Named Entity Recognition aims to identify and to classify rigid designators in text such as proper names, biological species, and temporal expressions into some predefined categories. There has been growing interest in this field of research since the early 1990s. Named Entity Recognition has a vital role in different fields of natural language processing such as Machine Translation, Information Extraction, Question Answering System and various other fields. In this paper, Named Entity Recognition for Nepali text, based on the Support Vector Machine (SVM) is presented which is one of machine learning approaches for the classification task. A set of features are extracted from training data set. Accuracy and efficiency of SVM classifier are analyzed in three different sizes of training data set. Recognition systems are tested with ten datasets for Nepali text. The strength of this work is the efficient feature extraction and the comprehensive recognition techniques. The Support Vector Machine based Named Entity Recognition is limited to use a certain set of features and it uses a small dictionary which affects its performance. The learning performance of recognition system is observed. It is found that system can learn well from the small set of training data and increase

Cutouts are often provided in composite structural components for practical reasons. For instance, aircraft components such as wingspar, fuselage and ribs are provided with cutouts for access, inspection, fuel lines and electric lines or to reduce the overall weight. This paper addresses the effect of boundary condition on buckling and postbuckling responses, failure loads, and failure characteristics of composite laminate with various shaped cutouts (i.e., circular, square, diamond, elliptical-vertical and elliptical-horizontal) and having different lay-ups under in-plane shear (positive and negative) load, using finite-element method. The FEM formulation is based on the first order shear deformation theory in conjunction with geometric nonlinearity using von Karman’s assumptions. The 3-D Tsai-Hill criterion is used to predict the failure of a lamina while the onset of delamination is predicted by the interlaminar failure criterion. It is observed that the effect of boundary condition on buckling, first-ply failure and ultimate failure loads of a quasi-isotropic laminate with cutout is more for positive shear load than that for the negative shear load for almost all cutout shapes. It is also noted that under in-plane shear loads postbuckling stiffness of (0/90)_4s laminate with circular cutout is maximum, while it is minimum for (45/—45)_4s laminate with circular cutout, irrespective of boundary conditions.

Spam is a universal problem with which everyone is familiar. A number of approaches are used for Spam filtering. The most common filtering technique is content-based filtering which uses the actual text of message to determine whether it is Spam or not. The content is very dynamic and it is very challenging to represent all information in a mathematical model of classification. For instance, in content-based Spam filtering, the characteristics used by the filter to identify Spam message are constantly changing over time. Na?ve Bayes method represents the changing nature of message using probability theory and support vector machine (SVM) represents those using different features. These two methods of classification are efficient in different domains and the case of Nepali SMS or Text classification has not yet been in consideration; these two methods do not consider the issue and it is interesting to find out the performance of both the methods in the problem of Nepali Text classification. In this paper, the Na?ve Bayes and SVM-based classification techniques are implemented to classify the Nepali SMS as Spam and non-Spam. An empirical analysis for various text cases has been done to evaluate accuracy measure of the classification methodologies used in this study. And, it is found to be 87.15% accurate in SVM and 92.74% accurate in the case of Na?ve Bayes.