Bishop Heber College

About: Bishop Heber College is a based out in . It is known for research contribution in the topics: Thin film & Band gap. The organization has 548 authors who have published 692 publications receiving 7144 citations.

Evolutionary clustering algorithm using criterion-knowledge-ranking for multi-objective optimization

Abstract: There are variety of methods available to solve multi-objective optimization problems, very few utilizes criterion linkage between data objects in the searching phase, to improve final result. This article proposes an evolutionary clustering algorithm for multi-objective optimization. This paper aims to identify more relevant features based on criterion knowledge from the given data sets and also adopts neighborhood learning to improve the diversity and efficacy of the algorithm. This research is an extension of the previous work named neighborhood learning using k-means genetic algorithm (FS-NLMOGA) which maximizes the compactness of the cluster and accuracy of the solution through constrained feature selection. The proposed objective finds the closest feature subset from the selected features of the data sets that also minimizes the cost while maintain the quality of the solution. The resultant cluster were analyzed and validated using cluster validity indexes. The proposed algorithm is tested with several UCI real-life data sets. The experimental results substantiates that the algorithm is efficient and robust.

A Parameter Uniform Almost First Order Convergent Numerical Method for a Semi-Linear System of Singularly Perturbed Delay Differential Equations

Abstract: In this paper an initial value problem for asemi-linear system of two singularly perturbed first order delay differential equations is considered on the interval(0,2]. The components of the solution of this system exhibit initial layers at 0 and interior layers at 1. A numerical method composed of a classical finite difference scheme on a piecewise uniform Shishkin mesh is suggested. This method is proved to be first order convergent in the maximum norm uniformly in the perturbation parameters.

Emu oil decreases atherogenic plaque formation in cafeteria diet-induced obese rats.

Abstract: BACKGROUND Atherosclerosis-induced coronary heart disease – caused by elevated levels of low-density lipoproteins (LDL) and inflammation – is one of the most prevalent diseases. Monounsaturated fatty acids are reported to prevent atherosclerosis; emu oil is a rich source of monounsaturated fatty acid, and we hypothesize that emu oil supplementation could lower inflammation and prevent atherosclerosis in diet-induced obese (DIO) animals. Male Wistar rats were randomly divided into five groups (n = 6), and fed with normal diet (chow pellet; ND), or with cafeteria diet (CD), or with CD along with emu oil supplementation at three different doses: ED1 (2 mL), ED2 (4 mL) and ED3 (8 mL) kg−1 body weight (BW), respectively. RESULTS After 12 weeks, the animals were sacrificed and serum was analysed for measuring lipid profile, C-reactive proteins, testosterone and luteinizing hormone. Histopathological studies were performed to observe atherogenic changes in thoracic aorta. Restoration of altered lipid and hormonal profiles, and inhibition of atherogenic changes in thoracic aorta, were observed with supplementation of emu oil, confirming its anti-atherosclerotic activity. CONCLUSION The high content of oleic acid in emu oil could have orchestrated – either solely or in combination with linoleic and linolenic acids – causing the upregulation of testosterone biosynthesis and inhibition of atheromatous plaque formation in diet-induced obese animals. © 2015 Society of Chemical Industry

A parameter uniform essentially first order convergent numerical method for a parabolic singularly perturbed differential equation of reaction-diffusion type with initial and Robin boundary conditions

Abstract: In this paper, a class of linear parabolic systems of singularly perturbed second-order differential equations of reaction–diffusion type with initial and Robin boundary conditions is considered. T...

Support vector machine using efficient instant selection for micro array data sets

Abstract: Supervised leaning classifier is usually constructing based on models through learning to achieve high accuracy. Support Vector Machine (SVM) is more useful classification technique in supervised learning model. In this paper, we examined SVM with linear kernel function and pre-computed kernel function using micro array data sets. In this observation is focused major three aspects such as accuracy, iteration and support vectors. These datasets are received from UCI machine learning repository. Pre-computed kernel support vector machine has shown best accuracy and minimum execution time to select instances in all data sets in our experiment.