Search results for: retail foreign market selection.

Authors: Mingook Lee, Sungjoo Lee

Abstract:

As technology-based service industries grow drastically worldwide; companies are recognizing the importance of market preoccupancy and have made an effort to capture a large market to gain the upper hand. To this end, a focus on patents can be used to determine the properties of a technology, as well as to capture advantages in technical skills, in comparison with the firm’s competitors. However, technology-based services largely depend not only on their technological value but also their economic value, due to the recognized worth that is passed to a plurality of users. Thus, it is important to determine whether there are any competitors in the target areas and what services they provide in any field. Despite this importance, little effort has been made to systematically benchmark competitors in order to identify business opportunities. Thus, this study aims to not only identify each position of technology-centered service companies in complex market dynamics, but also to discover new business opportunities. For this, we try to consider both technology and market environments simultaneously by utilizing patent data as a representative proxy for technology and trademark dates as an index for a firm’s target goods and services. Theoretically, this is one of the earliest attempts to combine patent data and trademark data to analyze corporate strategies. In practice, the research results are expected to be used as a decision criterion to diagnose the economic value that companies can obtain by entering the market, as well as the technological value to be passed onto their customers. Thus, the proposed approach can be useful to support effective technology and business strategies in a firm.

Keywords: Business opportunity, patent, Portfolio analysis, trademark.

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Authors: Syeda Maria Ali Shah, Asif Mansoor, Talat Sharafat Rehmani, Safia Mirza

Abstract:

The stock market of any country acts as a predictor of the economy. The spread of the COVID-19 pandemic has severely impacted the global financial markets. Besides, it has also critically affected the economy of Pakistan. In this study, we consider the role of the Karachi Stock Exchange (KSE) with regard to the Pakistan Stock Exchange and quantify the impact on macroeconomic variables in presence of COVID-19. The suitable macroeconomic variables are used to quantify the impact of COVID-19 by developing the stochastic model. The sufficiency of the computed model is attained by means of available techniques in the literature. The estimated equations are used to forecast the impact of pandemic on macroeconomic variables. The constructed model can help the policymakers take counteractive measures for restricting the influence of viruses on the Karachi Stock Market.

Keywords: COVID-19, Karachi Stock Market, macroeconomic variables, stochastic model, forecasting.

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Authors: Kristýna Pokorná, Petr Teplý

Abstract:

This paper focuses on sovereign credit risk meaning a hot topic related to the current Eurozone crisis. In the light of the recent financial crisis, market perception of the creditworthiness of individual sovereigns has changed significantly. Before the outbreak of the financial crisis, market participants did not differentiate between credit risk born by individual states despite different levels of public indebtedness. In the proceeding of the financial crisis, the market participants became aware of the worsening fiscal situation in the European countries and started to discriminate among government issuers. Concerns about the increasing sovereign risk were reflected in surging sovereign risk premium. The main of this paper is to shed light on the characteristics of the sovereign risk with the special attention paid to the mutual relation between credit spread and the CDS premium as the main measures of the sovereign risk premium.

Keywords: cointegration, credit default swap, credit risk, credit spread, sovereign risk

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Authors: Rajendra M. Sonar

Abstract:

The recent trend has been using hybrid approach rather than using a single intelligent technique to solve the problems. In this paper, we describe and discuss a framework to develop enterprise solutions that are backed by intelligent techniques. The framework not only uses intelligent techniques themselves but it is a complete environment that includes various interfaces and components to develop the intelligent solutions. The framework is completely Web-based and uses XML extensively. It can work like shared plat-form to be accessed by multiple developers, users and decision makers.

Keywords: Intelligent System Development Framework, WebbasedIntelligent Systems, Retail Banking.

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Authors: Phruksaphanrat B.

Abstract:

This research presents a fuzzy multi-objective model for a machine selection problem in a flexible manufacturing system of a tire company. Two main objectives are minimization of an average machine error and minimization of the total setup time. Conventionally, the working team uses trial and error in selecting a pressing machine for each task due to the complexity and constraints of the problem. So, both objectives may not satisfy. Moreover, trial and error takes a lot of time to get the final decision. Therefore, in this research preemptive fuzzy goal programming model is developed for solving this multi-objective problem. The proposed model can obtain the appropriate results that the Decision Making (DM) is satisfied for both objectives. Besides, alternative choice can be easily generated by varying the satisfaction level. Additionally, decision time can be reduced by using the model, which includes all constraints of the system to generate the solutions. A numerical example is also illustrated to show the effectiveness of the proposed model.

