ITM University, Gurgaon, Haryana

About: ITM University, Gurgaon, Haryana is a education organization based out in Gurgaon, India. It is known for research contribution in the topics: Encryption & Cryptosystem. The organization has 749 authors who have published 1159 publications receiving 12997 citations.

Antimicrobial Activity of Some Cyanobacteria

Abstract: Many green and blue-green algae were examined for their antibacterial and antifungal properties using their extracts in three different solvents, ethanol, methanol-toluene (3:1) and acetone, on solidified agar nutrient medium by agar well diffusion method. Extracts of only five screened cyanobateria, Westiellopsis prolifica ARM 365, Hapalosiphon hibernicus ARM 178, Nostoc muscorm ARM 221, Fischerella sp. ARM 354 and Scytonema sp., were found to have antimicrobial activity against the bacteria Pseuomonas striata, Bacillus subtilis, Escherichia coli, Bradyrhizobium sp. and fungi Aspergillus niger, Alternaria brassicae and Fusarium moniliforme. Ethanol extracts of Westiellopsis prolifica ARM 365 and H. hibernicus ARM 178 showed the greatest antibacterial as well as antifungal activity against the tested bacteria and fungi as revealed by the largest inhibition zone formation on solidified agar nutrient medium. The test bacterium P. striata and the test fungus A. niger were found to be the most sensitive to ethanol extracts of H. hibernicus ARM 178 and W. prolifica ARM 365. These cyanobacterial extracts inhibited test bacteria more strongly than test fungi. The strongest antimicrobial activity of H. hibernicus ARM 178 and W. prolifica ARM 365 was observed on the tenth day of their growth and their ethanol extracts showed the maximum antimicrobial activity at 30 °C temperature. These antimicrobial compounds may have potential pharmaceutical, biocontrol or agricultural applications.

Machine-Learning Based Channel Quality and Stability Estimation for Stream-Based Multichannel Wireless Sensor Networks

Abstract: Wireless sensor networks (WSNs) have become more and more diversified and are today able to also support high data rate applications, such as multimedia. In this case, per-packet channel handshaking/switching may result in inducing additional overheads, such as energy consumption, delays and, therefore, data loss. One of the solutions is to perform stream-based channel allocation where channel handshaking is performed once before transmitting the whole data stream. Deciding stream-based channel allocation is more critical in case of multichannel WSNs where channels of different quality/stability are available and the wish for high performance requires sensor nodes to switch to the best among the available channels. In this work, we will focus on devising mechanisms that perform channel quality/stability estimation in order to improve the accommodation of stream-based communication in multichannel wireless sensor networks. For performing channel quality assessment, we have formulated a composite metric, which we call channel rank measurement (CRM), that can demarcate channels into good, intermediate and bad quality on the basis of the standard deviation of the received signal strength indicator (RSSI) and the average of the link quality indicator (LQI) of the received packets. CRM is then used to generate a data set for training a supervised machine learning-based algorithm (which we call Normal Equation based Channel quality prediction (NEC) algorithm) in such a way that it may perform instantaneous channel rank estimation of any channel. Subsequently, two robust extensions of the NEC algorithm are proposed (which we call Normal Equation based Weighted Moving Average Channel quality prediction (NEWMAC) algorithm and Normal Equation based Aggregate Maturity Criteria with Beta Tracking based Channel weight prediction (NEAMCBTC) algorithm), that can perform channel quality estimation on the basis of both current and past values of channel rank estimation. In the end, simulations are made using MATLAB, and the results show that the Extended version of NEAMCBTC algorithm (Ext-NEAMCBTC) outperforms the compared techniques in terms of channel quality and stability assessment. It also minimizes channel switching overheads (in terms of switching delays and energy consumption) for accommodating stream-based communication in multichannel WSNs.

Sentiment Analysis of Twitter Data: A Hybrid Approach

Abstract: Over the past few years, the novel appeal and increasing popularity of social networks as a medium for users to express their opinions and views have created an accumulation of a massive amount of data. This evolving mountain of data is commonly termed Big Data. Accordingly, one area in which the application of new techniques in data mining research has significant potential to achieve more precise classification of hidden knowledge in Big Data is sentiment analysis (aka optimal mining). A hybrid approach using Naïve Bayes and Random Forest on mining Twitter datasets is presented here as an extension of previous work. Briefly, relevant data sets are collected from Twitter using Twitter API; then, use of the hybrid methodology is illustrated and evaluated against one with only Naïve Bayes classifier. Results show better accuracy and efficiency in the sentiment classification for the hybrid approach.