- For Authors
- For Reviewers
Outlier detection refers to task of identifying patterns. They don’t conform establish regular behavior. Outlier detection in high-dimensional data presents various challenges resulting from the “curse of dimensionality”. The current view is that distance concentration that is tendency of distances in high-dimensional data to become in discernible making distance-based methods label all points as almost equally good outliers. This paper provides evidence by demonstrating the distance based method can produce more contrasting outlier in high dimensional setting. The high dimensional can have a different impact, by reexamining the notion of reverse nearest neighbors. It is observed the distribution of point reverse count become skewed in high dimensional which resulting in the phenomenon known as Hubness. This provide insight into how some points (anti hubs) appear very infrequently ink-NN lists of other points, and explain the connection between anti hubs, outliers, and existing unsupervised outlier-detection methods. It crucial to understand increasing dimensionality so than have searching is different using maximum segment algorithm. Optimal interval search problem in a one dimensional space whose search space is significantly smaller than search space in two dimensional spaces.
Keywords : Outlier Detection, Reverse nearest Neighbours, High-Dimensional Data, Distance Concentration
The present investigation deals with effect of bentonite clay on concrete. The concrete was produced by partial replacement of OPC with mineral admixture bentonite clay in percentages of 0%, 5%, 10%, 15% and 20%. The M20 grade of concrete was designed. The compressive strength, split tensile strength and flexural strength were found for the specimens. The present investigation is to study the effect of bentonite clay on concrete. The results indicate that the concrete with 5% Bentonite Clay replacement shows better performance when compared to other samples. The compressive strength, split tensile strength and flexural strength at 5% bentonite clay replacement and it was less for the remaining samples.
Keywords : Bentonite clay, flexural strength, compressive strength, split tensile strength etc.
Effect of DMSO on the protonation equilibria of L-Glutamic acid and L-Histidine have been studied in varying concentrations (0-60% v/v) of DMSO–water mixtures maintaining an ionic strength of 0.16 mol/l at 303 K using pH metric method. The protonation constants have been calculated with the computer program MINIQUAD75 and the best fit models are arrived at based on statistical grounds employing crystallographic R factor, χ2, skewness and kurtosis. The variation of protonation constants with dielectric constant of the medium is attributed to the electrostatic and non-electrostatic forces. The effect of errors on the protonation constants has also been presented.
Keywords : Protonation equilibria, DMSO, Glutamic acid, Histidine, MINIQUAD75
Face recognition is for recognizing human faces from single images out of a large database. The task is difficult because of image variation in terms of position, size, expression, and pose and it is important because this uses in different areas such as surveillance at airports, border crossings, security systems etc. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the studies of machine recognition of faces. There are many methods exist to perform face detection, on this, Some use flesh tones, some use contours, and other are even more complex involving templates, neural networks, or filters. This all methods generally utilize 2D images for feature extraction and matching. Nowadays some of the algorithms make use of 3D images for the same. It will give more accurate and higher resilience towards covariates, such as expression, illumination and pose variations. But it is challenging because of the high cost of 3D sensors. RGB-D face recognition is introduced to reduce this problem of high cost of 3D sensors, which makes use of the features of both original image and depth image for the face recognition. This survey focuses on different 2D or 3D or combination of both face recognition techniques under different covariates, such as illuminations, expressions, noise, disguised conditions and different pose variations.
Keywords : Face recognition, RGB-D, LBP, SURF features, Kinect, Entropy, Saliency
Automatic facial expression recognition has become a progressive research area since it plays a major role in human computer interaction. The facial expression recognition finds its major application in areas like social interaction and social intelligence. However it is not an easy task because the facial image, facial occlusion, face color/shape etc. is not easy to deal with. In this paper, various techniques for feature extraction like Gabor filters, Principal Component Analysis (PCA), Local Binary Patterns (LBP), Linear Discriminant Analysis (LDA), DCT , with different classifiers like Support Vector Machine(SVM) and Neural Networks, which are used to recognize human expression in various conditions on different databases are being examined.
Keywords : Facial expression, Geometric features, Appearance features, PCA, LBP, Gabor, LDA