Keywords: Machine Selection, Preemptive Fuzzy Goal Programming, Mixed Integer Programming, Application of Tire Industry.

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Authors: C. Ardil

Abstract:

Intuitionistic fuzzy sets (IFS), Pythagorean fuzzy sets (PyFS), Picture fuzzy sets (PFS), q-rung orthopair fuzzy sets (q-ROF), Spherical fuzzy sets (SFS), T-spherical FS, and Neutrosophic sets (NS) are reviewed as multidimensional extensions of fuzzy sets in order to more explicitly and informatively describe the opinions of decision-making experts under uncertainty. To handle operations with standard fuzzy sets (SFS), the necessary operators; weighted arithmetic mean (WAM), weighted geometric mean (WGM), and Minkowski distance function are defined. The algorithm of the proposed proximity measure method (PMM) is provided with a multiple criteria group decision making method (MCDM) for use in a standard fuzzy set environment. To demonstrate the feasibility of the proposed method, the problem of selecting the best drone for an Air Force procurement request is used. The proximity measure method (PMM) based multidimensional standard fuzzy sets (SFS) is introduced to demonstrate its use with an issue involving unmanned combat aircraft selection.

Keywords: standard fuzzy sets (SFS), unmanned combat aircraft selection, multiple criteria decision making (MCDM), proximity measure method (PMM).

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Authors: Mihai Gheorghe

Abstract:

Freelancing in IT has seen an increased popularity during the last years mainly because of the fast Internet adoption in the countries with emerging economies, correlated with the continuous seek for reduced development costs as well with the rise of online platforms which address planning, coordination and various development tasks. This paper conducts an overview of the most relevant Freelance Marketplaces available and studies the market structure, distribution of the workforce and trends in IT freelancing.

Keywords: Freelancing in IT, Freelance Marketplaces, Freelance Market Structure, Globalization, Online Staffing, Trends in Freelancing.

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Authors: Mahdieh Khalilinezhad, Silvana Dellepiane, Gianni Vernazza

Abstract:

The aim of this work is to build a model based on tissue characterization that is able to discriminate pathological and non-pathological regions from three-phasic CT images. With our research and based on a feature selection in different phases, we are trying to design a neural network system with an optimal neuron number in a hidden layer. Our approach consists of three steps: feature selection, feature reduction, and classification. For each region of interest (ROI), 6 distinct sets of texture features are extracted such as: first order histogram parameters, absolute gradient, run-length matrix, co-occurrence matrix, autoregressive model, and wavelet, for a total of 270 texture features. When analyzing more phases, we show that the injection of liquid cause changes to the high relevant features in each region. Our results demonstrate that for detecting HCC tumor phase 3 is the best one in most of the features that we apply to the classification algorithm. The percentage of detection between pathology and healthy classes, according to our method, relates to first order histogram parameters with accuracy of 85% in phase 1, 95% in phase 2, and 95% in phase 3.

Keywords: Feature selection, Multi-phasic liver images, Neural network, Texture analysis.

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Authors: Nebi Gedik

Abstract:

Electroencephalography (EEG) investigations of the brain computer interfaces are based on the electrical signals resulting from neural activities in the brain. In this paper, it is offered a method for classifying motor imagery EEG signals. The suggested method classifies EEG signals into two classes using the wave atom transform, and the transform coefficients are assessed, creating the feature set. Classification is done with SVM and k-NN algorithms with and without feature selection. For feature selection t-test approaches are utilized. A test of the approach is performed on the BCI competition III dataset IIIa.

Keywords: motor imagery, EEG, wave atom transform, SVM, k-NN, t-test

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Authors: Walid S. Abd El-hamid, Sherif S. El-etriby, Mohiy M. Hadhoud

Abstract:

This paper presents a new methodology to select test cases from regression test suites. The selection strategy is based on analyzing the dynamic behavior of the applications that written in any programming language. Methods based on dynamic analysis are more safe and efficient. We design a technique that combine the code based technique and model based technique, to allow comparing the object oriented of an application that written in any programming language. We have developed a prototype tool that detect changes and select test cases from test suite.

