Qualification: 
MCA, M.Tech
veenag@am.amrita.edu

Veena G. currently serves as the Assistant Professor at the Department of Computer Science Applications at Amrita School of Engineering, Amritapuri. She has completed her M. Tech. in Computer Science.

Publications

Publication Type: Journal Article

Year of Publication Publication Type Title

2016

Journal Article

G. Veena, Peter, A. S., Rajkumari, K. A., and Ramanan, N., “A concept-based model for query management in service desks”, Advances in Intelligent Systems and Computing, vol. 413, pp. 255-265, 2016.[Abstract]


Thousands of email queries are often received by help desks of large organizations nowadays. It is a cumbersome and time-consuming task to manage these emails manually. Also, the support staff who initially answers the query may not always be technically sound to do this themselves. In that case, they forward the queries to higher authorities, unnecessarily wasting their precious time. A large amount of time and human effort is being wasted for this manual classification and query management process. So, in this paper, we propose a new concept-based semantic classification technique to automatically classify the help desk queries into multiple categories. Our system also proposes an approach for retrieving powerful information related to the queries. In our work, the dataset is represented using a graph model and the concept of ontology is used for representing semantics of data.

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Publication Type: Conference Paper

Year of Publication Publication Type Title

2014

Conference Paper

G. Veena and Lekha, N. K., “A concept based clustering model for document similarity”, in Proceedings - 2014 International Conference on Data Science and Engineering, ICDSE 2014, Cochin University of Science and Technology, 2014, pp. 118-123.[Abstract]


A lot of research work has been done in the area of concept mining and document similarity in past few years. But all these works were based on the statistical analysis of keywords. The major challenge in this area involves the preservation of semantics of the terms or phrases. Our paper proposes a graph model to represent the concept in the sentence level. The concept follows a triplet representation. A modified DB scan algorithm is used to cluster the extracted concepts. This cluster forms a belief network or probabilistic network. We use this network for extracting the most probable concepts in the document. In this paper we also proposes a new algorithm for document similarity. © 2014 IEEE.

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Faculty Research Interest: 
207
PROGRAMS
OFFERED
6
AMRITA
CAMPUSES
15
CONSTITUENT
SCHOOLS
A
GRADE BY
NAAC, MHRD
8th
RANK(INDIA):
NIRF 2018
150+
INTERNATIONAL
PARTNERS