Informatics Publishing Limited

S. Sruthi ¹, V. N. Rajavarman ¹

Affiliations
1 Dr.MGR Educational and Research Institute, Maduravoyal, Chennai-95, Tamil Nadu, IN

Abstract

Mobile Q&A systems, where mobile nodes access the Q&A systems through Internet, are very promising considering the rapid increase of mobile users and the convenience of practical use. However, such systems cannot directly use the previous centralized methods or broadcasting methods, which generate high cost of mobile Internet access, node overload, and high server bandwidth cost with the tremendous number of mobile users. Proposing a distributed Social-based mobile Q&A System (QSS) with low overhead and system cost as well as quick response to question askers. QSS enables mobile users to forward questions to potential answerers in their friend lists in a decentralized manner for a number of hops before resorting to the server. It leverages lightweight knowledge engineering techniques to accurately identify friends who are able to and willing to answer questions, thus reducing the search and computation costs of mobile nodes. It will give quick response for non-factual queries as well as colloquial languages.

Keywords

Centralized, Broadcasting, QsS, Non-Factual Queries, Colloquial.

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S. Sruthi ¹, M. Arun Kumar ², S. Valarmathy ²

Affiliations
1 Department of ECE, Bannari Amman Institute of Technology, Erode, IN
2 Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Erode, Tamilnadu, IN

Abstract

Face recognition can be used for both verification and identification. Today face recognition technology is being used to combat passport fraud, support law enforcement, identify missing children, and minimize benefit or identity fraud. The two main steps in a face recognition system are: (i) to define an effective representation of the face images, which includes sufficient information of the face for future classification, (ii) to classify a new face image with the chosen representation. In this paper, Extreme Learning Machine method for face recognition is proposed and compared with Euclidean and Mahalanobis distance methods for better face recognition rate. The Mahalanobis distance is a metric which is better adapted than the usual Euclidean distance to settings involving non spherically symmetric distribution, where as extreme learning machine (ELM) is an efficient learning algorithm for generalized single hidden layer feed forward networks (SLFNs), which performs well in classification applications. This will further enhance the quality of facial image authentication. Various experiments are done for 400 samples from ORL database for the three methods and the results are analyzed.

Keywords

Eigenfaces, Extreme Learning Machine, Principal Component Analysis (PCA), Mahalanobis Distance.

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K. Vijaya Sri ¹, S. Sruthi ¹, M. A. Madhuri ¹

Affiliations
1 Department of Pharmaceutical Analysis, Malla Reddy College of Pharmacy, (Affiliated to Osmania University) Maisammaguda, Secunderabad-500 014, IN

Abstract

A simple, fast, precise and accurate RP-HPLC by internal standard method were developed and validated for the estimation of Tolvaptan as per ICH guidelines. Methanol: 0.2M phosphate buffer (70:30v/v) was used as the solvent. Sumatriptan was selected as an internal standard. The method was developed in Eclipse C₁₈ column (100 mm × 4.6 mm, 3.5 μm particle size). The method was found to be linear in the range of 5-100μg/ml with a correlation coefficient value of 0.999. The accuracy studies of RP-HPLC by internal standard method was performed at three different levels, i.e., 50%, 100%, and 150% and recovery was found to be in the range 99.01 to 100.7%. The % RSD is <2% which indicates the accuracy and precision of the method. The above method was a rapid tool for routine analysis of Tolvaptan in the bulk and in the pharmaceutical dosage form.

Keywords

Tolvaptan, RP-HPLC Method, Internal Standard, Development, Validation and Pharmaceutical Formulation.

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