- familiarity with these sites. In order to clarify the effects of the Internet on the taboos of marriage, the non – internet parameters also considered to be controlled. The ttest held among the internet users and non-users, indicated that internet users possess lower taboos of marriage. Extraction of the effects of internet via considering the effects of non-internet parameters, indicate that addiction to the internet, creating a cordial atmosphere, emotional communication, and message attractive factors have significant effects on the family's traditional values.

Keywords: Internet, taboos of marriage, family, masscommunication, computer mediate communication.

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Authors: Raissa A. Muzychkina, Irina M. Korulkina, Dmitriy Yu. Korulkin

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In article the data of acute toxicity for pre-clinical researches of Ramon preparation is described. Ramon effects to clinical characteristics of blood, cardio-vascular system, hepatotoxic and diuretic effects were studied.

Keywords: Cancer, toxicity, antitumor activity, pre-clinical testing, anthraquinones, phytopreparation, Ramon.

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Authors: S. Nandagopalan, Dr. B. S. Adiga, N. Deepak

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In this paper a novel approach for generalized image retrieval based on semantic contents is presented. A combination of three feature extraction methods namely color, texture, and edge histogram descriptor. There is a provision to add new features in future for better retrieval efficiency. Any combination of these methods, which is more appropriate for the application, can be used for retrieval. This is provided through User Interface (UI) in the form of relevance feedback. The image properties analyzed in this work are by using computer vision and image processing algorithms. For color the histogram of images are computed, for texture cooccurrence matrix based entropy, energy, etc, are calculated and for edge density it is Edge Histogram Descriptor (EHD) that is found. For retrieval of images, a novel idea is developed based on greedy strategy to reduce the computational complexity. The entire system was developed using AForge.Imaging (an open source product), MATLAB .NET Builder, C#, and Oracle 10g. The system was tested with Coral Image database containing 1000 natural images and achieved better results.

Keywords: Content Based Image Retrieval (CBIR), Cooccurrencematrix, Feature vector, Edge Histogram Descriptor(EHD), Greedy strategy.

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Authors: Sanghyeok Oh, Keechul Jung

Abstract:

Although lots of research work has been done for human pose recognition, the view-point of cameras is still critical problem of overall recognition system. In this paper, view-point insensitive human pose recognition is proposed. The aims of the proposed system are view-point insensitivity and real-time processing. Recognition system consists of feature extraction module, neural network and real-time feed forward calculation. First, histogram-based method is used to extract feature from silhouette image and it is suitable for represent the shape of human pose. To reduce the dimension of feature vector, Principle Component Analysis(PCA) is used. Second, real-time processing is implemented by using Compute Unified Device Architecture(CUDA) and this architecture improves the speed of feed-forward calculation of neural network. We demonstrate the effectiveness of our approach with experiments on real environment.

Keywords: computer vision, neural network, pose recognition, view-point insensitive, PCA, CUDA.

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Authors: Rehab H. Alwan, Fadhil J. Kadhim, Ahmad T. Al-Taani

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In this study, a novel approach of image embedding is introduced. The proposed method consists of three main steps. First, the edge of the image is detected using Sobel mask filters. Second, the least significant bit LSB of each pixel is used. Finally, a gray level connectivity is applied using a fuzzy approach and the ASCII code is used for information hiding. The prior bit of the LSB represents the edged image after gray level connectivity, and the remaining six bits represent the original image with very little difference in contrast. The proposed method embeds three images in one image and includes, as a special case of data embedding, information hiding, identifying and authenticating text embedded within the digital images. Image embedding method is considered to be one of the good compression methods, in terms of reserving memory space. Moreover, information hiding within digital image can be used for security information transfer. The creation and extraction of three embedded images, and hiding text information is discussed and illustrated, in the following sections.

Keywords: Image embedding, Edge detection, gray level connectivity, information hiding, digital image compression.

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Authors: Chang Soo Kang, In Ho Im, Sergey Churayev, Timour Paltashev

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This paper describes the design of new method of propagation delay measurement in micro and nanostructures during characterization of ASIC standard library cell. Providing more accuracy timing information about library cell to the design team we can improve a quality of timing analysis inside of ASIC design flow process. Also, this information could be very useful for semiconductor foundry team to make correction in technology process. By comparison of the propagation delay in the CMOS element and result of analog SPICE simulation. It was implemented as digital IP core for semiconductor manufacturing process. Specialized method helps to observe the propagation time delay in one element of the standard-cell library with up-to picoseconds accuracy and less. Thus, the special useful solutions for VLSI schematic to parameters extraction, basic cell layout verification, design simulation and verification are announced.

