Current Issue : July - September Volume : 2014 Issue Number : 3 Articles : 6 Articles
This paper improves amethodwhich predictswhether evaluation objects such as companies and products are to be attractive in near\nfuture.The attractiveness is evaluated by trend rules. The trend rules represent relationships among evaluation objects, keywords,\nand numerical changes related to the evaluation objects. They are inductively acquired from text sequential data and numerical\nsequential data.The method assigns evaluation objects to the text sequential data by activating a topic dictionary.The dictionary\ndescribes keywords representing the numerical change. It can expand the amount of the training data. It is anticipated that the\nexpansion leads to the acquisition of more valid trend rules. This paper applies the method to a task which predicts attractive stock\nbrands based on both news headlines and stock price sequences. It shows that the method can improve the detection performance\nof evaluation objects through numerical experiments....
In the present paper a problem of optimal control for a single-product dynamical macroeconomic model is considered. In this\nmodel gross domestic product is divided into productive consumption, gross investment, and nonproductive consumption. The\nmodel is described by a fuzzy differential equation (FDE) to take into account imprecision inherent in the dynamics that may be\nnaturally conditioned by influence of various external factors, unforeseen contingencies of future, and so forth. The considered\nproblems are characterized by four criteria and by several important aspects. On one hand, the problem is complicated by the\npresence of fuzzy uncertainty as a result of a natural imprecision inherent in information about dynamics of real-world systems.\nOn the other hand, the number of the criteria is not small and most of them are integral criteria. Due to the above mentioned\naspects, solving the considered problem by using convolution of criteria into one criterion would lead to loss of information and\nalso would be counterintuitive and complex.We applied DEO (differential evolution optimization) method to solve the considered\nproblem....
We showed that solutions by the Haar wavelet-quasilinearization technique for the two problems, namely, (i) temperature\ndistribution equation in lumped system of combined convection-radiation in a slab made of materials with variable thermal\nconductivity and (ii) cooling of a lumped system by combined convection and radiation are strongly reliable and alsomore accurate\nthan the other numerical methods and are in good agreement with exact solution. According to theHaar wavelet-quasilinearization\ntechnique, we convert the nonlinear heat transfer equation to linear discretized equation with the help of quasilinearization\ntechnique and apply the Haar wavelet method at each iteration of quasilinearization technique to get the solution. The main aim of\npresent work is to show the reliability of the Haar wavelet-quasilinearization technique for heat transfer equations....
The aimof the paper is to propose a feature fusion based Audio-Visual Speaker Identification (AVSI) system with varied conditions\nof illumination environments.Among the different fusion strategies, feature level fusion has been used for the proposedAVSI system\nwhereHiddenMarkovModel (HMM) is used for learning and classification. Since the feature set contains richer information about\nthe raw biometric data than any other levels, integration at feature level is expected to provide better authentication results. In this\npaper, both Mel Frequency Cepstral Coefficients (MFCCs) and Linear Prediction Cepstral Coefficients (LPCCs) are combined to\nget the audio feature vectors and Active Shape Model (ASM) based appearance and shape facial features are concatenated to take\nthe visual feature vectors. These combined audio and visual features are used for the feature-fusion. To reduce the dimension of the\naudio and visual feature vectors, Principal Component Analysis (PCA) method is used. The VALID audio-visual database is used\nto measure the performance of the proposed system where four different illumination levels of lighting conditions are considered.\nExperimental results focus on the significance of the proposed audio-visual speaker identification system with various combinations\nof audio and visual features....
This paper presents improved versions of bacterial foraging algorithm (BFA). The chemotaxis feature of bacteria through random\nmotion is an effective strategy for exploring the optimum point in a search area. The selection of small step size value in the bacteria\nmotion leads to high accuracy in the solution but it offers slow convergence. On the contrary, defining a large step size in themotion\nprovides faster convergence but the bacteria will be unable to locate the optimum point hence reducing the fitness accuracy. In order\nto overcome such problems, novel linear and nonlinear mathematical relationships based on the index of iteration, index of bacteria,\nand fitness cost are adopted which can dynamically vary the step size of bacteria movement. The proposed algorithms are tested\nwith several unimodal andmultimodal benchmark functions in comparison with the original BFA.Moreover, the application of the\nproposed algorithms in modelling of a twin rotor system is presented. The results show that the proposed algorithms outperform\nthe predecessor algorithm in all test functions and acquire better model for the twin rotor system....
People often make decisions based on sensitivity rather than rationality. In the field of biological information processing,\nmethods are available for analyzing biological information directly based on electroencephalogram: EEG to determine the\npleasant/unpleasant reactions of users. In this study, we propose a sensitivity filtering technique for discriminating preferences\n(pleasant/unpleasant) for images using a sensitivity image filtering systembased on EEG.Using a set of images retrieved by similarity\nretrieval,we performthe sensitivity-based pleasant/unpleasant classification of images based on the affective features extracted from\nimages with the maximum entropy method: MEM. In the present study, the affective features comprised cross-correlation features\nobtained from EEGs produced when an individual observed an image. However, it is difficult to measure the EEG when a subject\nvisualizes an unknown image.Thus, we propose a solution where a linear regressionmethod based on canonical correlation is used\nto estimate the cross-correlation features from image features. Experiments were conducted to evaluate the validity of sensitivity\nfiltering compared with image similarity retrieval methods based on image features.We found that sensitivity filtering using color\ncorrelograms was suitable for the classification of preferred images,while sensitivity filtering using local binary patterns was suitable\nfor the classification of unpleasant images. Moreover, sensitivity filtering using local binary patterns for unpleasant images had a\n90% success rate.Thus, we conclude that the proposed method is efficient for filtering unpleasant images....
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