Search results for: hybrid methods

Authors: Mehrnoush Shamsfard, Azadeh Nematzadeh, Sarah Motiee

Abstract:

Increasing growth of information volume in the internet causes an increasing need to develop new (semi)automatic methods for retrieval of documents and ranking them according to their relevance to the user query. In this paper, after a brief review on ranking models, a new ontology based approach for ranking HTML documents is proposed and evaluated in various circumstances. Our approach is a combination of conceptual, statistical and linguistic methods. This combination reserves the precision of ranking without loosing the speed. Our approach exploits natural language processing techniques for extracting phrases and stemming words. Then an ontology based conceptual method will be used to annotate documents and expand the query. To expand a query the spread activation algorithm is improved so that the expansion can be done in various aspects. The annotated documents and the expanded query will be processed to compute the relevance degree exploiting statistical methods. The outstanding features of our approach are (1) combining conceptual, statistical and linguistic features of documents, (2) expanding the query with its related concepts before comparing to documents, (3) extracting and using both words and phrases to compute relevance degree, (4) improving the spread activation algorithm to do the expansion based on weighted combination of different conceptual relationships and (5) allowing variable document vector dimensions. A ranking system called ORank is developed to implement and test the proposed model. The test results will be included at the end of the paper.

Keywords: Document ranking, Ontology, Spread activation algorithm, Annotation.

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Authors: Jun Wang, Tingcun Wei

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The digital pulse width modulator (DPWM) is the crucial building block for digitally-controlled DC-DC switching converter, which converts the digital duty ratio signal into its analog counterpart to control the power MOSFET transistors on or off. With the increase of switching frequency of digitally-controlled DC-DC converter, the DPWM with higher time resolution is required. In this paper, a 15-bits DPWM with three-level hybrid structure is presented; the first level is composed of a7-bits counter and a comparator, the second one is a 5-bits delay line, and the third one is a 3-bits digital dither. The presented DPWM is designed and implemented using the PLL megafunction of FPGA (Field Programmable Gate Arrays), and the required frequency of clock signal is 128 times of switching frequency. The simulation results show that, for the switching frequency of 2 MHz, a DPWM which has the time resolution of 15 ps is achieved using a maximum clock frequency of 256MHz. The designed DPWM in this paper is especially useful for high-frequency digitally-controlled DC-DC switching converters.

Keywords: DPWM, PLL megafunction, FPGA, time resolution, digitally-controlled DC-DC switching converter.

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Authors: Amir Ansari, Ehsan Shahrjerdi, Ehsan Amini

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To investigate the behavior of sheet metals during forming tailor welded blanks (TWB) of various thickness made via Co2 Laser welding are under consideration. These blanks are formed used two different forming methods of rubber as well as the conventional punch and die methods. The main research objective is the effects of using a rubber die instead of a solid one the displacement of the weld line and the press force needed for forming. Specimens with thicknesses of 0.5, 0.6, 0.8 and 1mm are subjected to Erichsen two dimensional tests and the resulted force for each case are compared. This is followed by a theoretical and numerical study of press force and weld line displacement. It is concluded that using rubber pad forming (RPF) causes a reduction in weld line displacement and an increase in the press force.

Keywords: Rubber pad forming, Tailor welded blank, Thickness ratio, Weld line displacement.

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Authors: Surya Prakash, Sunil Kumar Sinha

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This paper deals with the application of artificial neural network (ANN) and fuzzy based Adaptive Neuro Fuzzy Inference System(ANFIS) approach to Load Frequency Control (LFC) of multi unequal area hydro-thermal interconnected power system. The proposed ANFIS controller combines the advantages of fuzzy controller as well as quick response and adaptability nature of ANN. Area-1 and area-2 consists of thermal reheat power plant whereas area-3 and area-4 consists of hydro power plant with electric governor. Performance evaluation is carried out by using intelligent controller like ANFIS, ANN and Fuzzy controllers and conventional PI and PID control approaches. To enhance the performance of intelligent and conventional controller sliding surface is included. The performances of the controllers are simulated using MATLAB/SIMULINK package. A comparison of ANFIS, ANN, Fuzzy, PI and PID based approaches shows the superiority of proposed ANFIS over ANN & fuzzy, PI and PID controller for 1% step load variation.