Keywords: phase error accumulation methodology, gatepropagation delay, Processor Testing, MEMS Testing

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Authors: Abderrahmane Amrouche, Jean Michel Rouvaen

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In this paper we present an efficient system for independent speaker speech recognition based on neural network approach. The proposed architecture comprises two phases: a preprocessing phase which consists in segmental normalization and features extraction and a classification phase which uses neural networks based on nonparametric density estimation namely the general regression neural network (GRNN). The relative performances of the proposed model are compared to the similar recognition systems based on the Multilayer Perceptron (MLP), the Recurrent Neural Network (RNN) and the well known Discrete Hidden Markov Model (HMM-VQ) that we have achieved also. Experimental results obtained with Arabic digits have shown that the use of nonparametric density estimation with an appropriate smoothing factor (spread) improves the generalization power of the neural network. The word error rate (WER) is reduced significantly over the baseline HMM method. GRNN computation is a successful alternative to the other neural network and DHMM.

Keywords: Speech Recognition, General Regression NeuralNetwork, Hidden Markov Model, Recurrent Neural Network, ArabicDigits.

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Authors: M. Margoudi, Z. Smyrnaiou

Abstract:

The current paper presents the results of a conducted case study. During the past few years the number of children diagnosed with Learning Difficulties has drastically augmented and especially the cases of ADHD (Attention Deficit Hyperactivity Disorder). One of the core characteristics of ADHD is a deficit in working memory functions. The review of the literature indicates a plethora of educational software that aim at training and enhancing the working memory. Nevertheless, in the current paper, the possibility of using for the same purpose free, online games will be explored. Another issue of interest is the potential effect of the working memory training to the core symptoms of ADHD. In order to explore the abovementioned research questions, three digital tests are employed, all of which are developed on the E-slate platform by the author, in order to check the levels of ADHD’s symptoms and to be used as diagnostic tools, both in the beginning and in the end of the case study. The tools used during the main intervention of the research are free online games for the training of working memory. The research and the data analysis focus on the following axes: a) the presence and the possible change in two of the core symptoms of ADHD, attention and impulsivity and b) a possible change in the general cognitive abilities of the individual. The case study was conducted with the participation of a thirteen year-old, female student, diagnosed with ADHD, during after-school hours. The results of the study indicate positive changes both in the levels of attention and impulsivity. Therefore, we conclude that the training of working memory through the use of free, online games has a positive impact on the characteristics of ADHD. Finally, concerning the second research question, the change in general cognitive abilities, no significant changes were noted.

Keywords: ADHD, attention, impulsivity, online games.

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Authors: Chapman Eze Nnadozie

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Relational databases constitute a very vital tool for the effective management and administration of both personal and organizational data. Data access ranges from a single user database management software to a more complex distributed server system. This paper intends to appraise the use a programming language extension like structured query language (SQL) to establish links to a relational database (Microsoft Access 2013) using Visual C++ 9 programming language environment. The methodology used involves the creation of tables to form a database using Microsoft Access 2013, which is Object Linking and Embedding (OLE) database compliant. The SQL command is used to query the tables in the database for easy extraction of expected records inside the visual C++ environment. The findings of this paper reveal that records can easily be accessed and manipulated to filter exactly what the user wants, such as retrieval of records with specified criteria, updating of records, and deletion of part or the whole records in a table.

Keywords: Data access, database, database management system, OLE, programming language, records, relational database, software, SQL, table.

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Authors: R. Hocine, A. Boudjemai, A. Amrani, K. Belkacemi

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Temperature rising is a negative factor in almost all systems. It could cause by self heating or ambient temperature. In solar photovoltaic cells this temperature rising affects on the behavior of cells. The ability of a PV module to withstand the effects of periodic hot-spot heating that occurs when cells are operated under reverse biased conditions is closely related to the properties of the cell semi-conductor material.

