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The objective of this paper is to design an algorithm to maximize the learning ability and knowledge about the target class while minimizing the number of training samples for support vector data description (SVDD). With this motivation, a novel training sample reduction algorithm is proposed in this paper that selects the most promising boundary data points as training set. The proposed approach uses the local geometry of the distribution to estimate the farthest boundary points (also known as extreme points). The legitimacy of the proposed algorithm is verified via experiments performed on MNIST, Iris, UCI default credit card, svmguide and Indian Pines datasets.

Over the past two decades, one-class classification (OCC) becomes very popular due to its diversified applicability in data mining and pattern recognition problems. Concerning to OCC, one-class support vector classifiers (OCSVCs) have been extensively studied and improved for the technology-driven applications; still, there is no comprehensive literature available to guide researchers for future exploration. This survey paper presents an up to date, structured and well-organized review on one-class support vector classifiers. This survey comprises available algorithms, parameter estimation techniques, feature selection strategies, sample reduction methodologies, workability in distributed environment and application domains related to OCSVCs. In this way, this paper offers a detailed overview to researchers looking for the state-of-the-art in this area.

Young generation are growing very fast in the field of social communication resulting development of stress and anxiety. The aim of this study was to find out the relationship between social communication and its cause (anxiety). This study included 116 subjects of professional course's students from the community. Each subject was given a questionnaire, focusing on (1) how many times recharge balance/month (2) how much recharge balance/month (3) frequency of use social networking sites (4) hours of use social networking sites/day and (5) preference to talk. Result was found positive correlation between number of recharge and social interaction anxiety scale (SIAS) which is highly significant, how much recharge with SIAS, frequency of use and SIAS, hours of use and SIAS, where as negative correlation found between preference to talk and SiAS.

Aim: To evaluate the effect of cavity preparation with erbium-doped:yttrium-aluminum-garnet (Er:YAG) laser on marginal integrity on resin composite restoration. Materials and Methods: A total of 40 human extracted teeth were divided into four groups, and class-V cavity of approximately 1.5 mm depth and 2 mm width were prepared on the buccal surface of each tooth. A total of 10 samples were kept as control group where cavities were prepared with carbide bur. Rest of 30 samples were divided into three groups with 10 samples each and cavity was prepared with Er:YAG laser keeping pulse energy constant at 200 mJ and pulse duration constant at super short pulse and frequencies used for three groups were 10, 15, and 20 Hz, respectively. For control group, restorations were done using etching and bonding method, and for the three laser groups, bonding agent was applied directly and composite resin was placed. After restoration, enamel cracks and gap formations at the cavosurface margins were evaluated using scanning electron microscope (SEM) on the surface and crosscut surface of the restoration. Results: Using SEM on the surface and crosscut surface of the restoration the preparation by Er:YAG laser at frequency 10 Hz showed better marginal integrity of resin composite restoration. Conclusion: Laser group showed better cavity preparation than that with rotary carbide bur.