Institution
Kongu Engineering College
About: Kongu Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Cluster analysis. The organization has 2001 authors who have published 1978 publications receiving 16923 citations.
Topics: Computer science, Cluster analysis, Control theory, Response surface methodology, Wireless sensor network
Papers published on a yearly basis
Papers
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TL;DR: The ANN model was more precise compared to the RSM model and it showed that, ANN is to be a powerful tool for modeling and optimizing FAME production.
128 citations
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TL;DR: Ultrasonic-assisted extraction could be used as an alternative method to extract pectin from sisal waste with the advantages of lower extraction temperatures, shorter extraction time and reduced energy consumption.
128 citations
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TL;DR: In this paper, the significance of the four reaction parameters such as methanol to oil ratio, catalyst concentration, mixing speed, and reaction time and their combined effect on biodiesel yield is investigated through twenty-nine of the pre-designed and performed experiments.
126 citations
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TL;DR: The results of the study demonstrated that, the tapioca starch based composites were showed a limited lifetime in biotic environment which make them suitable for being disposed in landfills after their use.
125 citations
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29 Apr 2013TL;DR: In proposed work, a new algorithm called Sentiment Fuzzy Classification algorithm with parts of speech tags is used to improve the classification accuracy on the benchmark dataset of Movies reviews dataset.
Abstract: Mining is used to help people to extract valuable information from large amount of data. Sentiment analysis focuses on the analysis and understanding of the emotions from the text patterns. It identifies the opinion or attitude that a person has towards a topic or an object and it seeks to identify the viewpoint underlying a text span. Sentiment analysis is useful in social media monitoring to automatically characterize the overall feeling or mood of consumers as reflected in social media toward a specific brand or company and determine whether they are viewed positively or negatively on the web. This new form of analysis has been widely adopted in customer relation management especially in the context of complaint management. For automating the task of classifying a single topic textual review, document-level sentiment classification is used for expressing a positive or negative sentiment. So analyzing sentiment using Multi-theme document is very difficult and the accuracy in the classification is less. The document level classification approximately classifies the sentiment using Bag of words in Support Vector Machine (SVM) algorithm. In proposed work, a new algorithm called Sentiment Fuzzy Classification algorithm with parts of speech tags is used to improve the classification accuracy on the benchmark dataset of Movies reviews dataset.
122 citations
Authors
Showing all 2001 results
Name | H-index | Papers | Citations |
---|---|---|---|
Thalappil Pradeep | 76 | 581 | 24664 |
Kumarasamy Thangaraj | 47 | 361 | 11869 |
Pagavathigounder Balasubramaniam | 46 | 268 | 6935 |
J. Prakash Maran | 34 | 56 | 3636 |
S. Saravanan | 30 | 209 | 3308 |
Rathanasamy Rajasekar | 23 | 86 | 2142 |
V. Sivakumar | 23 | 93 | 2265 |
K. Thirugnanasambandham | 21 | 31 | 1759 |
Subramaniam Shankar | 20 | 104 | 1510 |
P. Sivakumar | 19 | 132 | 1464 |
N. Sivarajasekar | 18 | 60 | 1025 |
S. Selvakumar | 18 | 68 | 1155 |
Zaharias D. Zaharis | 17 | 128 | 1179 |
P. Balasubramanie | 16 | 27 | 469 |
P. N. Palanisamy | 16 | 47 | 754 |