SYNTHESIS OF SILVER NANOPARTICLES FROM Ruellia tuberose Linn PLANT

Nanobiotechnology is emerging as a field of applied biological sciences and nanotechnology. The synthesis of nanoparticles is done by various physical and chemical methods, But biological methods are relatively simple, inexpensive, nontoxic and environmentally friendly methods. The current review focuses on the synthesis of nanoparticles, with particular emphasis on the use of parts of plants for the synthesis process, its applications and future prospectus

ANTIBACTERIAL PLANT EXTRACTS AND FORMULATION OF MOUTHWASH

In this study, we examined the medicinal properties of medicinal plants Achyranthes aspera Linn and Jatropha gossypifolia individually as well as their synergism. Different solvents system used to extract the principle compound present in these selected plants and solvent were chosen based on their polarity from high polar solvent to low polar. The ethanol extract of Achyranthes aspera and Jatropha gossypifolia has maximum activity against the microorganism Staphylococcus aureus. The effective compounds present in the extracted sample were evaluated by generating chromatogram in Thin Layer Chromatography technique. The quality and purity were confirmed by HPLC. The synergistic activity of ethanolic extract of these plants shows significant antibacterial activity

Sensing motion using spectral and spatial analysis of WLAN RSSI

In this paper we present how motion sensing can be obtained just by observing the WLAN radio signal strength and its fluctuations. The temporal, spectral and spatial characteristics of WLAN signal are analyzed. Our analysis confirms our claim that ’signal strength from access points appear to jump around more vigorously when the device is moving compared to when it is still and the number of detectable access points vary considerably while the user is on the move’. Using this observation, we present a novel motion detection algorithm, Spectrally Spread Motion Detection (SpecSMD) based on the spectral analysis of WLAN signal’s RSSI. To benchmark the proposed algorithm, we used Spatially Spread Motion Detection (SpatSMD), which is inspired by the recent work of Sohn et al. Both algorithms were evaluated by carrying out extensive measurements in a diverse set of conditions (indoors in different buildings and outdoors - city center, parking lot, university campus etc.,) and tested against the same data sets. The 94% average classification accuracy of the proposed SpecSMD is outperforming the accuracy of SpatSMD (accuracy 87%). The motion detection algorithms presented in this paper provide ubiquitous methods for deriving the state of the user. The algorithms can be implemented and run on a commodity device with WLAN capability without the need of any additional hardware support

Best Points Selection Procedure for Estimating Location and Scatter in Multivate Data with Application to Discriminant Analysisari

Multivariate data analysis is rely on the two measures namely location and scatter. The most widely used such estimators; sample mean and covariance matrix are extremely sensitive to outliers, then the results obtained with these estimators are inaccurate. Many robust alternatives are established and perform well while handling the data with outliers. But even still a challenging task while handling the large number of cases and/or variables with reference to the features such as dimensionality of data, heterogeneous of data, computing time, adequacy of the results and applications. This paper provides a procedure for the selection of best data points in order to estimate multivariate location and scatter. The obtained results also compared with the established robust procedures such as various MCD algorithmic techniques and MVE by a real environment. The application aspect of the procedure is also executed in the context of discriminant analysis of multivariate grouped data. The results such as apparent error rate, confusion matrix of classical and various robust discriminant procedures are also provided

Using time-of-flight for WLAN localization: feasibility study

Although signal strength based techniques are widely employed for WLAN localization, they generally suffer from providing highly accurate location information. In this paper, we first present the general shortcomings of the signal strength based approaches used for WLANbased localization and then state reasons why time-of-flight could be an attractive alternative. We subsequently analyze the feasibility of using time-of-flight technique for WLAN localization by synchronizing the clock using Network Time Protocol (NTP) as well as measuring the time at (i) network layer level, (ii) data link layer level, and (iii) firmware level. We conclude that at present using TOF is not a feasible approach because of the limitation of current hardware and protocols