Keywords: Load Frequency Control (LFC), ANFIS, ANN & Fuzzy, PI, PID Controllers, Area Control Error (ACE), Tie-line, MATLAB / SIMULINK.

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Authors: S. Sarumathi, N. Shanthi, P. Ranjetha

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Data mining is the procedure of determining interesting patterns from the huge amount of data. With the intention of accessing the data faster the most supporting processes needed is clustering. Clustering is the process of identifying similarity between data according to the individuality present in the data and grouping associated data objects into clusters. Cluster ensemble is the technique to combine various runs of different clustering algorithms to obtain a general partition of the original dataset, aiming for consolidation of outcomes from a collection of individual clustering outcomes. The performances of clustering ensembles are mainly affecting by two principal factors such as diversity and quality. This paper presents the overview about the different cluster ensemble algorithm along with their methods used in cluster ensemble to improve the diversity and quality in the several cluster ensemble related papers and shows the comparative analysis of different cluster ensemble also summarize various cluster ensemble methods. Henceforth this clear analysis will be very useful for the world of clustering experts and also helps in deciding the most appropriate one to determine the problem in hand.

Keywords: Cluster Ensemble, Consensus Function, CSPA, Diversity, HGPA, MCLA.

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Authors: I. Erins, J. Erina

Abstract:

The aim of the present article is to determine the impact of the external and internal factors of bank performance on the profitability indicators of the CEE countries banks in the period from 2006 to 2012. On the basis of research conducted abroad on bank and macroeconomic profitability indicators, in order to obtain research results, the authors evaluated return on average assets (ROAA) and return on average equity (ROAE) indicators of the CEE countries banks. The authors analyzed profitability indicators of banks using descriptive methods, SPSS data analysis methods, as well as data correlation and linear regression analysis. The authors concluded that most internal and external indicators of bank performance have no direct influence the profitability of the banks in the CEE countries. The only exceptions are credit risk and bank size, which affect one of the measures of bank profitability – return on average equity.

Keywords: Banks, CEE countries, Profitability ROAA, ROAE.

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Authors: M. Zeydan, G. Toğa

Abstract:

This study deals with a multi-criteria optimization problem which has been transformed into a single objective optimization problem using Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Grey Relational Analyses (GRA) approach. Grey-RSM and Grey-ANN are hybrid techniques which can be used for solving multi-criteria optimization problem. There have been two main purposes of this research as follows. 1. To determine optimum and robust fiber dyeing process conditions by using RSM and ANN based on GRA, 2. To obtain the best suitable model by comparing models developed by different methodologies. The design variables for fiber dyeing process in textile are temperature, time, softener, anti-static, material quantity, pH, retarder, and dispergator. The quality characteristics to be evaluated are nominal color consistency of fiber, maximum strength of fiber, minimum color of dyeing solution. GRA-RSM with exact level value, GRA-RSM with interval level value and GRA-ANN models were compared based on GRA output value and MSE (Mean Square Error) performance measurement of outputs with each other. As a result, GRA-ANN with interval value model seems to be suitable reducing the variation of dyeing process for GRA output value of the model.

Keywords: Artificial Neural Network, Grey Relational Analysis, Optimization, Response Surface Methodology

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Authors: C. Iraklis, G. Evmiridis, A. Iraklis

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Renewable energy sources and distributed power generation units already have an important role in electrical power generation. A mixture of different technologies penetrating the electrical grid, adds complexity in the management of distribution networks. High penetration of distributed power generation units creates node over-voltages, huge power losses, unreliable power management, reverse power flow and congestion. This paper presents an optimization algorithm capable of reducing congestion and power losses, both described as a function of weighted sum. Two factors that describe congestion are being proposed. An upgraded selective particle swarm optimization algorithm (SPSO) is used as a solution tool focusing on the technique of network reconfiguration. The upgraded SPSO algorithm is achieved with the addition of a heuristic algorithm specializing in reduction of power losses, with several scenarios being tested. Results show significant improvement in minimization of losses and congestion while achieving very small calculation times.

Keywords: Congestion, distribution networks, loss reduction, particle swarm optimization, smart grid.