Keywords: Regression testing, Model based testing, Dynamicbehavior.

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Authors: Raja Kannusamy

Abstract:

The research focuses on the applicable technologies in the automobile industry and their effects on the productivity and annual revenue of the industry. A study has been conducted on six major automobile manufacturing industries represented in this research as M1, M2, M3, M4, M5 and M6. The results indicate that M1, which is a pioneer in technological applications, remains the market leader, followed by M5 and M2 taking the second and third positions, respectively. M3, M6 and M4 are the followers and are placed next in positions. It has also been observed that M1 and M2 have entered into an agreement to share the basic structural technologies and they maintain long-term and trusted relationships with their suppliers through the Keiretsu system. With technological giants such as Apple, Microsoft, Uber and Google entering the automobile industry in recent years, an upward trend is expected in the futuristic market with self-driving cars to dominate the automobile sector. To keep up with the market trend, it is essential for automobile manufacturers to understand the importance of developing technological capabilities and skills to be competitive in the marketplace.

Keywords: automobile manufacturing industries, competitiveness, performance improvement, technological applications

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Authors: Alexandru George Floares

Abstract:

Chronic hepatitis B can evolve to cirrhosis and liver cancer. Interferon is the only effective treatment, for carefully selected patients, but it is very expensive. Some of the selection criteria are based on liver biopsy, an invasive, costly and painful medical procedure. Therefore, developing efficient non-invasive selection systems, could be in the patients benefit and also save money. We investigated the possibility to create intelligent systems to assist the Interferon therapeutical decision, mainly by predicting with acceptable accuracy the results of the biopsy. We used a knowledge discovery in integrated medical data - imaging, clinical, and laboratory data. The resulted intelligent systems, tested on 500 patients with chronic hepatitis B, based on C5.0 decision trees and boosting, predict with 100% accuracy the results of the liver biopsy. Also, by integrating the other patients selection criteria, they offer a non-invasive support for the correct Interferon therapeutic decision. To our best knowledge, these decision systems outperformed all similar systems published in the literature, and offer a realistic opportunity to replace liver biopsy in this medical context.

Keywords: Interferon, chronic hepatitis B, intelligent virtualbiopsy.

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Authors: Agneta Sundström, Zahra Ahmadi, Akmal Hyder

Abstract:

The purpose of this paper is to develop a typology based on market orientation (MO) and innovation orientation (IO), and to illustrate to what extent housing companies in Sweden fit within this framework. A qualitative study on 11 public housing companies in the central part of Sweden has been conducted by the help of open and semi-structured questions for data collection. Four public housing company types- i.e. reactive prospector, proactive prospector, reactive defender and proactive defender have been identified by the combination of MO-IO dimensions. Future research can include other dimensions like entrepreneurship and network to observe how it particularly affects MO. An empirical study can compare public and private housing companies on the basis of MO and IO dimensions. One major contribution of the paper is the proposition of typology which can be used to describe public housing companies and deciding their future course of actions.

Keywords: Customer-led, economy, innovativeness, market orientation.

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Authors: Piotr Prewysz-Kwinto

Abstract:

The market of financial instruments with high risk is developing very dynamically in recent years and attracts more and more interest of investors. It consists essentially of two groups of instruments, i.e. derivatives and exchange traded product (ETP), and each year new types are introduced and offered to investors. The aim of this paper is to present the principles concerning financial instruments with high investment risk available on the Warsaw Stock Exchange (WSE), because they have quite complex constructions, and to evaluate the development of this market. In order to achieve this aim, statistical data from 2014-2016 was analyzed. The results confirm that the financial instruments with high investment risk available on the WSE constitute a diversified and the most numerous group of financial instruments and attract the most interest of investors. Responsible investing requires, however, a good knowledge of how they work and how they can generate profit to not expose oneself to unexpected losses.

Keywords: Derivatives, exchange traded products, financial instruments, financial market, risk, stock exchange.