A STUDY ON THE PROBLEMS FACED BY THE COLLEGE STUDENTS IN THEIR ONLINE SHOPPING

Online shopping - an wonderful invention, in which the consumers can directly buy goods or services from a seller in real time with the help of online shopping mechanism, with no intermediary service, over the Internet . It is also known as online shop, e-shop, e-store, internet shop, webshop, webstore, online store, or virtual store. It is a form of electronic commerce where in the sale or purchase transaction is completed electronically and interactively in real -time. Recently, a large percentage of electronic commerce is conducted entirely in electronic form. This process is called Business – to – Consumers (B2C). Online retailers are also known as e-tailers and online retail is sometimes known as e- tail. Almost all big retailers are now electronically present on the World Wide Web. Online shopping, such an easy way of doing shopping mechanism provides the changed face of retailing to the shoppers. Initially, the consumers very well go through the websites of the stores before travelling to the stores to purchase. Later, now, many shoppers are just bypassing the direct visit store and order for the required products or services through online directly from the websites. Even with all the great efforts of online stores to improve their system or mechanism, there are few problems that the consumers still have to face while their online shopping. This paper aims to analyze the issues in online shopping system

Systematic mapping review on student’s performance analysis using big data predictive model

This paper classify the various existing predicting models that are used for monitoring andimproving students’ performance at schools and higher learning institutions. It analyses all theareas within the educational data mining methodology. Two databases were chosen for thisstudy and a systematic mapping study was performed. Due to the very infant stage of thisresearch area, only 114 articles published from 2012 till 2016 were identified. Within this, atotal of 59 articles were reviewed and classified. There is an increased interest and research inthe area of educational data mining, particularly in improving students’ performance withvarious predictive and prescriptive models. Most of the models are devised for pedagogicalimprovements ultimately. It is a huge scarcity in producing portable predictive models that fitsinto any educational environment. There is more research needed in the educational big data.Keywords: predictive analysis; student’s performance; big data; big data analytics; datamining; systematic mapping study

Stochastic Budget Optimization in Internet Advertising

Internet advertising is a sophisticated game in which the many advertisers "play" to optimize their return on investment. There are many "targets" for the advertisements, and each "target" has a collection of games with a potentially different set of players involved. In this paper, we study the problem of how advertisers allocate their budget across these "targets". In particular, we focus on formulating their best response strategy as an optimization problem. Advertisers have a set of keywords ("targets") and some stochastic information about the future, namely a probability distribution over scenarios of cost vs click combinations. This summarizes the potential states of the world assuming that the strategies of other players are fixed. Then, the best response can be abstracted as stochastic budget optimization problems to figure out how to spread a given budget across these keywords to maximize the expected number of clicks. We present the first known non-trivial poly-logarithmic approximation for these problems as well as the first known hardness results of getting better than logarithmic approximation ratios in the various parameters involved. We also identify several special cases of these problems of practical interest, such as with fixed number of scenarios or with polynomial-sized parameters related to cost, which are solvable either in polynomial time or with improved approximation ratios. Stochastic budget optimization with scenarios has sophisticated technical structure. Our approximation and hardness results come from relating these problems to a special type of (0/1, bipartite) quadratic programs inherent in them. Our research answers some open problems raised by the authors in (Stochastic Models for Budget Optimization in Search-Based Advertising, Algorithmica, 58 (4), 1022-1044, 2010).Comment: FINAL versio

Cross-Document Pattern Matching

We study a new variant of the string matching problem called cross-document string matching, which is the problem of indexing a collection of documents to support an efficient search for a pattern in a selected document, where the pattern itself is a substring of another document. Several variants of this problem are considered, and efficient linear-space solutions are proposed with query time bounds that either do not depend at all on the pattern size or depend on it in a very limited way (doubly logarithmic). As a side result, we propose an improved solution to the weighted level ancestor problem