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Authors: Hossein Pazhoumand-Dar, Amir Mohsen Toliyat Abolhassani, Ehsan Saeedi

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Speed estimation is one of the important and practical tasks in machine vision, Robotic and Mechatronic. the availability of high quality and inexpensive video cameras, and the increasing need for automated video analysis has generated a great deal of interest in machine vision algorithms. Numerous approaches for speed estimation have been proposed. So classification and survey of the proposed methods can be very useful. The goal of this paper is first to review and verify these methods. Then we will propose a novel algorithm to estimate the speed of moving object by using fuzzy concept. There is a direct relation between motion blur parameters and object speed. In our new approach we will use Radon transform to find direction of blurred image, and Fuzzy sets to estimate motion blur length. The most benefit of this algorithm is its robustness and precision in noisy images. Our method was tested on many images with different range of SNR and is satisfiable.

Keywords: Blur Analysis, Fuzzy sets, Speed estimation.

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Authors: Renáta Iváncsy, István Vajk

Abstract:

The various types of frequent pattern discovery problem, namely, the frequent itemset, sequence and graph mining problems are solved in different ways which are, however, in certain aspects similar. The main approach of discovering such patterns can be classified into two main classes, namely, in the class of the levelwise methods and in that of the database projection-based methods. The level-wise algorithms use in general clever indexing structures for discovering the patterns. In this paper a new approach is proposed for discovering frequent sequences and tree-like patterns efficiently that is based on the level-wise issue. Because the level-wise algorithms spend a lot of time for the subpattern testing problem, the new approach introduces the idea of using automaton theory to solve this problem.

Keywords: Frequent pattern discovery, graph mining, pushdownautomaton, sequence mining, state machine, tree mining.

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Authors: Mahdi Asghari

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The cavitation phenomenon is the formation and production of micro-bubbles and eventually the bursting of the micro-bubbles inside the liquid fluid, which results in localized high pressure and temperature, causing physical and chemical fluid changes. This pressure and temperature are predicted to be 2000 atmospheres and 5000 °C, respectively. As a result of small bubbles bursting from this process, temperature and pressure increase momentarily and locally, so that the intensity and magnitude of these temperatures and pressures provide the energy needed to break the molecular bonds of heavy compounds such as fuel oil. In this paper, we study the theory of cavitation and the methods of cavitation production by acoustic and hydrodynamic methods and the necessary mechanical equipment and reactors for industrial application of the hydrodynamic cavitation method to break down the molecular bonds of the fuel oil and convert it into useful and economical products.

Keywords: Cavitation, hydrodynamic cavitation, cavitation reactor, fuel oil.

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Authors: Hee-Jeong Ahn, Kee-Won Kim, Seung-Hoon Kim

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The knowledge of the relationship between characters can help readers to understand the overall story or plot of the literary fiction. In this paper, we present a method for extracting the specific relationship between characters from a Korean literary fiction. Generally, methods for extracting relationships between characters in text are statistical or computational methods based on the sentence distance between characters without considering Korean linguistic features. Furthermore, it is difficult to extract the relationship with direction from text, such as one-sided love, because they consider only the weight of relationship, without considering the direction of the relationship. Therefore, in order to identify specific relationships between characters, we propose a statistical method considering linguistic features, such as syntactic patterns and speech verbs in Korean. The result of our method is represented by a weighted directed graph of the relationship between the characters. Furthermore, we expect that proposed method could be applied to the relationship analysis between characters of other content like movie or TV drama.

Keywords: Data mining, Korean linguistic feature, literary fiction, relationship extraction.

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Authors: Jiri Brozovsky

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Modulus of elasticity is one of the important parameters of construction materials, which considerably influence their deformation properties and which can also be determined by means of non-destructive test methods like ultrasonic pulse method. However, measurement results of ultrasonic pulse methods are influenced by various factors, one of which is the natural frequency of the transducers. The paper states knowledge about influence of natural frequency of the transducers (54; 82 and 150kHz) on ultrasonic pulse velocity and dynamic modulus of elasticity (Young's Dynamic modulus of elasticity). Differences between ultrasonic pulse velocity and dynamic modulus of elasticity were found with the same smallest dimension of test specimen in the direction of sounding and density their value decreases as the natural frequency of transducers grew.

Keywords: Calcium silicate brick, ultrasonic pulse method, ultrasonic pulse velocity, dynamic modulus of elasticity.