In addition, the thermal effect also influences the estimation of the maximum power point (MPP) and electrical parameters for the PV modules, such as maximum output power, maximum conversion efficiency, internal efficiency, reliability, and lifetime. The cells junction temperature is a critical parameter that significantly affects the electrical characteristics of PV modules. For practical applications of PV modules, it is very important to accurately estimate the junction temperature of PV modules and analyze the thermal characteristics of the PV modules. Once the temperature variation is taken into account, we can then acquire a more accurate MPP for the PV modules, and the maximum utilization efficiency of the PV modules can also be further achieved.

In this paper, the three-Dimensional Transmission Line Matrix (3D-TLM) method was used to map the surface temperature distribution of solar cells while in the reverse bias mode. It was observed that some cells exhibited an inhomogeneity of the surface temperature resulting in localized heating (hot-spot). This hot-spot heating causes irreversible destruction of the solar cell structure. Hot spots can have a deleterious impact on the total solar modules if individual solar cells are heated. So, the results show clearly that the solar cells are capable of self-generating considerable amounts of heat that should be dissipated very quickly to increase PV module's lifetime.

Keywords: Thermal effect, Conduction, Heat dissipation, Thermal conductivity, Solar cell, PV module, Nodes, 3D-TLM.

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Authors: Khadijat T. Bamigbade, Olufade F. W. Onifade

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The field of automatic facial expression analysis has been an active research area in the last two decades. Its vast applicability in various domains has drawn so much attention into developing techniques and dataset that mirror real life scenarios. Many techniques such as Local Binary Patterns and its variants (CLBP, LBP-TOP) and lately, deep learning techniques, have been used for facial expression recognition. However, the problem of occlusion has not been sufficiently handled, making their results not applicable in real life situations. This paper develops a simple, yet highly efficient method tagged Local Binary Pattern-Histogram of Gradient (LBP-HOG) with occlusion detection in face image, using a multi-class SVM for Action Unit and in turn expression recognition. Our method was evaluated on three publicly available datasets which are JAFFE, CK, SFEW. Experimental results showed that our approach performed considerably well when compared with state-of-the-art algorithms and gave insight to occlusion detection as a key step to handling expression in wild.

Keywords: Automatic facial expression analysis, local binary pattern, LBP-HOG, occlusion detection.

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Authors: Anupam Kumar, Abdul Hamid Bhat, Pramod Agarwal

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Bidirectional energy transfer capability with high efficiency and reduced cost is fast gaining prominence in the central part of a lot of power conversion systems in Direct Current (DC) microgrid. Preferably, under the economics constraints, these systems utilise a single high efficiency power electronics conversion system and a dual active bridge converter. In this paper, modeling and performance of Dual Active Bridge (DAB) converter with Extended Phase Shift (EPS) is evaluated with two batteries on both sides of DC bus and bidirectional energy transfer is facilitated and this is further compared with the Single Phase Shift (SPS) mode of operation. Optimum operating zone is identified through exhaustive simulations using MATLAB/Simulink and SimPowerSystem software. Reduced rules based fuzzy logic controller is implemented for closed loop control of DAB converter. The control logic enables the bidirectional energy transfer within the batteries even at lower duty ratios. Charging and discharging of batteries is supervised by the fuzzy logic controller. State of charge, current and voltage for both the batteries are plotted in the battery characteristics. Power characteristics of batteries are also obtained using MATLAB simulations.

Keywords: Fuzzy logic controller, rule base, membership functions, dual active bridge converter, bidirectional power flow, duty ratio, extended phase shift, state of charge.

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Authors: Candido. R.G., Godoy, G.G., Gonçalves, A.R

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To produce sugar and ethanol, sugarcane processing generates several agricultural residues, being straw and bagasse is considered as the main among them. And what to do with this residues has been subject of many studies and experiences in an industry that, in recent years, highlighted by the ability to transform waste into valuable products such as electric power. Cellulose is the main component of these materials. It is the most common organic polymer and represents about 1.5 x 1012 tons of total production of biomass per year and is considered an almost inexhaustible source of raw material. Pretreatment with mineral acids is one of the most widely used as stage of cellulose extraction from lignocellulosic materials for solubilizing most of the hemicellulose content. This study had as goal to find the best reaction time of sugarcane bagasse pretreatment with sulfuric acid in order to minimize the losses of cellulose concomitantly with the highest possible removal of hemicellulose and lignin. It was found that the best time for this reaction was 40 minutes, in which it was reached a loss of hemicelluloses around 70% and lignin and cellulose, around 15%. Over this time, it was verified that the cellulose loss increased and there was no loss of lignin and hemicellulose.