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Authors: Kayvan Ahmadi, Hossein Shamsi

Abstract:

This paper proposes a low-voltage and low-power fully integrated digitally tuned continuous-time channel selection filter for WiMAX applications. A 5th-order elliptic low-pass filter is realized in a Gm-C topology. The bandwidth of the fully differential filter is reconfigurable from 2.5MHz to 20MHz (8x) for different requirements in WiMAX applications. The filter is simulated in a standard 90nm CMOS process. Simulation results show the THD (@Vout =100mVpp) is less than -66dB. The in-band ripple of the filter is about 0.15dB. The filter consumes 1.5mW from a supply voltage of 0.9V.

Keywords: Common-mode feedback, continuous-time, fully differential transconductor, Gm-C topology, low-voltage

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Authors: Usman Sarwar, Gopinath Sinniah Rao, Zeldi Suryady, Reza Khoshdelniat

Abstract:

The low power wireless sensor devices which usually uses the low power wireless private area network (IEEE 802.15.4) standard are being widely deployed for various purposes and in different scenarios. IPv6 low power wireless private area network (6LoWPAN) was adopted as part of the IETF standard for the wireless sensor devices so that it will become an open standard compares to other dominated proprietary standards available in the market. 6LoWPAN also allows the integration and communication of sensor nodes with the Internet more viable. This paper presents a comparative study on different available IPv6 platforms for wireless sensor networks including open and close sources. It also discusses about the platforms used by these stacks. Finally it evaluates and provides appropriate suggestions which can be use for selection of required IPv6 stack for low power devices.

Keywords: 6LoWPAN Stacks, 6LoWPAN Platforms, m-Stack, NanoStack, uIPv6, PhyNet 6LoWPAN, Jennic 6LoWPAN.

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Authors: Abd Rasid Abd Rahman

Abstract:

For the past thirty years the Malaysian economy has been said to contribute well to the progress of the nations. However, the intensification of global economy activity and the extensive use of Information Communication Technologies (ICTs) in recent years are challenging government-s effort to further develop Malaysian society. The competition posed by the low wage economies such as China and Vietnam have made the government realise the importance of engaging in high-skill and high technology industries. It is hoped this will be the basis of attracting more foreign direct investment (FDI) in order to help the country to compete in globalised world. Using Vision 2020 as it targeted vision, the government has decided to engage in the use of ICTs and introduce many policies pertaining to it. Mainly based on the secondary analysis approach, the findings show that policy pertaining to ICTs in Malaysia contributes to economic growth, but the consequences of this have resulted in greater division within society. Although some of the divisions such as gender and ethnicity are narrowing down, the gap in important areas such as regions and class differences is becoming wider. The widespread use of ICTs might contribute to the further establishment of democracy in Malaysia, but the increasing number of foreign entities such as FDI and foreign workers, cultural hybridisation and to some extent cultural domination are contributing to neocolonialism in Malaysia. This has obvious consequences for the government-s effort to create a Malaysian national identity. An important finding of this work is that there are contradictions within ICT policy between the effort to develop the economy and society.

Keywords: Globalisation, ICTs, ICT Policy, Malaysia, National Identity, Vision 2020

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Authors: Lidia Martínez Murillo

Abstract:

Since the 1990s the American furniture industry faces a transition period. Manufacturers, one of its most important actors made its entrance into the retail industry. This shift has had deep consequences not only for the American furniture industry as a whole, but also for other international furniture industries, especially the Chinese. The present work aims to analyze this actor based on the distinction provided by the Global Commodity Chain Theory. It stresses its characteristics, structure, operational way and importance for both the U.S. and the Chinese furniture industries.

Keywords: M&RC, blended strategy, U.S. furniture industry, Chinese furniture industry.

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Authors: S. C. Căescu, I. Dumitru

Abstract:

The main objective of the paper has been represented by the identification of the changes that occurred in the competitive environment and their impact on the strategic marketing management of companies in B2B market. At Romania-s level there has not yet been done a similar research that studies change management in crises on business to business field. In order to answer to the paper-s objectives, a qualitative marketing research (in-depth structured interview) was conducted, within the top management of 27 companies in Romanian business to business field. The main results of the research highlight the necessity of a management of change, as a result of the crises, as follows: changes in the corporate objectives (from development objectives to maintaining objectives), changes market segmentation and in competitive advantages, changes at the level of market strategies and of the marketing mix.

Keywords: change management, competitive environment, marketing management strategies, strategic marketing.