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Authors: E. Grochowska-Niedworok, K. Brukało, M. Hadasik, M. Kardas

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Consumption of vegetables by school children and adolescents is essential for their normal growth, development and health, but a significant minority of the world's population consumes the right amount of these products. The aim of the study was to evaluate the preferences and frequency of consumption of vegetables by school children and adolescents. It has been assumed that effectively implemented nutrition education programs should have an impact on increasing the frequency of vegetable consumption among the recipients. The study covered 514 students of five schools in the Opole Voivodeship aged 9 years to 22 years. The research tool was an author's questionnaire, which consisted of closed questions on the frequency of vegetable consumption and the use of 10 ways to treat them. Preferences and frequencies are shown in percentages, while correlations were estimated on the basis of Cramer`s V and gamma coefficients. In each of the examined age groups, the relationship between sex and vegetable consumption (the Cramer`s V coefficient value was 0.06 to 0.38) was determined and the various methods of culinary processing were used (V Craméra was 0.08 to 0.34). For both sexes, the relationship between age and frequency of vegetable consumption was shown (gamma values ranged from ~ 0.00 to 0.39) and different cooking methods (gamma values were 0.01 to 0.22). The most important determinant of nutritional choices is the taste and availability of products. The fact that they have a positive effect on their health is only in third position. As has been shown, obesity prevention programs can not only address nutrition education but also teach about new flavors and increase the availability of healthy foods. In addition, the frequency of vegetable consumption can be a good indicator reflecting the healthy behaviors of children and adolescents.

Keywords: Children and adolescents, frequency, welfare rate, vegetables.

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Authors: M. Sankari, C. Meena

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Enhancing the quality of two dimensional signals is one of the most important factors in the fields of video surveillance and computer vision. Usually in real-life video surveillance, false detection occurs due to the presence of random noise, illumination and shadow artifacts. The detection methods based on background subtraction faces several problems in accurately detecting objects in realistic environments: In this paper, we propose a noise removal algorithm using neighborhood comparison method with thresholding. The illumination variations correction is done in the detected foreground objects by using an amalgamation of techniques like homomorphic decomposition, curvelet transformation and gamma adjustment operator. Shadow is removed using chromaticity estimator with local relation estimator. Results are compared with the existing methods and prove as high robustness in the video surveillance.

Keywords: Chromaticity Estimator, Curvelet Transformation, Denoising, Gamma correction, Homomorphic, Neighborhood Assessment.

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Authors: Martin J.-D. Otis, Thien-Ly Nguyen-Dang, Thierry Laliberte, Denis Ouellet, Denis Laurendeau, Clement Gosselin

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A Cable-Driven Locomotion Interface provides a low inertia haptic interface and is used as a way of enabling the user to walk and interact with virtual surfaces. These surfaces generate Cartesian wrenches which must be optimized for each motorized reel in order to reproduce a haptic sensation in both feet. However, the use of wrench control requires a measure of the cable tensions applied to the moving platform. The latter measure may be inaccurate if it is based on sensors located near the reel. Moreover, friction hysteresis from the reel moving parts needs to be compensated for with an evaluation of low angular velocity of the motor shaft. Also, the pose of the platform is not known precisely due to cable sagging and mechanical deformation. This paper presents a non-ideal motorized reel design with its corresponding control strategy that aims at overcoming the aforementioned issues. A transfert function of the reel based on frequency responses in function of cable tension and cable length is presented with an optimal adaptative PIDF controller. Finally, an hybrid position/tension control is discussed with an analysis of the stability for achieving a complete functionnality of the haptic platform.

Keywords: haptic, reel, transparency, cable, tension, control

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Authors: Thashmee Karunaratne, Henrik Böstrom

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Logic based methods for learning from structured data is limited w.r.t. handling large search spaces, preventing large-sized substructures from being considered by the resulting classifiers. A novel approach to learning from structured data is introduced that employs a structure transformation method, called finger printing, for addressing these limitations. The method, which generates features corresponding to arbitrarily complex substructures, is implemented in a system, called DIFFER. The method is demonstrated to perform comparably to an existing state-of-art method on some benchmark data sets without requiring restrictions on the search space. Furthermore, learning from the union of features generated by finger printing and the previous method outperforms learning from each individual set of features on all benchmark data sets, demonstrating the benefit of developing complementary, rather than competing, methods for structure classification.

Keywords: Machine learning, Structure classification, Propositionalization.