Keywords: cellulose, acid pretreatment, hemicellulose removal, sugarcane bagasse

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Authors: Shibesh Dutta, Sivaramakrishnan R., Sundar Gopalan, Balakrishnan Shankar

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MOSCAPs of various combinations of Hafnium oxide and Titanium oxide of varying thickness with Aluminum as gate electrode have been fabricated and electrically characterized. The effects of voltage stress on the I-V characteristics for prolonged time durations have been studied and compared. Results showed hard breakdown and negligible degradation of reliability under stress.

Keywords: breakdown, MOSCAP, voltage stress.

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Authors: Muhammad Faisal Zafar, Dzulkifli Mohamad, Razib M. Othman

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On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this paper presents a simple approach to extract the useful character information. This work evaluates the use of the counter- propagation neural network (CPN) and presents feature extraction mechanism in full detail to work with on-line handwriting recognition. The obtained recognition rates were 60% to 94% using the CPN for different sets of character samples. This paper also describes a performance study in which a recognition mechanism with multiple thresholds is evaluated for counter-propagation architecture. The results indicate that the application of multiple thresholds has significant effect on recognition mechanism. The method is applicable for off-line character recognition as well. The technique is tested for upper-case English alphabets for a number of different styles from different peoples.

Keywords: On-line character recognition, character digitization, counter-propagation neural networks, extreme coordinates.

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Authors: Henry Munoz, Muhammad Mohsan, Takashi Kiyota

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Strength and deformability characteristics of a liquefiable sand deposit including the development of earthquake-induced shear stress and shear strain as well as soil softening via the progressive degradation of shear modulus were studied via shaking table experiments. To do so, a model of a liquefiable sand deposit was constructed and densely instrumented where accelerations, pressures, and displacements at different locations were continuously monitored. Furthermore, the confinement effects on the strength and deformation characteristics of the liquefiable sand deposit due to an external surcharge by placing a heavy concrete slab (i.e. the model of an actual structural rigid pavement) on the ground surface were examined. The results indicate that as the number of seismic-loading cycles increases, the sand deposit softens progressively as large shear strains take place in different sand elements. Liquefaction state is reached after the combined effects of the progressive degradation of the initial shear modulus associated with the continuous decrease in the mean principal stress, and the buildup of the excess of pore pressure takes place in the sand deposit. Finally, the confinement effects given by a concrete slab placed on the surface of the sand deposit resulted in a favorable increasing in the initial shear modulus, an increase in the mean principal stress and a decrease in the softening rate (i.e. the decreasing rate in shear modulus) of the sand, thus making the onset of liquefaction to take place at a later stage. This is, only after the sand deposit having a concrete slab experienced a higher number of seismic loading cycles liquefaction took place, in contrast to an ordinary sand deposit having no concrete slab.

Keywords: Liquefaction, shaking table, shear modulus degradation, earthquake.

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Authors: A. Amrani, O. Allali, A. Ben Hamida, F. Defrance, S. Morland, E. Pineau, T. Lacroix

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The energy planning process is a very complex task that involves several stakeholders and requires the consideration of several local and global factors and constraints. In order to optimize and simplify this process, we propose a tool-based iterative approach applied to district heating planning. We build our tool with the collaboration of a French territory using actual district data and implementing the European incentives. We set up an iterative process including data visualization and analysis, identification and extraction of information related to the area concerned by the operation, design of sustainable planning scenarios leveraging local renewable and recoverable energy sources, and finally, the evaluation of scenarios. The last step is performed by a dynamic digital twin replica of the city. Territory’s energy experts confirm that the tool provides them with valuable support towards sustainable energy planning.

Keywords: Climate change, data management, decision support, digital twin, district heating, energy planning, renewables, smart city.

