Hybrid Model

Online auction, shopping, electronic billing etc. all such types of application involves problems of fraudulent transactions. Online fraud occurrence and its detection is one of the challenging fields for web development and online phantom transaction. As no-secure specification of online frauds is in research database, so the techniques to evaluate and stop them are also in study. We are providing an approach with Hidden Markov Model (HMM) and mobile implicit authentication to find whether the user interacting online is a fraud or not. We propose a model based on these approaches to counter the occurred fraud and prevent the loss of the customer. Our technique is more parameterized than traditional approaches and so,chances of detecting legitimate user as a fraud will reduce.

Forecasting stock exchange rates is an important financial problem that is receiving increasing attention. During the last few years, a number of neural network models and hybrid models have been proposed for obtaining accurate prediction results, in an attempt to outperform the traditional linear and nonlinear approaches. This paper evaluates the effectiveness of neural network models; recurrent neural network (RNN), dynamic artificial neural network (DAN2) and the hybrid neural networks which use generalized autoregressive conditional heteroscedasticity (GARCH) and exponential generalized autoregressive conditional heteroscedasticity (EGARCH) to extract new input variables. The comparison for each model is done in two view points: MSE and MAD using real exchange daily rate values of Istanbul Stock Exchange (ISE) index XU10).

It is generally agreed that faces are not recognized only by utilizing some holistic search among all learned faces, but also through a feature analysis that aimed to specify more important features of each specific face. This paper addresses a novel decision strategy that efficiently uses both holistic and facial component (eye, nose and mouth) feature analysis to recognize faces. The proposed algorithm first performs a holistic search using the whole image features and selects probable candidates for further processing. Then the facial features of these probable candidates are compared and classified with the stored patterns in the train set. Finally the classification results are fused with a weighted majority voting to form the final decision. Simulation studies justify the superior performance of the proposed method as compared to that of Eigenface method.

Abstract: MIX is an ESPRIT project aimed at developing strategies and tools for integrating symbolic and neural methods in hybrid systems. The project arose from the observation that current hybrid systems are generally small-scale experimental systems which couple one symbolic and ...

A model-based optimization of an industrial fed- batch sugar crystallisation process is considered in this paper. The objective is to define the optimal profiles of the manipulated process inputs, the feeding rate of liquor/syrup and the steam supply rate, such that the crystal content and the crystal size distribution (CSD) measures at the end of the batch cycle reach the

The present study examined the extent to which scores on the Dispositional Flow Scale-2 (DFS-2) could differentiate individuals who frequently experience flow characteristics in physical activity from those who do not. A total of 993 participants completed the Japanese version of the DFS-2. Latent class factor analysis (LCFA), which combines the strengths of both latent class analysis and factor analysis, was conducted on the DFS-2 responses. Six classes were identified through a series of LCFAs and the patterns of the item-average scores for the nine flow attributes were found to be parallel among these classes. The top two and bottom two classes (19.3% and 13.4% of the whole sample) were considered the groups who experience flow characteristics frequently and seldom, respectively. These results indicated that individuals who often experience flow attributes in physical activity could be differentiated from those who do not based on their DFS-2 scores. Note. The article published i...

We seek to address criticisms of the concept of moral panics by offering a hybrid model of moral panics (MPs) that synthesizes theory and practice of MPs research. A review of the literature on MPs from sociology, media studies and related fields shows a wide variety of usage and lack of conceptual clarity of the term moral panic. Yet there

In this paper we use First–Order Hybrid Petri nets (FOHPN), an hybrid model that combines fluid and discrete event dynamics, to model the concurrent activ-ities of manufacturing systems. In particular we consider an existing mineral water bottling plant and we show how the ...

The main objective of this research paper is to build an appropriate mathematical model that helps in forecasting third party claim amount for different categories of vehicles based on the chosen characteristics of the data. In actuarial research, predicting the insurance claim amount for different vehicle categories is a challenging task, and minimal empirical research studies were done to forecast the claims. In the present study, the annual time series historical data were collected for a period of 34 years. We had built the machine learning predictive models to modeling the claim amount with different categories of vehicles effectively. In this context, we exhibited the feasibility of using a statistical machine learning approach such as Linear regression Model, the Exponential Smoothing Model, autoregressive integrated moving average (ARIMA), artificial neural network (ANN), and hybrid ARIMA-ANN models to predict the various categories of vehicles claim amount. The data were analyzed, compared, and the empirical analysis showed that Artificial Neural Network is a better predictive model among the other time series models based on performance evaluation metrics RMSE and MAPE with lesser variance. Therefore, the machine learning approach for forecasting third party claim amounts will help the Insurance Companies in India to provide a better predictive model, which ensures better claims settlement and management for different categories of vehicles.

A hybrid RANS/LES model is set up by blending a k-\varepsilon model and the Smagorinsky model according to the LNS idea [1], i.e. using the minimum value among the eddy viscosities given by the RANS and the LES closures. The Navier-Stokes equations are discretized by a mixed finite-volume/finite-element formulation applicable to unstrucured grid. A low diffusion MUSCL scheme, designed for LES, is adopted [2]. Both explicit (Runge-Kutta) and implicit (second-order accurate) time advancing can be used. The hybrid model is applied to the simulation of the flows around a square cylinder and around a sphere. Results are compared with those obtained with RANS and LES and with experimental data. REFERENCES: [1] P.Batten.LNS-An approach towards embedded LES. In phAIAA Paper 2002-0427. American Institute of Aeronautics and Astronautics, 2002. [2] S.Camarri,M.V.Salvetti,B.Koobus and A.Dervieux. A low-diffusion MUSCL scheme for LES on unstructured grids. phComputers and Fluids 2003; 33:1101-1129.

Throughout the world, the distinctive organizational forms adopted by social enterprises depend on the existing legal frameworks, on the political economy of welfare provision and on the cultural and historical traditions of non-profit development in each country. Consequently, the social enterprise sector globally encompasses both new typologies of organizations and traditional third sector organizations, re-imagined through the lens of sustainable income-generation.In this thought leadership article (TLA), we consider the landscape of the existing research on social enterprises, early forms of social enterprises in Bangladesh, evolution of the sector in the 1980s and 1990s, and intersections between businesses and social entrepreneurship. We identify key sectoral trends, assess the policy environment and envision a roadmap for future development of the social enterprise space.