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Authors: Yousef Amer, Mehdi Moayyedian, Zeinab Hajiabolhasani, Lida Moayyedian

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Moulded parts contribute to more than 70% of components in products. However, common defects particularly in plastic injection moulding exist such as: warpage, shrinkage, sink marks, and weld lines. In this paper Taguchi experimental design methods are applied to reduce the warpage defect of thin plate Acrylonitrile Butadiene Styrene (ABS) and are demonstrated in two levels; namely, orthogonal arrays of Taguchi and the Analysis of Variance (ANOVA). Eight trials have been run in which the optimal parameters that can minimize the warpage defect in factorial experiment are obtained. The results obtained from ANOVA approach analysis with respect to those derived from MINITAB illustrate the most significant factors which may cause warpage in injection moulding process. Moreover, ANOVA approach in comparison with other approaches like S/N ratio is more accurate and with the interaction of factors it is possible to achieve higher and the better outcomes.

Keywords: Analysis of variance, ANOVA, plastic injection mould, Taguchi methods, Warpage.

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Authors: A. K. Pradhan, M. K. Mohanty, S. K. Kar

Abstract:

The power system utility has started to think about the green power technology in order to have an eco-friendly environment. The green power technology utilizes renewable energy sources for reduction of GHG emissions. Odisha state (India) is very rich in potential of renewable energy sources especially in solar energy (about 300 solar days), for installation of grid connected photovoltaic system. This paper focuses on the utilization of photovoltaic systems in an Institute building of Bhubaneswar city, Odisha. Different data like solar insolation (kW/m²/day), sunshine duration has been collected from metrological stations for Bhubaneswar city. The required electrical power and cost are calculated for daily load of 1.0 kW. The HOMER (Hybrid Optimization Model of Electric Renewable) software is used to estimate system size and its performance analysis. The simulation result shows that the cost of energy (COE) is $ 0.194/kWh, the Operating cost is $63/yr and the net present cost (NPC) is $3,917. The energy produced from PV array is 1,756kWh/yr and energy purchased from grid is 410kWh/yr. The AC primary load consumption is 1314 kWh/yr and the Grid sales are 746 kWh/yr. One battery is connected in parallel with 12V DC Bus and the usable nominal capacity 2.4 kWh with 9.6 h autonomy capacity.

Keywords: Economic assessment, HOMER, Optimization, Photovoltaic (PV), Renewable energy.

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Authors: A. O. Salami

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The transportation problems are primarily concerned with the optimal way in which products produced at different plants (supply origins) are transported to a number of warehouses or customers (demand destinations). The objective in a transportation problem is to fully satisfy the destination requirements within the operating production capacity constraints at the minimum possible cost. The objective of this study is to determine ways of minimizing transportation cost in order to maximum profit. Data were sourced from the records of the Distribution Department of 7-Up Bottling Company Plc., Ilorin, Kwara State, Nigeria. The data were computed and analyzed using the three methods of solving transportation problem. The result shows that the three methods produced the same total transportation costs amounting to N1, 358, 019, implying that any of the method can be adopted by the company in transporting its final products to the wholesale dealers in order to minimize total production cost. 

Keywords: Allocation problem, Cost Minimization, Distribution system, Resources utilization.

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Authors: Andy Handouyahia, Tony Haddad, Frank Eaton

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Recent quasi-experimental evaluation of the Canadian Active Labour Market Policies (ALMP) by Human Resources and Skills Development Canada (HRSDC) has provided an opportunity to examine alternative methods to estimating the incremental effects of Employment Benefits and Support Measures (EBSMs) on program participants. The focus of this paper is to assess the efficiency and robustness of inverse probability weighting (IPW) relative to kernel matching (KM) in the estimation of program effects. To accomplish this objective, the authors compare pairs of 1,080 estimates, along with their associated standard errors, to assess which type of estimate is generally more efficient and robust. In the interest of practicality, the authorsalso document the computationaltime it took to produce the IPW and KM estimates, respectively.

Keywords: Treatment effect, causal inference, observational studies, Propensity score based matching, Kernel Matching, Inverse Probability Weighting, Estimation methods for incremental effect.