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Authors: Jingling Li, Yi Zhang, Wei Liang, Tao Cui, Jun Li

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Compared with terrestrial network, the traffic of spatial information network has both self-similarity and short correlation characteristics. By studying its traffic prediction method, the resource utilization of spatial information network can be improved, and the method can provide an important basis for traffic planning of a spatial information network. In this paper, considering the accuracy and complexity of the algorithm, the spatial information network traffic is decomposed into approximate component with long correlation and detail component with short correlation, and a time series hybrid prediction model based on wavelet decomposition is proposed to predict the spatial network traffic. Firstly, the original traffic data are decomposed to approximate components and detail components by using wavelet decomposition algorithm. According to the autocorrelation and partial correlation smearing and truncation characteristics of each component, the corresponding model (AR/MA/ARMA) of each detail component can be directly established, while the type of approximate component modeling can be established by ARIMA model after smoothing. Finally, the prediction results of the multiple models are fitted to obtain the prediction results of the original data. The method not only considers the self-similarity of a spatial information network, but also takes into account the short correlation caused by network burst information, which is verified by using the measured data of a certain back bone network released by the MAWI working group in 2018. Compared with the typical time series model, the predicted data of hybrid model is closer to the real traffic data and has a smaller relative root means square error, which is more suitable for a spatial information network.

Keywords: Spatial Information Network, Traffic prediction, Wavelet decomposition, Time series model.

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Authors: Liping Jing, Michael K. Ng, Xinhua Yang, Joshua Zhexue Huang

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This paper presents a text clustering system developed based on a k-means type subspace clustering algorithm to cluster large, high dimensional and sparse text data. In this algorithm, a new step is added in the k-means clustering process to automatically calculate the weights of keywords in each cluster so that the important words of a cluster can be identified by the weight values. For understanding and interpretation of clustering results, a few keywords that can best represent the semantic topic are extracted from each cluster. Two methods are used to extract the representative words. The candidate words are first selected according to their weights calculated by our new algorithm. Then, the candidates are fed to the WordNet to identify the set of noun words and consolidate the synonymy and hyponymy words. Experimental results have shown that the clustering algorithm is superior to the other subspace clustering algorithms, such as PROCLUS and HARP and kmeans type algorithm, e.g., Bisecting-KMeans. Furthermore, the word extraction method is effective in selection of the words to represent the topics of the clusters.

Keywords: Subspace Clustering, Text Mining, Feature Weighting, Cluster Interpretation, Ontology

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Authors: Yanwen Li, Shuguo Xie

Abstract:

In electromagnetic imaging, because of the diffraction limited system, the pixel values could change slowly near the edge of the image targets and they also change with the location in the same target. Using traditional digital image segmentation methods to segment electromagnetic gradient images could result in lots of errors because of this change in pixel values. To address this issue, this paper proposes a novel image segmentation and extraction algorithm based on Mean-Shift and dictionary learning. Firstly, the preliminary segmentation results from adaptive bandwidth Mean-Shift algorithm are expanded, merged and extracted. Then the overlap rate of the extracted image block is detected before determining a segmentation region with a single complete target. Last, the gradient edge of the extracted targets is recovered and reconstructed by using a dictionary-learning algorithm, while the final segmentation results are obtained which are very close to the gradient target in the original image. Both the experimental results and the simulated results show that the segmentation results are very accurate. The Dice coefficients are improved by 70% to 80% compared with the Mean-Shift only method.

Keywords: Gradient image, segmentation and extract, mean-shift algorithm, dictionary learning.

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Authors: Vaibhav Kant Singh, Vijay Shah, Yogendra Kumar Jain, Anupam Shukla, A.S. Thoke, Vinay KumarSingh, Chhaya Dule, Vivek Parganiha

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Data mining, which is the exploration of knowledge from the large set of data, generated as a result of the various data processing activities. Frequent Pattern Mining is a very important task in data mining. The previous approaches applied to generate frequent set generally adopt candidate generation and pruning techniques for the satisfaction of the desired objective. This paper shows how the different approaches achieve the objective of frequent mining along with the complexities required to perform the job. This paper will also look for hardware approach of cache coherence to improve efficiency of the above process. The process of data mining is helpful in generation of support systems that can help in Management, Bioinformatics, Biotechnology, Medical Science, Statistics, Mathematics, Banking, Networking and other Computer related applications. This paper proposes the use of both upward and downward closure property for the extraction of frequent item sets which reduces the total number of scans required for the generation of Candidate Sets.

Keywords: Data Mining, Candidate Sets, Frequent Item set, Pruning.

