National Institute of Technology, Karnataka

About: National Institute of Technology, Karnataka is a education organization based out in Mangalore, Karnataka, India. It is known for research contribution in the topics: Corrosion & Cloud computing. The organization has 5017 authors who have published 7057 publications receiving 70367 citations.

Green Synthesis of Nanomaterials

Abstract: The nanotechnology industry is increasingly promoting nano as a “green” technology that will improve the environmental performance of existing industries, reduce consumption of resources and energy, and allow achievement of environmentally benign economic expansion. Eco-friendly solutions are gaining popularity in the contemporary world. A green method for nanoparticle synthesis should be assessed considering three aspects: the solvent, the capping agent, and the reducing agent compared to physical and chemical methods. Particularly, the plant extracts mediated process is a good and advantageous method for the development of metal nanoparticles compared to using microorganisms, in which the cell maintenance time is limited. Synthesis and characterization of nanoparticles are important steps to be adopted to apply nanoparticles in field applications, and these steps include preparation of leaf extract, Phytochemical screening, and preparation of precursor. Various factors affecting the synthesis of nanoparticles, such as pH, temperature, and time, will be discussed. The degradation of any organic compounds by the green approach (plant extracts) is mainly due to the presence of polyphenols in the biodegradable material. Although the synthesis of nanoparticles has been trending higher, their application in the area of waste water treatment has been limited until recently. Hence, the application of nanoparticles to waste water treatment will be discussed with a view toward paving the way for an alternate source of water.

Performance analysis of Ensemble methods on Twitter sentiment analysis using NLP techniques

Abstract: Mining opinions and analyzing sentiments from social network data help in various fields such as even prediction, analyzing overall mood of public on a particular social issue and so on. This paper involves analyzing the mood of the society on a particular news from Twitter posts. The key idea of the paper is to increase the accuracy of classification by including Natural Language Processing Techniques (NLP) especially semantics and Word Sense Disambiguation. The mined text information is subjected to Ensemble classification to analyze the sentiment. Ensemble classification involves combining the effect of various independent classifiers on a particular classification problem. Experiments conducted demonstrate that ensemble classifier outperforms traditional machine learning classifiers by 3-5%.