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Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

Abstract:

Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.

Keywords: Classification algorithms; data mining; tourism; knowledge discovery.

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Authors: Akrem Sellami, Imed Riadh Farah

Abstract:

Hyperspectral imagery (HSI) typically provides a wealth of information captured in a wide range of the electromagnetic spectrum for each pixel in the image. Hence, a pixel in HSI is a high-dimensional vector of intensities with a large spectral range and a high spectral resolution. Therefore, the semantic interpretation is a challenging task of HSI analysis. We focused in this paper on object classification as HSI semantic interpretation. However, HSI classification still faces some issues, among which are the following: The spatial variability of spectral signatures, the high number of spectral bands, and the high cost of true sample labeling. Therefore, the high number of spectral bands and the low number of training samples pose the problem of the curse of dimensionality. In order to resolve this problem, we propose to introduce the process of dimensionality reduction trying to improve the classification of HSI. The presented approach is a semi-supervised band selection method based on spatial hypergraph embedding model to represent higher order relationships with different weights of the spatial neighbors corresponding to the centroid of pixel. This semi-supervised band selection has been developed to select useful bands for object classification. The presented approach is evaluated on AVIRIS and ROSIS HSIs and compared to other dimensionality reduction methods. The experimental results demonstrate the efficacy of our approach compared to many existing dimensionality reduction methods for HSI classification.

Keywords: Hyperspectral image, spatial hypergraph, dimensionality reduction, semantic interpretation, band selection, feature extraction.

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Authors: Mafarja Majdi, Salwani Abdullah, Najmeh S. Jaddi

Abstract:

One of the global combinatorial optimization problems in machine learning is feature selection. It concerned with removing the irrelevant, noisy, and redundant data, along with keeping the original meaning of the original data. Attribute reduction in rough set theory is an important feature selection method. Since attribute reduction is an NP-hard problem, it is necessary to investigate fast and effective approximate algorithms. In this paper, we proposed two feature selection mechanisms based on memetic algorithms (MAs) which combine the genetic algorithm with a fuzzy record to record travel algorithm and a fuzzy controlled great deluge algorithm, to identify a good balance between local search and genetic search. In order to verify the proposed approaches, numerical experiments are carried out on thirteen datasets. The results show that the MAs approaches are efficient in solving attribute reduction problems when compared with other meta-heuristic approaches.

Keywords: Rough Set Theory, Attribute Reduction, Fuzzy Logic, Memetic Algorithms, Record to Record Algorithm, Great Deluge Algorithm.

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Authors: Lidia Martínez Murillo

Abstract:

Since the 1990s the American furniture industry faces a transition period. Manufacturers, one of its most important actors made its entrance into the retail industry. This shift has had deep consequences not only for the American furniture industry as a whole, but also for other international furniture industries, especially the Chinese. The present work aims to analyze this actor based on the distinction provided by the Global Commodity Chain Theory. It stresses its characteristics, structure, operational way and importance for both the U.S. and the Chinese furniture industries.

Keywords: M&RC, blended strategy, U.S. furniture industry, Chinese furniture industry.

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Authors: Chin Wen Cheong

Abstract:

This study employs a bivariate asymmetric GARCH model to reveal the hidden dynamics price changes and volatility among the emerging markets of Thailand and Malaysian after the Asian financial crisis from January 2001 to December 2008. Our results indicated that the equity markets are sharing the common information (shock) that transmitted among each others. These empirical findings are used to demonstrate the importance of shock and volatility dynamic transmissions in the cross-market hedging and market risk.

Keywords: multivariate ARCH, structural change, value at risk.

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Authors: S. Turkan

Abstract:

In this paper, the Turkey’s Top 500 Industrial Enterprises data in 2014 were analyzed by data envelopment analysis. Data envelopment analysis is used to detect efficient decision-making units such as universities, hospitals, schools etc. by using inputs and outputs. The decision-making units in this study are enterprises. To detect efficient enterprises, some financial ratios are determined as inputs and outputs. For this reason, financial indicators related to productivity of enterprises are considered. The efficient foreign weighted owned capital enterprises are detected via super efficiency model. According to the results, it is said that Mercedes-Benz is the most efficient foreign weighted owned capital enterprise in Turkey.