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Authors: A. Hortacsu, A. Tektas

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This paper aims to develop a model that assists the international retailer in selecting the country that maximizes the degree of fit between the retailer-s goals and the country characteristics in his initial internationalization move. A two-stage multi criteria decision model is designed integrating the Analytic Hierarchy Process (AHP) and Goal Programming. Ethical, cultural, geographic and economic proximity are identified as the relevant constructs of the internationalization decision. The constructs are further structured into sub-factors within analytic hierarchy. The model helps the retailer to integrate, rank and weigh a number of hard and soft factors and prioritize the countries accordingly. The model has been implemented on a Turkish luxury goods retailer who was planning to internationalize. Actual entry of the specific retailer in the selected country is a support for the model. Implementation on a single retailer limits the generalizability of the results; however, the emphasis of the paper is on construct identification and model development. The paper enriches the existing literature by proposing a hybrid multi objective decision model which introduces new soft dimensions i.e. perceived distance, ethical proximity, humane orientation to the decision process and facilitates effective decision making.

Keywords: Analytic hierarchy process, culture, ethics, goal programming, retail foreign market selection.

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Authors: Darren Zou

Abstract:

Forecasting the stock market is a very challenging task. Various economic indicators such as GDP, exchange rates, interest rates, and unemployment have a substantial impact on the stock market. Time series models are the traditional methods used to predict stock market changes. In this paper, a machine learning method, Bayesian Additive Regression Tree (BART) is used in predicting stock market indexes based on multiple economic indicators. BART can be used to model heterogeneous treatment effects, and thereby works well when models are misspecified. It also has the capability to handle non-linear main effects and multi-way interactions without much input from financial analysts. In this research, BART is proposed to provide a reliable prediction on day-to-day stock market activities. By comparing the analysis results from BART and with time series method, BART can perform well and has better prediction capability than the traditional methods.

Keywords: Bayesian, Forecast, Stock, BART.

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Authors: Shigeaki Sakurai, Youichi Kitahara, Ryohei Orihara

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Sequential mining methods efficiently discover all frequent sequential patterns included in sequential data. These methods use the support, which is the previous criterion that satisfies the Apriori property, to evaluate the frequency. However, the discovered patterns do not always correspond to the interests of analysts, because the patterns are common and the analysts cannot get new knowledge from the patterns. The paper proposes a new criterion, namely, the sequential interestingness, to discover sequential patterns that are more attractive for the analysts. The paper shows that the criterion satisfies the Apriori property and how the criterion is related to the support. Also, the paper proposes an efficient sequential mining method based on the proposed criterion. Lastly, the paper shows the effectiveness of the proposed method by applying the method to two kinds of sequential data.

Keywords: Sequential mining, Support, Confidence, Apriori property

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Authors: Priyadarshinee Adhikari, Neeta Gargote, Jyothi Digge, B.G. Hogade

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A number of automated shot-change detection methods for indexing a video sequence to facilitate browsing and retrieval have been proposed in recent years. This paper emphasizes on the simulation of video shot boundary detection using one of the methods of the color histogram wherein scaling of the histogram metrics is an added feature. The difference between the histograms of two consecutive frames is evaluated resulting in the metrics. Further scaling of the metrics is performed to avoid ambiguity and to enable the choice of apt threshold for any type of videos which involves minor error due to flashlight, camera motion, etc. Two sample videos are used here with resolution of 352 X 240 pixels using color histogram approach in the uncompressed media. An attempt is made for the retrieval of color video. The simulation is performed for the abrupt change in video which yields 90% recall and precision value.

Keywords: Abrupt change, color histogram, ground-truthing, precision, recall, scaling, threshold.

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Authors: Hui-Min Ting, Tsair-Fwu Lee, Ming-Yuan Cho, Pei-Ju Chao, Chun-Ming Chang, Long-Chang Chen, Fu-Min Fang

Abstract:

To evaluate the ability to predict xerostomia after radiotherapy, we constructed and compared neural network and logistic regression models. In this study, 61 patients who completed a questionnaire about their quality of life (QoL) before and after a full course of radiation therapy were included. Based on this questionnaire, some statistical data about the condition of the patients’ salivary glands were obtained, and these subjects were included as the inputs of the neural network and logistic regression models in order to predict the probability of xerostomia. Seven variables were then selected from the statistical data according to Cramer’s V and point-biserial correlation values and were trained by each model to obtain the respective outputs which were 0.88 and 0.89 for AUC, 9.20 and 7.65 for SSE, and 13.7% and 19.0% for MAPE, respectively. These parameters demonstrate that both neural network and logistic regression methods are effective for predicting conditions of parotid glands.