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Authors: Manatchanok Tantiphiphatthana, Lin Peng, Rujira Jitrwung, Kunio Yoshikawa

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Hydrothermal liquefaction (HTL) is a technique for obtaining clean biofuel from biomass in the presence of heat and pressure in an aqueous medium which leads to a decomposition of this biomass to the formation of various products. A role of operating conditions is essential for the bio-oil and other products’ yield and also quality of the products. The effects of these parameters were investigated in regards to the composition and yield of the products. Chlorellaceae microalgae were tested under different HTL conditions to clarify suitable conditions for extracting bio-oil together with value-added co-products. Firstly, different microalgae loading rates (5-30%) were tested and found that this parameter has not much significant to product yield. Therefore, 10% microalgae loading rate was selected as a proper economical solution for conditioned schedule at 250oC and 30 min-reaction time. Next, a range of temperature (210-290oC) was applied to verify the effects of each parameter by keeping the reaction time constant at 30 min. The results showed no linkage with the increase of the reaction temperature and some reactions occurred that lead to different product yields. Moreover, some nutrients found in the aqueous product are possible to be utilized for nutrient recovery.

Keywords: Bio-oil, Hydrothermal Liquefaction, Microalgae, Aqueous co-product.

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Authors: V. A. C. Vale, E. T. L. Cöuras Ford

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In view of the requirements of the current industrial processes, the instrumentation plays a critical role. In this context, this work aims to raise some the operating characteristics of the level and flow transmitters, in addition to observing their similarities and possible limitations configurations.

Keywords: Flow, level, instrumentation, configurations of meters, method of choice of the meters, instrumentation in the industrial processes.

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Authors: Mazliham Mohd Su'ud, Pierre Loonis, Idris Abu Seman

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This paper deals with the extraction of information from the experts to automatically identify and recognize Ganoderma infection in oil palm stem using tomography images. Expert-s knowledge are used as rules in a Fuzzy Inference Systems to classify each individual patterns observed in he tomography image. The classification is done by defining membership functions which assigned a set of three possible hypotheses : Ganoderma infection (G), non Ganoderma infection (N) or intact stem tissue (I) to every abnormalities pattern found in the tomography image. A complete comparison between Mamdani and Sugeno style,triangular, trapezoids and mixed triangular-trapezoids membership functions and different methods of aggregation and defuzzification is also presented and analyzed to select suitable Fuzzy Inference System methods to perform the above mentioned task. The results showed that seven out of 30 initial possible combination of available Fuzzy Inference methods in MATLAB Fuzzy Toolbox were observed giving result close to the experts estimation.

Keywords: Fuzzy Inference Systems, Tomography analysis, Modelizationof expert's information, Ganoderma Infection pattern recognition

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Authors: Sher Muhammad, Mirza Muhammad Waqar

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It is necessary to monitor and identify mangroves types and spatial extent near coastal areas because it plays an important role in coastal ecosystem and environmental protection. This research aims at identifying and mapping mangroves types along Karachi coast ranging from 24.79⁰ to 24.85⁰ in latitude and 66.91⁰ to 66.97⁰ in longitude using hyperspectral remote sensing data and techniques. Image acquired during February, 2012 through Hyperion sensor have been used for this research. Image pre processing includes geometric and radiometric correction followed by Minimum Noise Fraction (MNF) and Pixel Purity Index (PPI). The output of MNF and PPI has been analyzed by visualizing it in n-dimensions for end member extraction. Well distributed clusters on the n-dimensional scatter plot have been selected with the region of interest (ROI) tool as end members. These end members have been used as an input for classification techniques applied to identify and map mangroves species including Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF) and Spectral Information Diversion (SID). Only two types of mangroves namely Avicennia Marina (White Mangroves) and Avicennia germinans (Black Mangroves) have been observed throughout the study area.

Keywords: Mangrove, Hyperspectral, SAM, SFF, SID.

