In this paper, we study MHD asymmetric steady incompressible viscous flow of an electrically conducting fluid between two large stationary coaxial porous disks of different permeability in the presence of uniform transverse magnetic field. The governing nonlinear momentum equations in cylindrical co- ordinates together with relevant boundary conditions are reduced to nonlinear ordinary differential equation (NODE) using similarity transformations. The resulting NODE is solved by Computer Extended Series Solution (CESS) and Homotopy Analysis Method (HAM). The analytical solutions are explicitly expressed by recurrence relation for determining the universal coefficients. The nearest singularity is obtained at R=4.2981 with help of Domb-Sykes plot which restricts the convergence of the series, using Euler transformation the singularity is mapped to infinity. The obtained solutions are valid for all values of the Reynolds number, magnetic parameter and permeability parameter are presented through graphs and tabular forms to discuss the important features of the flow. The resulting solutions are compared with the earlier literatures which are found to be in good agreement. Further, the region of validity of the series is extended for much larger values of R up to infinity by Pade’ approximants.

Accurate diagnosis of Alzheimer's disease (AD) and its prodromal stage mild cognitive impairment (MCI) is very important for patients and clinicians. There are many useful medical data have been discovered to be remarkable for diagnosis i.e., structural MR imaging (MRI), functional imaging (e.g., FDG-PET and FIB-PET). Multimodal classification model is needed to combine these biomarkers to improve the diagnose performance. Some methods have been proposed such as linear mixed kernel, combined embedding and nonlinear graph fusion. These methods have efficiently employed the multimodal data, but they ignore the influence of noise and outliers. Noise is easily generated in image analysis and measurement. To enhance robustness, mixture distributions were applied in nonlinear regression models. Gaussian mixture model is successfully applied in many domains. In this paper, we generalize nonlinear multimodal classification model based on GMM. The performance on real dataset: 22 AD, 23 MCI and 25 NC (health) is comparable to other methods.

Abstract - Network security situational awareness systems helps in better managing the security concerns of a network, by monitoring for any anomalies in the network connections and recommending remedial actions upon detecting an attack. An Intrusion Detection System helps in identifying the security concerns of a network, by monitoring for any anomalies in the network connections. We have proposed a CRF based IDS system using genetic search feature selection algorithm for network security situational awareness to detect any anomalies in the network. The conditional random fields being discriminative models are capable of directly modeling the conditional probabilities rather than joint probabilities there by achieving better classification accuracy. The genetic search feature selection algorithm is capable of identifying the optimal subset among the features based on the best population of features associated with the target class. The proposed system, when trained and tested on the bench mark NSL-KDD dataset exhibited higher accuracy in identifying an attack and also classifying the attack category.

This study examined the important factors and metrics for achieving goals in small scale digital marketing campaigns and there relative impact on the path of conversion to design a consolidated value to evaluate and optimize conversion. The study uses both survey and real time data analysis. Further analysis include statistical tools like t-test, regression analysis, correlation analysis and factor analysis were used. The findings shows that factors considered relevant by sellers have significant impact on conversion. The different types interactions those impact conversion can be expressed in a single value to estimate conversion. Practical implication of the study is the consolidated value to explain conversion. In low volume analytics a macro figure is important as highly segregated values are extremely small to explain any impact. Conversion Path Analysis from ratio correlation and regression analysis to design conversion equation with input factor flexibility is new concept in developing conversion strategy.

In the stock market, the investor's investment sentiment has a serious impact on other individual investors. In the past, people used the infectious disease transmission model to conduct in-depth research on the spread of various diseases. In this paper, we will use the infectious disease model. Based on and improved, the time delay caused by information delay and the saturation rate due to people's limited social ability are added to the homogenous network, which makes the established model more realistic. First, we establish the goal of emotional communication. The positive sentiment-based SIR propagation model is then proved by a series of mathematical methods to the local stability and global stability of the model. Finally, we use the numerical simulation study to verify the results we obtained, and the results show that: When the number of people stays in equilibrium and the information lagging factors are kept under control, the market will remain stable.

The aim of this paper is to apply Galerkin weighted residual method for solving tenth and twelfth order linear and nonlinear boundary value problems (BVPs). A trial function is assumed which is made to satisfy the boundary conditions given, and used to generate the residual to be minimized. The method is formulated as a rigorous matrix form. To investigate the effectiveness of the method, numerical examples were considered which were compared with both the analytic solutions and the solutions obtained by our method. It is observed that, the proposed method is very accurate, better, efficient and appropriate. All problems are computed using the software MATLAB.

This paper proposes a new class of nonlinear systems called generalized Lorenz-like systems which can be used to describe many usual three-dimensional chaotic systems such as Lorenz system, Lü system, Chen system, Liu system, etc. Then the control and synchronization problems for generalized Lorenz-like system via a single input are studied and two control laws are proposed based on partial feedback linearization with asymptotically stable zero dynamics. Finally, the numerical simulations demonstrate the correctness and effectiveness of the proposed control strategies.