Keywords: NPC, ANN, logistic regression, xerostomia.

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Authors: M. Poveda, S. Lozecznik, J. Oleszkiewicz, Q. Yuan

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The goal of this experiment is to evaluate the effectiveness of different leachate pre-treatment options in terms of COD and ammonia removal. This research focused on the evaluation of physical-chemical methods for pre-treatment of leachate that would be effective and rapid in order to satisfy the requirements of the sewer discharge by-laws. The four pre-treatment options evaluated were: air stripping, chemical coagulation, electrocoagulation and advanced oxidation with sodium ferrate. Chemical coagulation reported the best COD removal rate at 43%, compared to 18% for both air stripping and electro-coagulation, and 20% for oxidation with sodium ferrate. On the other hand, air stripping was far superior to the other treatment options in terms of ammonia removal with 86%. Oxidation with sodium ferrate reached only 16%, while chemical coagulation and electro-coagulation removed less than 10%. When combined, air stripping and chemical coagulation removed up to 50% COD and 85% ammonia.

Keywords: Leachate pretreatment, air stripping, chemical coagulation, electro-coagulation, oxidation.

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Authors: Samir Al-Zobaidi

Abstract:

This study attempts to understand the effect of different UV irradiation methods on the intercalation of LDPE/MMT nanocomposites, and its molecular behavior at certain isothermal crystallization temperature. Three different methods of UV exposure were employed using single composition of LDPE/MMT nanocomposites. All samples were annealed for 5 hours at a crystallization temperature of 100^oC. The crystallization temperature was chosen to be at large supercooling temperature to ensure quick and complete crystallization. The raw material of LDPE consisted of two stable monoclinic and orthorhombic phases according to XRD results. The thermal behavior of both phases acted differently when UV exposure method was changed. The monoclinic phase was more dependent on the method used compared to the orthorhombic phase. The intercalation of clay, as well as, the non-isothermal crystallization temperature, has also shown a clear dependency on the type of UV exposure. A third phase that is thermally less stable was also observed. Its respond to UV irradiation was greater since it contains low molecular weight entities which make it more vulnerable to any UV exposure.

Keywords: LDPE/MMt nanocomposites, crystallization, UV irradiation, intercalation.

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Authors: Ali Ganoun

Abstract:

This paper proposes evaluation of sound parameterization methods in recognizing some spoken Arabic words, namely digits from zero to nine. Each isolated spoken word is represented by a single template based on a specific recognition feature, and the recognition is based on the Euclidean distance from those templates. The performance analysis of recognition is based on four parameterization features: the Burg Spectrum Analysis, the Walsh Spectrum Analysis, the Thomson Multitaper Spectrum Analysis and the Mel Frequency Cepstral Coefficients (MFCC) features. The main aim of this paper was to compare, analyze, and discuss the outcomes of spoken Arabic digits recognition systems based on the selected recognition features. The results acqired confirm that the use of MFCC features is a very promising method in recognizing Spoken Arabic digits.

Keywords: Speech Recognition, Spectrum Analysis, Burg Spectrum, Walsh Spectrum Analysis, Thomson Multitaper Spectrum, MFCC.

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Authors: Zenaide Carvalho da Silva, Leandro Rodrigues Ferreira, Andrey Ricardo Pimentel

Abstract:

Learning styles (LS) refer to the ways and forms that the student prefers to learn in the teaching and learning process. Each student has their own way of receiving and processing information throughout the learning process. Therefore, knowing their LS is important to better understand their individual learning preferences, and also, understand why the use of some teaching methods and techniques give better results with some students, while others it does not. We believe that knowledge of these styles enables the possibility of making propositions for teaching; thus, reorganizing teaching methods and techniques in order to allow learning that is adapted to the individual needs of the student. Adapting learning would be possible through the creation of online educational resources adapted to the style of the student. In this context, this article presents the structure of a learning object interface adaptation based on the LS. The structure created should enable the creation of the adapted learning object according to the student's LS and contributes to the increase of student’s motivation in the use of a learning object as an educational resource.

Keywords: Adaptation, interface, learning object, learning style.

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