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Authors: Isao Taguchi, Yasuo Sugai

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This paper proposes an efficient learning method for the layered neural networks based on the selection of training data and input characteristics of an output layer unit. Comparing to recent neural networks; pulse neural networks, quantum neuro computation, etc, the multilayer network is widely used due to its simple structure. When learning objects are complicated, the problems, such as unsuccessful learning or a significant time required in learning, remain unsolved. Focusing on the input data during the learning stage, we undertook an experiment to identify the data that makes large errors and interferes with the learning process. Our method devides the learning process into several stages. In general, input characteristics to an output layer unit show oscillation during learning process for complicated problems. The multi-stage learning method proposes by the authors for the function approximation problems of classifying learning data in a phased manner, focusing on their learnabilities prior to learning in the multi layered neural network, and demonstrates validity of the multi-stage learning method. Specifically, this paper verifies by computer experiments that both of learning accuracy and learning time are improved of the BP method as a learning rule of the multi-stage learning method. In learning, oscillatory phenomena of a learning curve serve an important role in learning performance. The authors also discuss the occurrence mechanisms of oscillatory phenomena in learning. Furthermore, the authors discuss the reasons that errors of some data remain large value even after learning, observing behaviors during learning.

Keywords: data selection, function approximation problem, multistage leaning, neural network, voluntary oscillation.

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Authors: Edith Esparza-Young, Ángel M. Matos, Yaritza Gonzalez, Kirthana Sugunathevan

Abstract:

Introduction: This research describes the existing medical school program which supports a multicultural setting and bilingualism. The rise of Spanish speakers in the United States has led to the recruitment of bilingual medical students who can serve the evolving demographics. This paper includes anecdotal evidence, narratives and the latest research on the outcomes of supporting a multilingual academic experience in medical school and beyond. People in the United States will continue to need health care from physicians who have experience with multicultural competence. Physicians who are bilingual and possess effective communication skills will be in high demand. Methodologies: This research is descriptive. Through this descriptive research, the researcher will describe the qualities and characteristics of the existing medical school programs, curriculum, and student services. Additionally, the researcher will shed light on the existing curriculum in the medical school and also describe specific programs which help to serve as safety nets to support diverse populations. The method included observations of the existing program and the implementation of the medical school program, specifically the Accelerated Review Program, the Language Education and Professional Communication Program, student organizations and the Global Health Institute. Concluding Statement: This research identified and described characteristics of the medical school’s program. The research explained and described the current and present phenomenon of this medical program, which has focused on increasing the graduation of bilingual and minority physicians. The findings are based on observations of the curriculum, programs and student organizations which evolves and remains innovative to stay current with student enrollment.

Keywords: Bilingual, English, medicine, doctor.

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Authors: G. Blázquez, A. Gálvez-Pérez, M. Calero, I. Iáñez-Rodríguez, M. A. Martín-Lara, A. Pérez

Abstract:

The production of olive oil is considered as one of the most important agri-food industries. However, some of the by-products generated in the process are potential pollutants and cause environmental problems. Consequently, the management of these by-products is currently considered as a challenge for the olive oil industry. In this context, several technologies have been developed and tested. In this sense, the autohydrolysis of these by-products could be considered as a promising technique. Therefore, this study focused on autohydrolysis treatments of a solid residue from the olive oil industry denominated olive cake. This one comes from the olive pomace extraction with hexane. Firstly, a water washing was carried out to eliminate the water soluble compounds. Then, an experimental design was developed for the autohydrolysis experiments carried out in the hydrothermal pressure reactor. The studied variables were temperature (30, 60 and 90 ºC) and time (30, 60, 90 min). On the other hand, aliquots of liquid obtained fractions were analysed by HPLC to determine the fructose and sucrose contents present in the liquid fraction. Finally, the obtained results of sugars contents and the yields of the different experiments were fitted to a neuro-fuzzy and to a polynomial model.

Keywords: ANFIS, olive cake, polyols, saccharides.

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Authors: Daniel Joseph Whittaker

Abstract:

This paper examines the broad array of historic sites from which Chicago common brick has emerged, and the methods this brick has been utilized within and around a new hybrid structure recently completed-and periodically opened to the public, as a private art, architecture, design, and social activism gallery space. Various technical aspects regarding the structural and aesthetic reuse methods of salvaged brick within the interior and exterior of this new Tadao Ando-designed building in Lincoln Park, Chicago, are explored. This paper expands specifically upon the multiple possible origins of Chicago common brick, as well as the extant brick currently composing the surrounding alley which is integral to demarcating the southern site boundary of the old apartment building now gallery. Themes encompassing Chicago’s archeological and architectural history, local resource extraction, and labor practices permeate this paper’s investigation into urban, social and architectural history and building construction technology advancements through time.

Keywords: Masonry construction, history brickmaking, private museums, Chicago Illinois, Tadao Ando.

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