Keywords: Data envelopment analysis, super efficiency, financial ratios, BCC model.

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Authors: Babu Sena Paul, Ratnajit Bhattacharjee

Abstract:

Relay based communication has gained considerable importance in the recent years. In this paper we find the end-toend statistics of a two hop non-regenerative relay branch, each hop being Nakagami-m faded. Closed form expressions for the probability density functions of the signal envelope at the output of a selection combiner and a maximal ratio combiner at the destination node are also derived and analytical formulations are verified through computer simulation. These density functions are useful in evaluating the system performance in terms of bit error rate and outage probability.

Keywords: co-operative diversity, diversity combining, maximal ratio combining, selection combining.

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Authors: Samraj Andrews, Ramaswamy Palaniappan, Nidal Kamel

Abstract:

In single trial analysis, when using Principal Component Analysis (PCA) to extract Visual Evoked Potential (VEP) signals, the selection of principal components (PCs) is an important issue. We propose a new method here that selects only the appropriate PCs. We denote the method as selective eigen-rate (SER). In the method, the VEP is reconstructed based on the rate of the eigen-values of the PCs. When this technique is applied on emulated VEP signals added with background electroencephalogram (EEG), with a focus on extracting the evoked P3 parameter, it is found to be feasible. The improvement in signal to noise ratio (SNR) is superior to two other existing methods of PC selection: Kaiser (KSR) and Residual Power (RP). Though another PC selection method, Spectral Power Ratio (SPR) gives a comparable SNR with high noise factors (i.e. EEGs), SER give more impressive results in such cases. Next, we applied SER method to real VEP signals to analyse the P3 responses for matched and non-matched stimuli. The P3 parameters extracted through our proposed SER method showed higher P3 response for matched stimulus, which confirms to the existing neuroscience knowledge. Single trial PCA using KSR and RP methods failed to indicate any difference for the stimuli.

Keywords: Electroencephalogram, P3, Single trial VEP.

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Authors: Masahiro Ohmura, Koh Kakusho, Takeshi Okadome

Abstract:

This paper presents a regression model with autocorrelated errors in which the inputs are social moods obtained by analyzing the adjectives in Twitter posts using a document topic model, where document topics are extracted using LDA. The regression model predicts Dow Jones Industrial Average (DJIA) more precisely than autoregressive moving-average models.

Keywords: Regression model, social mood, stock market prediction, Twitter.

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Authors: Adriano Z. Zambom, Preethi Ravikumar

Abstract:

One of the biggest challenges in nonparametric regression is the curse of dimensionality. Additive models are known to overcome this problem by estimating only the individual additive effects of each covariate. However, if the model is misspecified, the accuracy of the estimator compared to the fully nonparametric one is unknown. In this work the efficiency of completely nonparametric regression estimators such as the Loess is compared to the estimators that assume additivity in several situations, including additive and non-additive regression scenarios. The comparison is done by computing the oracle mean square error of the estimators with regards to the true nonparametric regression function. Then, a backward elimination selection procedure based on the Akaike Information Criteria is proposed, which is computed from either the additive or the nonparametric model. Simulations show that if the additive model is misspecified, the percentage of time it fails to select important variables can be higher than that of the fully nonparametric approach. A dimension reduction step is included when nonparametric estimator cannot be computed due to the curse of dimensionality. Finally, the Boston housing dataset is analyzed using the proposed backward elimination procedure and the selected variables are identified.

Keywords: Additive models, local polynomial regression, residuals, mean square error, variable selection.

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Authors: P. Soda, G.Iannello

Abstract:

In this paper we address the issue of classifying the fluorescent intensity of a sample in Indirect Immuno-Fluorescence (IIF). Since IIF is a subjective, semi-quantitative test in its very nature, we discuss a strategy to reliably label the image data set by using the diagnoses performed by different physicians. Then, we discuss image pre-processing, feature extraction and selection. Finally, we propose two ANN-based classifiers that can separate intrinsically dubious samples and whose error tolerance can be flexibly set. Measured performance shows error rates less than 1%, which candidates the method to be used in daily medical practice either to perform pre-selection of cases to be examined, or to act as a second reader.

Keywords: Artificial neural networks, computer aided diagnosis, image classification, indirect immuno-fluorescence, pattern recognition.

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