Indian Institute of Technology Guwahati

About: Indian Institute of Technology Guwahati is a education organization based out in Guwahati, Assam, India. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 6933 authors who have published 17102 publications receiving 257351 citations.

Potentials of Exopolysaccharides from Lactic Acid Bacteria

Abstract: Recent research in the area of importance of microbes has revealed the immense industrial potential of exopolysaccharides and their derivative oligosaccharides from lactic acid bacteria. However, due to lack of adequate technological knowledge, the exopolysaccharides have remained largely under exploited. In the present review, the enormous potentials of different types of exopolysaccharides from lactic acid bacteria are described. This also summarizes the recent advances in the applications of exopolysaccharides, certain problems associated with their commercial production and the remedies.

A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data

Abstract: Collaborative filtering (CF) is the most successful approach for personalized product or service recommendations Neighborhood based collaborative filtering is an important class of CF, which is simple, intuitive and efficient product recommender system widely used in commercial domain Typically, neighborhood-based CF uses a similarity measure for finding similar users to an active user or similar products on which she rated Traditional similarity measures utilize ratings of only co-rated items while computing similarity between a pair of users Therefore, these measures are not suitable in a sparse data In this paper, we propose a similarity measure for neighborhood based CF, which uses all ratings made by a pair of users Proposed measure finds importance of each pair of rated items by exploiting Bhattacharyya similarity To show effectiveness of the measure, we compared performances of neighborhood based CFs using state-of-the-art similarity measures with the proposed measured based CF Recommendation results on a set of real data show that proposed measure based CF outperforms existing measures based CFs in various evaluation metrics

NIR- and FRET-Based Sensing of Cu2+ and S2- in Physiological Conditions and in Live Cells

Abstract: We have synthesized a new indole functionalized rhodamine derivative L1 which specifically binds to Cu2+ in the presence of large excess of other competing ions with visually observable changes in their electronic and fluorescence spectral behavior. These spectral changes are significant enough in the NIR and visible region of the spectrum and thus enable naked eye detection. The receptor, L1, could be employed as a resonance energy transfer (RET) based sensor for detection of Cu2+ based on the process involving the donor indole and the acceptor Cu2+ bound xanthene fragment. Studies reveal that L1–Cu complex is selectively and fully reversible in presence of sulfide anions. Further, fluorescence microscopic studies confirmed that the reagent L1 could also be used as an imaging probe for detection of uptake of these ions in HeLa cells.

Classification of ECG arrhythmias using multi-resolution analysis and neural networks

Abstract: Automatic detection and classification of cardiac arrhythmias is important for diagnosis of cardiac abnormalities. We propose a method to accurately classify ECG arrhythmias through a combination of wavelets and artificial neural networks (ANN). The ability of the wavelet transform to decompose signal at various resolutions allows accurate extraction/detection of features from non-stationary signals like ECG. A set of discrete wavelet transform (DWT) coefficients, which contain the maximum information about the arrhythmia, is selected from the wavelet decomposition. These coefficients are fed to the back-propagation neural network which classifies the arrhythmias. The proposed method is capable of distinguishing the normal sinus rhythm and 12 different arrhythmias and is robust against noise. The overall accuracy of classification of the proposed approach is 96.77%.